TF2.2_Product.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/bin/python\r\n"
     ]
    }
   ],
   "source": [
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.initializers import Initializer\n",
    "from tensorflow.keras.layers import Input, Conv2D, Reshape, Activation, UpSampling2D, MaxPool2D, Concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.initializers import  RandomNormal\n",
    "from tensorflow.keras.layers import Layer, InputSpec\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "BatchNormalization._USE_V2_BEHAVIOR = False\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "BACKBONE           = 'resnet50'\n",
    "SNAPSHOT_PATH      = './snapshots'\n",
    "SNAPSHOT           = None#'iou_resnet50_csv_06.h5'\n",
    "TENSORBOARD_DIR    = './tensorboard'\n",
    "RANDOM_TRANSFORM   = True\n",
    "IMAGE_MIN_SIDE     = 800\n",
    "IMAGE_MAX_SIDE     = 1333\n",
    "FREEZE_BACKBONE    = True\n",
    "NO_SNAPSHOTS       = True\n",
    "EPOCHS             = 150\n",
    "STEPS              = 10000\n",
    "ANNOTATIONS        = '/home/unreal/Documents/SKU110K/annotations/annotations_train.csv'\n",
    "CLASSES            = 'object_detector_retinanet/keras_retinanet/bin/class_mappings.csv'\n",
    "VAL_ANNOTATIONS    = '/home/unreal/Documents/SKU110K/annotations/annotations_val.csv'\n",
    "BASE_DIR           = '/home/unreal/Documents/SKU110K/images'\n",
    "DATASET_TYPE       = 'csv'\n",
    "BATCH_SIZE         = 1\n",
    "WEIGHTS            = None\n",
    "MULTI_GPU          = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "def bbox_transform_inv(boxes, deltas, mean=None, std=None):\n",
    "    \"\"\" Applies deltas (usually regression results) to boxes (usually anchors).\n",
    "\n",
    "    Before applying the deltas to the boxes, the normalization that was previously applied (in the generator) has to be removed.\n",
    "    The mean and std are the mean and std as applied in the generator. They are unnormalized in this function and then applied to the boxes.\n",
    "\n",
    "    Args\n",
    "        boxes : np.array of shape (B, N, 4), where B is the batch size, N the number of boxes and 4 values for (x1, y1, x2, y2).\n",
    "        deltas: np.array of same shape as boxes. These deltas (d_x1, d_y1, d_x2, d_y2) are a factor of the width/height.\n",
    "        mean  : The mean value used when computing deltas (defaults to [0, 0, 0, 0]).\n",
    "        std   : The standard deviation used when computing deltas (defaults to [0.2, 0.2, 0.2, 0.2]).\n",
    "\n",
    "    Returns\n",
    "        A np.array of the same shape as boxes, but with deltas applied to each box.\n",
    "        The mean and std are used during training to normalize the regression values (networks love normalization).\n",
    "    \"\"\"\n",
    "    if mean is None:\n",
    "        mean = [0, 0, 0, 0]\n",
    "    if std is None:\n",
    "        std = [0.2, 0.2, 0.2, 0.2]\n",
    "\n",
    "    width  = boxes[:, :, 2] - boxes[:, :, 0]\n",
    "    height = boxes[:, :, 3] - boxes[:, :, 1]\n",
    "\n",
    "    x1 = boxes[:, :, 0] + (deltas[:, :, 0] * std[0] + mean[0]) * width\n",
    "    y1 = boxes[:, :, 1] + (deltas[:, :, 1] * std[1] + mean[1]) * height\n",
    "    x2 = boxes[:, :, 2] + (deltas[:, :, 2] * std[2] + mean[2]) * width\n",
    "    y2 = boxes[:, :, 3] + (deltas[:, :, 3] * std[3] + mean[3]) * height\n",
    "\n",
    "    pred_boxes = tf.stack([x1, y1, x2, y2], axis=2)\n",
    "\n",
    "    return pred_boxes\n",
    "\n",
    "class Anchors(Layer):\n",
    "    \"\"\" Keras layer for generating achors for a given shape.\n",
    "    \"\"\"\n",
    "    def __init__(self, size, stride, ratios=None, scales=None, *args, **kwargs):\n",
    "        super(self.__class__,self).__init__()\n",
    "\n",
    "        \"\"\" Initializer for an Anchors layer.\n",
    "\n",
    "        Args\n",
    "            size: The base size of the anchors to generate.\n",
    "            stride: The stride of the anchors to generate.\n",
    "            ratios: The ratios of the anchors to generate (defaults to [0.5, 1, 2]).\n",
    "            scales: The scales of the anchors to generate (defaults to [2^0, 2^(1/3), 2^(2/3)]).\n",
    "        \"\"\"\n",
    "        \n",
    "        self.size   = size\n",
    "        self.stride = stride\n",
    "        self.ratios = ratios\n",
    "        self.scales = scales\n",
    "\n",
    "        if ratios is None:\n",
    "            self.ratios  = np.array([0.5, 1, 2], keras.backend.floatx())\n",
    "        elif isinstance(ratios, list):\n",
    "            self.ratios  = np.array(ratios)\n",
    "        if scales is None:\n",
    "            self.scales  = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)], keras.backend.floatx())\n",
    "        elif isinstance(scales, list):\n",
    "            self.scales  = np.array(scales)\n",
    "\n",
    "        self.num_anchors = len(ratios) * len(scales)\n",
    "        self.anchors     = keras.backend.variable(utils_anchors.generate_anchors(\n",
    "            base_size=size,\n",
    "            ratios=ratios,\n",
    "            scales=scales,\n",
    "        ))\n",
    "\n",
    "        super(Anchors, self).__init__(*args, **kwargs)\n",
    "\n",
    "    def call(self, inputs, **kwargs):\n",
    "        features = inputs\n",
    "        features_shape = keras.backend.shape(features)[:3]\n",
    "\n",
    "        # generate proposals from bbox deltas and shifted anchors\n",
    "        anchors = backend.shift(features_shape[1:3], self.stride, self.anchors)\n",
    "        anchors = keras.backend.tile(keras.backend.expand_dims(anchors, axis=0), (features_shape[0], 1, 1))\n",
    "\n",
    "        return anchors\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        if None not in input_shape[1:]:\n",
    "            total = np.prod(input_shape[1:3]) * self.num_anchors\n",
    "            return (input_shape[0], total, 4)\n",
    "        else:\n",
    "            return (input_shape[0], None, 4)\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super(Anchors, self).get_config()\n",
    "        config.update({\n",
    "            'size'   : self.size,\n",
    "            'stride' : self.stride,\n",
    "            'ratios' : self.ratios.tolist(),\n",
    "            'scales' : self.scales.tolist(),\n",
    "        })\n",
    "\n",
    "        return config\n",
    "\n",
    "class UpsampleLike(Layer):\n",
    "    \"\"\" Keras layer for upsampling a Tensor to be the same shape as another Tensor.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(self.__class__,self).__init__()\n",
    "    \n",
    "        self.methods = {\n",
    "            'bilinear': tf.image.ResizeMethod.BILINEAR,\n",
    "            'nearest' : tf.image.ResizeMethod.NEAREST_NEIGHBOR,\n",
    "            'bicubic' : tf.image.ResizeMethod.BICUBIC,\n",
    "            'area'    : tf.image.ResizeMethod.AREA,\n",
    "        }\n",
    "\n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        source, target = inputs\n",
    "        target_shape = tf.shape(target)\n",
    "        return tf.image.resize(source, (target_shape[1], target_shape[2]), self.methods['nearest'])\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0][0],) + input_shape[1][1:3] + (input_shape[0][-1],)\n",
    "\n",
    "class RegressBoxes(Layer):\n",
    "    \"\"\" Keras layer for applying regression values to boxes.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, mean=None, std=None):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \"\"\" Initializer for the RegressBoxes layer.\n",
    "\n",
    "        Args\n",
    "            mean: The mean value of the regression values which was used for normalization.\n",
    "            std: The standard value of the regression values which was used for normalization.\n",
    "        \"\"\"\n",
    "        if mean is None:\n",
    "            mean = np.array([0, 0, 0, 0])\n",
    "        if std is None:\n",
    "            std = np.array([0.2, 0.2, 0.2, 0.2])\n",
    "\n",
    "        if isinstance(mean, (list, tuple)):\n",
    "            mean = np.array(mean)\n",
    "        elif not isinstance(mean, np.ndarray):\n",
    "            raise ValueError('Expected mean to be a np.ndarray, list or tuple. Received: {}'.format(type(mean)))\n",
    "\n",
    "        if isinstance(std, (list, tuple)):\n",
    "            std = np.array(std)\n",
    "        elif not isinstance(std, np.ndarray):\n",
    "            raise ValueError('Expected std to be a np.ndarray, list or tuple. Received: {}'.format(type(std)))\n",
    "\n",
    "        self.mean = mean\n",
    "        self.std  = std\n",
    "        super(RegressBoxes, self).__init__()\n",
    "\n",
    "    def call(self, inputs):\n",
    "        anchors, regression = inputs\n",
    "        return bbox_transform_inv(anchors, regression, mean=self.mean, std=self.std)\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return input_shape[0]\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super(RegressBoxes, self).get_config()\n",
    "        config.update({\n",
    "            'mean': self.mean.tolist(),\n",
    "            'std' : self.std.tolist(),\n",
    "        })\n",
    "\n",
    "        return config\n",
    "\n",
    "class ClipBoxes(Layer):\n",
    "    \"\"\" Keras layer to clip box values to lie inside a given shape.\n",
    "    \"\"\"\n",
    "    def __init__(self, feature_size):\n",
    "        super(self.__class__,self).__init__()\n",
    "\n",
    "    def call(self, inputs):\n",
    "        image, boxes = inputs\n",
    "        shape = keras.backend.cast(keras.backend.shape(image), keras.backend.floatx())\n",
    "\n",
    "        x1 = tf.clip_by_value(boxes[:, :, 0], 0, shape[2])\n",
    "        y1 = tf.clip_by_value(boxes[:, :, 1], 0, shape[1])\n",
    "        x2 = tf.clip_by_value(boxes[:, :, 2], 0, shape[2])\n",
    "        y2 = tf.clip_by_value(boxes[:, :, 3], 0, shape[1])\n",
    "\n",
    "        return tf.stack([x1, y1, x2, y2], axis=2)\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return input_shape[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backbone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RESNET 50 / 101   { Resnet('resnet50/101 ' , stage_5=True,train_bn=True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "source": [
    "#RESNET LAYERS\n",
    "class AdditionLayer(Layer):\n",
    "    def __init__(self):\n",
    "        super(self.__class__,self).__init__()\n",
    "        self.add = tf.keras.layers.Add()\n",
    "  \n",
    "    @tf.function\n",
    "    def call(self,input1,input2):\n",
    "        return self.add([input1,input2])\n",
    "class BatchNorm(Layer):\n",
    "    def __init__(self,scale=True, center=True,name=None):\n",
    "        super(self.__class__,self).__init__()\n",
    "        self.bn = BatchNormalization(scale=True,center=True,trainable=True,name=name)\n",
    "\n",
    "    @tf.function\n",
    "    def call(self,inputs,training=True):\n",
    "        return self.bn(inputs,training=training)\n",
    "class Relu(Layer):\n",
    "    def __init__(self,name='None'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        # self.relu = tf.keras.layers.Activation('relu',name=name)\n",
    "        self.relu = tf.nn.relu\n",
    "        self._name = name\n",
    "\n",
    "  \n",
    "    @tf.function\n",
    "    def call(self,inputs):\n",
    "        return self.relu(inputs)\n",
    "class Convolution(Layer):\n",
    "    def __init__(self,filters,kernel_size,conv_name='None',bn_name='None',padding = 'valid',stride = (1,1),use_bias = True,train_bn = True):\n",
    "        super(self.__class__,self).__init__()   \n",
    "        self.conv = tf.keras.layers.Conv2D(filters=filters, kernel_size = kernel_size,name=conv_name\n",
    "                                       , padding = padding, strides = stride,use_bias=use_bias)\n",
    "        self.bn = BatchNorm(name=bn_name)\n",
    "        self.train_bn=train_bn\n",
    "\n",
    "    @tf.function\n",
    "    def call(self,inputs):\n",
    "        x = self.conv(inputs)\n",
    "        return self.bn(x,training=self.train_bn)\n",
    "class conv_block(Layer):\n",
    "    def __init__(self,kernel_size,filters,stage,block,strides = (2,2),use_bias=True,train_bn=True):\n",
    "        super(self.__class__,self).__init__()\n",
    "        self.nb_filter1, self.nb_filter2, self.nb_filter3 = filters\n",
    "        self.conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "        self.bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "        self.l1 = Convolution(filters = self.nb_filter1, kernel_size = (1,1), conv_name = self.conv_name_base + '2a', \n",
    "                    stride=strides ,bn_name = self.bn_name_base + '2a',use_bias=use_bias,train_bn=train_bn)\n",
    "        self.l2 = Relu()\n",
    "\n",
    "        self.l3 = Convolution(filters = self.nb_filter2, kernel_size = (kernel_size,kernel_size), \n",
    "                          conv_name = self.conv_name_base + '2b',bn_name = self.bn_name_base + '2b',\n",
    "                          padding='same',use_bias=use_bias,train_bn=train_bn)\n",
    "        self.l4 = Relu()\n",
    "\n",
    "        self.l5 = Convolution(filters = self.nb_filter3, kernel_size = (1,1), conv_name = self.conv_name_base + '2c', \n",
    "                    bn_name = self.bn_name_base + '2c',use_bias=use_bias,train_bn=train_bn)\n",
    "    \n",
    "        self.shortcut_l6 = Convolution(filters = self.nb_filter3, kernel_size = (1,1), conv_name = self.conv_name_base + '1', \n",
    "                    stride=strides ,bn_name = self.bn_name_base + '1',use_bias=use_bias,train_bn=train_bn)\n",
    "    \n",
    "        self.Addition_l7 = AdditionLayer()\n",
    "\n",
    "        self.l8 = Relu(name='res' + str(stage) + block + '_out')\n",
    "\n",
    "\n",
    "    @tf.function\n",
    "    def call(self,inputs):\n",
    "        x = self.l1(inputs)\n",
    "        x = self.l2(x)\n",
    "\n",
    "        x = self.l3(x)\n",
    "        x = self.l4(x)\n",
    "\n",
    "        x = self.l5(x)\n",
    "    \n",
    "        shortcut = self.shortcut_l6(inputs)\n",
    "    \n",
    "\n",
    "        x = self.Addition_l7(x,shortcut)\n",
    "\n",
    "        x = self.l8(x)\n",
    "\n",
    "        return x\n",
    "class identity_block(Layer):\n",
    "    def __init__(self,kernel_size,filters,stage,block,use_bias=True,train_bn=True):\n",
    "        super(self.__class__,self).__init__()\n",
    "        self.nb_filter1, self.nb_filter2, self.nb_filter3 = filters\n",
    "        self.conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "        self.bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "        self.l1 = Convolution(filters = self.nb_filter1, kernel_size = (1,1), conv_name = self.conv_name_base + '2a', \n",
    "                    bn_name = self.bn_name_base + '2a')\n",
    "        self.l2 = Relu()\n",
    "        self.l3 = Convolution(filters = self.nb_filter2, kernel_size = (kernel_size,kernel_size), conv_name = self.conv_name_base + '2b', \n",
    "                    bn_name = self.bn_name_base + '2b',padding='same')\n",
    "    \n",
    "        self.l4 = Relu()\n",
    "        self.l5 = Convolution(filters = self.nb_filter3, kernel_size = (1,1), conv_name = self.conv_name_base + '2c', \n",
    "                    bn_name = self.bn_name_base + '2c')\n",
    "        self.l6 = AdditionLayer()\n",
    "        self.l7 = Relu(name='res' + str(stage) + block + '_out')\n",
    "\n",
    "    @tf.function\n",
    "    def call(self,inputs):\n",
    "        x = self.l1(inputs)\n",
    "        x = self.l2(x)\n",
    "\n",
    "        x = self.l3(x)\n",
    "        x = self.l4(x)\n",
    "\n",
    "        x = self.l5(x)\n",
    "        x = self.l6(x,inputs)\n",
    "\n",
    "        x = self.l7(x)\n",
    "\n",
    "        return x "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "class Resnet(Model):\n",
    "    def __init__(self,architecture,stage_5=True,train_bn=True):\n",
    "        super(self.__class__,self).__init__()\n",
    "        assert architecture in ['resnet50','resnet101']\n",
    "        # self.C1,self.C2,self.C3,self.C4,self.C5 = None , None , None , None , None\n",
    "        \n",
    "        #stage 1\n",
    "        self.l1 = tf.keras.layers.ZeroPadding2D((3,3))\n",
    "        self.l2 = Convolution(filters=64,kernel_size=(7,7),conv_name='conv1',bn_name='conv1',use_bias=True,train_bn=True)\n",
    "        self.l3 = Relu()\n",
    "\n",
    "        self.l4 = tf.keras.layers.MaxPool2D((3,3),strides=(2,2),padding='same')\n",
    "\n",
    "        #stage 2\n",
    "        self.l5 = conv_block(kernel_size=3,filters=[64,64,256],stage=2,block='a',strides=(1,1),train_bn=train_bn)\n",
    "        self.l6 = identity_block(kernel_size=3,filters=[64,64,256],stage=2,block='b',train_bn=train_bn)\n",
    "        self.l7 = identity_block(kernel_size=3,filters=[64,64,256],stage=2,block='c',train_bn=train_bn)\n",
    "\n",
    "        #stage 3\n",
    "        self.l8 = conv_block(kernel_size=3,filters=[128,128,512],stage=3,block='a',train_bn=train_bn)\n",
    "        self.l9 = identity_block(kernel_size=3,filters=[128,128,512],stage=3,block='a',train_bn=train_bn)\n",
    "        self.l10 = identity_block(kernel_size=3,filters=[128,128,512],stage=3,block='a',train_bn=train_bn)\n",
    "        self.l11 = identity_block(kernel_size=3,filters=[128,128,512],stage=3,block='a',train_bn=train_bn)\n",
    "\n",
    "        #stage 4\n",
    "        self.l12 = conv_block(kernel_size=3,filters=[256,256,1024],stage=4,block='a',train_bn=train_bn)\n",
    "        self.block_count = {\"resnet50\": 5, \"resnet101\": 22}[architecture]\n",
    "        self.arch_wise_layers = list()\n",
    "    \n",
    "        for i in range(self.block_count):\n",
    "            self.arch_wise_layers.append(identity_block(kernel_size=3,filters=[256,256,1024],stage=4,block=chr(98 + i),train_bn=train_bn))\n",
    "    \n",
    "        #stage5\n",
    "        self.stage5=False\n",
    "        if stage_5:\n",
    "            self.stage5=True\n",
    "        \n",
    "        self.s5_l1 = conv_block(kernel_size=3,filters=[512,512,2048],stage=5,block='a',train_bn = train_bn)\n",
    "        self.s5_l2 = identity_block(kernel_size=3,filters=[512,512,2048],stage=5,block='b',train_bn = train_bn)\n",
    "        self.s5_l3 = identity_block(kernel_size=3,filters=[512,512,2048],stage=5,block='c',train_bn = train_bn)\n",
    "\n",
    "    def build(self,input_shape):\n",
    "        super(self.__class__,self).build(input_shape)\n",
    "\n",
    "    @tf.function\n",
    "    def call(self,inputs):\n",
    "        #stage 1\n",
    "        x = self.l1(inputs)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "    \n",
    "        x = self.l2(x)\n",
    "\n",
    "        # print('shap check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l3(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l4(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "        C1 = x\n",
    "\n",
    "        #stage 2\n",
    "        x = self.l5(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l6(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l7(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        C2 = x\n",
    "\n",
    "        #stage 3\n",
    "        x = self.l8(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l9(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l10(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        x = self.l11(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        C3 = x\n",
    "\n",
    "        #stage 4\n",
    "        x = self.l12(x)\n",
    "        # print('shape check')\n",
    "        # print(x.shape)\n",
    "\n",
    "        # return x\n",
    "        for l in self.arch_wise_layers:\n",
    "            x = l(x)\n",
    "            # print('shape-l check')\n",
    "            # print(x.shape)\n",
    "        C4 = x\n",
    "\n",
    "        #stage 5\n",
    "        if self.stage5:\n",
    "            x = self.s5_l1(x)\n",
