[example] Move

example/README.md

@@ -7,7 +7,6 @@ Notebooks
 * [composite symbol](notebooks/composite_symbol.ipynb) gives you a demo of how to composite a symbolic Inception-BatchNorm Network
 * [cifar-10 recipe](notebooks/cifar-recipe.ipynb) gives you a step by step demo of how to use MXNet
 * [cifar-100](notebooks/cifar-100.ipynb) gives you a demo of how to train a 75.68% accuracy CIFAR-100 model
-* [predict with pretained model](notebooks/predict-with-pretrained-model.ipynb) gives you a demo of use a pretrained Inception-BN Network
 * [simple bind](notebooks/simple_bind.ipynb) gives you a demo of some details in ```mx.model``` module.
 
 Contents

example/imagenet/README.md

@@ -12,7 +12,11 @@ Note: A commonly mistake is forgetting shuffle the image list. This will lead fa
 
 - [alexnet.py](alexnet.py) : alexnet with 5 convolution layers followed by 3
   fully connnected layers
-- [inception.py](inception.py) : inception + batch norm network
+- [inception.py](inception.py) : inception + batch norm network for ImageNet with 1000 classes problem
+- [inception-full.py](inception-full.py) : This inception network is used for ImageNet with 21841 classes
+
+## Notebooks
+- [predict with pretained model](predict-with-pretrained-model.ipynb) gives you a demo of use a pretrained Inception-BN Network
 
 ## Results
 

example/imagenet/inception-full.py

@@ -0,0 +1,101 @@
+# pylint: skip-file
+import sys
+sys.path.insert(0, "../mxnet/python")
+import mxnet as mx
+import logging
+from data import ilsvrc12_iterator
+
+
+logger = logging.getLogger()
+logger.setLevel(logging.DEBUG)
+
+def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0), name=None, suffix=''):
+    conv = mx.symbol.Convolution(data=data, workspace=512, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, name='conv_%s%s' %(name, suffix))
+    bn = mx.symbol.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))
+    act = mx.symbol.Activation(data=bn, act_type='relu', name='relu_%s%s' %(name, suffix))
+    return act
+
+def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red, num_d3x3, pool, proj, name):
+    # 1x1
+    c1x1 = ConvFactory(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_1x1' % name))
+    # 3x3 reduce + 3x3
+    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')
+    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_3x3' % name))
+    # double 3x3 reduce + double 3x3
+    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1), name=('%s_double_3x3' % name), suffix='_reduce')
+    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_0' % name))
+    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), name=('%s_double_3x3_1' % name))
+    # pool + proj
+    pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
+    cproj = ConvFactory(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_proj' %  name))
+    # concat
+    concat = mx.symbol.Concat(*[c1x1, c3x3, cd3x3, cproj], name='ch_concat_%s_chconcat' % name)
+    return concat
+
+def InceptionFactoryB(data, num_3x3red, num_3x3, num_d3x3red, num_d3x3, name):
+    # 3x3 reduce + 3x3
+    c3x3r = ConvFactory(data=data, num_filter=num_3x3red, kernel=(1, 1), name=('%s_3x3' % name), suffix='_reduce')
+    c3x3 = ConvFactory(data=c3x3r, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=('%s_3x3' % name))
+    # double 3x3 reduce + double 3x3
+    cd3x3r = ConvFactory(data=data, num_filter=num_d3x3red, kernel=(1, 1),  name=('%s_double_3x3' % name), suffix='_reduce')
+    cd3x3 = ConvFactory(data=cd3x3r, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=('%s_double_3x3_0' % name))
+    cd3x3 = ConvFactory(data=cd3x3, num_filter=num_d3x3, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=('%s_double_3x3_1' % name))
+    # pool + proj
+    pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type="max", name=('max_pool_%s_pool' % name))
+    # concat
+    concat = mx.symbol.Concat(*[c3x3, cd3x3, pooling], name='ch_concat_%s_chconcat' % name)
+    return concat
+
+def inception(nhidden, grad_scale):
+    # data
+    data = mx.symbol.Variable(name="data")
+    # stage 1
+    conv1 = ConvFactory(data=data, num_filter=96, kernel=(7, 7), stride=(2, 2), pad=(3, 3), name='conv1')
+    pool1 = mx.symbol.Pooling(data=conv1, kernel=(3, 3), stride=(2, 2), name='pool1', pool_type='max')
+    # stage 2
+    conv2red = ConvFactory(data=pool1, num_filter=128, kernel=(1, 1), stride=(1, 1), name='conv2red')
+    conv2 = ConvFactory(data=conv2red, num_filter=288, kernel=(3, 3), stride=(1, 1), pad=(1, 1), name='conv2')
+    pool2 = mx.symbol.Pooling(data=conv2, kernel=(3, 3), stride=(2, 2), name='pool2', pool_type='max')
+    # stage 2
+    in3a = InceptionFactoryA(pool2, 96, 96, 96, 96, 144, "avg", 48, '3a')
+    in3b = InceptionFactoryA(in3a, 96, 96, 144, 96, 144, "avg", 96, '3b')
+    in3c = InceptionFactoryB(in3b, 192, 240, 96, 144, '3c')
+    # stage 3
+    in4a = InceptionFactoryA(in3c, 224, 64, 96, 96, 128, "avg", 128, '4a')
+    in4b = InceptionFactoryA(in4a, 192, 96, 128, 96, 128, "avg", 128, '4b')
+    in4c = InceptionFactoryA(in4b, 160, 128, 160, 128, 160, "avg", 128, '4c')
+    in4d = InceptionFactoryA(in4c, 96, 128, 192, 160, 96, "avg", 128, '4d')
+    in4e = InceptionFactoryB(in4d, 128, 192, 192, 256, '4e')
+    # stage 4
+    in5a = InceptionFactoryA(in4e, 352, 192, 320, 160, 224, "avg", 128, '5a')
+    in5b = InceptionFactoryA(in5a, 352, 192, 320, 192, 224, "max", 128, '5b')
+    # global avg pooling
+    avg = mx.symbol.Pooling(data=in5b, kernel=(7, 7), stride=(1, 1), name="global_pool", pool_type='avg')
+    # linear classifier
+    flatten = mx.symbol.Flatten(data=avg, name='flatten')
+    fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=nhidden, name='fc1')
+    softmax = mx.symbol.Softmax(data=fc1, name='softmax')
+    return softmax
+
+softmax = inception(21841, 1.0)
+
+batch_size = 64
+num_gpu = 4
+gpus = [mx.gpu(i) for i in range(num_gpu)]
+input_shape = (3, 224, 224)
+
+train = ilsvrc12_iterator(batch_size=batch_size, input_shape=(3,224,224))
+
+model_prefix = "model/Inception-Full"
+num_round = 10
+
+logging.info("This script is used to train ImageNet fullset over 21841 classes.")
+logging.info("For noraml 1000 classes problem, please use inception.py")
+
+model = mx.model.FeedForward(ctx=gpus, symbol=softmax, num_round=num_round,
+                             learning_rate=0.05, momentum=0.9, wd=0.00001)
+
+model.fit(X=train,
+          eval_metric="acc",
+          epoch_end_callback=[mx.callback.Speedometer(batch_size), mx.callback.log_train_metric(100)],
+	  iter_end_callback=mx.callback.do_checkpoint(model_prefix))