Commit
•
80fd191
1
Parent(s):
78444e2
Upload folder using huggingface_hub
Browse files- .DS_Store +0 -0
- .gitattributes +1 -0
- README.md +6 -5
- app.py +92 -0
- input.png +3 -0
- ormbg.pth +3 -0
- ormbg.py +473 -0
- requirements.txt +10 -0
.DS_Store
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Binary file (6.15 kB). View file
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.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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input.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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-
title:
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-
emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: ORMBG
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emoji: 💻
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colorFrom: red
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colorTo: red
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sdk: gradio
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sdk_version: 4.16.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import numpy as np
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import torch
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import torch.nn.functional as F
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import gradio as gr
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from ormbg import ORMBG
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from PIL import Image
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def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
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if len(im.shape) < 3:
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im = im[:, :, np.newaxis]
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
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im_tensor = F.interpolate(
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torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
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).type(torch.uint8)
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image = torch.divide(im_tensor, 255.0)
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return image
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def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
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result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result - mi) / (ma - mi)
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im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
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im_array = np.squeeze(im_array)
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return im_array
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def inference(orig_image):
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model_path = "ormbg.pth"
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net = ORMBG()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():
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net.load_state_dict(torch.load(model_path))
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net = net.cuda()
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else:
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net.load_state_dict(torch.load(model_path, map_location="cpu"))
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net.eval()
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model_input_size = [1024, 1024]
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orig_im_size = orig_image.shape[0:2]
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image = preprocess_image(orig_image, model_input_size).to(device)
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result = net(image)
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# post process
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result_image = postprocess_image(result[0][0], orig_im_size)
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# save result
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pil_im = Image.fromarray(result_image)
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no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
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no_bg_image.paste(orig_image, mask=pil_im)
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return no_bg_image
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gr.Markdown("## Open Remove Background Model (ormbg)")
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gr.HTML(
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"""
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<p style="margin-bottom: 10px; font-size: 94%">
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This is a demo for Open Remove Background Model (ormbg) that using
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<a href="https://huggingface.co/schirrmacher/ormbg" target="_blank">Open Remove Background Model (ormbg) model</a> as backbone.
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</p>
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"""
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)
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title = "Background Removal"
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description = r"""
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This model is a fully open-source background remover optimized for images with humans.
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It is based on <a href='https://github.com/xuebinqin/DIS' target='_blank'>Highly Accurate Dichotomous Image Segmentation research</a>.
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You can find more about the model <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>here</a>.
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"""
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examples = [
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["./input.png"],
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]
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demo = gr.Interface(
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fn=inference,
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inputs="image",
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outputs="image",