    "            x = self.s5_l2(x)\n",
    "            x = self.s5_l3(x)\n",
    "            C5 = x\n",
    "\n",
    "        # print(type(x))\n",
    "        return C1, C2, C3, C4, C5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tert = np.random.rand(1,300,300,3).astype(np.float32)\n",
    "resnet = Resnet('resnet50')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert = resnet(tert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([1, 150, 150, 64]),\n",
       " TensorShape([1, 150, 150, 256]),\n",
       " TensorShape([1, 75, 75, 512]),\n",
       " TensorShape([1, 38, 38, 1024]),\n",
       " TensorShape([1, 19, 19, 2048]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert[0].shape, bert[1].shape, bert[2].shape, bert[3].shape, bert[4].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": [
     0,
     42,
     75,
     113,
     160,
     178,
     184
    ]
   },
   "outputs": [],
   "source": [
    "class Default_Classification_Model(Model):\n",
    "    def __init__(self, num_classes, num_anchors,  pyramid_feature_size=256,    \n",
    "                 prior_probability=0.01,classification_feature_size=256,\n",
    "                 name='classification_submodel'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "        options = {\n",
    "                'kernel_size': 3,\n",
    "                'strides': 1,\n",
    "                'padding': 'same',\n",
    "            }\n",
    "        self.conv1 = Conv2D(filters=classification_feature_size, activation='relu',name='pyramid_classification_1',\n",
    "                            kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),\n",
    "                            bias_initializer='zeros',**options)\n",
    "        self.conv2 = Conv2D(filters=classification_feature_size, activation='relu',name='pyramid_classification_2',\n",
    "                            kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),\n",
    "                            bias_initializer='zeros',**options)\n",
    "        self.conv3 = Conv2D(filters=classification_feature_size, activation='relu',name='pyramid_classification_3',\n",
    "                            kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),\n",
    "                            bias_initializer='zeros',**options)\n",
    "        self.conv4 = Conv2D(filters=classification_feature_size, activation='relu',name='pyramid_classification_4',\n",
    "                            kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),\n",
    "                            bias_initializer='zeros',**options)\n",
    "\n",
    "        self.conv5 = Conv2D(filters=num_classes * num_anchors,kernel_initializer=tf.keras.initializers.Zeros(),\n",
    "#                             bias_initializer=PriorProbability(probability=prior_probability),\n",
    "                            name='pyramid_classification',\n",
    "                            **options)\n",
    "        self.reshape = Reshape((-1, num_classes), name='pyramid_classification_reshape')\n",
    "        self.act = Activation('sigmoid', name='pyramid_classification_sigmoid')\n",
    "        \n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        x = self.conv1(inputs)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.reshape(x)\n",
    "        x = self.act(x)\n",
    "\n",
    "        return x\n",
    "class Default_Regression_Model(Model):\n",
    "    def __init__(self, num_anchors, pyramid_feature_size=256, regression_feature_size=256,\n",
    "                             name='regression_submodel'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "        options = { 'kernel_size': 3,\n",
    "                    'strides': 1,\n",
    "                    'padding': 'same',\n",
    "                    'kernel_initializer': RandomNormal(mean=0.0, stddev=0.01, seed=None),\n",
    "                    'bias_initializer': 'zeros'}\n",
    "        self.conv1 = Conv2D(filters=regression_feature_size, activation='relu',# kernel_size\n",
    "                                         name='pyramid_regression_1',**options)\n",
    "        self.conv2 = Conv2D(filters=regression_feature_size, activation='relu',\n",
    "                                         name='pyramid_regression_2',**options)\n",
    "        self.conv3 = Conv2D(filters=regression_feature_size, activation='relu',\n",
    "                                         name='pyramid_regression_3',**options)\n",
    "        self.conv4 = Conv2D(filters=regression_feature_size, activation='relu',\n",
    "                                         name='pyramid_regression_4',**options)\n",
    "        \n",
    "        self.conv5 =  Conv2D(num_anchors * 4, name='pyramid_regression', **options)\n",
    "        self.reshape =Reshape((-1, 4), name='pyramid_regression_reshape')\n",
    "\n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        \n",
    "\n",
    "        x = self.conv1(inputs)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.reshape(x)        \n",
    "        return x\n",
    "class Default_IOU_Model(Model):\n",
    "    def __init__(self, num_classes, num_anchors,  pyramid_feature_size=256, \n",
    "                 prior_probability=0.01, iou_feature_size=256, name='iou_submodel'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "        options = { 'kernel_size': 3,\n",
    "                    'strides': 1,\n",
    "                    'padding': 'same',\n",
    "                  } \n",
    "            \n",
    "        self.conv1 = Conv2D(filters=iou_feature_size,activation='relu',name='pyramid_iou_1',\n",
    "                           kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),\n",
    "                           bias_initializer='zeros',**options)\n",
    "        self.conv2 = Conv2D(filters=iou_feature_size,activation='relu',name='pyramid_iou_2',\n",
    "                           kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),\n",
    "                           bias_initializer='zeros',**options)\n",
    "        self.conv3 = Conv2D(filters=iou_feature_size,activation='relu',name='pyramid_iou_3',\n",
    "                           kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),\n",
    "                           bias_initializer='zeros',**options)\n",
    "        self.conv4 = Conv2D(filters=iou_feature_size,activation='relu',name='pyramid_iou_4',\n",
    "                           kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),\n",
    "                           bias_initializer='zeros',**options)\n",
    "        self.conv5 = Conv2D(filters=num_classes * num_anchors,bias_initializer='zeros',name='pyramid_iou',\n",
    "                            kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),**options)\n",
    "        self.reshape = keras.layers.Reshape((-1, num_classes), name='pyramid_iou_reshape')\n",
    "        self.act = keras.layers.Activation('sigmoid', name='pyramid_iou_sigmoid')\n",
    "    \n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        x = self.conv1(inputs)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.reshape(x)\n",
    "        x = self.act(x)\n",
    "\n",
    "        return x\n",
    "class Pyramid(Model):\n",
    "    def __init__(self, feature_size):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "        self.P5_conv1 = Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C5_reduced')#C5\n",
    "        self.P5_upsampled = UpsampleLike()#([P5, C4])\n",
    "        self.P5_conv2 = Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P5')#(P5)\n",
    "\n",
    "        # add P5 elementwise to C4\n",
    "        self.P4_conv1 = Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C4_reduced')#(C4)\n",
    "        self.P4_add = AdditionLayer()#([P5_upsampled, P4])\n",
    "        self.P4_upsampled = UpsampleLike()#([P4, C3])\n",
    "        self.P4_conv2 = Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P4')#(P4)\n",
    "\n",
    "        # add P4 elementwise to C3\n",
    "        self.P3_conv1 = Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C3_reduced')#(C3)\n",
    "        self.P3_add = AdditionLayer()#([P4_upsampled, P3])\n",
    "        self.P3_conv2 = Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P3')#(P3)\n",
    "\n",
    "        # \"P6 is obtained via a 3x3 stride-2 conv on C5\"\n",
    "        self.P6_conv1 = Conv2D(feature_size, kernel_size=3, strides=2, padding='same', name='P6')#(C5)\n",
    "\n",
    "    # \"P7 is computed by applying ReLU followed by a 3x3 stride-2 conv on P6\"\n",
    "        self.P7_act = tf.keras.layers.Activation('relu', name='C6_relu')#(P6)\n",
    "        self.P7_conv1 = Conv2D(feature_size, kernel_size=3, strides=2, padding='same', name='P7')#(P7)\n",
    "\n",
    "    \n",
    "        \n",
    "    @tf.function\n",
    "    def call(self, C3, C4, C5):\n",
    "        P5 = self.P5_conv1(C5)\n",
    "        P5_upsampled = self.P5_upsampled([P5, C4])\n",
    "        P5 =self.P5_conv2(P5)\n",
    "        P4 = self.P4_conv1(C4)\n",
    "        P4 = self.P4_add(P5_upsampled, P4)\n",
    "        P4_upsampled = self.P4_upsampled([P4, C3])\n",
    "        P4 = self.P4_conv2(P4)\n",
    "        P3 = self.P3_conv1(C3)\n",
    "        P3 = self.P3_add(P4_upsampled, P3)\n",
    "        P3 = self.P3_conv2(P3)\n",
    "\n",
    "        P6 = self.P6_conv1(C5)\n",
    "\n",
    "        P7 = self.P7_act(P6)\n",
    "        P7 = self.P7_conv1(P7) \n",
    "        \n",
    "        return  [P3, P4, P5, P6, P7]\n",
    "class AnchorParameters:\n",
    "    \"\"\" The parameteres that define how anchors are generated.\n",
    "\n",
    "    Args\n",
    "        sizes   : List of sizes to use. Each size corresponds to one feature level.\n",
    "        strides : List of strides to use. Each stride correspond to one feature level.\n",
    "        ratios  : List of ratios to use per location in a feature map.\n",
    "        scales  : List of scales to use per location in a feature map.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, sizes, strides, ratios, scales):\n",
    "        self.sizes = sizes\n",
    "        self.strides = strides\n",
    "        self.ratios = ratios\n",
    "        self.scales = scales\n",
    "\n",
    "    def num_anchors(self):\n",
    "        return len(self.ratios) * len(self.scales)\n",
    "AnchorParameters.default = AnchorParameters(\n",
    "    sizes=[32, 64, 128, 256, 512],\n",
    "    strides=[8, 16, 32, 64, 128],\n",
    "    ratios=np.array([0.5, 1, 2], dtype=np.float32),\n",
    "    scales=np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)], dtype = np.float32))\n",
    "#### TESTING\n",
    "class PriorProbability(Initializer):\n",
    "    def __init__(self, feature_size):\n",
    "        super(self.__class__,self).__init__()\n",
    "        self.probability = probability\n",
    "\n",
    "    def get_config(self):\n",
    "        return {\n",
    "            'probability': self.probability\n",
    "        }\n",
    "\n",
    "    def __call__(self, shape, dtype=None):\n",
    "        # set bias to -log((1 - p)/p) for foreground\n",
    "        result = np.ones(shape, dtype=dtype) * -math.log((1 - self.probability) / self.probability)\n",
    "\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "class Retinanet(Model):\n",
    "    def __init__(self ,num_classes,num_anchors=9,submodels=None,name='retinanet'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "#         submodels = default_submodels(num_classes, num_anchors)\n",
    "        self.backbone = Resnet('resnet50')\n",
    "        self.pyramid = Pyramid(feature_size=256)\n",
    "        self.regression_model = Default_Regression_Model(num_anchors) \n",
    "        self.classification_model = Default_Classification_Model(num_classes, num_anchors)\n",
    "        self.concat1 = Concatenate(axis=1)\n",
    "        self.concat2 = Concatenate(axis=1)\n",
    "        \n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        \n",
    "        _,_,C3, C4, C5 = self.backbone(inputs)\n",
    "        features = self.pyramid(C3, C4, C5)\n",
    "        \n",
    "        c1 = self.classification_model(features[0])\n",
    "        c2 = self.classification_model(features[1])\n",
    "        c3 = self.classification_model(features[2])\n",
    "        c4 = self.classification_model(features[3])\n",
    "        c5 = self.classification_model(features[4])\n",
    "        C1 = self.concat1([c1,c2,c3,c4,c5])\n",
    "        \n",
    "        r1  = self.regression_model(features[0])\n",
    "        r2  = self.regression_model(features[1])\n",
    "        r3  = self.regression_model(features[2])\n",
    "        r4  = self.regression_model(features[3])\n",
    "        r5  = self.regression_model(features[4])\n",
    "        R1  = self.concat2([r1,r2,r3,r4,r5])\n",
    "        \n",
    "        print(C1.shape ,R1.shape)\n",
    "        return features\n",
    "#INCOMEPLETE BBOX Make prediction model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Retinanet(10,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "code_folding": [
     0,
     11,
     49,
     98,
     125
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def focal(alpha=0.25, gamma=2.0):\n",
    "    \"\"\" Create a functor for computing the focal loss.\n",
    "\n",
    "    Args\n",
    "        alpha: Scale the focal weight with alpha.\n",
    "        gamma: Take the power of the focal weight with gamma.\n",
    "\n",
    "    Returns\n",
    "        A functor that computes the focal loss using the alpha and gamma.\n",
    "    \"\"\"\n",
    "\n",
    "    def _focal(y_true, y_pred):\n",
    "        \"\"\" Compute the focal loss given the target tensor and the predicted tensor.\n",
    "\n",
    "        As defined in https://arxiv.org/abs/1708.02002\n",
    "\n",
    "        Args\n",
    "            y_true: Tensor of target data from the generator with shape (B, N, num_classes).\n",
    "            y_pred: Tensor of predicted data from the network with shape (B, N, num_classes).\n",
    "\n",
    "        Returns\n",
    "            The focal loss of y_pred w.r.t. y_true.\n",
    "        \"\"\"\n",
    "        labels = y_true\n",
    "        classification = y_pred\n",
    "\n",
    "        # filter out \"ignore\" anchors\n",
    "        anchor_state = keras.backend.max(labels, axis=2)  # -1 for ignore, 0 for background, 1 for object\n",
    "        indices = backend.where(keras.backend.not_equal(anchor_state, -1))\n",
    "        labels = backend.gather_nd(labels, indices)\n",
    "        classification = backend.gather_nd(classification, indices)\n",
    "\n",
    "        # compute the focal loss\n",
    "        alpha_factor = keras.backend.ones_like(labels) * alpha\n",
    "        alpha_factor = backend.where(keras.backend.equal(labels, 1), alpha_factor, 1 - alpha_factor)\n",
    "        focal_weight = backend.where(keras.backend.equal(labels, 1), 1 - classification, classification)\n",
    "        focal_weight = alpha_factor * focal_weight ** gamma\n",
    "\n",
    "        cls_loss = focal_weight * keras.backend.binary_crossentropy(labels, classification)\n",
    "\n",
    "        # compute the normalizer: the number of positive anchors\n",
    "        normalizer = backend.where(keras.backend.equal(anchor_state, 1))\n",
    "        normalizer = keras.backend.cast(keras.backend.shape(normalizer)[0], keras.backend.floatx())\n",
    "        normalizer = keras.backend.maximum(1.0, normalizer)\n",
    "\n",
    "        return keras.backend.sum(cls_loss) / normalizer\n",
    "\n",
    "    return _focal\n",
    "def smooth_l1(sigma=3.0):\n",
    "    \"\"\" Create a smooth L1 loss functor.\n",
    "\n",
    "    Args\n",
    "        sigma: This argument defines the point where the loss changes from L2 to L1.\n",
    "\n",
    "    Returns\n",
    "        A functor for computing the smooth L1 loss given target data and predicted data.\n",
    "    \"\"\"\n",
    "    sigma_squared = sigma ** 2\n",
    "\n",
    "    def _smooth_l1(y_true, y_pred):\n",
    "        \"\"\" Compute the smooth L1 loss of y_pred w.r.t. y_true.\n",
    "\n",
    "        Args\n",
    "            y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).\n",
    "            y_pred: Tensor from the network of shape (B, N, 4).\n",
    "\n",
    "        Returns\n",
    "            The smooth L1 loss of y_pred w.r.t. y_true.\n",
    "        \"\"\"\n",
    "        # separate target and state\n",
    "        regression = y_pred\n",
    "        regression_target = y_true[:, :, :4]\n",
    "        anchor_state = y_true[:, :, 4]\n",
    "\n",
    "        # filter out \"ignore\" anchors\n",
    "        indices = backend.where(keras.backend.equal(anchor_state, 1))\n",
    "        regression = backend.gather_nd(regression, indices)\n",
    "        regression_target = backend.gather_nd(regression_target, indices)\n",
    "\n",
    "        # compute smooth L1 loss\n",
    "        # f(x) = 0.5 * (sigma * x)^2          if |x| < 1 / sigma / sigma\n",
    "        #        |x| - 0.5 / sigma / sigma    otherwise\n",
    "        regression_diff = regression - regression_target\n",
    "        regression_diff = keras.backend.abs(regression_diff)\n",
    "        regression_loss = backend.where(\n",
    "            keras.backend.less(regression_diff, 1.0 / sigma_squared),\n",
    "            0.5 * sigma_squared * keras.backend.pow(regression_diff, 2),\n",
    "            regression_diff - 0.5 / sigma_squared\n",
    "        )\n",
    "\n",
    "        # compute the normalizer: the number of positive anchors\n",
    "        normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])\n",
    "        normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())\n",
    "        return keras.backend.sum(regression_loss) / normalizer\n",
    "\n",
    "    return _smooth_l1\n",
    "def iou_score():\n",
    "    def iou_loss(y_true, y_pred):\n",
    "        # separate target and state\n",
    "        regression = y_pred[:, :, :4]\n",
    "        y_pred_iou = y_pred[:, :, 4]\n",
    "        y_pred_hard_scores = y_pred[:, :, 5]\n",
    "        regression_target = y_true[:, :, :4]\n",
    "        anchor_state = y_true[:, :, 4]\n",
    "\n",
    "        # filter out \"ignore\" anchors\n",
    "        indices = backend.where((keras.backend.equal(anchor_state, 1) & (keras.backend.greater(y_pred_hard_scores, 0.1))))\n",
    "        regression = backend.gather_nd(regression, indices)\n",
    "        y_pred_iou = backend.gather_nd(y_pred_iou, indices)\n",
    "        y_pred_iou = keras.backend.expand_dims(y_pred_iou)\n",
    "\n",
    "        regression_target = backend.gather_nd(regression_target, indices)\n",
    "\n",
    "        y_true_iou = intersection_over_union(regression_target, regression)\n",
    "        iou_loss = keras.backend.binary_crossentropy(output=y_pred_iou, target=y_true_iou)\n",
    "\n",
    "        # compute the normalizer: the number of positive anchors\n",
    "        normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])\n",
    "        normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())\n",
    "        return keras.backend.sum(iou_loss) / normalizer\n",
    "\n",
    "    return iou_loss\n",
    "def intersection_over_union(y_true_masks, y_pred_masks):\n",
    "    w_true = y_true_masks[:, 2::4] - y_true_masks[:, 0::4]\n",
    "    h_true = y_true_masks[:, 3::4] - y_true_masks[:, 1::4]\n",
    "    gt_area = w_true * h_true\n",
    "\n",
    "    w_pred = y_pred_masks[:, 2::4] - y_pred_masks[:, 0::4]\n",
    "    h_pred = y_pred_masks[:, 3::4] - y_pred_masks[:, 1::4]\n",
    "    pred_area = w_pred * h_pred\n",
    "    w_intersection = keras.backend.maximum(0., keras.backend.minimum(y_true_masks[:, 2::4],\n",
    "                                                                     y_pred_masks[:, 2::4]) - keras.backend.maximum(\n",
    "        y_true_masks[:, 0::4], y_pred_masks[:, 0::4]))\n",
    "    h_intersection = keras.backend.maximum(0., keras.backend.minimum(y_true_masks[:, 3::4],\n",
