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import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from arch_model import EBlock, DBlock
from arch_util import CustomSequential
except:
from archs.arch_model import EBlock, DBlock
from .arch_util import CustomSequential
class DarkIR(nn.Module):
def __init__(self, img_channel=3,
width=32,
middle_blk_num_enc=2,
middle_blk_num_dec=2,
enc_blk_nums=[1, 2, 3],
dec_blk_nums=[3, 1, 1],
dilations = [1, 4, 9],
extra_depth_wise = True):
super(DarkIR, self).__init__()
self.intro = nn.Conv2d(in_channels=img_channel, out_channels=width, kernel_size=3, padding=1, stride=1, groups=1,
bias=True)
self.ending = nn.Conv2d(in_channels=width, out_channels=img_channel, kernel_size=3, padding=1, stride=1, groups=1,
bias=True)
self.encoders = nn.ModuleList()
self.decoders = nn.ModuleList()
self.middle_blks = nn.ModuleList()
self.ups = nn.ModuleList()
self.downs = nn.ModuleList()
chan = width
for num in enc_blk_nums:
self.encoders.append(
CustomSequential(
*[EBlock(chan, extra_depth_wise=extra_depth_wise) for _ in range(num)]
)
)
self.downs.append(
nn.Conv2d(chan, 2*chan, 2, 2)
)
chan = chan * 2
self.middle_blks_enc = \
CustomSequential(
*[EBlock(chan, extra_depth_wise=extra_depth_wise) for _ in range(middle_blk_num_enc)]
)
self.middle_blks_dec = \
CustomSequential(
*[DBlock(chan, dilations=dilations, extra_depth_wise=extra_depth_wise) for _ in range(middle_blk_num_dec)]
)
for num in dec_blk_nums:
self.ups.append(
nn.Sequential(
nn.Conv2d(chan, chan * 2, 1, bias=False),
nn.PixelShuffle(2)
)
)
chan = chan // 2
self.decoders.append(
CustomSequential(
*[DBlock(chan, dilations=dilations, extra_depth_wise=extra_depth_wise) for _ in range(num)]
)
)
self.padder_size = 2 ** len(self.encoders)
# this layer is needed for the computing of the middle loss. It isn't necessary for anything else
self.side_out = nn.Conv2d(in_channels = width * 2**len(self.encoders), out_channels = img_channel,
kernel_size = 3, stride=1, padding=1)
def forward(self, input, side_loss = False, use_adapter = None):
_, _, H, W = input.shape
input = self.check_image_size(input)
x = self.intro(input)
skips = []
for encoder, down in zip(self.encoders, self.downs):
x = encoder(x)
skips.append(x)
x = down(x)
# we apply the encoder transforms
x_light = self.middle_blks_enc(x)
if side_loss:
out_side = self.side_out(x_light)
# apply the decoder transforms
x = self.middle_blks_dec(x_light)
x = x + x_light
for decoder, up, skip in zip(self.decoders, self.ups, skips[::-1]):
x = up(x)
x = x + skip
x = decoder(x)
x = self.ending(x)
x = x + input
out = x[:, :, :H, :W] # we recover the original size of the image
if side_loss:
return out_side, out
else:
return out
def check_image_size(self, x):
_, _, h, w = x.size()
mod_pad_h = (self.padder_size - h % self.padder_size) % self.padder_size
mod_pad_w = (self.padder_size - w % self.padder_size) % self.padder_size
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), value = 0)
return x
if __name__ == '__main__':
img_channel = 3
width = 64
enc_blks = [1, 2, 3]
middle_blk_num_enc = 2
middle_blk_num_dec = 2
dec_blks = [3, 1, 1]
residual_layers = None
dilations = [1, 4, 9]
extra_depth_wise = True
net = DarkIR(img_channel=img_channel,
width=width,
middle_blk_num_enc=middle_blk_num_enc,
middle_blk_num_dec= middle_blk_num_dec,
enc_blk_nums=enc_blks,
dec_blk_nums=dec_blks,
dilations = dilations,
extra_depth_wise = extra_depth_wise)
new_state_dict = net.state_dict()
inp_shape = (3, 256, 256)
net.load_state_dict(new_state_dict)
from ptflops import get_model_complexity_info
macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=False)
print(macs, params)
weights = net.state_dict()
adapter_weights = {k: v for k, v in weights.items() if 'adapter' not in k}
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