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loss.py
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loss.py
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import tensorflow as tf
import os
import numpy as np
import inspect
def adversarial_loss(outputs, is_real, is_disc=None, type='nsgan'):
r"""
Adversarial loss
https://arxiv.org/abs/1711.10337
"""
outputs = tf.reshape(outputs, [-1])
if type == 'hinge':
if is_disc:
if is_real:
outputs = -outputs
return tf.reduce_mean(tf.nn.relu(1 + outputs))
else:
return tf.reduce_mean(-outputs)
elif type == 'nsgan':
labels = tf.ones_like(outputs) if is_real else tf.zeros_like(outputs)
loss = tf.keras.metrics.binary_crossentropy(labels, outputs)
return loss
elif type == 'lsgan':
labels = tf.ones_like(outputs) if is_real else tf.zeros_like(outputs)
loss = tf.keras.metrics.mean_squared_error(labels, outputs)
return loss
def seg_loss(pred, mask):
y_pred = tf.nn.softmax(pred, axis=-1)
pred1, pred2 = tf.split(y_pred, [1, 1], axis=-1)
label = tf.to_float(mask)
log1 = tf.log(tf.clip_by_value(pred1, 1e-8, 1.0 - 1e-8))
log2 = tf.log(tf.clip_by_value(pred2, 1e-8, 1.0 - 1e-8))
loss = - tf.transpose(tf.multiply(1 - label, log1), perm=[1, 2, 3, 0]) - \
tf.transpose(tf.multiply(label, log2), perm=[1, 2, 3, 0])
loss = tf.transpose(loss, perm=[3, 0, 1, 2])
loss = tf.reduce_mean(loss)
return loss
def focal_loss(pred, mask, ratio, gamma=0):
y_pred = tf.nn.softmax(pred, axis=-1)
pred1, pred2 = tf.split(y_pred, [1, 1], axis=-1)
label = tf.to_float(mask)
log1 = tf.multiply(pred2 ** gamma, tf.log(tf.clip_by_value(pred1, 1e-8, 1.0 - 1e-8)))
log2 = tf.multiply(pred1 ** gamma, tf.log(tf.clip_by_value(pred2, 1e-8, 1.0 - 1e-8)))
loss = - ratio * tf.transpose(tf.multiply(1 - label, log1), perm=[1, 2, 3, 0]) - \
(1 - ratio) * tf.transpose(tf.multiply(label, log2), perm=[1, 2, 3, 0])
loss = tf.transpose(loss, perm=[3, 0, 1, 2])
loss = tf.reduce_mean(loss)
return loss
def l1_loss(inputs, targets):
inputs = tf.reshape(inputs, [-1])
targets = tf.reshape(targets, [-1])
loss = tf.reduce_mean(tf.abs(inputs - targets))
return loss
def perceptual_loss(x, y, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
r"""
Perceptual loss, VGG-based
https://arxiv.org/abs/1603.08155
https://github.com/dxyang/StyleTransfer/blob/master/utils.py
"""
x_vgg = Vgg16(x)
y_vgg = Vgg16(y)
content_loss = 0.0
content_loss += l1_loss(x_vgg.pool1 / 255, y_vgg.pool1 / 255)
content_loss += l1_loss(x_vgg.pool2 / 255, y_vgg.pool2 / 255)
content_loss += l1_loss(x_vgg.pool3 / 255, y_vgg.pool3 / 255)
return content_loss
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg16:
def __init__(self, rgb, vgg16_npy_path=None):
if vgg16_npy_path is None:
path = inspect.getfile(Vgg16)
path = os.path.abspath(os.path.join(path, os.pardir))
path = os.path.join(path, "vgg16.npy")
vgg16_npy_path = path
# print(path)
self.data_dict = np.load(vgg16_npy_path, allow_pickle=True, encoding='latin1').item()
# print("npy file loaded")
self.build(rgb)
def build(self, rgb):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
"""
# print("build model started")
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
# assert red.get_shape().as_list()[1:] == [224, 224, 1]
# assert green.get_shape().as_list()[1:] == [224, 224, 1]
# assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
# assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
self.pool5 = self.max_pool(self.conv5_3, 'pool5')
self.data_dict = None
def avg_pool(self, bottom, name):
return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
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(self, bottom, name):
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 fc_layer(self, bottom, name):
with tf.variable_scope(name):
shape = bottom.get_shape().as_list()
dim = 1
for d in shape[1:]:
dim *= d
x = tf.reshape(bottom, [-1, dim])
weights = self.get_fc_weight(name)
biases = self.get_bias(name)
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
return fc
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 get_fc_weight(self, name):
return tf.constant(self.data_dict[name][0], name="weights")