    "                                                                     y_pred_masks[:, 3::4]) - keras.backend.maximum(\n",
    "        y_true_masks[:, 1::4], y_pred_masks[:, 1::4]))\n",
    "    intersection_area = w_intersection * h_intersection\n",
    "\n",
    "    union = pred_area + gt_area - intersection_area + keras.backend.epsilon()\n",
    "    return intersection_area / union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "        loss={\n",
    "            'regression': smooth_l1(),\n",
    "            'classification': focal()\n",
    "        },\n",
    "        optimizer=tf.keras.optimizers.Adam(lr=1e-5, clipnorm=0.001)\n",
    "        # optimizer=keras.optimizers.SGD()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regression': <function __main__.smooth_l1.<locals>._smooth_l1(y_true, y_pred)>,\n",
       " 'classification': <function __main__.focal.<locals>._focal(y_true, y_pred)>}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.loss\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### ----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model.fit_generator(\n",
    "        generator=train_generator,\n",
    "        steps_per_epoch=STEPS,\n",
    "        epochs=EPOCHS,\n",
    "        verbose=1,\n",
    "        callbacks=callbacks,\n",
    "        validation_data=validation_generator,\n",
    "        validation_steps=validation_generator.size()\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Efficient B7 (Noise Student)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# model = tf.keras.applications.EfficientNetB7(include_top=False, weights=\"imagenet\",)\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# file = open('modelweights.pkl', 'rb')\n",
    "# data = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(model.weights[3:]), len(data[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.weights[3:] = data[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'get_default_graph'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-7dc0eca4a5d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mefficientnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mefn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mefn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEfficientNetB7\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'noisy-student'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/efficientnet/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'models'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'utils'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/efficientnet/model.py\u001b[0m in \u001b[0;36mEfficientNetB7\u001b[0;34m(include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0minput_tensor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m         \u001b[0mpooling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpooling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    607\u001b[0m     )\n\u001b[1;32m    608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/efficientnet/model.py\u001b[0m in \u001b[0;36mEfficientNet\u001b[0;34m(width_coefficient, depth_coefficient, default_resolution, dropout_rate, drop_connect_rate, depth_divisor, blocks_args, model_name, include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m         \u001b[0mimg_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tensorflow'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36mInput\u001b[0;34m(shape, batch_shape, name, dtype, sparse, tensor)\u001b[0m\n\u001b[1;32m   1455\u001b[0m                              \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1456\u001b[0m                              \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1457\u001b[0;31m                              input_tensor=tensor)\n\u001b[0m\u001b[1;32m   1458\u001b[0m     \u001b[0;31m# Return tensor including _keras_shape and _keras_history.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1459\u001b[0m     \u001b[0;31m# Note that in this case train_output and test_output are the same pointer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     90\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)\u001b[0m\n\u001b[1;32m   1317\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m             \u001b[0mprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'input'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m             \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_uid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1320\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mInputLayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/tf/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mget_uid\u001b[0;34m(prefix)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \"\"\"\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_GRAPH_UID_DICTS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m     \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_GRAPH_UID_DICTS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0m_GRAPH_UID_DICTS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'get_default_graph'"
     ]
    }
   ],
   "source": [
    "# import efficientnet.keras as efn \n",
    "# model = efn.EfficientNetB7(weights='noisy-student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save(\"efficient_noisy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# weigh= model.get_weights();    \n",
    "# pklfile= \"modelweights.pkl\"\n",
    "# try:\n",
    "#     fpkl= open(pklfile, 'wb')    #Python 3     \n",
    "#     pickle.dump(weigh, fpkl, protocol= pickle.HIGHEST_PROTOCOL)\n",
    "#     fpkl.close()\n",
    "# except:\n",
    "#     print(\"jb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDDICENT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_100475) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4j_expand_activation_layer_call_and_return_conditional_losses_60267) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4c_activation_layer_call_and_return_conditional_losses_57999) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5i_se_reduce_layer_call_and_return_conditional_losses_113508) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6b_se_reduce_layer_call_and_return_conditional_losses_64249) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6m_expand_activation_layer_call_and_return_conditional_losses_67798) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5a_activation_layer_call_and_return_conditional_losses_60655) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2g_activation_layer_call_and_return_conditional_losses_103911) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3c_expand_activation_layer_call_and_return_conditional_losses_104904) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2f_activation_layer_call_and_return_conditional_losses_54451) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5i_activation_layer_call_and_return_conditional_losses_113468) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5d_activation_layer_call_and_return_conditional_losses_61598) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5d_activation_layer_call_and_return_conditional_losses_111603) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block7a_expand_activation_layer_call_and_return_conditional_losses_68130) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4h_activation_layer_call_and_return_conditional_losses_59659) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5d_se_reduce_layer_call_and_return_conditional_losses_111643) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2f_se_reduce_layer_call_and_return_conditional_losses_54498) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4c_expand_activation_layer_call_and_return_conditional_losses_57943) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3b_expand_activation_layer_call_and_return_conditional_losses_55339) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1b_se_reduce_layer_call_and_return_conditional_losses_100827) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4g_activation_layer_call_and_return_conditional_losses_59327) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5e_expand_activation_layer_call_and_return_conditional_losses_61874) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5g_expand_activation_layer_call_and_return_conditional_losses_62538) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_64534) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_activation_layer_call_and_return_conditional_losses_115659) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6m_se_reduce_layer_call_and_return_conditional_losses_67901) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2d_activation_layer_call_and_return_conditional_losses_102792) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_expand_activation_layer_call_and_return_conditional_losses_65142) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_60878) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_expand_activation_layer_call_and_return_conditional_losses_115580) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2a_activation_layer_call_and_return_conditional_losses_101720) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6i_activation_layer_call_and_return_conditional_losses_66526) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3a_se_reduce_layer_call_and_return_conditional_losses_104324) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6h_expand_activation_layer_call_and_return_conditional_losses_66138) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block7d_activation_layer_call_and_return_conditional_losses_120088) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4d_expand_activation_layer_call_and_return_conditional_losses_58275) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5e_expand_activation_layer_call_and_return_conditional_losses_111897) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1d_activation_layer_call_and_return_conditional_losses_101347) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_116445) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4e_expand_activation_layer_call_and_return_conditional_losses_58607) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6l_activation_layer_call_and_return_conditional_losses_118270) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3f_se_reduce_layer_call_and_return_conditional_losses_106142) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_100554) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6f_se_reduce_layer_call_and_return_conditional_losses_65577) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4g_se_reduce_layer_call_and_return_conditional_losses_59374) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4f_expand_activation_layer_call_and_return_conditional_losses_108587) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1c_se_reduce_layer_call_and_return_conditional_losses_101107) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_96216) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4e_expand_activation_layer_call_and_return_conditional_losses_108214) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5h_activation_layer_call_and_return_conditional_losses_113095) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5b_expand_activation_layer_call_and_return_conditional_losses_110778) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3d_expand_activation_layer_call_and_return_conditional_losses_105277) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block5g_activation_layer_call_and_return_conditional_losses_62594) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_115326) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6h_activation_layer_call_and_return_conditional_losses_116778) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_107095) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_107174) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2c_se_reduce_layer_call_and_return_conditional_losses_102459) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6a_activation_layer_call_and_return_conditional_losses_63923) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3c_se_reduce_layer_call_and_return_conditional_losses_105023) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6c_activation_layer_call_and_return_conditional_losses_114913) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6g_se_reduce_layer_call_and_return_conditional_losses_65909) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2f_expand_activation_layer_call_and_return_conditional_losses_54395) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4f_se_reduce_layer_call_and_return_conditional_losses_59042) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6m_se_reduce_layer_call_and_return_conditional_losses_118683) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5b_activation_layer_call_and_return_conditional_losses_60934) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5j_se_reduce_layer_call_and_return_conditional_losses_63637) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2c_activation_layer_call_and_return_conditional_losses_53455) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5c_expand_activation_layer_call_and_return_conditional_losses_111151) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_51831) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block7a_activation_layer_call_and_return_conditional_losses_119016) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block4e_activation_layer_call_and_return_conditional_losses_58663) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block7d_se_reduce_layer_call_and_return_conditional_losses_69176) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block2e_expand_activation_layer_call_and_return_conditional_losses_103086) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_stem_activation_layer_call_and_return_conditional_losses_51728) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block6j_activation_layer_call_and_return_conditional_losses_117524) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block2b_expand_activation_layer_call_and_return_conditional_losses_101967) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block6k_activation_layer_call_and_return_conditional_losses_117897) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block4g_se_reduce_layer_call_and_return_conditional_losses_109079) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_114953) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5f_activation_layer_call_and_return_conditional_losses_112349) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4e_activation_layer_call_and_return_conditional_losses_108293) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_top_activation_layer_call_and_return_conditional_losses_120382) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2b_se_reduce_layer_call_and_return_conditional_losses_53170) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block6a_expand_activation_layer_call_and_return_conditional_losses_114135) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block2c_expand_activation_layer_call_and_return_conditional_losses_102340) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6d_se_reduce_layer_call_and_return_conditional_losses_64913) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6a_se_reduce_layer_call_and_return_conditional_losses_63970) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6m_expand_activation_layer_call_and_return_conditional_losses_118564) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_activation_layer_call_and_return_conditional_losses_65198) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3g_se_reduce_layer_call_and_return_conditional_losses_57102) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6i_expand_activation_layer_call_and_return_conditional_losses_117072) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block7d_activation_layer_call_and_return_conditional_losses_69129) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5f_expand_activation_layer_call_and_return_conditional_losses_112270) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4h_activation_layer_call_and_return_conditional_losses_109412) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4f_se_reduce_layer_call_and_return_conditional_losses_108706) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6b_activation_layer_call_and_return_conditional_losses_64202) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1a_se_reduce_layer_call_and_return_conditional_losses_100594) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_se_reduce_layer_call_and_return_conditional_losses_65245) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1a_activation_layer_call_and_return_conditional_losses_51784) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2g_se_reduce_layer_call_and_return_conditional_losses_54830) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4i_expand_activation_layer_call_and_return_conditional_losses_59935) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2f_activation_layer_call_and_return_conditional_losses_103538) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5i_expand_activation_layer_call_and_return_conditional_losses_113389) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6i_se_reduce_layer_call_and_return_conditional_losses_66573) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6f_expand_activation_layer_call_and_return_conditional_losses_65474) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4b_expand_activation_layer_call_and_return_conditional_losses_57611) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_53123) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3e_expand_activation_layer_call_and_return_conditional_losses_56335) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5e_activation_layer_call_and_return_conditional_losses_61930) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2e_activation_layer_call_and_return_conditional_losses_54119) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block7b_activation_layer_call_and_return_conditional_losses_68465) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6b_se_reduce_layer_call_and_return_conditional_losses_114580) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2c_expand_activation_layer_call_and_return_conditional_losses_53399) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5d_expand_activation_layer_call_and_return_conditional_losses_61542) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4d_se_reduce_layer_call_and_return_conditional_losses_58378) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3e_expand_activation_layer_call_and_return_conditional_losses_105650) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2b_activation_layer_call_and_return_conditional_losses_102046) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4j_se_reduce_layer_call_and_return_conditional_losses_110198) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6d_activation_layer_call_and_return_conditional_losses_64866) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference__wrapped_model_32910) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block5f_activation_layer_call_and_return_conditional_losses_62262) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5g_expand_activation_layer_call_and_return_conditional_losses_112643) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block5c_activation_layer_call_and_return_conditional_losses_61266) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block7c_expand_activation_layer_call_and_return_conditional_losses_68741) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3g_activation_layer_call_and_return_conditional_losses_106475) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4b_activation_layer_call_and_return_conditional_losses_57667) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6l_activation_layer_call_and_return_conditional_losses_67522) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3e_se_reduce_layer_call_and_return_conditional_losses_56438) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block6c_expand_activation_layer_call_and_return_conditional_losses_64478) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block2f_expand_activation_layer_call_and_return_conditional_losses_103459) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block1d_se_reduce_layer_call_and_return_conditional_losses_101387) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block1b_se_reduce_layer_call_and_return_conditional_losses_52038) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block1b_activation_layer_call_and_return_conditional_losses_100787) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block2e_se_reduce_layer_call_and_return_conditional_losses_103205) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block6k_activation_layer_call_and_return_conditional_losses_67190) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block1c_activation_layer_call_and_return_conditional_losses_52251) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_block4g_expand_activation_layer_call_and_return_conditional_losses_59271) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6c_se_reduce_layer_call_and_return_conditional_losses_64581) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block1c_activation_layer_call_and_return_conditional_losses_101067) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5a_expand_activation_layer_call_and_return_conditional_losses_110452) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5f_se_reduce_layer_call_and_return_conditional_losses_62309) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6l_se_reduce_layer_call_and_return_conditional_losses_118310) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
