get_dct_weights() cannot be found

[Yung-zi]

不是说和SENet相比就修改一行代码吗 ， 而且找不到get_dct_weights()这个函数

[cfzd]

FcaNet/model/layer.py

Lines 65 to 117 in aa5fb63

	class MultiSpectralDCTLayer(nn.Module):
	"""
	Generate dct filters
	"""
	def __init__(self, height, width, mapper_x, mapper_y, channel):
	super(MultiSpectralDCTLayer, self).__init__()
	assert len(mapper_x) == len(mapper_y)
	assert channel % len(mapper_x) == 0
	self.num_freq = len(mapper_x)
	# fixed DCT init
	self.register_buffer('weight', self.get_dct_filter(height, width, mapper_x, mapper_y, channel))
	# fixed random init
	# self.register_buffer('weight', torch.rand(channel, height, width))
	# learnable DCT init
	# self.register_parameter('weight', self.get_dct_filter(height, width, mapper_x, mapper_y, channel))
	# learnable random init
	# self.register_parameter('weight', torch.rand(channel, height, width))
	# num_freq, h, w
	def forward(self, x):
	assert len(x.shape) == 4, 'x must been 4 dimensions, but got ' + str(len(x.shape))
	# n, c, h, w = x.shape
	x = x * self.weight
	result = torch.sum(x, dim=[2,3])
	return result
	def build_filter(self, pos, freq, POS):
	result = math.cos(math.pi * freq * (pos + 0.5) / POS) / math.sqrt(POS)
	if freq == 0:
	return result
	else:
	return result * math.sqrt(2)
	def get_dct_filter(self, tile_size_x, tile_size_y, mapper_x, mapper_y, channel):
	dct_filter = torch.zeros(channel, tile_size_x, tile_size_y)
	c_part = channel // len(mapper_x)
	for i, (u_x, v_y) in enumerate(zip(mapper_x, mapper_y)):
	for t_x in range(tile_size_x):
	for t_y in range(tile_size_y):
	dct_filter[i * c_part: (i+1)*c_part, t_x, t_y] = self.build_filter(t_x, u_x, tile_size_x) * self.build_filter(t_y, v_y, tile_size_y)
	return dct_filter

[SmallBirdMing]

好像还是找不到pre_computed_dct_weights这个函数呀

[cfzd]

@SmallBirdMing
没有pre_computed_dct_weights这个函数