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vgg16.py
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vgg16.py
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# Adapted from : VGG 16 model : https://github.com/machrisaa/tensorflow-vgg
import time
import os
import inspect
import numpy as np
from termcolor import colored
import tensorflow as tf
from hed.losses import sigmoid_cross_entropy_balanced
from hed.utils.io import IO
class Vgg16():
def __init__(self, cfgs, run='training'):
self.cfgs = cfgs
self.io = IO()
base_path = os.path.abspath(os.path.dirname(__file__))
weights_file = os.path.join(base_path, self.cfgs['model_weights_path'])
self.data_dict = np.load(weights_file, encoding='latin1').item()
self.io.print_info("Model weights loaded from {}".format(self.cfgs['model_weights_path']))
self.images = tf.placeholder(tf.float32, [None, self.cfgs[run]['image_height'], self.cfgs[run]['image_width'], self.cfgs[run]['n_channels']])
self.edgemaps = tf.placeholder(tf.float32, [None, self.cfgs[run]['image_height'], self.cfgs[run]['image_width'], 1])
self.define_model()
def define_model(self):
"""
Load VGG params from disk without FC layers A
Add branch layers (with deconv) after each CONV block
"""
start_time = time.time()
self.conv1_1 = self.conv_layer_vgg(self.images, "conv1_1")
self.conv1_2 = self.conv_layer_vgg(self.conv1_1, "conv1_2")
self.side_1 = self.side_layer(self.conv1_2, "side_1", 1)
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.io.print_info('Added CONV-BLOCK-1+SIDE-1')
self.conv2_1 = self.conv_layer_vgg(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer_vgg(self.conv2_1, "conv2_2")
self.side_2 = self.side_layer(self.conv2_2, "side_2", 2)
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.io.print_info('Added CONV-BLOCK-2+SIDE-2')
self.conv3_1 = self.conv_layer_vgg(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer_vgg(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer_vgg(self.conv3_2, "conv3_3")
self.side_3 = self.side_layer(self.conv3_3, "side_3", 4)
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.io.print_info('Added CONV-BLOCK-3+SIDE-3')
self.conv4_1 = self.conv_layer_vgg(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer_vgg(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer_vgg(self.conv4_2, "conv4_3")
self.side_4 = self.side_layer(self.conv4_3, "side_4", 8)
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.io.print_info('Added CONV-BLOCK-4+SIDE-4')
self.conv5_1 = self.conv_layer_vgg(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer_vgg(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer_vgg(self.conv5_2, "conv5_3")
self.side_5 = self.side_layer(self.conv5_3, "side_5", 16)
self.io.print_info('Added CONV-BLOCK-5+SIDE-5')
self.side_outputs = [self.side_1, self.side_2, self.side_3, self.side_4, self.side_5]
w_shape = [1, 1, len(self.side_outputs), 1]
self.fuse = self.conv_layer(tf.concat(self.side_outputs, axis=3),
w_shape, name='fuse_1', use_bias=False,
w_init=tf.constant_initializer(0.2))
self.io.print_info('Added FUSE layer')
# complete output maps from side layer and fuse layers
self.outputs = self.side_outputs + [self.fuse]
self.data_dict = None
self.io.print_info("Build model finished: {:.4f}s".format(time.time() - start_time))
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def conv_layer_vgg(self, bottom, name):
"""
Adding a conv layer + weight parameters from a dict
"""
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def conv_layer(self, x, W_shape, b_shape=None, name=None,
padding='SAME', use_bias=True, w_init=None, b_init=None):
W = self.weight_variable(W_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
if use_bias:
b = self.bias_variable([b_shape], b_init)
tf.summary.histogram('biases_{}'.format(name), b)
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding)
return conv + b if use_bias else conv
def deconv_layer(self, x, upscale, name, padding='SAME', w_init=None):
x_shape = tf.shape(x)
in_shape = x.shape.as_list()
w_shape = [upscale * 2, upscale * 2, in_shape[-1], 1]
strides = [1, upscale, upscale, 1]
W = self.weight_variable(w_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
out_shape = tf.stack([x_shape[0], x_shape[1], x_shape[2], w_shape[2]]) * tf.constant(strides, tf.int32)
deconv = tf.nn.conv2d_transpose(x, W, out_shape, strides=strides, padding=padding)
return deconv
def side_layer(self, inputs, name, upscale):
"""
https://github.com/s9xie/hed/blob/9e74dd710773d8d8a469ad905c76f4a7fa08f945/examples/hed/train_val.prototxt#L122
1x1 conv followed with Deconvoltion layer to upscale the size of input image sans color
"""
with tf.variable_scope(name):
in_shape = inputs.shape.as_list()
w_shape = [1, 1, in_shape[-1], 1]
classifier = self.conv_layer(inputs, w_shape, b_shape=1,
w_init=tf.constant_initializer(),
b_init=tf.constant_initializer(),
name=name + '_reduction')
classifier = self.deconv_layer(classifier, upscale=upscale,
name='{}_deconv_{}'.format(name, upscale),
w_init=tf.truncated_normal_initializer(stddev=0.1))
return classifier
def get_conv_filter(self, name):
return tf.constant(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name="biases")
def weight_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def bias_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def setup_testing(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs for predictions
"""
self.predictions = []
for idx, b in enumerate(self.outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
self.predictions.append(output)
def setup_training(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs
Compute total loss := side_layer_loss + fuse_layer_loss
Compute predicted edge maps from fuse layer as pseudo performance metric to track
"""
self.predictions = []
self.loss = 0
self.io.print_warning('Deep supervision application set to {}'.format(self.cfgs['deep_supervision']))
for idx, b in enumerate(self.side_outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
cost = sigmoid_cross_entropy_balanced(b, self.edgemaps, name='cross_entropy{}'.format(idx))
self.predictions.append(output)
if self.cfgs['deep_supervision']:
self.loss += (self.cfgs['loss_weights'] * cost)
fuse_output = tf.nn.sigmoid(self.fuse, name='fuse')
fuse_cost = sigmoid_cross_entropy_balanced(self.fuse, self.edgemaps, name='cross_entropy_fuse')
self.predictions.append(fuse_output)
self.loss += (self.cfgs['loss_weights'] * fuse_cost)
pred = tf.cast(tf.greater(fuse_output, 0.5), tf.int32, name='predictions')
error = tf.cast(tf.not_equal(pred, tf.cast(self.edgemaps, tf.int32)), tf.float32)
self.error = tf.reduce_mean(error, name='pixel_error')
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('error', self.error)
self.merged_summary = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(self.cfgs['save_dir'] + '/train', session.graph)
self.val_writer = tf.summary.FileWriter(self.cfgs['save_dir'] + '/val')