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slow_fast_net.py
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import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers import Conv3D
import numpy as np
def swish(x, beta=1):
return x * tf.nn.sigmoid(beta * x)
def mish(x):
return x * tf.nn.tanh(tf.nn.softplus(x))
tf.keras.utils.get_custom_objects().update({"swish": swish})
tf.keras.utils.get_custom_objects().update({"mish": mish})
def auto_pad(inputs, kernel_size, data_format):
"""
This function replaces the padding implementation in original tensorflow.
It also avoids negative dimension by automatically padding given the input kernel size (for each dimension).
"""
islist = isinstance(kernel_size, tuple)
kernel_size = np.array(kernel_size)
pad_total = kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
if islist:
paddings = np.concatenate(
[pad_beg[:, np.newaxis], pad_end[:, np.newaxis]], axis=1
)
paddings = [list(p) for p in paddings]
else:
paddings = [[pad_beg, pad_end]] * 3
if data_format == "channels_first":
padded_inputs = tf.pad(tensor=inputs, paddings=[[0, 0], [0, 0]] + paddings)
else:
padded_inputs = tf.pad(tensor=inputs, paddings=[[0, 0]] + paddings + [[0, 0]])
return padded_inputs
class ConvXD(tf.keras.layers.Layer):
def __init__(
self,
filters,
kernel_size,
strides,
padding="valid",
use_bias=False,
name="conv_3d",
data_format="channels_last",
**kwargs
):
super(ConvXD, self).__init__(name=name, **kwargs)
# self.name = name
self.pad = False
if isinstance(strides, list) or isinstance(strides, tuple):
self.pad = any(np.array(strides) > 1)
else:
self.pad = strides > 1
if self.pad:
padding = "valid"
else:
padding = "same"
self.conv = Conv3D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
use_bias=use_bias,
)
self.data_format = data_format
self.kernel_size = kernel_size
@tf.function
def call(self, inputs, training=None):
if self.pad:
inputs = auto_pad(
inputs=inputs,
kernel_size=self.kernel_size,
data_format=self.data_format,
)
outputs = self.conv(inputs)
return outputs
class PreactBlock(tf.keras.layers.Layer):
def __init__(self, activation="swish", **kwargs):
super(PreactBlock, self).__init__()
self.batch_norm = layers.BatchNormalization(epsilon=1e-5)
self.act = layers.Activation(activation)
def get_config(self):
return super().get_config()
@tf.function
def call(self, inputs, training=False):
x = self.batch_norm(inputs, training)
x = self.act(x)
return x
class DataLayer(layers.Layer):
def __init__(self, stride):
super(DataLayer, self).__init__()
self.stride = stride
@tf.function
def call(self, inputs):
x = inputs[:, :: self.stride, :, :, :]
return x
def get_config(self):
config = super(DataLayer, self).get_config()
config.update({"stride": self.stride})
return config
class InitBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, use_bias=False):
super(InitBlock, self).__init__()
self.conv3d = ConvXD(
filters, kernel_size, strides=(1, 2, 2), padding="same", use_bias=use_bias
)
self.maxpl3d = layers.MaxPool3D(
pool_size=(1, 3, 3), strides=(1, 2, 2), padding="same"
)
def get_config(self):
return super().get_config()
@tf.function
def call(self, inputs, training=False):
x = self.conv3d(inputs)
x = self.maxpl3d(x)
return x
class ResBlock(tf.keras.layers.Layer):
def __init__(
self,
filters,
time_kernel_size=1,
stride=1,
shortcut=None,
shortcut_stride=1,
use_bias=False,
**kwargs
):
super(ResBlock, self).__init__()
self.conv3d1 = ConvXD(
filters, (time_kernel_size, 1, 1), 1, padding="same", use_bias=use_bias
)
self.preact1 = PreactBlock()
self.conv3d2 = ConvXD(
filters, (1, 3, 3), (1, stride, stride), padding="same", use_bias=use_bias
)
self.preact2 = PreactBlock()
self.conv3d3 = ConvXD(
filters * 4, 1, strides=1, padding="same", use_bias=use_bias
)
self.conv3d3_batch = layers.BatchNormalization(epsilon=1e-5)
if shortcut == True:
self.shortcut = ConvXD(
filters * 4,
1,
(1, shortcut_stride, shortcut_stride),
padding="same",
use_bias=use_bias,
)
else:
self.shortcut = None
@tf.function
def call(self, inputs, training=False):
x = self.conv3d1(inputs)
x = self.preact1(x, training)
x = self.conv3d2(x)
x = self.preact2(x, training)
x = self.conv3d3(x)
x = self.conv3d3_batch(x)
if self.shortcut:
