Convert a simple cnn from keras to pytorch - keras

Question

Can anyone please help me to convert this model to PyTorch? I already tried to convert from Keras to PyTorch like this How can I convert this keras cnn model to pytorch version but training results were different. Thank you.
input_3d = (1, 64, 96, 96)
pool_3d = (2, 2, 2)
model = Sequential()
model.add(Convolution3D(8, 3, 3, 3, name='conv1', input_shape=input_3d,
data_format='channels_first'))
model.add(MaxPooling3D(pool_size=pool_3d, name='pool1'))
model.add(Convolution3D(8, 3, 3, 3, name='conv2',data_format='channels_first'))
model.add(MaxPooling3D(pool_size=pool_3d, name='pool2'))
model.add(Convolution3D(8, 3, 3, 3, name='conv3',data_format='channels_first'))
model.add(MaxPooling3D(pool_size=pool_3d, name='pool3'))
model.add(Flatten())
model.add(Dense(2000, activation='relu', name='dense1'))
model.add(Dropout(0.5, name='dropout1'))
model.add(Dense(500, activation='relu', name='dense2'))
model.add(Dropout(0.5, name='dropout2'))
model.add(Dense(3, activation='softmax', name='softmax'))
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1 (Conv3D) (None, 8, 60, 94, 94) 224
_________________________________________________________________
pool1 (MaxPooling3D) (None, 8, 30, 47, 47) 0
_________________________________________________________________
conv2 (Conv3D) (None, 8, 28, 45, 45) 1736
_________________________________________________________________
pool2 (MaxPooling3D) (None, 8, 14, 22, 22) 0
_________________________________________________________________
conv3 (Conv3D) (None, 8, 12, 20, 20) 1736
_________________________________________________________________
pool3 (MaxPooling3D) (None, 8, 6, 10, 10) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 4800) 0
_________________________________________________________________
dense1 (Dense) (None, 2000) 9602000
_________________________________________________________________
dropout1 (Dropout) (None, 2000) 0
_________________________________________________________________
dense2 (Dense) (None, 500) 1000500
_________________________________________________________________
dropout2 (Dropout) (None, 500) 0
_________________________________________________________________
softmax (Dense) (None, 3) 1503
=================================================================

Answer 1

Your PyTorch equivalent of the Keras model would look like this:
class CNN(nn.Module):
def __init__(self, ):
super(CNN, self).__init__()
self.maxpool = nn.MaxPool3d((2, 2, 2))
self.conv1 = nn.Conv3d(in_channels=1, out_channels=8, kernel_size=3)
self.conv2 = nn.Conv3d(in_channels=8, out_channels=8, kernel_size=3)
self.conv3 = nn.Conv3d(in_channels=8, out_channels=8, kernel_size=3)
self.linear1 = nn.Linear(4800, 2000)
self.dropout1 = nn.Dropout3d(0.5)
self.linear2 = nn.Linear(2000, 500)
self.dropout2 = nn.Dropout3d(0.5)
self.linear3 = nn.Linear(500, 3)
def forward(self, x):
out = self.maxpool(self.conv1(x))
out = self.maxpool(self.conv2(out))
out = self.maxpool(self.conv3(out))
# Flattening process
b, c, d, h, w = out.size() # batch_size, channels, depth, height, width
out = out.view(-1, c * d * h * w)
out = self.dropout1(self.linear1(out))
out = self.dropout2(self.linear2(out))
out = self.linear3(out)
out = torch.softmax(out, 1)
return out
A driver program to test the model:
inputs = torch.randn(8, 1, 64, 96, 96)
model = CNN()
outputs = model(inputs)
print(outputs.shape) # torch.Size([8, 3])

Answer 2

You can save keras weight and reload then in pytorch.
the steps are
Step 0: Train a Model in Keras. ...
Step 1: Recreate & Initialize Your Model Architecture in PyTorch. ...
Step 2: Import Your Keras Model and Copy the Weights. ...
Step 3: Load Those Weights onto Your PyTorch Model. ...
Step 4: Test and Save Your Pytorch Model.
You Can follow example here https://gereshes.com/2019/06/24/how-to-transfer-a-simple-keras-model-to-pytorch-the-hard-way/