python 수 동 볼 륨 작업 실현

주로 볼 륨 작업 의 공식 을 고려 하면 된다.

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)