for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
# block: object, planes: output channel, blocks: the num of blocks
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion # the input channel num become 4 times
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet50(pretrained = True):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3])
# model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
model.load_state_dict(torch.load('./resnet50_20170907_state_dict.pth'))
return model
cnn = resnet50(pretrained=True) # the output number is 9
cnn.cuda()
cnn.eval()
criterion = nn.MSELoss().cuda()
for i, (test_images, test_labels, fn) in enumerate(test_loader): # the first i in index, and the () is the content
test_images = Variable(test_images.cuda())
test_labels = Variable(test_labels.cuda())
outputs = cnn(test_images)
print(outputs.data[0])
print(fn)
loss = criterion(outputs, test_labels)
print("Iter [%d/%d] Test_Loss: %.4f" % (i + 1, 781, loss.data[0]))
着重看定义ｄａｔａｌｏａｄｅｒ以及返回图像名称的一段代码：
def random_choose_data(label_path):
random.seed(1)
file = open(label_path)
lines = file.readlines()
slice_initial = random.sample(lines, 200000) # if don't change this ,it will be all the same
slice = list(set(lines)-set(slice_initial))
random.shuffle(slice)
train_label = slice[:150000]
test_label = slice[150000:200000]
return train_label, test_label # output the list and delvery it into ImageFolder