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Configuration error
Configuration error
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from .common import *
_norm = bn
# _norm = pixelnorm
def norm(channels):
return _norm(channels)
def skip(
num_input_channels=2,
num_output_channels=3,
num_channels_down=[16, 32, 64, 128, 128],
num_channels_up=[16, 32, 64, 128, 128],
num_channels_skip=[4, 4, 4, 4, 4],
filter_size_down=3,
filter_size_up=3,
filter_skip_size=1,
need_sigmoid=True,
need_tanh=False,
need_bias=True,
pad="reflection",
upsample_mode="bilinear",
downsample_mode="stride",
act_fun="LeakyReLU",
need1x1_up=True,
decorr_rgb=True,
):
"""Assembles encoder-decoder with skip connections.
Arguments:
act_fun: Either string 'LeakyReLU|Swish|ELU|none' or module (e.g. nn.ReLU)
pad (string): zero|reflection (default: 'zero')
upsample_mode (string): 'nearest|bilinear' (default: 'nearest')
downsample_mode (string): 'stride|avg|max|lanczos2' (default: 'stride')
"""
assert len(num_channels_down) == len(num_channels_up) == len(num_channels_skip)
n_scales = len(num_channels_down)
if not (isinstance(upsample_mode, list) or isinstance(upsample_mode, tuple)):
upsample_mode = [upsample_mode] * n_scales
if not (isinstance(downsample_mode, list) or isinstance(downsample_mode, tuple)):
downsample_mode = [downsample_mode] * n_scales
if not (isinstance(filter_size_down, list) or isinstance(filter_size_down, tuple)):
filter_size_down = [filter_size_down] * n_scales
if not (isinstance(filter_size_up, list) or isinstance(filter_size_up, tuple)):
filter_size_up = [filter_size_up] * n_scales
last_scale = n_scales - 1
cur_depth = None
model = nn.Sequential()
# model.add(transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]))
model_tmp = model
input_depth = num_input_channels
for i in range(len(num_channels_down)):
deeper = nn.Sequential()
skip = nn.Sequential()
if num_channels_skip[i] != 0:
model_tmp.add(Concat(1, skip, deeper))
else:
model_tmp.add(deeper)
model_tmp.add(norm(num_channels_skip[i] + (num_channels_up[i + 1] if i < last_scale else num_channels_down[i])))
if num_channels_skip[i] != 0:
skip.add(conv(input_depth, num_channels_skip[i], filter_skip_size, bias=need_bias, pad=pad))
skip.add(norm(num_channels_skip[i]))
skip.add(act(act_fun))
# skip.add(Concat(2, GenNoise(nums_noise[i]), skip_part))
deeper.add(
conv(
input_depth,
num_channels_down[i],
filter_size_down[i],
2,
bias=need_bias,
pad=pad,
downsample_mode=downsample_mode[i],
)
)
deeper.add(norm(num_channels_down[i]))
deeper.add(act(act_fun))
deeper.add(conv(num_channels_down[i], num_channels_down[i], filter_size_down[i], bias=need_bias, pad=pad))
deeper.add(norm(num_channels_down[i]))
deeper.add(act(act_fun))
deeper_main = nn.Sequential()
if i == len(num_channels_down) - 1:
# The deepest
k = num_channels_down[i]
else:
deeper.add(deeper_main)
k = num_channels_up[i + 1]
deeper.add(nn.Upsample(scale_factor=2, mode=upsample_mode[i]))
model_tmp.add(conv(num_channels_skip[i] + k, num_channels_up[i], filter_size_up[i], 1, bias=need_bias, pad=pad))
# if i > 0:
# model_tmp.add(norm(num_channels_up[i]))
model_tmp.add(norm(num_channels_up[i]))
model_tmp.add(act(act_fun))
if need1x1_up:
model_tmp.add(conv(num_channels_up[i], num_channels_up[i], 1, bias=need_bias, pad=pad))
if i > 0:
model_tmp.add(norm(num_channels_up[i]))
model_tmp.add(act(act_fun))
input_depth = num_channels_down[i]
model_tmp = deeper_main
model.add(conv(num_channels_up[0], num_output_channels, 1, bias=need_bias, pad=pad))
if decorr_rgb:
model.add(DecorrelatedColorsToRGB())
if need_sigmoid:
model.add(nn.Sigmoid())
elif need_tanh:
model.add(nn.Tanh())
return model
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