Baseline arch

basicsr/models/archs/Baseline_arch.py

@@ -0,0 +1,202 @@
+# ------------------------------------------------------------------------
+# Copyright (c) 2022 megvii-model. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+'''
+Simple Baselines for Image Restoration
+
+@article{chen2022simple,
+ title={Simple Baselines for Image Restoration},
+ author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
+ journal={arXiv preprint arXiv:2204.04676},
+ year={2022}
+}
+'''
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from basicsr.models.archs.arch_util import LayerNorm2d
+from basicsr.models.archs.local_arch import Local_Base
+
+class BaselineBlock(nn.Module):
+ def __init__(self, c, DW_Expand=1, FFN_Expand=2, drop_out_rate=0.):
+ super().__init__()
+ dw_channel = c * DW_Expand
+ self.conv1 = nn.Conv2d(in_channels=c, out_channels=dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
+ self.conv2 = nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel, kernel_size=3, padding=1, stride=1, groups=dw_channel,
+ bias=True)
+ self.conv3 = nn.Conv2d(in_channels=dw_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
+
+ # Channel Attention
+ self.se = nn.Sequential(
+ nn.AdaptiveAvgPool2d(1),
+ nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1,
+ groups=1, bias=True),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel, kernel_size=1, padding=0, stride=1,
+ groups=1, bias=True),
+ nn.Sigmoid()
+ )
+
+ # GELU
+ self.gelu = nn.GELU()
+
+ ffn_channel = FFN_Expand * c
+ self.conv4 = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
+ self.conv5 = nn.Conv2d(in_channels=ffn_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
+
+ self.norm1 = LayerNorm2d(c)
+ self.norm2 = LayerNorm2d(c)
+
+ self.dropout1 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
+ self.dropout2 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
+
+ self.beta = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True)
+ self.gamma = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True)
+
+ def forward(self, inp):
+ x = inp
+
+ x = self.norm1(x)
+
+ x = self.conv1(x)
+ x = self.conv2(x)
+ x = self.gelu(x)
+ x = x * self.se(x)
+ x = self.conv3(x)
+
+ x = self.dropout1(x)
+
+ y = inp + x * self.beta
+
+ x = self.conv4(self.norm2(y))
+ x = self.gelu(x)
+ x = self.conv5(x)
+
+ x = self.dropout2(x)
+
+ return y + x * self.gamma
+
+
+class Baseline(nn.Module):
+
+ def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[], dw_expand=1, ffn_expand=2):
+ super().__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(
+ nn.Sequential(
+ *[BaselineBlock(chan, dw_expand, ffn_expand) for _ in range(num)]
+ )
+ )
+ self.downs.append(
+ nn.Conv2d(chan, 2*chan, 2, 2)
+ )
+ chan = chan * 2
+
+ self.middle_blks = \
+ nn.Sequential(
+ *[BaselineBlock(chan, dw_expand, ffn_expand) for _ in range(middle_blk_num)]
+ )
+
+ 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(
+ nn.Sequential(
+ *[BaselineBlock(chan, dw_expand, ffn_expand) for _ in range(num)]
+ )
+ )
+
+ self.padder_size = 2 ** len(self.encoders)
+
+ def forward(self, inp):
+ B, C, H, W = inp.shape
+ inp = self.check_image_size(inp)
+
+ x = self.intro(inp)
+
+ encs = []
+
+ for encoder, down in zip(self.encoders, self.downs):
+ x = encoder(x)
+ encs.append(x)
+ x = down(x)
+
+ x = self.middle_blks(x)
+
+ for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]):
+ x = up(x)
+ x = x + enc_skip
+ x = decoder(x)
+
+ x = self.ending(x)
+ x = x + inp
+
+ return x[:, :, :H, :W]
+
+ 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))
+ return x
+
+class BaselineLocal(Local_Base, Baseline):
+ def __init__(self, *args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs):
+ Local_Base.__init__(self)
+ Baseline.__init__(self, *args, **kwargs)
+
+ N, C, H, W = train_size
+ base_size = (int(H * 1.5), int(W * 1.5))
+
+ self.eval()
+ with torch.no_grad():
+ self.convert(base_size=base_size, train_size=train_size, fast_imp=fast_imp)
+
+if __name__ == '__main__':
+ img_channel = 3
+ width = 32
+
+ dw_expand = 1
+ ffn_expand = 2
+
+ # enc_blks = [2, 2, 4, 8]
+ # middle_blk_num = 12
+ # dec_blks = [2, 2, 2, 2]
+
+ enc_blks = [1, 1, 1, 28]
+ middle_blk_num = 1
+ dec_blks = [1, 1, 1, 1]
+
+ net = Baseline(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num,
+ enc_blk_nums=enc_blks, dec_blk_nums=dec_blks, dw_expand=dw_expand, ffn_expand=ffn_expand)
+
+ inp_shape = (3, 256, 256)
+
+ from ptflops import get_model_complexity_info
+
+ macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=False)
+
+ params = float(params[:-3])
+ macs = float(macs[:-4])
+
+ print(macs, params)