Feature/mps

config_collab.yaml

@@ -17,25 +17,27 @@ num_epochs_pretraining: 270
 num_epochs_finetuning: 100
 val_split: 0.2
 random_seed: 42
-cosine_annealing: False
+cosine_annealing: True
 iso_range: [0, 999999]
 
 # --- Experiment Settings ---
 experiment: NAF_test
 mlflow_experiment: NAFNet_variations
 
 # --- Run Configuration ---:
-run_name: NAF_ps
+run_name: NAF_debayer_in
 run_path: NAF_deep_test_align
 model_params:
-  chans: [64, 128, 256, 512, 512, 512]
+  # chans: [64, 128, 256, 512, 512, 512]
+  chans: [32, 64, 128, 256, 256, 256]
+
   enc_blk_nums: [2, 2, 2, 3, 4]
   middle_blk_num: 12
   dec_blk_nums: [2, 2, 2, 2, 2]
   cond_input: 1
-  in_channels: 4
+  in_channels: 3
   out_channels: 3
-  rggb: True
+  rggb: False
   use_CondFuserV2: False
   use_add: False
   use_CondFuserV3: False

src/Restorer/Cond_NAF.py

@@ -900,3 +900,29 @@ def make_full_model_PS(params, model_name=None):
         model.load_state_dict(state_dict)
     return model
 
+
+
+class ModelWrapperDemoIn(nn.Module):
+    def __init__(self, **kwargs):
+        super().__init__()
+        if 'gamma' in kwargs:
+            kwargs.pop('gamma')
+
+        self.demosaicer = DemosaicingFromRGGB()
+        self.model = Restorer(
+            **kwargs
+        )
+
+    def forward(self, rggb, cond, *args):
+        debayered = self.demosaicer(rggb, cond)
+        output = self.model(debayered, cond)
+        output = (debayered + output)
+        return output
+
+
+def make_full_model_RGGB_DemoIn(params, model_name=None):
+    model = ModelWrapperDemoIn(**params)
+    if not model_name is None:
+        state_dict = torch.load(model_name, map_location="cpu")
+        model.load_state_dict(state_dict)
+    return model

src/Restorer/Cond_NAF_SSID.py

@@ -0,0 +1,278 @@
+# ------------------------------------------------------------------------
+# 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
+
+class LayerNorm2dAdjusted(nn.Module):
+    def __init__(self, channels, eps=1e-6):
+        super(LayerNorm2d, self).__init__()
+        self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
+        self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
+        self.eps = eps
+
+    def forward(self, x, target_mu, target_var):
+        mu = x.mean(1, keepdim=True)
+        var = (x - mu).pow(2).mean(1, keepdim=True)
+
+        y = (x - mu) / torch.sqrt(var + self.eps)
+
+        y = y * torch.sqrt(target_var + self.eps) + target_mu
+
+        weight_view = self.weight.view(1, self.weight.size(0), 1, 1)
+        bias_view = self.bias.view(1, self.bias.size(0), 1, 1)
+
+        y = weight_view * y + bias_view
+        return y
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, channels, eps=1e-6):
+        super(LayerNorm2d, self).__init__()
+        self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
+        self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
+        self.eps = eps
+
+    def forward(self, x):
+        mu = x.mean(1, keepdim=True)
+        var = (x - mu).pow(2).mean(1, keepdim=True)
+
+        y = (x - mu) / torch.sqrt(var + self.eps)
+
+        weight_view = self.weight.view(1, self.weight.size(0), 1, 1)
+        bias_view = self.bias.view(1, self.bias.size(0), 1, 1)
+
+        y = weight_view * y + bias_view
+        return y
+
+class ChannelAttention(nn.Module):
+    def __init__(self, dims):
+        super().__init__()
+        self.sca = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels=dims, out_channels=dims, kernel_size=1, padding=0, stride=1,
+                      groups=1, bias=True),
+        )
+
+    def forward(self, x):
+        return self.sca(x)
+
+class CondFuser(nn.Module):
+    def __init__(self, chan):
+        super().__init__()
+        self.cca = ChannelAttention(chan * 2)
+
+    def forward(self, x1, x2):
+        x = torch.cat([x1, x2], dim=1)
+        x = self.cca(x) * x
+
+        x1, x2 = x.chunk(2, dim=1)
+        return x1 + x2
+
+class CondFuserAdd(nn.Module):
+    def __init__(self, chan):
+        super().__init__()
+
+    def forward(self, x1, x2):
+        return x1 + x2
+
+class SimpleGate(nn.Module):
+    def forward(self, x):
+        x1, x2 = x.chunk(2, dim=1)
+        return x1 * x2
+
+class NAFBlock(nn.Module):
+    def __init__(self, c, DW_Expand=2, 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 // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
+
+        # Simplified Channel Attention
+        self.sca = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1,
+                      groups=1, bias=True),
+        )
+
+        # SimpleGate
+        self.sg = SimpleGate()
+
+        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 // 2, 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.sg(x)
+        x = x * self.sca(x)
+        x = self.conv3(x)
+
+        x = self.dropout1(x)
+
+        y = inp + x * self.beta
+
+        x = self.conv4(self.norm2(y))
+        x = self.sg(x)
+        x = self.conv5(x)
+
+        x = self.dropout2(x)
+
+        return y + x * self.gamma
+
+
+class NAFNet(nn.Module):
+
+    def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[],
+                use_add = False):
+        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()
+        self.merges = nn.ModuleList()
+
+        chan = width
+        for num in enc_blk_nums:
+            self.encoders.append(
+                nn.Sequential(
+                    *[NAFBlock(chan) for _ in range(num)]
+                )
+            )
+            self.downs.append(
+                nn.Conv2d(chan, 2*chan, 2, 2)
+            )
+            chan = chan * 2
+
+        self.middle_blks = \
+            nn.Sequential(
+                *[NAFBlock(chan) 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(
+                    *[NAFBlock(chan) for _ in range(num)]
+                )
+            )
+
+            if use_add:
+                self.merges.append(CondFuserAdd(chan))
+            else:
+                self.merges.append(CondFuser(chan))
+
+        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, merge, enc_skip in zip(self.decoders, self.ups, self.merges, encs[::-1]):
+            x = up(x)
+            x = merge(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 NAFNetLocal(Local_Base, NAFNet):
+    def __init__(self, *args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs):
+        Local_Base.__init__(self)
+        NAFNet.__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
+
+    # 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 = NAFNet(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num,
+                      enc_blk_nums=enc_blks, dec_blk_nums=dec_blks)
+
+
+    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)