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examples=examples,
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title=title,
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description=description,
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)
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if __name__ == "__main__":
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demo.launch(share=False)
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input.png
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![]() |
Git LFS Details
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ormbg.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ba387a8348526875024f59aa97d23af9cacfff77abf4e9af14332bf477c088fa
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size 176719216
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ormbg.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# https://github.com/xuebinqin/DIS/blob/main/IS-Net/models/isnet.py
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class REBNCONV(nn.Module):
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def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
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super(REBNCONV, self).__init__()
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self.conv_s1 = nn.Conv2d(
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in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
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)
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self.bn_s1 = nn.BatchNorm2d(out_ch)
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self.relu_s1 = nn.ReLU(inplace=True)
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def forward(self, x):
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hx = x
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xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
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return xout
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## upsample tensor 'src' to have the same spatial size with tensor 'tar'
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def _upsample_like(src, tar):
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src = F.interpolate(src, size=tar.shape[2:], mode="bilinear")
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return src
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|
33 |
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|
34 |
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### RSU-7 ###
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class RSU7(nn.Module):
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36 |
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37 |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
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38 |
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super(RSU7, self).__init__()
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39 |
+
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self.in_ch = in_ch
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41 |
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self.mid_ch = mid_ch
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42 |
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self.out_ch = out_ch
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43 |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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48 |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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51 |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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54 |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
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56 |
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self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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57 |
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58 |
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
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59 |
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self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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60 |
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self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
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62 |
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self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
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64 |
+
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65 |
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self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
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66 |
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self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
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67 |
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self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
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68 |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
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69 |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
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70 |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
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71 |
+
|
72 |
+
def forward(self, x):
|
73 |
+
b, c, h, w = x.shape
|
74 |
+
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75 |
+
hx = x
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76 |
+
hxin = self.rebnconvin(hx)
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77 |
+
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78 |
+
hx1 = self.rebnconv1(hxin)
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79 |
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hx = self.pool1(hx1)
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80 |
+
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81 |
+
hx2 = self.rebnconv2(hx)
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82 |
+
hx = self.pool2(hx2)
|
83 |
+
|
84 |
+
hx3 = self.rebnconv3(hx)
|
85 |
+
hx = self.pool3(hx3)
