Batch size = 32
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block1): Block(
(skip): Conv2d(128, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(skipbn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): SeparableConv2d(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(pointwise): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): SeparableConv2d(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(pointwise): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
)
(block2): Block(
(skip): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(skipbn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(pointwise): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(pointwise): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
)
(block3): Block(
(skip): Conv2d(256, 728, kernel_size=(1, 1), stride=(2, 2), bias=False)
(skipbn): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(pointwise): Conv2d(256, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
)
(block4): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block5): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block6): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block7): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block8): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block9): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block10): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block11): Block(
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU(inplace=True)
(7): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block12): Block(
(skip): Conv2d(728, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(skipbn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(rep): Sequential(
(0): ReLU()
(1): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): SeparableConv2d(
(conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False)
(pointwise): Conv2d(728, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
)
(conv3): SeparableConv2d(
(conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False)
(pointwise): Conv2d(1024, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bn3): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv4): SeparableConv2d(
(conv1): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)
(pointwise): Conv2d(1536, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
for i,data in enumerate(train_loader):
#dataset[0][0]
#dataset = dataset[0].view(dataset[0].size(0), -1)
image,label1,label2,label3 = data
print(image.size())
yhat = model(image.float()) """
However model doesnt take input and wants an input of size [128 128 1 1]
How do I give the images as input to the model?