I'm a beginner with constructing neural networks in pytorch and Keras. I have the following Keras code for a variation of the AlexNet model:
def shallownet(nb_classes):
global img_size
model = Sequential()
model.add(Conv2D(64, (5, 5), input_shape=img_size, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same', data_format='channels_first'))
model.add(Conv2D(64, (5, 5), padding='same', data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same', data_format='channels_first'))
model.add(Flatten())
model.add(Dense(384))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(192))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
return model
This is based on the C1/C3 model described on page 12 of this paper: https://arxiv.org/pdf/1609.04836.pdf And I want to convert this to the Pytorch version of the NN. Specifically I'm trying to get it in the form of:
class AlexNetOWT_BN(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNetOWT_BN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2,
bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(384),
nn.Conv2d(384, 256, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256)
)
self.classifier = nn.Sequential(
nn.Linear(256 * 6 * 6, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(4096, 4096, bias=False),
nn.BatchNorm1d(4096),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(4096, num_classes)
)
#self.regime = {
# 0: {'optimizer': 'SGD', 'lr': 1e-2,
# 'weight_decay': 5e-4, 'momentum': 0.9},
# 10: {'lr': 5e-3},
# 15: {'lr': 1e-3, 'weight_decay': 0},
# 20: {'lr': 5e-4},
# 25: {'lr': 1e-4}
#}
self.regime = {
0: {'optimizer': 'SGD', 'lr': 1e-2,
'weight_decay': 5e-4, 'momentum': 0.9},
20: {'lr': 1e-3},
40: {'lr': 1e-4}
}
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.input_transform = {
'train': transforms.Compose([
transforms.Scale(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]),
'eval': transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
])
}
def forward(self, x):
x = self.features(x)
x = x.view(-1, 256 * 6 * 6)
x = self.classifier(x)
return x
The original AlexNet code for self.features is here (I switched the order of things above; not sure if that's allowed or not):
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2,
bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(384),
nn.Conv2d(384, 256, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256)
)
I am specifically confused about how to change the features and the classifier. The description of the model I want is here: https://arxiv.org/pdf/1609.04836.pdf on page 12, section B.3.
Thanks so much
Here's the code:
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=5, padding=2,
bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=5, padding=2, bias=False),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.classifier = nn.Sequential(
nn.Linear(64 * 7 * 7, 384, bias=False),
nn.BatchNorm1d(384),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(384, 192, bias=False),
nn.BatchNorm1d(192),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(192, num_classes)
)
self.regime = {
0: {'optimizer': 'SGD', 'lr': 1e-3,
'weight_decay': 5e-4, 'momentum': 0.9},
60: {'lr': 1e-2},
120: {'lr': 1e-3},
180: {'lr': 1e-4}
}
def forward(self, x):
x = self.features(x)
x = x.view(-1, 64 * 7 * 7)
x = self.classifier(x)
return F.log_softmax(x)
def cifar10_shallow(**kwargs):
num_classes = getattr(kwargs, 'num_classes', 10)
return AlexNet(num_classes)
def cifar100_shallow(**kwargs):
num_classes = getattr(kwargs, 'num_classes', 100)
return AlexNet(num_classes)
Written by Wei Wen. smoothout repository
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