#!/usr/bin/env python
# -*- coding: utf-8 -*-
import chainer.functions as F
import chainer.links as L
from chainer import Chain
class VGG_single(Chain):
def __init__(self):
super(VGG_single, self).__init__(
conv1_1=L.Convolution2D(3, 64, 3, stride=1, pad=1),
conv1_2=L.Convolution2D(64, 64, 3, stride=1, pad=1),
conv2_1=L.Convolution2D(64, 128, 3, stride=1, pad=1),
conv2_2=L.Convolution2D(128, 128, 3, stride=1, pad=1),
conv3_1=L.Convolution2D(128, 256, 3, stride=1, pad=1),
conv3_2=L.Convolution2D(256, 256, 3, stride=1, pad=1),
conv3_3=L.Convolution2D(256, 256, 3, stride=1, pad=1),
conv4_1=L.Convolution2D(256, 512, 3, stride=1, pad=1),
conv4_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
conv4_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
conv5_1=L.Convolution2D(512, 512, 3, stride=1, pad=1),
conv5_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
conv5_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
fc6=L.Linear(2048, 4096),
fc7=L.Linear(4096, 4096),
fc8=L.Linear(4096, 256),
)
self.train = True
def __call__(self, x, t):
h = F.relu(self.conv1_1(x))
h = F.relu(self.conv1_2(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.conv2_2(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.relu(self.conv3_3(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv4_1(h))
h = F.relu(self.conv4_2(h))
h = F.relu(self.conv4_3(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.conv5_1(h))
h = F.relu(self.conv5_2(h))
h = F.relu(self.conv5_3(h))
h = F.max_pooling_2d(h, 2, stride=2)
h = F.relu(self.fc6(h))
h = F.relu(self.fc7(h))
h = self.fc8(h)
self.pred = F.reshape(h, (x.data.shape[0], 1, 16, 16))
if t is not None:
self.loss = F.softmax_cross_entropy(self.pred, t, normalize=False)
self.loss /= 16 * 16
return self.loss
else:
self.pred = F.sigmoid(self.pred)
return self.pred
model = VGG_single()
