python 手动实现卷积操作

ゝ一世哀愁。 2024-04-17 05:51 54阅读 0赞

主要考虑卷积操作的公式即可：

import numpy as np 
    def conv2d(img, in_channels, out_channels ,kernels, bias, stride=1, padding=0):
    	N, C, H, W = img.shape 
    	kh, kw = kernels.shape
    	p = padding
    	assert C == in_channels, "kernels' input channels do not match with img"
    
    	if p:
    		img = np.pad(img, ((0,0),(0,0),(p,p),(p,p)), 'constant') # padding along with all axis
    
    	out_h = (H + 2*padding - kh) // stride + 1
    	out_w = (W + 2*padding - kw) // stride + 1
    
    	outputs = np.zeros([N, out_channels, out_h, out_w])
    	# print(img)
    	for n in range(N):
    		for out in range(out_channels):
    			for i in range(in_channels):
    				for h in range(out_h):
    					for w in range(out_w):
    						for x in range(kh):
    							for y in range(kw):
    								outputs[n][out][h][w] += img[n][i][h * stride + x][w * stride + y] * kernels[x][y]
    				# if i == in_channels - 1:
    				# 	outputs[n][out][:][:] += bias[n][out]
    	return outputs
    
    if __name__ == '__main__':
    	img = np.asarray(
    		[[
    			[
    				[0,1,2,3,4],
    				[1,2,3,4,5],
    				[2,3,4,5,6],
    				[3,4,5,6,7],
    				[4,5,6,7,8],
    			],
    			[
    				[1,2,3,4,5],
    				[2,3,4,5,6],
    				[3,4,5,6,7],
    				[4,5,6,7,8],
    				[5,6,7,8,9]
    			],
    			[
    				[2,3,4,5,6],
    				[3,4,5,6,7],
    				[4,5,6,7,8],
    				[5,6,7,8,9],
    				[6,7,8,9,0]
    			],
    		]]
    		)
    
    	kernels = np.asarray(
    		[
    			[1,1,1],
    			[1,1,1],
    			[1,1,1]
    		]
    		)
    	bias = np.asarray([[0,1],[0,0],[0,0]])
    
    	in_channels = 3
    	out_channels = 3
    
    	outputs = conv2d(img, in_channels, out_channels, kernels, bias, 2, 1)
    	print(outputs)