/
fr_model.py
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/
fr_model.py
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import numpy as np
import chainer
from chainer import Variable
import chainer.functions as F
import chainer.links as L
from chainer import computational_graph
from chainer import cuda
from chainer import optimizers
from chainer import serializers
class FRModel(chainer.Chain):
def __init__(self, top="patchwise"):
super(FRModel, self).__init__(
# feature extraction
conv1 = L.Convolution2D(3, 32, 3, pad=1),
conv2 = L.Convolution2D(32, 32, 3, pad=1),
conv3 = L.Convolution2D(32, 64, 3, pad=1),
conv4 = L.Convolution2D(64, 64, 3, pad=1),
conv5 = L.Convolution2D(64, 128, 3, pad=1),
conv6 = L.Convolution2D(128, 128, 3, pad=1),
conv7 = L.Convolution2D(128, 256, 3, pad=1),
conv8 = L.Convolution2D(256, 256, 3, pad=1),
conv9 = L.Convolution2D(256, 512, 3, pad=1),
conv10 = L.Convolution2D(512, 512, 3, pad=1),
# quality regression
fc1 = L.Linear(512 * 3, 512),
fc2 = L.Linear(512, 1)
)
self.top = top
if top == "weighted":
fc1_a = L.Linear(512 * 3, 512)
fc2_a = L.Linear(512, 1)
self.add_link("fc1_a", fc1_a)
self.add_link("fc2_a", fc2_a)
def extract_features(self, x, train=True):
h = F.relu(self.conv1(x))
h = F.relu(self.conv2(h))
self.h1 = h
h = F.max_pooling_2d(h,2)
h = F.relu(self.conv3(h))
h = F.relu(self.conv4(h))
self.h2 = h
h = F.max_pooling_2d(h,2)
h = F.relu(self.conv5(h))
h = F.relu(self.conv6(h))
self.h3 = h
h = F.max_pooling_2d(h,2)
h = F.relu(self.conv7(h))
h = F.relu(self.conv8(h))
self.h4 = h
h = F.max_pooling_2d(h,2)
h = F.relu(self.conv9(h))
h = F.relu(self.conv10(h))
self.h5 = h
h = F.max_pooling_2d(h,2)
return h
def forward(self, x_data, x_ref_data, y_data, train=True,
n_patches_per_image=32):
xp = cuda.cupy
if not isinstance(x_data, Variable):
x = Variable(x_data)
else:
x = x_data
x_data = x.data
self.n_images = y_data.shape[0]
self.n_patches = x_data.shape[0]
self.n_patches_per_image = n_patches_per_image
x_ref = Variable(x_ref_data)
h = self.extract_features(x)
self.h = h
h_ref = self.extract_features(x_ref)
h = F.concat((h-h_ref, h, h_ref))
h_ = h # save intermediate features
h = F.dropout(F.relu(self.fc1(h)), ratio=0.5)
h = self.fc2(h)
if self.top == "weighted":
a = F.dropout(F.relu(self.fc1_a(h_)), ratio=0.5)
a = F.relu(self.fc2_a(a))+0.000001
t = Variable(y_data)
self.weighted_loss(h, a, t)
elif self.top == "patchwise":
a = Variable(xp.ones_like(h.data))
t = Variable(xp.repeat(y_data, n_patches_per_image))
self.patchwise_loss(h, a, t)
if train:
return self.loss
else:
return self.loss, self.y
def patchwise_loss(self, h, a, t):
self.loss = F.sum(abs(h - F.reshape(t, (-1,1))))
self.loss /= self.n_patches
if self.n_images > 1:
h = F.split_axis(h, self.n_images, 0)
a = F.split_axis(a, self.n_images, 0)
else:
h, a = [h], [a]
self.y = h
self.a = a
def weighted_loss(self, h, a, t):
self.loss = 0
if self.n_images > 1:
h = F.split_axis(h, self.n_images, 0)
a = F.split_axis(a, self.n_images, 0)
t = F.split_axis(t, self.n_images, 0)
else:
h, a, t = [h], [a], [t]
for i in range(self.n_images):
y = F.sum(h[i]*a[i], 0) / F.sum(a[i], 0)
self.loss += abs(y - F.reshape(t[i], (1,)))
self.loss /= self.n_images
self.y = h
self.a = a