padding ='same'转换为PyTorch padding =#

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问题描述

我正在尝试将以下Keras模型代码转换为pytorch，但是在处理padding ='same'时遇到问题.

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'))

产生以下摘要:

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

现在，我会写:

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 = ?),
        )

其中填充应具有数值的地方.我想知道是否有更简单的方法来计算此值，因为我们使用的是padding ='same'.

Where padding should have numerical value. I was wondering if there is an easier way to calculate this since we're using padding='same'.

此外，Keras模型的下一行如下所示:

Also, the next line of the Keras model looks like:

model.add(Conv2D(128, (3, 3), padding='same'))

所以我真的需要重新学习如何计算填充，特别是在大步之后. 从粗略的角度看，填充2是吗?

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?

padding

padding ='same'转换为PyTorch padding =#

问题描述

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