|
86 |
+
|
87 |
+
hx4 = self.rebnconv4(hx)
|
88 |
+
hx = self.pool4(hx4)
|
89 |
+
|
90 |
+
hx5 = self.rebnconv5(hx)
|
91 |
+
hx = self.pool5(hx5)
|
92 |
+
|
93 |
+
hx6 = self.rebnconv6(hx)
|
94 |
+
|
95 |
+
hx7 = self.rebnconv7(hx6)
|
96 |
+
|
97 |
+
hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
|
98 |
+
hx6dup = _upsample_like(hx6d, hx5)
|
99 |
+
|
100 |
+
hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
|
101 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
102 |
+
|
103 |
+
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
|
104 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
105 |
+
|
106 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
107 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
108 |
+
|
109 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
110 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
111 |
+
|
112 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
113 |
+
|
114 |
+
return hx1d + hxin
|
115 |
+
|
116 |
+
|
117 |
+
### RSU-6 ###
|
118 |
+
class RSU6(nn.Module):
|
119 |
+
|
120 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
121 |
+
super(RSU6, self).__init__()
|
122 |
+
|
123 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
124 |
+
|
125 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
126 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
127 |
+
|
128 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
129 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
130 |
+
|
131 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
132 |
+
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
133 |
+
|
134 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
135 |
+
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
136 |
+
|
137 |
+
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
138 |
+
|
139 |
+
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
140 |
+
|
141 |
+
self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
142 |
+
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
143 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
144 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
145 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
146 |
+
|
147 |
+
def forward(self, x):
|
148 |
+
|
149 |
+
hx = x
|
150 |
+
|
151 |
+
hxin = self.rebnconvin(hx)
|
152 |
+
|
153 |
+
hx1 = self.rebnconv1(hxin)
|
154 |
+
hx = self.pool1(hx1)
|
155 |
+
|
156 |
+
hx2 = self.rebnconv2(hx)
|
157 |
+
hx = self.pool2(hx2)
|
158 |
+
|
159 |
+
hx3 = self.rebnconv3(hx)
|
160 |
+
hx = self.pool3(hx3)
|
161 |
+
|
162 |
+
hx4 = self.rebnconv4(hx)
|
163 |
+
hx = self.pool4(hx4)
|
164 |
+
|
165 |
+
hx5 = self.rebnconv5(hx)
|
166 |
+
|
167 |
+
hx6 = self.rebnconv6(hx5)
|
168 |
+
|
169 |
+
hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
|
170 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
171 |
+
|
172 |
+
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
|
173 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
174 |
+
|
175 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
176 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
177 |
+
|
178 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
179 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
180 |
+
|
181 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
182 |
+
|
183 |
+
return hx1d + hxin
|
184 |
+
|
185 |
+
|
186 |
+
### RSU-5 ###
|
187 |
+
class RSU5(nn.Module):
|
188 |
+
|
189 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
190 |
+
super(RSU5, self).__init__()
|
191 |
+
|
192 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
193 |
+
|
194 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
195 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
196 |
+
|
197 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
198 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
199 |
+
|
200 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
201 |
+
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
202 |
+
|
203 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
204 |
+
|
205 |
+
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
206 |
+
|
207 |
+
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
208 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
209 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
210 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
211 |
+
|
212 |
+
def forward(self, x):
|
213 |
+
|
214 |
+
hx = x
|
215 |
+
|
216 |
+
hxin = self.rebnconvin(hx)
|
217 |
+
|
218 |
+
hx1 = self.rebnconv1(hxin)
|
219 |
+
hx = self.pool1(hx1)
|
220 |
+
|
221 |
+
hx2 = self.rebnconv2(hx)
|
222 |
+
hx = self.pool2(hx2)
|
223 |
+
|
224 |
+
hx3 = self.rebnconv3(hx)
|
225 |
+
hx = self.pool3(hx3)
|
226 |
+
|
227 |
+
hx4 = self.rebnconv4(hx)
|
228 |
+
|
229 |
+
hx5 = self.rebnconv5(hx4)
|
230 |
+
|
231 |
+
hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
|
232 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
233 |
+
|
234 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
235 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
236 |
+
|
237 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
238 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
239 |
+
|
240 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
241 |
+
|
242 |
+
return hx1d + hxin
|
243 |
+
|
244 |
+
|
245 |
+
### RSU-4 ###
|
246 |
+
class RSU4(nn.Module):
|
247 |
+
|
248 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
249 |
+
super(RSU4, self).__init__()
|
250 |
+
|
251 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
252 |
+
|
253 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
254 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
255 |
+
|
256 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
257 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
258 |
+
|
259 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
260 |
+
|
261 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
262 |
+
|
263 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
264 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
265 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
266 |
+
|
267 |
+
def forward(self, x):
|
268 |
+
|
269 |
+
hx = x
|
270 |
+
|
271 |
+
hxin = self.rebnconvin(hx)
|
272 |
+
|
273 |
+
hx1 = self.rebnconv1(hxin)
|
274 |
+
hx = self.pool1(hx1)
|
275 |
+
|
276 |
+
hx2 = self.rebnconv2(hx)
|
277 |
+
hx = self.pool2(hx2)
|
278 |
+
|
279 |
+