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      "WARNING:tensorflow:Importing a function (__inference_block5d_expand_activation_layer_call_and_return_conditional_losses_111524) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_efficientnetb7_layer_call_and_return_conditional_losses_90802) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6b_activation_layer_call_and_return_conditional_losses_114540) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4c_activation_layer_call_and_return_conditional_losses_107547) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6e_se_reduce_layer_call_and_return_conditional_losses_115699) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5j_activation_layer_call_and_return_conditional_losses_113841) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6h_expand_activation_layer_call_and_return_conditional_losses_116699) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block5a_activation_layer_call_and_return_conditional_losses_110531) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block4e_se_reduce_layer_call_and_return_conditional_losses_108333) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block6l_expand_activation_layer_call_and_return_conditional_losses_118191) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:Importing a function (__inference_block3d_se_reduce_layer_call_and_return_conditional_losses_105396) with ops with custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "model=tf.keras.models.load_model(\"efficient_noisy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"efficientnetb7\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "rescaling (Rescaling)           (None, None, None, 3 0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "normalization (Normalization)   (None, None, None, 3 7           rescaling[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv_pad (ZeroPadding2D)   (None, None, None, 3 0           normalization[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv (Conv2D)              (None, None, None, 6 1728        stem_conv_pad[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_bn (BatchNormalization)    (None, None, None, 6 256         stem_conv[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_activation (Activation)    (None, None, None, 6 0           stem_bn[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1a_dwconv (DepthwiseConv2D (None, None, None, 6 576         stem_activation[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1a_bn (BatchNormalization) (None, None, None, 6 256         block1a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1a_activation (Activation) (None, None, None, 6 0           block1a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_squeeze (GlobalAvera (None, 64)           0           block1a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reshape (Reshape)    (None, 1, 1, 64)     0           block1a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reduce (Conv2D)      (None, 1, 1, 16)     1040        block1a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_expand (Conv2D)      (None, 1, 1, 64)     1088        block1a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_excite (Multiply)    (None, None, None, 6 0           block1a_activation[0][0]         \n",
      "                                                                 block1a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_conv (Conv2D)   (None, None, None, 3 2048        block1a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_bn (BatchNormal (None, None, None, 3 128         block1a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_dwconv (DepthwiseConv2D (None, None, None, 3 288         block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_bn (BatchNormalization) (None, None, None, 3 128         block1b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1b_activation (Activation) (None, None, None, 3 0           block1b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_squeeze (GlobalAvera (None, 32)           0           block1b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_excite (Multiply)    (None, None, None, 3 0           block1b_activation[0][0]         \n",
      "                                                                 block1b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_conv (Conv2D)   (None, None, None, 3 1024        block1b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_bn (BatchNormal (None, None, None, 3 128         block1b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_drop (Dropout)          (None, None, None, 3 0           block1b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_add (Add)               (None, None, None, 3 0           block1b_drop[0][0]               \n",
      "                                                                 block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1c_dwconv (DepthwiseConv2D (None, None, None, 3 288         block1b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block1c_bn (BatchNormalization) (None, None, None, 3 128         block1c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1c_activation (Activation) (None, None, None, 3 0           block1c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1c_se_squeeze (GlobalAvera (None, 32)           0           block1c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1c_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1c_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1c_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1c_se_excite (Multiply)    (None, None, None, 3 0           block1c_activation[0][0]         \n",
      "                                                                 block1c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1c_project_conv (Conv2D)   (None, None, None, 3 1024        block1c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1c_project_bn (BatchNormal (None, None, None, 3 128         block1c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1c_drop (Dropout)          (None, None, None, 3 0           block1c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1c_add (Add)               (None, None, None, 3 0           block1c_drop[0][0]               \n",
      "                                                                 block1b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block1d_dwconv (DepthwiseConv2D (None, None, None, 3 288         block1c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block1d_bn (BatchNormalization) (None, None, None, 3 128         block1d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1d_activation (Activation) (None, None, None, 3 0           block1d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1d_se_squeeze (GlobalAvera (None, 32)           0           block1d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1d_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1d_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1d_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1d_se_excite (Multiply)    (None, None, None, 3 0           block1d_activation[0][0]         \n",
      "                                                                 block1d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1d_project_conv (Conv2D)   (None, None, None, 3 1024        block1d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1d_project_bn (BatchNormal (None, None, None, 3 128         block1d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1d_drop (Dropout)          (None, None, None, 3 0           block1d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1d_add (Add)               (None, None, None, 3 0           block1d_drop[0][0]               \n",
      "                                                                 block1c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_conv (Conv2D)    (None, None, None, 1 6144        block1d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_bn (BatchNormali (None, None, None, 1 768         block2a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_activation (Acti (None, None, None, 1 0           block2a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv_pad (ZeroPadding (None, None, None, 1 0           block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv (DepthwiseConv2D (None, None, None, 1 1728        block2a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_bn (BatchNormalization) (None, None, None, 1 768         block2a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2a_activation (Activation) (None, None, None, 1 0           block2a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_squeeze (GlobalAvera (None, 192)          0           block2a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reshape (Reshape)    (None, 1, 1, 192)    0           block2a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reduce (Conv2D)      (None, 1, 1, 8)      1544        block2a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_expand (Conv2D)      (None, 1, 1, 192)    1728        block2a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_excite (Multiply)    (None, None, None, 1 0           block2a_activation[0][0]         \n",
      "                                                                 block2a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_conv (Conv2D)   (None, None, None, 4 9216        block2a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_bn (BatchNormal (None, None, None, 4 192         block2a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_conv (Conv2D)    (None, None, None, 2 13824       block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_bn (BatchNormali (None, None, None, 2 1152        block2b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_activation (Acti (None, None, None, 2 0           block2b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2b_bn (BatchNormalization) (None, None, None, 2 1152        block2b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2b_activation (Activation) (None, None, None, 2 0           block2b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_squeeze (GlobalAvera (None, 288)          0           block2b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_excite (Multiply)    (None, None, None, 2 0           block2b_activation[0][0]         \n",
      "                                                                 block2b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_conv (Conv2D)   (None, None, None, 4 13824       block2b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block2b_project_bn (BatchNormal (None, None, None, 4 192         block2b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_drop (Dropout)          (None, None, None, 4 0           block2b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_add (Add)               (None, None, None, 4 0           block2b_drop[0][0]               \n",
      "                                                                 block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_conv (Conv2D)    (None, None, None, 2 13824       block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_bn (BatchNormali (None, None, None, 2 1152        block2c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_activation (Acti (None, None, None, 2 0           block2c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2c_bn (BatchNormalization) (None, None, None, 2 1152        block2c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2c_activation (Activation) (None, None, None, 2 0           block2c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_squeeze (GlobalAvera (None, 288)          0           block2c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_excite (Multiply)    (None, None, None, 2 0           block2c_activation[0][0]         \n",
      "                                                                 block2c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_conv (Conv2D)   (None, None, None, 4 13824       block2c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_bn (BatchNormal (None, None, None, 4 192         block2c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2c_drop (Dropout)          (None, None, None, 4 0           block2c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_add (Add)               (None, None, None, 4 0           block2c_drop[0][0]               \n",
      "                                                                 block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_conv (Conv2D)    (None, None, None, 2 13824       block2c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_bn (BatchNormali (None, None, None, 2 1152        block2d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_activation (Acti (None, None, None, 2 0           block2d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2d_bn (BatchNormalization) (None, None, None, 2 1152        block2d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2d_activation (Activation) (None, None, None, 2 0           block2d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_squeeze (GlobalAvera (None, 288)          0           block2d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_excite (Multiply)    (None, None, None, 2 0           block2d_activation[0][0]         \n",
      "                                                                 block2d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_project_conv (Conv2D)   (None, None, None, 4 13824       block2d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_project_bn (BatchNormal (None, None, None, 4 192         block2d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2d_drop (Dropout)          (None, None, None, 4 0           block2d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_add (Add)               (None, None, None, 4 0           block2d_drop[0][0]               \n",
      "                                                                 block2c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2e_expand_conv (Conv2D)    (None, None, None, 2 13824       block2d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2e_expand_bn (BatchNormali (None, None, None, 2 1152        block2e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2e_expand_activation (Acti (None, None, None, 2 0           block2e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2e_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2e_bn (BatchNormalization) (None, None, None, 2 1152        block2e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2e_activation (Activation) (None, None, None, 2 0           block2e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2e_se_squeeze (GlobalAvera (None, 288)          0           block2e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2e_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2e_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2e_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2e_se_excite (Multiply)    (None, None, None, 2 0           block2e_activation[0][0]         \n",
      "                                                                 block2e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2e_project_conv (Conv2D)   (None, None, None, 4 13824       block2e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2e_project_bn (BatchNormal (None, None, None, 4 192         block2e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2e_drop (Dropout)          (None, None, None, 4 0           block2e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2e_add (Add)               (None, None, None, 4 0           block2e_drop[0][0]               \n",
      "                                                                 block2d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2f_expand_conv (Conv2D)    (None, None, None, 2 13824       block2e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2f_expand_bn (BatchNormali (None, None, None, 2 1152        block2f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2f_expand_activation (Acti (None, None, None, 2 0           block2f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2f_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2f_bn (BatchNormalization) (None, None, None, 2 1152        block2f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2f_activation (Activation) (None, None, None, 2 0           block2f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2f_se_squeeze (GlobalAvera (None, 288)          0           block2f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2f_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2f_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2f_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2f_se_excite (Multiply)    (None, None, None, 2 0           block2f_activation[0][0]         \n",
      "                                                                 block2f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2f_project_conv (Conv2D)   (None, None, None, 4 13824       block2f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2f_project_bn (BatchNormal (None, None, None, 4 192         block2f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2f_drop (Dropout)          (None, None, None, 4 0           block2f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2f_add (Add)               (None, None, None, 4 0           block2f_drop[0][0]               \n",
      "                                                                 block2e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2g_expand_conv (Conv2D)    (None, None, None, 2 13824       block2f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2g_expand_bn (BatchNormali (None, None, None, 2 1152        block2g_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2g_expand_activation (Acti (None, None, None, 2 0           block2g_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2g_dwconv (DepthwiseConv2D (None, None, None, 2 2592        block2g_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2g_bn (BatchNormalization) (None, None, None, 2 1152        block2g_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2g_activation (Activation) (None, None, None, 2 0           block2g_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2g_se_squeeze (GlobalAvera (None, 288)          0           block2g_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2g_se_reshape (Reshape)    (None, 1, 1, 288)    0           block2g_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2g_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block2g_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2g_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block2g_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2g_se_excite (Multiply)    (None, None, None, 2 0           block2g_activation[0][0]         \n",
