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

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get_dct_weights() cannot be found about fcanet HOT 3 OPEN

cfzd commented on June 29, 2024

get_dct_weights() cannot be found

from fcanet.

Comments (3)

cfzd commented on June 29, 2024

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

from fcanet.

SmallBirdMing commented on June 29, 2024

好像还是找不到pre_computed_dct_weights这个函数呀

from fcanet.

cfzd commented on June 29, 2024

@SmallBirdMing
没有pre_computed_dct_weights这个函数

from fcanet.

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	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

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