hx3 = self.rebnconv3(hx)
|
280 |
+
|
281 |
+
hx4 = self.rebnconv4(hx3)
|
282 |
+
|
283 |
+
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
|
284 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
285 |
+
|
286 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
287 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
288 |
+
|
289 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
290 |
+
|
291 |
+
return hx1d + hxin
|
292 |
+
|
293 |
+
|
294 |
+
### RSU-4F ###
|
295 |
+
class RSU4F(nn.Module):
|
296 |
+
|
297 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
298 |
+
super(RSU4F, self).__init__()
|
299 |
+
|
300 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
301 |
+
|
302 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
303 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
304 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
|
305 |
+
|
306 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
|
307 |
+
|
308 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
|
309 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
|
310 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
311 |
+
|
312 |
+
def forward(self, x):
|
313 |
+
|
314 |
+
hx = x
|
315 |
+
|
316 |
+
hxin = self.rebnconvin(hx)
|
317 |
+
|
318 |
+
hx1 = self.rebnconv1(hxin)
|
319 |
+
hx2 = self.rebnconv2(hx1)
|
320 |
+
hx3 = self.rebnconv3(hx2)
|
321 |
+
|
322 |
+
hx4 = self.rebnconv4(hx3)
|
323 |
+
|
324 |
+
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
|
325 |
+
hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
|
326 |
+
hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
|
327 |
+
|
328 |
+
return hx1d + hxin
|
329 |
+
|
330 |
+
|
331 |
+
class myrebnconv(nn.Module):
|
332 |
+
def __init__(
|
333 |
+
self,
|
334 |
+
in_ch=3,
|
335 |
+
out_ch=1,
|
336 |
+
kernel_size=3,
|
337 |
+
stride=1,
|
338 |
+
padding=1,
|
339 |
+
dilation=1,
|
340 |
+
groups=1,
|
341 |
+
):
|
342 |
+
super(myrebnconv, self).__init__()
|
343 |
+
|
344 |
+
self.conv = nn.Conv2d(
|
345 |
+
in_ch,
|
346 |
+
out_ch,
|
347 |
+
kernel_size=kernel_size,
|
348 |
+
stride=stride,
|
349 |
+
padding=padding,
|
350 |
+
dilation=dilation,
|
351 |
+
groups=groups,
|
352 |
+
)
|
353 |
+
self.bn = nn.BatchNorm2d(out_ch)
|
354 |
+
self.rl = nn.ReLU(inplace=True)
|
355 |
+
|
356 |
+
def forward(self, x):
|
357 |
+
return self.rl(self.bn(self.conv(x)))
|
358 |
+
|
359 |
+
|
360 |
+
class ORMBG(nn.Module):
|
361 |
+
|
362 |
+
def __init__(self, in_ch=3, out_ch=1):
|
363 |
+
super(ORMBG, self).__init__()
|
364 |
+
|
365 |
+
self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
|
366 |
+
self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
367 |
+
|
368 |
+
self.stage1 = RSU7(64, 32, 64)
|
369 |
+
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
370 |
+
|
371 |
+
self.stage2 = RSU6(64, 32, 128)
|
372 |
+
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
373 |
+
|
374 |
+
self.stage3 = RSU5(128, 64, 256)
|
375 |
+
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
376 |
+
|
377 |
+
self.stage4 = RSU4(256, 128, 512)
|
378 |
+
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
379 |
+
|
380 |
+
self.stage5 = RSU4F(512, 256, 512)
|
381 |
+
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
382 |
+
|
383 |
+
self.stage6 = RSU4F(512, 256, 512)
|
384 |
+
|
385 |
+
# decoder
|
386 |
+
self.stage5d = RSU4F(1024, 256, 512)
|
387 |
+
self.stage4d = RSU4(1024, 128, 256)
|
388 |
+
self.stage3d = RSU5(512, 64, 128)
|
389 |
+
self.stage2d = RSU6(256, 32, 64)
|
390 |
+
self.stage1d = RSU7(128, 16, 64)
|
391 |
+
|
392 |
+
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
|
393 |
+
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
|
394 |
+
self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
|
395 |
+
self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
|
396 |
+
self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
|
397 |
+
self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
|
398 |
+
|
399 |
+
# self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
|
400 |
+
|
401 |
+
def forward(self, x):
|
402 |
+
|
403 |
+
hx = x
|
404 |
+
|
405 |
+
hxin = self.conv_in(hx)
|
406 |
+
# hx = self.pool_in(hxin)
|
407 |
+
|
408 |
+
# stage 1
|
409 |
+
hx1 = self.stage1(hxin)
|
410 |
+
hx = self.pool12(hx1)
|
411 |
+
|
412 |
+
# stage 2
|
413 |
+
hx2 = self.stage2(hx)
|
414 |
+
hx = self.pool23(hx2)
|
415 |
+
|
416 |
+
# stage 3
|
417 |
+
hx3 = self.stage3(hx)
|
418 |
+
hx = self.pool34(hx3)
|
419 |
+
|
420 |
+
# stage 4
|
421 |
+
hx4 = self.stage4(hx)
|
422 |
+
hx = self.pool45(hx4)
|
423 |
+
|
424 |
+
# stage 5
|
425 |
+
hx5 = self.stage5(hx)
|
426 |
+
hx = self.pool56(hx5)
|
427 |
+
|
428 |
+
# stage 6
|
429 |
+
hx6 = self.stage6(hx)
|
430 |
+
hx6up = _upsample_like(hx6, hx5)
|
431 |
+
|
432 |
+
# -------------------- decoder --------------------
|
433 |
+
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
|
434 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
435 |
+
|
436 |
+
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
|
437 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
438 |
+
|
439 |
+
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
|
440 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
441 |
+
|
442 |
+
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
|
443 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
444 |
+
|
445 |
+
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
|
446 |
+
|
447 |
+
# side output
|
448 |
+
d1 = self.side1(hx1d)
|
449 |
+
d1 = _upsample_like(d1, x)
|
450 |
+
|
451 |
+
d2 = self.side2(hx2d)
|
452 |
+
d2 = _upsample_like(d2, x)
|
453 |
+
|
454 |
+
d3 = self.side3(hx3d)
|
455 |
+
d3 = _upsample_like(d3, x)
|
456 |
+
|
457 |
+
d4 = self.side4(hx4d)
|
458 |
+
d4 = _upsample_like(d4, x)
|
459 |
+
|
460 |
+
d5 = self.side5(hx5d)
|
461 |
+
d5 = _upsample_like(d5, x)
|
462 |
+
|
463 |
+
d6 = self.side6(hx6)
|
464 |
+
d6 = _upsample_like(d6, x)
|
465 |
+
|
466 |
+
return [
|
467 |
+
F.sigmoid(d1),
|
468 |
+
F.sigmoid(d2),
|
469 |
+
F.sigmoid(d3),
|
470 |
+
F.sigmoid(d4),
|
471 |
+
F.sigmoid(d5),
|
472 |
+
F.sigmoid(d6),
|
473 |
+
], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]
|
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gradio
|
2 |
+
gradio_imageslider
|
3 |
+
torch
|
4 |
+
torchvision
|
5 |
+
scikit-image
|
6 |
+
pillow
|
7 |
+
numpy
|
8 |
+
typing
|
9 |
+
gitpython
|
10 |
+
huggingface_hub
|