      "                                                                 block2g_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2g_project_conv (Conv2D)   (None, None, None, 4 13824       block2g_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2g_project_bn (BatchNormal (None, None, None, 4 192         block2g_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2g_drop (Dropout)          (None, None, None, 4 0           block2g_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2g_add (Add)               (None, None, None, 4 0           block2g_drop[0][0]               \n",
      "                                                                 block2f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_conv (Conv2D)    (None, None, None, 2 13824       block2g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_bn (BatchNormali (None, None, None, 2 1152        block3a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_activation (Acti (None, None, None, 2 0           block3a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv_pad (ZeroPadding (None, None, None, 2 0           block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv (DepthwiseConv2D (None, None, None, 2 7200        block3a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_bn (BatchNormalization) (None, None, None, 2 1152        block3a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3a_activation (Activation) (None, None, None, 2 0           block3a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_squeeze (GlobalAvera (None, 288)          0           block3a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reshape (Reshape)    (None, 1, 1, 288)    0           block3a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block3a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block3a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_excite (Multiply)    (None, None, None, 2 0           block3a_activation[0][0]         \n",
      "                                                                 block3a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_conv (Conv2D)   (None, None, None, 8 23040       block3a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_bn (BatchNormal (None, None, None, 8 320         block3a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_conv (Conv2D)    (None, None, None, 4 38400       block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_bn (BatchNormali (None, None, None, 4 1920        block3b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_activation (Acti (None, None, None, 4 0           block3b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3b_bn (BatchNormalization) (None, None, None, 4 1920        block3b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3b_activation (Activation) (None, None, None, 4 0           block3b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_squeeze (GlobalAvera (None, 480)          0           block3b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_excite (Multiply)    (None, None, None, 4 0           block3b_activation[0][0]         \n",
      "                                                                 block3b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_conv (Conv2D)   (None, None, None, 8 38400       block3b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_bn (BatchNormal (None, None, None, 8 320         block3b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_drop (Dropout)          (None, None, None, 8 0           block3b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_add (Add)               (None, None, None, 8 0           block3b_drop[0][0]               \n",
      "                                                                 block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_conv (Conv2D)    (None, None, None, 4 38400       block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_bn (BatchNormali (None, None, None, 4 1920        block3c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_activation (Acti (None, None, None, 4 0           block3c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3c_bn (BatchNormalization) (None, None, None, 4 1920        block3c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3c_activation (Activation) (None, None, None, 4 0           block3c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_squeeze (GlobalAvera (None, 480)          0           block3c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_excite (Multiply)    (None, None, None, 4 0           block3c_activation[0][0]         \n",
      "                                                                 block3c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_conv (Conv2D)   (None, None, None, 8 38400       block3c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_bn (BatchNormal (None, None, None, 8 320         block3c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3c_drop (Dropout)          (None, None, None, 8 0           block3c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_add (Add)               (None, None, None, 8 0           block3c_drop[0][0]               \n",
      "                                                                 block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_conv (Conv2D)    (None, None, None, 4 38400       block3c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_bn (BatchNormali (None, None, None, 4 1920        block3d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_activation (Acti (None, None, None, 4 0           block3d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3d_bn (BatchNormalization) (None, None, None, 4 1920        block3d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3d_activation (Activation) (None, None, None, 4 0           block3d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_squeeze (GlobalAvera (None, 480)          0           block3d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_excite (Multiply)    (None, None, None, 4 0           block3d_activation[0][0]         \n",
      "                                                                 block3d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_project_conv (Conv2D)   (None, None, None, 8 38400       block3d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_project_bn (BatchNormal (None, None, None, 8 320         block3d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3d_drop (Dropout)          (None, None, None, 8 0           block3d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_add (Add)               (None, None, None, 8 0           block3d_drop[0][0]               \n",
      "                                                                 block3c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3e_expand_conv (Conv2D)    (None, None, None, 4 38400       block3d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3e_expand_bn (BatchNormali (None, None, None, 4 1920        block3e_expand_conv[0][0]        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "block3e_expand_activation (Acti (None, None, None, 4 0           block3e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3e_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3e_bn (BatchNormalization) (None, None, None, 4 1920        block3e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3e_activation (Activation) (None, None, None, 4 0           block3e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3e_se_squeeze (GlobalAvera (None, 480)          0           block3e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3e_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3e_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3e_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3e_se_excite (Multiply)    (None, None, None, 4 0           block3e_activation[0][0]         \n",
      "                                                                 block3e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3e_project_conv (Conv2D)   (None, None, None, 8 38400       block3e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3e_project_bn (BatchNormal (None, None, None, 8 320         block3e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3e_drop (Dropout)          (None, None, None, 8 0           block3e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3e_add (Add)               (None, None, None, 8 0           block3e_drop[0][0]               \n",
      "                                                                 block3d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3f_expand_conv (Conv2D)    (None, None, None, 4 38400       block3e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3f_expand_bn (BatchNormali (None, None, None, 4 1920        block3f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3f_expand_activation (Acti (None, None, None, 4 0           block3f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3f_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3f_bn (BatchNormalization) (None, None, None, 4 1920        block3f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3f_activation (Activation) (None, None, None, 4 0           block3f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3f_se_squeeze (GlobalAvera (None, 480)          0           block3f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3f_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3f_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3f_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3f_se_excite (Multiply)    (None, None, None, 4 0           block3f_activation[0][0]         \n",
      "                                                                 block3f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3f_project_conv (Conv2D)   (None, None, None, 8 38400       block3f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3f_project_bn (BatchNormal (None, None, None, 8 320         block3f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3f_drop (Dropout)          (None, None, None, 8 0           block3f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3f_add (Add)               (None, None, None, 8 0           block3f_drop[0][0]               \n",
      "                                                                 block3e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3g_expand_conv (Conv2D)    (None, None, None, 4 38400       block3f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3g_expand_bn (BatchNormali (None, None, None, 4 1920        block3g_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3g_expand_activation (Acti (None, None, None, 4 0           block3g_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3g_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block3g_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3g_bn (BatchNormalization) (None, None, None, 4 1920        block3g_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3g_activation (Activation) (None, None, None, 4 0           block3g_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3g_se_squeeze (GlobalAvera (None, 480)          0           block3g_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3g_se_reshape (Reshape)    (None, 1, 1, 480)    0           block3g_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3g_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block3g_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3g_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block3g_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3g_se_excite (Multiply)    (None, None, None, 4 0           block3g_activation[0][0]         \n",
      "                                                                 block3g_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3g_project_conv (Conv2D)   (None, None, None, 8 38400       block3g_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3g_project_bn (BatchNormal (None, None, None, 8 320         block3g_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3g_drop (Dropout)          (None, None, None, 8 0           block3g_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3g_add (Add)               (None, None, None, 8 0           block3g_drop[0][0]               \n",
      "                                                                 block3f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_conv (Conv2D)    (None, None, None, 4 38400       block3g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_bn (BatchNormali (None, None, None, 4 1920        block4a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_activation (Acti (None, None, None, 4 0           block4a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv_pad (ZeroPadding (None, None, None, 4 0           block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv (DepthwiseConv2D (None, None, None, 4 4320        block4a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_bn (BatchNormalization) (None, None, None, 4 1920        block4a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4a_activation (Activation) (None, None, None, 4 0           block4a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_squeeze (GlobalAvera (None, 480)          0           block4a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_excite (Multiply)    (None, None, None, 4 0           block4a_activation[0][0]         \n",
      "                                                                 block4a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_conv (Conv2D)   (None, None, None, 1 76800       block4a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_bn (BatchNormal (None, None, None, 1 640         block4a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_conv (Conv2D)    (None, None, None, 9 153600      block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_bn (BatchNormali (None, None, None, 9 3840        block4b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_activation (Acti (None, None, None, 9 0           block4b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4b_bn (BatchNormalization) (None, None, None, 9 3840        block4b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4b_activation (Activation) (None, None, None, 9 0           block4b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_squeeze (GlobalAvera (None, 960)          0           block4b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_excite (Multiply)    (None, None, None, 9 0           block4b_activation[0][0]         \n",
      "                                                                 block4b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_conv (Conv2D)   (None, None, None, 1 153600      block4b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_bn (BatchNormal (None, None, None, 1 640         block4b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_drop (Dropout)          (None, None, None, 1 0           block4b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_add (Add)               (None, None, None, 1 0           block4b_drop[0][0]               \n",
      "                                                                 block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_conv (Conv2D)    (None, None, None, 9 153600      block4b_add[0][0]                \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "block4c_expand_bn (BatchNormali (None, None, None, 9 3840        block4c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_activation (Acti (None, None, None, 9 0           block4c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4c_bn (BatchNormalization) (None, None, None, 9 3840        block4c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4c_activation (Activation) (None, None, None, 9 0           block4c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_squeeze (GlobalAvera (None, 960)          0           block4c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_excite (Multiply)    (None, None, None, 9 0           block4c_activation[0][0]         \n",
      "                                                                 block4c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_conv (Conv2D)   (None, None, None, 1 153600      block4c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_bn (BatchNormal (None, None, None, 1 640         block4c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4c_drop (Dropout)          (None, None, None, 1 0           block4c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_add (Add)               (None, None, None, 1 0           block4c_drop[0][0]               \n",
      "                                                                 block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_conv (Conv2D)    (None, None, None, 9 153600      block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_bn (BatchNormali (None, None, None, 9 3840        block4d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_activation (Acti (None, None, None, 9 0           block4d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4d_bn (BatchNormalization) (None, None, None, 9 3840        block4d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4d_activation (Activation) (None, None, None, 9 0           block4d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_squeeze (GlobalAvera (None, 960)          0           block4d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_excite (Multiply)    (None, None, None, 9 0           block4d_activation[0][0]         \n",
      "                                                                 block4d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_conv (Conv2D)   (None, None, None, 1 153600      block4d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_bn (BatchNormal (None, None, None, 1 640         block4d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4d_drop (Dropout)          (None, None, None, 1 0           block4d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_add (Add)               (None, None, None, 1 0           block4d_drop[0][0]               \n",
      "                                                                 block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_conv (Conv2D)    (None, None, None, 9 153600      block4d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_bn (BatchNormali (None, None, None, 9 3840        block4e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_activation (Acti (None, None, None, 9 0           block4e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4e_bn (BatchNormalization) (None, None, None, 9 3840        block4e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4e_activation (Activation) (None, None, None, 9 0           block4e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_squeeze (GlobalAvera (None, 960)          0           block4e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_excite (Multiply)    (None, None, None, 9 0           block4e_activation[0][0]         \n",
      "                                                                 block4e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_project_conv (Conv2D)   (None, None, None, 1 153600      block4e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_project_bn (BatchNormal (None, None, None, 1 640         block4e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4e_drop (Dropout)          (None, None, None, 1 0           block4e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_add (Add)               (None, None, None, 1 0           block4e_drop[0][0]               \n",
      "                                                                 block4d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_conv (Conv2D)    (None, None, None, 9 153600      block4e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_bn (BatchNormali (None, None, None, 9 3840        block4f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_activation (Acti (None, None, None, 9 0           block4f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4f_bn (BatchNormalization) (None, None, None, 9 3840        block4f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4f_activation (Activation) (None, None, None, 9 0           block4f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_squeeze (GlobalAvera (None, 960)          0           block4f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_excite (Multiply)    (None, None, None, 9 0           block4f_activation[0][0]         \n",
