卷积运算的意外结果

这是我编写的执行单个卷积并输出形状的代码。

使用来自的公式 http://cs231n.github.io/convolutional-networks/ 要计算输出大小:

你可以说服自己正确的计算公式 许多神经元__fit_由(w__f+2p)/s+1给出。

计算输出大小的公式如下所示:

def output_size(w , f , stride , padding) : 
        return (((w - f) + (2 * padding)) / stride) + 1

问题是 output_size 计算2690.5的大小,与1350的卷积结果不同:

%reset -f

import torch
import torch.nn.functional as F
import numpy as np
from PIL import Image
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from pylab import plt
plt.style.use('seaborn')
%matplotlib inline

width = 60
height = 30
kernel_size_param = 5
stride_param = 2
padding_param = 2

img = Image.new('RGB', (width, height), color = 'red')

in_channels = 3
out_channels = 3

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels, 
                      out_channels, 
                      kernel_size=kernel_size_param, 
                      stride=stride_param, 
                      padding=padding_param))

    def forward(self, x):
        out = self.layer1(x)

        return out

# w : input volume size
# f : receptive field size of the Conv Layer neurons
# output_size computes spatial size of output volume - spatial dimensions are (width, height)
def output_size(w , f , stride , padding) : 
    return (((w - f) + (2 * padding)) / stride) + 1

w = width * height * in_channels
f = kernel_size_param * kernel_size_param

print('output size :' , output_size(w , f , stride_param , padding_param))

model = ConvNet()

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=.001)

img_a = np.array(img)
img_pt = torch.tensor(img_a).float()
result = model(img_pt.view(3, width , height).unsqueeze_(0))
an = result.view(30 , 15 , out_channels).data.numpy()

# print(result.shape)
# print(an.shape)

# print(np.amin(an.flatten('F')))

print(30 * 15 * out_channels)

我是否正确实现了输出大小?如何修改此模型,使结果 Conv2d 形状与结果相同 输出大小 ?