Пытаюсь протестировать ResNet:
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class ASDataset(Dataset):
def __init__(self, client_file: str, imposter_file: str, transforms = None):
with open(client_file, "r") as f:
client_files = f.read().splitlines()
with open(imposter_file, "r") as f:
imposter_files = f.read().splitlines()
self.labels = torch.cat((torch.ones(len(client_files)), \
torch.zeros(len(imposter_files))))
self.imgs = client_files + imposter_files
self.transforms = transforms
def __len__(self):
return len(self.imgs)
def __getitem__(self, idx):
img_name = self.imgs[idx]
img = Image.open(img_name)
label = self.labels[idx]
if self.transforms:
img = self.transforms(img)
return img, label
class Block(nn.Module):
def __init__(self, num_layers, in_channels, out_channels, identity_downsample=None, stride=1):
assert num_layers in [18, 34, 50, 101, 152], "should be a a valid architecture"
super(Block, self).__init__()
self.num_layers = num_layers
if self.num_layers > 34:
self.expansion = 4
else:
self.expansion = 1
# ResNet50, 101, and 152 include additional layer of 1x1 kernels
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(out_channels)
if self.num_layers > 34:
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)
else:
# for ResNet18 and 34, connect input directly to (3x3) kernel (skip first (1x1))
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, stride=1, padding=0)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU()
self.identity_downsample = identity_downsample
def forward(self, x):
identity = x
if self.num_layers > 34:
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
if self.identity_downsample is not None:
identity = self.identity_downsample(identity)
x += identity
x = self.relu(x)
return x
class ResNet(nn.Module):
def __init__(self, num_layers, block, image_channels, num_classes, **kwargs):
assert num_layers in [18, 34, 50, 101, 152], f'ResNet{num_layers}: Unknown architecture! Number of layers has ' \
f'to be 18, 34, 50, 101, or 152 '
super(ResNet, self).__init__()
if num_layers < 50:
self.expansion = 1
else:
self.expansion = 4
if num_layers == 18:
layers = [2, 2, 2, 2]
elif num_layers == 34 or num_layers == 50:
layers = [3, 4, 6, 3]
elif num_layers == 101:
layers = [3, 4, 23, 3]
else:
layers = [3, 8, 36, 3]
self.in_channels = 64
self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# ResNetLayers
self.layer1 = self.make_layers(num_layers, block, layers[0], intermediate_channels=64, stride=1)
self.layer2 = self.make_layers(num_layers, block, layers[1], intermediate_channels=128, stride=2)
self.layer3 = self.make_layers(num_layers, block, layers[2], intermediate_channels=256, stride=2)
self.layer4 = self.make_layers(num_layers, block, layers[3], intermediate_channels=512, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * self.expansion, num_classes)
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.reshape(x.shape[0], -1)
x = self.fc(x)
return torch.sigmoid(x)
def make_layers(self, num_layers, block, num_residual_blocks, intermediate_channels, stride):
layers = []
identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels, intermediate_channels*self.expansion, kernel_size=1, stride=stride),
nn.BatchNorm2d(intermediate_channels*self.expansion))
layers.append(block(num_layers, self.in_channels, intermediate_channels, identity_downsample, stride))
self.in_channels = intermediate_channels * self.expansion # 256
for i in range(num_residual_blocks - 1):
layers.append(block(num_layers, self.in_channels, intermediate_channels)) # 256 -> 64, 64*4 (256) again
return nn.Sequential(*layers)
def ResNet18(img_channels=3, num_classes=1):
return ResNet(18, Block, img_channels, num_classes)
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
torch.manual_seed(42)
kwargs = {"nfeat":64, "nhid":64, "nclass":1, "nheads":49, "dropout":0.6, "alpha":0.01}
model = ResNet18()
test_dataset = ASDataset(client_file="raw/client_train_raw.txt", imposter_file="raw/imposter_train_raw.txt", \
transforms=preprocess)
train_dataset = ASDataset(client_file="raw/client_test_raw.txt", imposter_file="raw/imposter_test_raw.txt", \
transforms=preprocess)
train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=128, shuffle=True)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=15e-4, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.3)
EPOCHS = 10
device = "cpu"
Но получаю AUC = 100, EER = 0
Разве такое может быть?
Есть подозрения, что неверно расписан сам ResNet или как-то не так его использую.
Простой
Простой