      "                                                                 block4f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_project_conv (Conv2D)   (None, None, None, 1 153600      block4f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_project_bn (BatchNormal (None, None, None, 1 640         block4f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4f_drop (Dropout)          (None, None, None, 1 0           block4f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_add (Add)               (None, None, None, 1 0           block4f_drop[0][0]               \n",
      "                                                                 block4e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4g_expand_conv (Conv2D)    (None, None, None, 9 153600      block4f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4g_expand_bn (BatchNormali (None, None, None, 9 3840        block4g_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4g_expand_activation (Acti (None, None, None, 9 0           block4g_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4g_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4g_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4g_bn (BatchNormalization) (None, None, None, 9 3840        block4g_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4g_activation (Activation) (None, None, None, 9 0           block4g_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4g_se_squeeze (GlobalAvera (None, 960)          0           block4g_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4g_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4g_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4g_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4g_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4g_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4g_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4g_se_excite (Multiply)    (None, None, None, 9 0           block4g_activation[0][0]         \n",
      "                                                                 block4g_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4g_project_conv (Conv2D)   (None, None, None, 1 153600      block4g_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4g_project_bn (BatchNormal (None, None, None, 1 640         block4g_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4g_drop (Dropout)          (None, None, None, 1 0           block4g_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4g_add (Add)               (None, None, None, 1 0           block4g_drop[0][0]               \n",
      "                                                                 block4f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4h_expand_conv (Conv2D)    (None, None, None, 9 153600      block4g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4h_expand_bn (BatchNormali (None, None, None, 9 3840        block4h_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4h_expand_activation (Acti (None, None, None, 9 0           block4h_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block4h_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4h_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4h_bn (BatchNormalization) (None, None, None, 9 3840        block4h_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4h_activation (Activation) (None, None, None, 9 0           block4h_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4h_se_squeeze (GlobalAvera (None, 960)          0           block4h_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4h_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4h_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4h_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4h_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4h_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4h_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4h_se_excite (Multiply)    (None, None, None, 9 0           block4h_activation[0][0]         \n",
      "                                                                 block4h_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4h_project_conv (Conv2D)   (None, None, None, 1 153600      block4h_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4h_project_bn (BatchNormal (None, None, None, 1 640         block4h_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4h_drop (Dropout)          (None, None, None, 1 0           block4h_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4h_add (Add)               (None, None, None, 1 0           block4h_drop[0][0]               \n",
      "                                                                 block4g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4i_expand_conv (Conv2D)    (None, None, None, 9 153600      block4h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4i_expand_bn (BatchNormali (None, None, None, 9 3840        block4i_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4i_expand_activation (Acti (None, None, None, 9 0           block4i_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4i_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4i_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4i_bn (BatchNormalization) (None, None, None, 9 3840        block4i_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4i_activation (Activation) (None, None, None, 9 0           block4i_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4i_se_squeeze (GlobalAvera (None, 960)          0           block4i_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4i_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4i_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4i_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4i_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4i_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4i_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4i_se_excite (Multiply)    (None, None, None, 9 0           block4i_activation[0][0]         \n",
      "                                                                 block4i_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4i_project_conv (Conv2D)   (None, None, None, 1 153600      block4i_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4i_project_bn (BatchNormal (None, None, None, 1 640         block4i_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4i_drop (Dropout)          (None, None, None, 1 0           block4i_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4i_add (Add)               (None, None, None, 1 0           block4i_drop[0][0]               \n",
      "                                                                 block4h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4j_expand_conv (Conv2D)    (None, None, None, 9 153600      block4i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4j_expand_bn (BatchNormali (None, None, None, 9 3840        block4j_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4j_expand_activation (Acti (None, None, None, 9 0           block4j_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4j_dwconv (DepthwiseConv2D (None, None, None, 9 8640        block4j_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4j_bn (BatchNormalization) (None, None, None, 9 3840        block4j_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4j_activation (Activation) (None, None, None, 9 0           block4j_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4j_se_squeeze (GlobalAvera (None, 960)          0           block4j_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4j_se_reshape (Reshape)    (None, 1, 1, 960)    0           block4j_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4j_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block4j_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4j_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block4j_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4j_se_excite (Multiply)    (None, None, None, 9 0           block4j_activation[0][0]         \n",
      "                                                                 block4j_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4j_project_conv (Conv2D)   (None, None, None, 1 153600      block4j_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4j_project_bn (BatchNormal (None, None, None, 1 640         block4j_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4j_drop (Dropout)          (None, None, None, 1 0           block4j_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4j_add (Add)               (None, None, None, 1 0           block4j_drop[0][0]               \n",
      "                                                                 block4i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_conv (Conv2D)    (None, None, None, 9 153600      block4j_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_bn (BatchNormali (None, None, None, 9 3840        block5a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_activation (Acti (None, None, None, 9 0           block5a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_dwconv (DepthwiseConv2D (None, None, None, 9 24000       block5a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5a_bn (BatchNormalization) (None, None, None, 9 3840        block5a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5a_activation (Activation) (None, None, None, 9 0           block5a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_squeeze (GlobalAvera (None, 960)          0           block5a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_excite (Multiply)    (None, None, None, 9 0           block5a_activation[0][0]         \n",
      "                                                                 block5a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_conv (Conv2D)   (None, None, None, 2 215040      block5a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_bn (BatchNormal (None, None, None, 2 896         block5a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_conv (Conv2D)    (None, None, None, 1 301056      block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_bn (BatchNormali (None, None, None, 1 5376        block5b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_activation (Acti (None, None, None, 1 0           block5b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5b_bn (BatchNormalization) (None, None, None, 1 5376        block5b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5b_activation (Activation) (None, None, None, 1 0           block5b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_squeeze (GlobalAvera (None, 1344)         0           block5b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_excite (Multiply)    (None, None, None, 1 0           block5b_activation[0][0]         \n",
      "                                                                 block5b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_conv (Conv2D)   (None, None, None, 2 301056      block5b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_bn (BatchNormal (None, None, None, 2 896         block5b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_drop (Dropout)          (None, None, None, 2 0           block5b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_add (Add)               (None, None, None, 2 0           block5b_drop[0][0]               \n",
      "                                                                 block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_conv (Conv2D)    (None, None, None, 1 301056      block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_bn (BatchNormali (None, None, None, 1 5376        block5c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_activation (Acti (None, None, None, 1 0           block5c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5c_bn (BatchNormalization) (None, None, None, 1 5376        block5c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5c_activation (Activation) (None, None, None, 1 0           block5c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_squeeze (GlobalAvera (None, 1344)         0           block5c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_excite (Multiply)    (None, None, None, 1 0           block5c_activation[0][0]         \n",
      "                                                                 block5c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_conv (Conv2D)   (None, None, None, 2 301056      block5c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_bn (BatchNormal (None, None, None, 2 896         block5c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5c_drop (Dropout)          (None, None, None, 2 0           block5c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_add (Add)               (None, None, None, 2 0           block5c_drop[0][0]               \n",
      "                                                                 block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_conv (Conv2D)    (None, None, None, 1 301056      block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_bn (BatchNormali (None, None, None, 1 5376        block5d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_activation (Acti (None, None, None, 1 0           block5d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5d_bn (BatchNormalization) (None, None, None, 1 5376        block5d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5d_activation (Activation) (None, None, None, 1 0           block5d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_squeeze (GlobalAvera (None, 1344)         0           block5d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_excite (Multiply)    (None, None, None, 1 0           block5d_activation[0][0]         \n",
      "                                                                 block5d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block5d_project_conv (Conv2D)   (None, None, None, 2 301056      block5d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_project_bn (BatchNormal (None, None, None, 2 896         block5d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5d_drop (Dropout)          (None, None, None, 2 0           block5d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_add (Add)               (None, None, None, 2 0           block5d_drop[0][0]               \n",
      "                                                                 block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_conv (Conv2D)    (None, None, None, 1 301056      block5d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_bn (BatchNormali (None, None, None, 1 5376        block5e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_activation (Acti (None, None, None, 1 0           block5e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5e_bn (BatchNormalization) (None, None, None, 1 5376        block5e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5e_activation (Activation) (None, None, None, 1 0           block5e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_squeeze (GlobalAvera (None, 1344)         0           block5e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_excite (Multiply)    (None, None, None, 1 0           block5e_activation[0][0]         \n",
      "                                                                 block5e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_project_conv (Conv2D)   (None, None, None, 2 301056      block5e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_project_bn (BatchNormal (None, None, None, 2 896         block5e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5e_drop (Dropout)          (None, None, None, 2 0           block5e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_add (Add)               (None, None, None, 2 0           block5e_drop[0][0]               \n",
      "                                                                 block5d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_conv (Conv2D)    (None, None, None, 1 301056      block5e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_bn (BatchNormali (None, None, None, 1 5376        block5f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_activation (Acti (None, None, None, 1 0           block5f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5f_bn (BatchNormalization) (None, None, None, 1 5376        block5f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5f_activation (Activation) (None, None, None, 1 0           block5f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_squeeze (GlobalAvera (None, 1344)         0           block5f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_excite (Multiply)    (None, None, None, 1 0           block5f_activation[0][0]         \n",
      "                                                                 block5f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_project_conv (Conv2D)   (None, None, None, 2 301056      block5f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_project_bn (BatchNormal (None, None, None, 2 896         block5f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5f_drop (Dropout)          (None, None, None, 2 0           block5f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_add (Add)               (None, None, None, 2 0           block5f_drop[0][0]               \n",
      "                                                                 block5e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5g_expand_conv (Conv2D)    (None, None, None, 1 301056      block5f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5g_expand_bn (BatchNormali (None, None, None, 1 5376        block5g_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5g_expand_activation (Acti (None, None, None, 1 0           block5g_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5g_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5g_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5g_bn (BatchNormalization) (None, None, None, 1 5376        block5g_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5g_activation (Activation) (None, None, None, 1 0           block5g_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5g_se_squeeze (GlobalAvera (None, 1344)         0           block5g_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5g_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5g_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5g_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5g_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5g_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5g_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5g_se_excite (Multiply)    (None, None, None, 1 0           block5g_activation[0][0]         \n",
      "                                                                 block5g_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5g_project_conv (Conv2D)   (None, None, None, 2 301056      block5g_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5g_project_bn (BatchNormal (None, None, None, 2 896         block5g_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5g_drop (Dropout)          (None, None, None, 2 0           block5g_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5g_add (Add)               (None, None, None, 2 0           block5g_drop[0][0]               \n",
