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resnet_model.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: resnet_model.py
import tensorflow as tf
from tensorflow.contrib.layers import variance_scaling_initializer
from tensorpack.tfutils.argscope import argscope, get_arg_scope
from tensorpack.models import (
Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm, BNReLU, FullyConnected,
LinearWrap)
### return the shortcut given l as the input and n_out as the desired number of shortcut output channels
def resnet_shortcut(l, n_out, stride, nl = tf.identity):
data_format = get_arg_scope()['Conv2D']['data_format']
n_in = l.get_shape().as_list()[1 if data_format == 'NCHW' else 3]
if n_in != n_out: # change dimension via 1x1 conv when channel is not the same
return Conv2D('convshortcut', l, n_out, 1, stride=stride, nl=nl)
else:
return l
### return the processed branch and the shortcut branch
def apply_preactivation(l, preact):
if preact == 'bnrelu':
shortcut = l # preserve identity mapping
l = BNReLU('preact', l)
else:
shortcut = l
return l, shortcut
### return a BatchNorm layer given x, name and zero_init
def get_bn(zero_init=False):
"""
Zero init gamma is good for resnet. See https://arxiv.org/abs/1706.02677.
Besides, moving average deserves a more careful control
"""
if zero_init:
return lambda x, name: BatchNorm('bn', x, gamma_init=tf.zeros_initializer())
else:
return lambda x, name: BatchNorm('bn', x)
### a typical module of resnet block with pre-activation
### i --> 3x3 conv --> 3x3 conv --> + --> o
### | |
### -----( possible 1x1 conv )----
def preresnet_basicblock(l, ch_out, stride, preact):
l, shortcut = apply_preactivation(l, preact)
l = Conv2D('conv1', l, ch_out, 3, stride=stride, nl=BNReLU)
l = Conv2D('conv2', l, ch_out, 3)
return l + resnet_shortcut(shortcut, ch_out, stride)
### another version of resnet block with bottlebneck
### i --> 1x1 conv --> 3x3 conv --> 1x1 conv --> + -->
### | |
### ----------------> 1x1 conv ----------------
def preresnet_bottleneck(l, ch_out, stride, preact):
# stride is applied on the second conv, following fb.resnet.torch
l, shortcut = apply_preactivation(l, preact)
l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
l = Conv2D('conv3', l, ch_out * 4, 1)
return l + resnet_shortcut(shortcut, ch_out * 4, stride)
###
def preresnet_group(l, name, block_func, features, count, stride):
with tf.variable_scope(name):
for i in range(0, count):
with tf.variable_scope('block{}'.format(i)):
# first block doesn't need activation
l = block_func(l, features,
stride if i == 0 else 1,
'no_preact' if i == 0 else 'bnrelu')
# end of each group need an extra activation
l = BNReLU('bnlast', l)
return l
###
def resnet_basicblock(l, ch_out, stride):
shortcut = l
l = Conv2D('conv1', l, ch_out, 3, stride = stride, nl = BNReLU)
tf.identity(l, 'BNReLU1')
l = Conv2D('conv2', l, ch_out, 3, nl = get_bn(zero_init=True))
tf.identity(l, 'BNReLU2')
return l + resnet_shortcut(shortcut, ch_out, stride, nl = get_bn(zero_init = False))
###
def resnet_bottleneck(l, ch_out, stride, stride_first = False):
"""
stride_first: original resnet put stride on first conv. fb.resnet.torch put stride on second conv.
"""
shortcut = l
l = Conv2D('conv1', l, ch_out, 1, stride = stride if stride_first else 1, nl = BNReLU)
l = Conv2D('conv2', l, ch_out, 3, stride = 1 if stride_first else stride, nl = BNReLU)
l = Conv2D('conv3', l, ch_out * 4, 1, nl = get_bn(zero_init = True))
return l + resnet_shortcut(shortcut, ch_out * 4, stride, nl = get_bn(zero_init = False))
###
def se_resnet_bottleneck(l, ch_out, stride):
shortcut = l
l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
l = Conv2D('conv3', l, ch_out * 4, 1, nl=get_bn(zero_init=True))
squeeze = GlobalAvgPooling('gap', l)
squeeze = FullyConnected('fc1', squeeze, ch_out // 4, nl=tf.nn.relu)
squeeze = FullyConnected('fc2', squeeze, ch_out * 4, nl=tf.nn.sigmoid)
l = l * tf.reshape(squeeze, [-1, ch_out * 4, 1, 1])
return l + resnet_shortcut(shortcut, ch_out * 4, stride, nl=get_bn(zero_init=False))
###
def resnet_group(l, name, block_func, features, count, stride):
with tf.variable_scope(name):
for i in range(0, count):
with tf.variable_scope('block{}'.format(i)):
l = block_func(l, features, stride if i == 0 else 1)
# end of each block need an activation
# @ 20171122 to make it convenient to extract the output
# here we set the name as ReLU_output
l = tf.nn.relu(l, name = 'ReLU_output')
return l
### architecture of ResNet
### 7x7 conv -> four cascaded groups with increased channels -> global average pooling & FC -> softmax
def resnet_backbone(image, num_blocks, group_func, block_func, output_dims):
with argscope(Conv2D, nl = tf.identity, use_bias = False, W_init = variance_scaling_initializer(mode = 'FAN_OUT')):
logits = (LinearWrap(image)
.Conv2D('conv0', 64, 7, stride = 2, nl = BNReLU)
.MaxPooling('pool0', shape = 3, stride = 2, padding = 'SAME')
.apply(group_func, 'group0', block_func, 64, num_blocks[0], 1)
.apply(group_func, 'group1', block_func, 128, num_blocks[1], 2)
.apply(group_func, 'group2', block_func, 256, num_blocks[2], 2)
.apply(group_func, 'group3', block_func, 512, num_blocks[3], 2)
.GlobalAvgPooling('gap')
.FullyConnected('linear_C{}'.format(output_dims), output_dims, \
nl = tf.identity)())
return logits
### THE END