x = layers.Add()([x, self.shortcut(inputs)])
else:
x = layers.Add()([x, inputs])
x = layers.Activation(activation="swish")(x)
return x
class SlowBody(tf.keras.layers.Layer):
def __init__(self, stages, filters=64):
super(SlowBody, self).__init__()
self.stages = stages
self.res_blocks = []
self.concat = layers.Concatenate()
self.init_block = InitBlock(filters, (1, 7, 7))
for stage_num, conv_num in enumerate(stages):
for conv in range(conv_num):
time_kernel_size = 1 if stage_num < 1 else 3
shortcut = True if conv == 0 else None
if conv == 0:
if stage_num == 0:
self.res_blocks.append(
ResBlock(
filters,
time_kernel_size=time_kernel_size,
shortcut=shortcut,
stride=1,
shortcut_stride=1,
)
)
else:
self.res_blocks.append(
ResBlock(
filters,
time_kernel_size=time_kernel_size,
shortcut=shortcut,
stride=2,
shortcut_stride=2,
)
)
else:
self.res_blocks.append(ResBlock(filters, shortcut=shortcut))
filters = filters * 2
self.global_avgpool3d = layers.GlobalAveragePooling3D()
@tf.function
def call(self, x, laterals, training=False):
num_res_block = 0
x = self.init_block(x)
for conv_num, lateral in zip(self.stages, laterals):
x = self.concat([x, lateral])
for _ in range(conv_num):
x = self.res_blocks[num_res_block](x, training)
num_res_block += 1
x = self.global_avgpool3d(x)
return x
class FastBody(tf.keras.layers.Layer):
def __init__(self, stages, filters: int = 8):
super(FastBody, self).__init__()
self.stages = stages
self.filters = filters
self.main_res_blocks = []
self.last_res_blocks = []
self.conv3s = []
self.first_lateral = ConvXD(
filters * 2,
kernel_size=(5, 1, 1),
strides=(8, 1, 1),
padding="same",
use_bias=False,
)
self.init_block = InitBlock(filters, (5, 7, 7))
for stage_num, conv_num in enumerate(stages):
for conv in range(conv_num):
shortcut = True if conv == 0 else None
if conv == 0:
if stage_num == 0:
filters = filters * 1
self.main_res_blocks.append(
ResBlock(
filters,
time_kernel_size=3,
shortcut=shortcut,
shortcut_stride=1,
)
)
else:
filters = filters * 2
self.main_res_blocks.append(
ResBlock(
filters,
time_kernel_size=3,
shortcut=shortcut,
stride=2,
shortcut_stride=2,
)
)
else:
if stage_num == 0:
self.main_res_blocks.append(
ResBlock(filters, time_kernel_size=3, shortcut=None)
)
else:
self.main_res_blocks.append(
ResBlock(
filters,
time_kernel_size=3,
shortcut=None,
shortcut_stride=2,
)
)
self.last_res_blocks.append(
ResBlock(filters, time_kernel_size=3, shortcut=None)
)
self.conv3s.append(
ConvXD(
filters * 8,
kernel_size=(5, 1, 1),
strides=(8, 1, 1),
padding="same",
use_bias=False,
)
)
self.global_avg_pool3d = layers.GlobalAveragePooling3D()
@tf.function
def call(self, x, training=False):
with tf.init_scope():
laterals = []
cnt_main_res_block = 0
cnt_last_res_block = 0
cnt_conv3s = 0
x = self.init_block(x)
first_lateral = self.first_lateral(x)
laterals.append(first_lateral)
for conv_num in self.stages:
for _ in range(conv_num):
x = self.main_res_blocks[cnt_main_res_block](x, training)
cnt_main_res_block += 1
x = self.last_res_blocks[cnt_last_res_block](x, training)
cnt_last_res_block += 1
lateral = self.conv3s[cnt_conv3s](x)
cnt_conv3s += 1
laterals.append(lateral)
x = self.global_avg_pool3d(x)
return x, laterals
class SlowFastNet(tf.keras.Model):
resnet = {
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3],
}
def __init__(self, input_shapes, resnet_size=50, outputs=3):
super(SlowFastNet, self).__init__()
self.input_shapes = input_shapes
self.fast_data_layer = DataLayer(2)
self.slow_data_layer = DataLayer(16)
self.fast_body = FastBody(
self.resnet[resnet_size], filters=input_shapes[0] // 8
)
self.slow_body = SlowBody(self.resnet[resnet_size], filters=input_shapes[0])
self.concat = layers.Concatenate()
self.dropout = layers.Dropout(0.6)
self.dense = layers.Dense(outputs, activation="softmax")
@tf.function
def call(self, x, training=False, mask=None):
fast_inputs = self.fast_data_layer(x)
slow_inputs = self.slow_data_layer(x)
fast_x, laterals = self.fast_body(fast_inputs, training)
slow_x = self.slow_body(slow_inputs, laterals, training)
x = self.concat([slow_x, fast_x])
x = self.dropout(x)
x = self.dense(x)
return x
def build_graph(self):
x = layers.Input(self.input_shape)
return tf.keras.Model(inputs=[x], outputs=self.call(x))