      "                                                                 block5f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5h_expand_conv (Conv2D)    (None, None, None, 1 301056      block5g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5h_expand_bn (BatchNormali (None, None, None, 1 5376        block5h_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5h_expand_activation (Acti (None, None, None, 1 0           block5h_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5h_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5h_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5h_bn (BatchNormalization) (None, None, None, 1 5376        block5h_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5h_activation (Activation) (None, None, None, 1 0           block5h_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5h_se_squeeze (GlobalAvera (None, 1344)         0           block5h_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5h_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5h_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block5h_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5h_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5h_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5h_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5h_se_excite (Multiply)    (None, None, None, 1 0           block5h_activation[0][0]         \n",
      "                                                                 block5h_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5h_project_conv (Conv2D)   (None, None, None, 2 301056      block5h_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5h_project_bn (BatchNormal (None, None, None, 2 896         block5h_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5h_drop (Dropout)          (None, None, None, 2 0           block5h_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5h_add (Add)               (None, None, None, 2 0           block5h_drop[0][0]               \n",
      "                                                                 block5g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5i_expand_conv (Conv2D)    (None, None, None, 1 301056      block5h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5i_expand_bn (BatchNormali (None, None, None, 1 5376        block5i_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5i_expand_activation (Acti (None, None, None, 1 0           block5i_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5i_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5i_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5i_bn (BatchNormalization) (None, None, None, 1 5376        block5i_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5i_activation (Activation) (None, None, None, 1 0           block5i_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5i_se_squeeze (GlobalAvera (None, 1344)         0           block5i_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5i_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5i_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5i_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5i_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5i_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5i_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5i_se_excite (Multiply)    (None, None, None, 1 0           block5i_activation[0][0]         \n",
      "                                                                 block5i_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5i_project_conv (Conv2D)   (None, None, None, 2 301056      block5i_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5i_project_bn (BatchNormal (None, None, None, 2 896         block5i_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5i_drop (Dropout)          (None, None, None, 2 0           block5i_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5i_add (Add)               (None, None, None, 2 0           block5i_drop[0][0]               \n",
      "                                                                 block5h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5j_expand_conv (Conv2D)    (None, None, None, 1 301056      block5i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5j_expand_bn (BatchNormali (None, None, None, 1 5376        block5j_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5j_expand_activation (Acti (None, None, None, 1 0           block5j_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5j_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block5j_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5j_bn (BatchNormalization) (None, None, None, 1 5376        block5j_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5j_activation (Activation) (None, None, None, 1 0           block5j_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5j_se_squeeze (GlobalAvera (None, 1344)         0           block5j_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5j_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block5j_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5j_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block5j_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5j_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block5j_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5j_se_excite (Multiply)    (None, None, None, 1 0           block5j_activation[0][0]         \n",
      "                                                                 block5j_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5j_project_conv (Conv2D)   (None, None, None, 2 301056      block5j_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5j_project_bn (BatchNormal (None, None, None, 2 896         block5j_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5j_drop (Dropout)          (None, None, None, 2 0           block5j_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5j_add (Add)               (None, None, None, 2 0           block5j_drop[0][0]               \n",
      "                                                                 block5i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_conv (Conv2D)    (None, None, None, 1 301056      block5j_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_bn (BatchNormali (None, None, None, 1 5376        block6a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_activation (Acti (None, None, None, 1 0           block6a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv_pad (ZeroPadding (None, None, None, 1 0           block6a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv (DepthwiseConv2D (None, None, None, 1 33600       block6a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_bn (BatchNormalization) (None, None, None, 1 5376        block6a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6a_activation (Activation) (None, None, None, 1 0           block6a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_squeeze (GlobalAvera (None, 1344)         0           block6a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reshape (Reshape)    (None, 1, 1, 1344)   0           block6a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reduce (Conv2D)      (None, 1, 1, 56)     75320       block6a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_expand (Conv2D)      (None, 1, 1, 1344)   76608       block6a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_excite (Multiply)    (None, None, None, 1 0           block6a_activation[0][0]         \n",
      "                                                                 block6a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_conv (Conv2D)   (None, None, None, 3 516096      block6a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_bn (BatchNormal (None, None, None, 3 1536        block6a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_conv (Conv2D)    (None, None, None, 2 884736      block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_bn (BatchNormali (None, None, None, 2 9216        block6b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_activation (Acti (None, None, None, 2 0           block6b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6b_bn (BatchNormalization) (None, None, None, 2 9216        block6b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6b_activation (Activation) (None, None, None, 2 0           block6b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_squeeze (GlobalAvera (None, 2304)         0           block6b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_excite (Multiply)    (None, None, None, 2 0           block6b_activation[0][0]         \n",
      "                                                                 block6b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_conv (Conv2D)   (None, None, None, 3 884736      block6b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_bn (BatchNormal (None, None, None, 3 1536        block6b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_drop (Dropout)          (None, None, None, 3 0           block6b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_add (Add)               (None, None, None, 3 0           block6b_drop[0][0]               \n",
      "                                                                 block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block6c_expand_conv (Conv2D)    (None, None, None, 2 884736      block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_bn (BatchNormali (None, None, None, 2 9216        block6c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_activation (Acti (None, None, None, 2 0           block6c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6c_bn (BatchNormalization) (None, None, None, 2 9216        block6c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6c_activation (Activation) (None, None, None, 2 0           block6c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_squeeze (GlobalAvera (None, 2304)         0           block6c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_excite (Multiply)    (None, None, None, 2 0           block6c_activation[0][0]         \n",
      "                                                                 block6c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_conv (Conv2D)   (None, None, None, 3 884736      block6c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_bn (BatchNormal (None, None, None, 3 1536        block6c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6c_drop (Dropout)          (None, None, None, 3 0           block6c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_add (Add)               (None, None, None, 3 0           block6c_drop[0][0]               \n",
      "                                                                 block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_conv (Conv2D)    (None, None, None, 2 884736      block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_bn (BatchNormali (None, None, None, 2 9216        block6d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_activation (Acti (None, None, None, 2 0           block6d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6d_bn (BatchNormalization) (None, None, None, 2 9216        block6d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6d_activation (Activation) (None, None, None, 2 0           block6d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_squeeze (GlobalAvera (None, 2304)         0           block6d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_excite (Multiply)    (None, None, None, 2 0           block6d_activation[0][0]         \n",
      "                                                                 block6d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_conv (Conv2D)   (None, None, None, 3 884736      block6d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_bn (BatchNormal (None, None, None, 3 1536        block6d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6d_drop (Dropout)          (None, None, None, 3 0           block6d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_add (Add)               (None, None, None, 3 0           block6d_drop[0][0]               \n",
      "                                                                 block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_conv (Conv2D)    (None, None, None, 2 884736      block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_bn (BatchNormali (None, None, None, 2 9216        block6e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_activation (Acti (None, None, None, 2 0           block6e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6e_bn (BatchNormalization) (None, None, None, 2 9216        block6e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6e_activation (Activation) (None, None, None, 2 0           block6e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_squeeze (GlobalAvera (None, 2304)         0           block6e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_excite (Multiply)    (None, None, None, 2 0           block6e_activation[0][0]         \n",
      "                                                                 block6e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_project_conv (Conv2D)   (None, None, None, 3 884736      block6e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_project_bn (BatchNormal (None, None, None, 3 1536        block6e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6e_drop (Dropout)          (None, None, None, 3 0           block6e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_add (Add)               (None, None, None, 3 0           block6e_drop[0][0]               \n",
      "                                                                 block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6f_expand_conv (Conv2D)    (None, None, None, 2 884736      block6e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6f_expand_bn (BatchNormali (None, None, None, 2 9216        block6f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6f_expand_activation (Acti (None, None, None, 2 0           block6f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6f_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6f_bn (BatchNormalization) (None, None, None, 2 9216        block6f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6f_activation (Activation) (None, None, None, 2 0           block6f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6f_se_squeeze (GlobalAvera (None, 2304)         0           block6f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6f_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6f_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6f_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6f_se_excite (Multiply)    (None, None, None, 2 0           block6f_activation[0][0]         \n",
      "                                                                 block6f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6f_project_conv (Conv2D)   (None, None, None, 3 884736      block6f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6f_project_bn (BatchNormal (None, None, None, 3 1536        block6f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6f_drop (Dropout)          (None, None, None, 3 0           block6f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6f_add (Add)               (None, None, None, 3 0           block6f_drop[0][0]               \n",
      "                                                                 block6e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6g_expand_conv (Conv2D)    (None, None, None, 2 884736      block6f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6g_expand_bn (BatchNormali (None, None, None, 2 9216        block6g_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6g_expand_activation (Acti (None, None, None, 2 0           block6g_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6g_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6g_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6g_bn (BatchNormalization) (None, None, None, 2 9216        block6g_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6g_activation (Activation) (None, None, None, 2 0           block6g_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6g_se_squeeze (GlobalAvera (None, 2304)         0           block6g_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6g_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6g_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6g_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6g_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6g_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6g_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6g_se_excite (Multiply)    (None, None, None, 2 0           block6g_activation[0][0]         \n",
      "                                                                 block6g_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6g_project_conv (Conv2D)   (None, None, None, 3 884736      block6g_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6g_project_bn (BatchNormal (None, None, None, 3 1536        block6g_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6g_drop (Dropout)          (None, None, None, 3 0           block6g_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6g_add (Add)               (None, None, None, 3 0           block6g_drop[0][0]               \n",
      "                                                                 block6f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6h_expand_conv (Conv2D)    (None, None, None, 2 884736      block6g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6h_expand_bn (BatchNormali (None, None, None, 2 9216        block6h_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6h_expand_activation (Acti (None, None, None, 2 0           block6h_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6h_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6h_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6h_bn (BatchNormalization) (None, None, None, 2 9216        block6h_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6h_activation (Activation) (None, None, None, 2 0           block6h_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6h_se_squeeze (GlobalAvera (None, 2304)         0           block6h_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6h_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6h_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6h_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6h_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6h_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6h_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6h_se_excite (Multiply)    (None, None, None, 2 0           block6h_activation[0][0]         \n",
      "                                                                 block6h_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6h_project_conv (Conv2D)   (None, None, None, 3 884736      block6h_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6h_project_bn (BatchNormal (None, None, None, 3 1536        block6h_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6h_drop (Dropout)          (None, None, None, 3 0           block6h_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6h_add (Add)               (None, None, None, 3 0           block6h_drop[0][0]               \n",
      "                                                                 block6g_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6i_expand_conv (Conv2D)    (None, None, None, 2 884736      block6h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6i_expand_bn (BatchNormali (None, None, None, 2 9216        block6i_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6i_expand_activation (Acti (None, None, None, 2 0           block6i_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6i_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6i_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6i_bn (BatchNormalization) (None, None, None, 2 9216        block6i_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6i_activation (Activation) (None, None, None, 2 0           block6i_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6i_se_squeeze (GlobalAvera (None, 2304)         0           block6i_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6i_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6i_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6i_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6i_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6i_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6i_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6i_se_excite (Multiply)    (None, None, None, 2 0           block6i_activation[0][0]         \n",
