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ntddk/ResNet_A.py

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Deep Residual Network definition by Chainer
Raw
ResNet_A.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import chainer
import chainer.links as L
import chainer.functions as F
def Convolution2D(in_size, ch, ksize, stride, pad=0):
return L.Convolution2D(in_size, ch, ksize, stride, pad,
math.sqrt(2 / (ksize ** 2 * in_size)))
class BottleNeck(chainer.Chain):
def __init__(self, in_size, ch1, ch2, ch3, stride=1, ksize=1):
super(BottleNeck, self).__init__(
conv1=Convolution2D(in_size, ch1, ksize, stride),
bn1=L.BatchNormalization(ch1),
conv2=Convolution2D(ch1, ch2, 3, 1, 1),
bn2=L.BatchNormalization(ch2),
conv3=Convolution2D(ch2, ch3, 1, 1),
bn3=L.BatchNormalization(ch3),
)
def __call__(self, x, train):
h = F.relu(self.bn1(self.conv1(x), test=not train))
h = F.relu(self.bn2(self.conv2(h), test=not train))
h = self.bn3(self.conv3(h), test=not train)
if x.data.shape != h.data.shape:
xp = chainer.cuda.get_array_module(x.data)
n, c, hh, ww = x.data.shape
pad_c = h.data.shape[1] - c
p = xp.zeros((n, pad_c, hh, ww), dtype=xp.float32)
p = chainer.Variable(p, volatile=not train)
x = F.concat((p, x))
if x.data.shape[2:] != h.data.shape[2:]:
x = F.average_pooling_2d(x, 1, 2)
return F.relu(h + x)
class ResNet(chainer.Chain):
insize = 224
def __init__(self, block_class):
super(ResNet, self).__init__()
links = [('conv1', Convolution2D(3, 64, 7, 2))]
links += [('bn1', L.BatchNormalization(64))]
links += [('_mpool1', F.MaxPooling2D(3, 2, 0, True, True))]
for i in range(3):
links += [('res{}'.format(len(links)),
block_class(256 if i > 0 else 64, 64, 64, 256))]
for i in range(8):
links += [('res{}'.format(len(links)),
block_class(512 if i > 0 else 256, 128, 128, 512,
1 if i > 0 else 2))]
for i in range(36):
links += [('res{}'.format(len(links)),
block_class(1024 if i > 0 else 512, 256, 256, 1024,
1 if i > 0 else 2))]
for i in range(3):
links += [('res{}'.format(len(links)),
block_class(2048 if i > 0 else 1024, 512, 512, 2048,
1 if i > 0 else 2))]
links += [('_apool{}'.format(len(links)),
F.AveragePooling2D(7, 1, 0, False, True))]
links += [('fc{}'.format(len(links)),
L.Linear(2048, 1000))]
for link in links:
if not link[0].startswith('_'):
self.add_link(*link)
self.forward = links
self.train = True
def clear(self):
self.loss = None
self.accuracy = None
def __call__(self, x, t=None):
self.clear()
for name, f in self.forward:
if 'res' in name:
x = f(x, self.train)
else:
x = f(x)
if t is not None:
self.loss = F.softmax_cross_entropy(x, t)
self.accuracy = F.accuracy(x, t)
return self.loss
else:
return x
model = ResNet(BottleNeck)
Raw
ResNet_B.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import chainer
import chainer.links as L
import chainer.functions as F
def Convolution2D(in_size, ch, ksize, stride, pad=0):
return L.Convolution2D(in_size, ch, ksize, stride, pad,
math.sqrt(2 / (ksize ** 2 * in_size)))
class BottleNeck(chainer.Chain):
def __init__(self, in_size, ch1, ch2, ch3, proj=False, stride=1, ksize=1):
super(BottleNeck, self).__init__(
conv1=Convolution2D(in_size, ch1, ksize, stride),
bn1=L.BatchNormalization(ch1),
conv2=Convolution2D(ch1, ch2, 3, 1, 1),
bn2=L.BatchNormalization(ch2),
conv3=Convolution2D(ch2, ch3, 1, 1),
bn3=L.BatchNormalization(ch3),
)
if proj:
self.add_link('res', Convolution2D(in_size, ch3, 1, stride))
def __call__(self, x, train):
h = F.relu(self.bn1(self.conv1(x), test=not train))
h = F.relu(self.bn2(self.conv2(h), test=not train))
h = self.bn3(self.conv3(h), test=not train)
if 'res' in self._children:
x = self.res(x)
return F.relu(h + x)
class ResNet(chainer.Chain):
insize = 224
def __init__(self, block_class):
super(ResNet, self).__init__()
links = [('conv1', Convolution2D(3, 64, 7, 2))]
links += [('bn1', L.BatchNormalization(64))]
links += [('_mpool1', F.MaxPooling2D(3, 2, 0, True, True))]
for i in range(3):
links += [('res{}'.format(len(links)),
block_class(256 if i > 0 else 64, 64, 64, 256,
False if i > 0 else True))]
for i in range(8):
links += [('res{}'.format(len(links)),
block_class(512 if i > 0 else 256, 128, 128, 512,
False if i > 0 else True,
1 if i > 0 else 2))]
for i in range(36):
links += [('res{}'.format(len(links)),
block_class(1024 if i > 0 else 512, 256, 256, 1024,
False if i > 0 else True,
1 if i > 0 else 2))]
for i in range(3):
links += [('res{}'.format(len(links)),
block_class(2048 if i > 0 else 1024, 512, 512, 2048,
False if i > 0 else True,
1 if i > 0 else 2))]
links += [('_apool{}'.format(len(links)),
F.AveragePooling2D(7, 1, 0, False, True))]
links += [('fc{}'.format(len(links)),
L.Linear(2048, 1000))]
for link in links:
if not link[0].startswith('_'):
self.add_link(*link)
self.forward = links
self.train = True
def clear(self):
self.loss = None
self.accuracy = None
def __call__(self, x, t=None):
self.clear()
for name, f in self.forward:
if 'res' in name:
x = f(x, self.train)
else:
x = f(x)
if t is not None:
self.loss = F.softmax_cross_entropy(x, t)
self.accuracy = F.accuracy(x, t)
return self.loss
else:
return x
model = ResNet(BottleNeck)
Raw
test_forward.py
import ResNet_A
import ResNet_B
import numpy as np
from chainer import Variable
from chainer import computational_graph
def forward_test(resnet, graph_fn):
x = np.random.rand(1, 3, 224, 224).astype(np.float32)
pred = resnet(Variable(x, volatile=False))
with open(graph_fn, 'w') as o:
g = computational_graph.build_computational_graph(
(pred,), remove_split=True)
o.write(g.dump())
return pred
resnet = ResNet_A.model
ret = forward_test(resnet, 'resnet_A.dot')
resnet = ResNet_B.model
forward_test(resnet, 'resnet_B.dot')
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