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ResNet50.py
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ResNet50.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import chainer
import chainer.functions as F
import chainer.links as L
import math
import numpy as np
class BottleNeckA(chainer.Chain):
def __init__(self, in_size, ch, out_size, stride=2):
w = math.sqrt(2)
super(BottleNeckA, self).__init__(
conv1=L.Convolution2D(in_size, ch, 1, stride, 0, w, nobias=True),
bn1=L.BatchNormalization(ch),
conv2=L.Convolution2D(ch, ch, 3, 1, 1, w, nobias=True),
bn2=L.BatchNormalization(ch),
conv3=L.Convolution2D(ch, out_size, 1, 1, 0, w, nobias=True),
bn3=L.BatchNormalization(out_size),
conv4=L.Convolution2D(
in_size, out_size, 1, stride, 0, w, nobias=True),
bn4=L.BatchNormalization(out_size),
)
def __call__(self, x, train):
h1 = F.relu(self.bn1(self.conv1(x), test=not train))
h1 = F.relu(self.bn2(self.conv2(h1), test=not train))
h1 = self.bn3(self.conv3(h1), test=not train)
h2 = self.bn4(self.conv4(x), test=not train)
return F.relu(h1 + h2)
class BottleNeckB(chainer.Chain):
def __init__(self, in_size, ch):
w = math.sqrt(2)
super(BottleNeckB, self).__init__(
conv1=L.Convolution2D(in_size, ch, 1, 1, 0, w, nobias=True),
bn1=L.BatchNormalization(ch),
conv2=L.Convolution2D(ch, ch, 3, 1, 1, w, nobias=True),
bn2=L.BatchNormalization(ch),
conv3=L.Convolution2D(ch, in_size, 1, 1, 0, w, nobias=True),
bn3=L.BatchNormalization(in_size),
)
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)
return F.relu(h + x)
class Block(chainer.Chain):
def __init__(self, layer, in_size, ch, out_size, stride=2):
super(Block, self).__init__()
links = [('a', BottleNeckA(in_size, ch, out_size, stride))]
for i in range(layer - 1):
links += [('b_{}'.format(i + 1), BottleNeckB(out_size, ch))]
for link in links:
self.add_link(*link)
self.forward = links
def __call__(self, x, train):
for name, _ in self.forward:
f = getattr(self, name)(x, train)
return x
class ResNet50(chainer.Chain):
def __init__(self):
self.train = True
w = math.sqrt(2)
super(ResNet50, self).__init__()
self.add_link('conv1', L.Convolution2D(3, 64, 7, 2, 3, w, nobias=True))
self.add_link('bn1', L.BatchNormalization(64))
self.add_link('res2', Block(3, 64, 64, 256, 1))
self.add_link('res3', Block(4, 256, 128, 512))
self.add_link('res4', Block(6, 512, 256, 1024))
self.add_link('res5', Block(3, 1024, 512, 2048))
def clear(self):
self.loss = None
self.pred = None
self.pre_rec = None
def __call__(self, x, t, before_fc=False):
self.clear()
h = self.bn1(self.conv1(x), test=not self.train)
h = F.max_pooling_2d(F.relu(h), 3, stride=2)
h = self.res2(h, self.train)
h = self.res3(h, self.train)
h = self.res4(h, self.train)
h = self.res5(h, self.train)
h = F.average_pooling_2d(h, h.data.shape[2], stride=1)
self.feature = h
return h