padding='same' 转换为 PyTorch padding=#

Question

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

    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'))

这产生以下摘要：

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 = ?),
        )

填充应该有数值的地方。我想知道是否有更简单的方法来计算这个，因为我们使用的是 padding='same'。

此外，Keras 模型的下一行看起来像：

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

所以我真的需要复习如何计算填充，尤其是在 stride 之后。仅从粗略的角度来看，填充是 2 吗？

Answer 1

W：输入体积大小

F：内核大小

S：跨步

P：填充量

输出音量大小=(W-F+2P)/S+1

例如

输入：7x7，内核：3x3，步幅：1，pad：0

输出大小 = (7-3+2*0)/1+1 = 5 =>5x5