      "                                                                 block6i_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6i_project_conv (Conv2D)   (None, None, None, 3 884736      block6i_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block6i_project_bn (BatchNormal (None, None, None, 3 1536        block6i_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6i_drop (Dropout)          (None, None, None, 3 0           block6i_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6i_add (Add)               (None, None, None, 3 0           block6i_drop[0][0]               \n",
      "                                                                 block6h_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6j_expand_conv (Conv2D)    (None, None, None, 2 884736      block6i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6j_expand_bn (BatchNormali (None, None, None, 2 9216        block6j_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6j_expand_activation (Acti (None, None, None, 2 0           block6j_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6j_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6j_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6j_bn (BatchNormalization) (None, None, None, 2 9216        block6j_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6j_activation (Activation) (None, None, None, 2 0           block6j_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6j_se_squeeze (GlobalAvera (None, 2304)         0           block6j_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6j_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6j_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6j_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6j_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6j_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6j_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6j_se_excite (Multiply)    (None, None, None, 2 0           block6j_activation[0][0]         \n",
      "                                                                 block6j_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6j_project_conv (Conv2D)   (None, None, None, 3 884736      block6j_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6j_project_bn (BatchNormal (None, None, None, 3 1536        block6j_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6j_drop (Dropout)          (None, None, None, 3 0           block6j_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6j_add (Add)               (None, None, None, 3 0           block6j_drop[0][0]               \n",
      "                                                                 block6i_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6k_expand_conv (Conv2D)    (None, None, None, 2 884736      block6j_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6k_expand_bn (BatchNormali (None, None, None, 2 9216        block6k_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6k_expand_activation (Acti (None, None, None, 2 0           block6k_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6k_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6k_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6k_bn (BatchNormalization) (None, None, None, 2 9216        block6k_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6k_activation (Activation) (None, None, None, 2 0           block6k_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6k_se_squeeze (GlobalAvera (None, 2304)         0           block6k_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6k_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6k_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6k_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6k_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6k_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6k_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6k_se_excite (Multiply)    (None, None, None, 2 0           block6k_activation[0][0]         \n",
      "                                                                 block6k_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6k_project_conv (Conv2D)   (None, None, None, 3 884736      block6k_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6k_project_bn (BatchNormal (None, None, None, 3 1536        block6k_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6k_drop (Dropout)          (None, None, None, 3 0           block6k_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6k_add (Add)               (None, None, None, 3 0           block6k_drop[0][0]               \n",
      "                                                                 block6j_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6l_expand_conv (Conv2D)    (None, None, None, 2 884736      block6k_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6l_expand_bn (BatchNormali (None, None, None, 2 9216        block6l_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6l_expand_activation (Acti (None, None, None, 2 0           block6l_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6l_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6l_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6l_bn (BatchNormalization) (None, None, None, 2 9216        block6l_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6l_activation (Activation) (None, None, None, 2 0           block6l_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6l_se_squeeze (GlobalAvera (None, 2304)         0           block6l_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6l_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6l_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6l_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6l_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6l_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6l_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6l_se_excite (Multiply)    (None, None, None, 2 0           block6l_activation[0][0]         \n",
      "                                                                 block6l_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6l_project_conv (Conv2D)   (None, None, None, 3 884736      block6l_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6l_project_bn (BatchNormal (None, None, None, 3 1536        block6l_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6l_drop (Dropout)          (None, None, None, 3 0           block6l_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6l_add (Add)               (None, None, None, 3 0           block6l_drop[0][0]               \n",
      "                                                                 block6k_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6m_expand_conv (Conv2D)    (None, None, None, 2 884736      block6l_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6m_expand_bn (BatchNormali (None, None, None, 2 9216        block6m_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6m_expand_activation (Acti (None, None, None, 2 0           block6m_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6m_dwconv (DepthwiseConv2D (None, None, None, 2 57600       block6m_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6m_bn (BatchNormalization) (None, None, None, 2 9216        block6m_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6m_activation (Activation) (None, None, None, 2 0           block6m_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6m_se_squeeze (GlobalAvera (None, 2304)         0           block6m_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6m_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block6m_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6m_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block6m_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6m_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block6m_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6m_se_excite (Multiply)    (None, None, None, 2 0           block6m_activation[0][0]         \n",
      "                                                                 block6m_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6m_project_conv (Conv2D)   (None, None, None, 3 884736      block6m_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6m_project_bn (BatchNormal (None, None, None, 3 1536        block6m_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6m_drop (Dropout)          (None, None, None, 3 0           block6m_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6m_add (Add)               (None, None, None, 3 0           block6m_drop[0][0]               \n",
      "                                                                 block6l_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_conv (Conv2D)    (None, None, None, 2 884736      block6m_add[0][0]                \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block7a_expand_bn (BatchNormali (None, None, None, 2 9216        block7a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_activation (Acti (None, None, None, 2 0           block7a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_dwconv (DepthwiseConv2D (None, None, None, 2 20736       block7a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7a_bn (BatchNormalization) (None, None, None, 2 9216        block7a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7a_activation (Activation) (None, None, None, 2 0           block7a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_squeeze (GlobalAvera (None, 2304)         0           block7a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reshape (Reshape)    (None, 1, 1, 2304)   0           block7a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reduce (Conv2D)      (None, 1, 1, 96)     221280      block7a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_expand (Conv2D)      (None, 1, 1, 2304)   223488      block7a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_excite (Multiply)    (None, None, None, 2 0           block7a_activation[0][0]         \n",
      "                                                                 block7a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_conv (Conv2D)   (None, None, None, 6 1474560     block7a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_bn (BatchNormal (None, None, None, 6 2560        block7a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_conv (Conv2D)    (None, None, None, 3 2457600     block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_bn (BatchNormali (None, None, None, 3 15360       block7b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_activation (Acti (None, None, None, 3 0           block7b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_dwconv (DepthwiseConv2D (None, None, None, 3 34560       block7b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7b_bn (BatchNormalization) (None, None, None, 3 15360       block7b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7b_activation (Activation) (None, None, None, 3 0           block7b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_squeeze (GlobalAvera (None, 3840)         0           block7b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_reshape (Reshape)    (None, 1, 1, 3840)   0           block7b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_reduce (Conv2D)      (None, 1, 1, 160)    614560      block7b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_expand (Conv2D)      (None, 1, 1, 3840)   618240      block7b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_excite (Multiply)    (None, None, None, 3 0           block7b_activation[0][0]         \n",
      "                                                                 block7b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_project_conv (Conv2D)   (None, None, None, 6 2457600     block7b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_project_bn (BatchNormal (None, None, None, 6 2560        block7b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7b_drop (Dropout)          (None, None, None, 6 0           block7b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_add (Add)               (None, None, None, 6 0           block7b_drop[0][0]               \n",
      "                                                                 block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7c_expand_conv (Conv2D)    (None, None, None, 3 2457600     block7b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7c_expand_bn (BatchNormali (None, None, None, 3 15360       block7c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7c_expand_activation (Acti (None, None, None, 3 0           block7c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7c_dwconv (DepthwiseConv2D (None, None, None, 3 34560       block7c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7c_bn (BatchNormalization) (None, None, None, 3 15360       block7c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7c_activation (Activation) (None, None, None, 3 0           block7c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7c_se_squeeze (GlobalAvera (None, 3840)         0           block7c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7c_se_reshape (Reshape)    (None, 1, 1, 3840)   0           block7c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7c_se_reduce (Conv2D)      (None, 1, 1, 160)    614560      block7c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7c_se_expand (Conv2D)      (None, 1, 1, 3840)   618240      block7c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7c_se_excite (Multiply)    (None, None, None, 3 0           block7c_activation[0][0]         \n",
      "                                                                 block7c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7c_project_conv (Conv2D)   (None, None, None, 6 2457600     block7c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7c_project_bn (BatchNormal (None, None, None, 6 2560        block7c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7c_drop (Dropout)          (None, None, None, 6 0           block7c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7c_add (Add)               (None, None, None, 6 0           block7c_drop[0][0]               \n",
      "                                                                 block7b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7d_expand_conv (Conv2D)    (None, None, None, 3 2457600     block7c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7d_expand_bn (BatchNormali (None, None, None, 3 15360       block7d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7d_expand_activation (Acti (None, None, None, 3 0           block7d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7d_dwconv (DepthwiseConv2D (None, None, None, 3 34560       block7d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7d_bn (BatchNormalization) (None, None, None, 3 15360       block7d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7d_activation (Activation) (None, None, None, 3 0           block7d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7d_se_squeeze (GlobalAvera (None, 3840)         0           block7d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7d_se_reshape (Reshape)    (None, 1, 1, 3840)   0           block7d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7d_se_reduce (Conv2D)      (None, 1, 1, 160)    614560      block7d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7d_se_expand (Conv2D)      (None, 1, 1, 3840)   618240      block7d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7d_se_excite (Multiply)    (None, None, None, 3 0           block7d_activation[0][0]         \n",
      "                                                                 block7d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7d_project_conv (Conv2D)   (None, None, None, 6 2457600     block7d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7d_project_bn (BatchNormal (None, None, None, 6 2560        block7d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7d_drop (Dropout)          (None, None, None, 6 0           block7d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7d_add (Add)               (None, None, None, 6 0           block7d_drop[0][0]               \n",
      "                                                                 block7c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "top_conv (Conv2D)               (None, None, None, 2 1638400     block7d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "top_bn (BatchNormalization)     (None, None, None, 2 10240       top_conv[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "top_activation (Activation)     (None, None, None, 2 0           top_bn[0][0]                     \n",
      "==================================================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 64,097,687\n",
      "Trainable params: 63,786,960\n",
      "Non-trainable params: 310,727\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "813"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "        loss={\n",
    "            'regression': smooth_l1(),\n",
    "            'classification': focal()\n",
    "        },\n",
    "        optimizer=tf.keras.optimizers.Adam(lr=1e-5, clipnorm=0.001)\n",
    "        # optimizer=keras.optimizers.SGD()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regression': <function __main__.smooth_l1.<locals>._smooth_l1(y_true, y_pred)>,\n",
       " 'classification': <function __main__.focal.<locals>._focal(y_true, y_pred)>}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.loss\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None, None, 2560)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.output_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "resnet = Resnet('resnet50')\n",
    "tert = np.random.rand(1,300,300,3).astype(np.float32)\n",
    "bert = resnet(tert)\n",
    "sert = model(tert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([1, 150, 150, 64]),\n",
       " TensorShape([1, 150, 150, 256]),\n",
       " TensorShape([1, 75, 75, 512]),\n",
       " TensorShape([1, 38, 38, 1024]),\n",
       " TensorShape([1, 19, 19, 2048]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert[0].shape, bert[1].shape, bert[2].shape, bert[3].shape, bert[4].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 9, 9, 2560])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sert.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "class Retinanet(Model):\n",
    "    def __init__(self ,num_classes,num_anchors=9,submodels=None,name='retinanet'):\n",
    "        super(self.__class__,self).__init__()\n",
    "        \n",
    "#         submodels = default_submodels(num_classes, num_anchors)\n",
    "        self.backbone = Resnet('resnet50')\n",
    "        self.pyramid = Pyramid(feature_size=256)\n",
    "        self.regression_model = Default_Regression_Model(num_anchors) \n",
    "        self.classification_model = Default_Classification_Model(num_classes, num_anchors)\n",
    "        self.concat1 = Concatenate(axis=1)\n",
    "        self.concat2 = Concatenate(axis=1)\n",
    "        \n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        \n",
    "        _,_,C3, C4, C5 = self.backbone(inputs)\n",
    "        features = self.pyramid(C3, C4, C5)\n",
    "        \n",
    "        c1 = self.classification_model(features[0])\n",
    "        c2 = self.classification_model(features[1])\n",
    "        c3 = self.classification_model(features[2])\n",
    "        c4 = self.classification_model(features[3])\n",
    "        c5 = self.classification_model(features[4])\n",
    "        C1 = self.concat1([c1,c2,c3,c4,c5])\n",
    "        \n",
    "        r1  = self.regression_model(features[0])\n",
    "        r2  = self.regression_model(features[1])\n",
    "        r3  = self.regression_model(features[2])\n",
    "        r4  = self.regression_model(features[3])\n",
    "        r5  = self.regression_model(features[4])\n",
    "        R1  = self.concat2([r1,r2,r3,r4,r5])\n",
    "        \n",
    "        print(C1.shape ,R1.shape)\n",
    "        return features\n",
    "#INCOMEPLETE BBOX Make prediction model"
   ]
  }
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