New version 1.3 update

.gitignore

@@ -3,6 +3,7 @@
 # Edit at https://www.toptal.com/developers/gitignore?templates=python,opencv
 
 ### Custom ###
+weight/
 *.mp4
 
 ### OpenCV ###

README.md

@@ -34,6 +34,22 @@ For this project, FGVC(Flow-edge Guided Video Completion) deep learning model wa
 | scipy          | 1.6.2      |
 
 
+# Usage
+
+```sh
+# Remove __pycache__.
+$ find . | grep -E "(__pycache__|\.pyc|\.pyo$)" | xargs rm -rf
+
+# Run video inpainting.
+$ python run_inpainting.py \
+>> --path 'D:/_data/f250_color' \
+>> --path_mask 'D:/_data/f250_mask' \
+>> --outroot 'D:/_data/f250_result_final' \
+>> --merge \
+>> --run
+```
+
+
 # Update History
 
 ### v1.2
@@ -58,4 +74,4 @@ For this project, FGVC(Flow-edge Guided Video Completion) deep learning model wa
 <br>
 
 ---
-**Updated :** 2021-11-20 22:42
+**Updated :** 2021-11-27 03:25

modules/DeepFill/DeepFill.py

@@ -0,0 +1,84 @@
+from .ops import *
+
+
+class Generator(nn.Module):
+    def __init__(self, first_dim=32, isCheck=False, device=None):
+        super(Generator, self).__init__()
+        self.isCheck = isCheck
+        self.device = device
+        self.stage_1 = CoarseNet(5, first_dim, device=device)
+        self.stage_2 = RefinementNet(5, first_dim, device=device)
+
+    def forward(self, masked_img, mask, small_mask): # mask : 1 x 1 x H x W
+
+        # border, maybe
+        mask = mask.expand(masked_img.size(0),1,masked_img.size(2),masked_img.size(3))
+        small_mask = small_mask.expand(masked_img.size(0), 1, masked_img.size(2) // 8, masked_img.size(3) // 8)
+        if self.device:
+            ones = to_var(torch.ones(mask.size()), device=self.device)
+        else:
+            ones = to_var(torch.ones(mask.size()))
+        # stage1
+        stage1_input = torch.cat([masked_img, ones, ones*mask], dim=1)
+        stage1_output, resized_mask = self.stage_1(stage1_input, mask)
+        # stage2
+        new_masked_img = stage1_output*mask.clone() + masked_img.clone()*(1.-mask.clone())
+        stage2_input = torch.cat([new_masked_img, ones.clone(), ones.clone()*mask.clone()], dim=1)
+        stage2_output, offset_flow = self.stage_2(stage2_input, small_mask)
+
+        return stage1_output, stage2_output, offset_flow
+
+
+class CoarseNet(nn.Module):
+    '''
+    # input: B x 5 x W x H
+    # after down: B x 128(32*4) x W/4 x H/4
+    # after atrous: same with the output size of the down module
+    # after up : same with the input size
+    '''
+    def __init__(self, in_ch, out_ch, device=None):
+        super(CoarseNet,self).__init__()
+        self.down = Down_Module(in_ch, out_ch)
+        self.atrous = Dilation_Module(out_ch*4, out_ch*4)
+        self.up = Up_Module(out_ch*4, 3)
+        self.device=device
+
+    def forward(self, x, mask):
+        x = self.down(x)
+        resized_mask = down_sample(mask, scale_factor=0.25, mode='nearest', device=self.device)
+        x = self.atrous(x)
+        x = self.up(x)
+
+        return x, resized_mask
+
+
+class RefinementNet(nn.Module):
+    '''
+    # input: B x 5 x W x H
+    # after down: B x 128(32*4) x W/4 x H/4
+    # after atrous: same with the output size of the down module
+    # after up : same with the input size
+    '''
+    def __init__(self, in_ch, out_ch, device=None):
+        super(RefinementNet,self).__init__()
+        self.down_conv_branch = Down_Module(in_ch, out_ch, isRefine=True)
+        self.down_attn_branch = Down_Module(in_ch, out_ch, activation=nn.ReLU(), isRefine=True, isAttn=True)
+        self.atrous = Dilation_Module(out_ch*4, out_ch*4)
+        self.CAttn = Contextual_Attention_Module(out_ch*4, out_ch*4, device=device)
+        self.up = Up_Module(out_ch*8, 3, isRefine=True)
+
+    def forward(self, x, resized_mask):
+        # conv branch
+        conv_x = self.down_conv_branch(x)
+        conv_x = self.atrous(conv_x)
+
+        # attention branch
+        attn_x = self.down_attn_branch(x)
+
+        attn_x, offset_flow = self.CAttn(attn_x, attn_x, mask=resized_mask)
+
+        # concat two branches
+        deconv_x = torch.cat([conv_x, attn_x], dim=1) # deconv_x => B x 256 x W/4 x H/4
+        x = self.up(deconv_x)
+
+        return x, offset_flow