I'm trying to convert the following Keras model code to pytorch, but am having problems dealing with padding='same'.
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=img_size))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
Which produces the following summary:
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 64) 1792
_________________________________________________________________
batch_normalization_1 (Batch (None, 30, 30, 64) 120
_________________________________________________________________
activation_1 (Activation) (None, 30, 30, 64) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 30, 30, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 30, 30, 64) 36928
_________________________________________________________________
batch_normalization_2 (Batch (None, 30, 30, 64) 120
_________________________________________________________________
activation_2 (Activation) (None, 30, 30, 64) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 64) 0
=================================================================
Total params: 38,960
Trainable params: 38,840
Non-trainable params: 120
Right now, I would write:
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3,
bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Dropout(0.3),
nn.Conv2d(64, 64, kernel_size=3, padding = ?
bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding = ?),
)
Where padding should have numerical value. I was wondering if there is an easier way to calculate this since we're using padding='same'.
Also, the next line of the Keras model looks like:
model.add(Conv2D(128, (3, 3), padding='same'))
So I really need to brush up on how to calculate padding, especially after stride too. From a rough eye only, is the padding 2?
W:input volume size
F:kernel size
S:stride
P:amount of padding
size of output volume = (W-F+2P)/S+1
e.g.
input:7x7, kernel:3x3, stride:1, pad:0
output size = (7-3+2*0)/1+1 = 5 =>5x5
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