Add Replicate demo and API

IS-Net/cog.yaml

@@ -0,0 +1,16 @@
+build:
+  gpu: true
+  python_version: 3.8
+  system_packages:
+    - libgl1-mesa-glx
+    - libglib2.0-0
+  python_packages:
+    - torch==1.9.0
+    - torchvision==0.10.0
+    - numpy==1.21.1
+    - opencv-python==4.5.5.64
+    - matplotlib==3.5.1
+    - tqdm==4.63.1
+    - scikit-image==0.19.2
+
+predict: "predict.py:Predictor"

IS-Net/predict.py

@@ -0,0 +1,74 @@
+import numpy as np
+import torch
+from torch.autograd import Variable
+from torchvision import transforms
+from torchvision.utils import save_image
+import torch.nn.functional as F
+from data_loader_cache import normalize, im_reader, im_preprocess
+from models.isnet import ISNetDIS
+
+from cog import BasePredictor, Path, Input
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+        self.net = ISNetDIS()
+        self.net.load_state_dict(torch.load("isnet.pth", map_location=device))
+        self.net.to(device)
+        self.net.eval()
+
+    def predict(
+            self,
+            input_image: Path = Input(description="Image to segment."),
+
+    ) -> Path:
+        cache_size = [1024,1024]
+        image, orig_size = load_image(str(input_image), cache_size)
+
+        image = image.type(torch.FloatTensor)
+
+        image = Variable(image, requires_grad=False).to(device)  # wrap inputs in Variable
+
+        ds_val = self.net(image)[0]  # list of 6 results
+
+        pred_val = ds_val[0][0, :, :, :]  # B x 1 x H x W    # we want the first one which is the most accurate prediction
+
+        ## recover the prediction spatial size to the orignal image size
+        pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val, 0), (orig_size[0][0], orig_size[0][1]), mode='bilinear'))
+
+        ma = torch.max(pred_val)
+        mi = torch.min(pred_val)
+        pred_val = (pred_val - mi) / (ma - mi)  # max = 1
+
+        if device == 'cuda':
+            torch.cuda.empty_cache()
+
+        output_path = "output.png"
+        save_image(pred_val, output_path, normalize=True)
+
+        return Path(output_path)
+
+
+class GOSNormalize(object):
+    '''
+    Normalize the Image using torch.transforms
+    '''
+
+    def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image):
+        image = normalize(image, self.mean, self.std)
+        return image
+
+
+def load_image(im_path, cache_size):
+    im = im_reader(im_path)
+    im, im_shp = im_preprocess(im, cache_size)
+    im = torch.divide(im, 255.0)
+    shape = torch.from_numpy(np.array(im_shp))
+    transform = transforms.Compose([GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0])])
+    return transform(im).unsqueeze(0), shape.unsqueeze(0)  # make a batch of image, shape

README.md

@@ -12,9 +12,9 @@
 <br>
 
 ## This is the official repo for our newly formulated DIS task: 
-[**Project Page**](https://xuebinqin.github.io/dis/index.html), [**Arxiv**](https://arxiv.org/pdf/2203.03041.pdf), [**中文**](https://github.com/xuebinqin/xuebinqin.github.io/blob/main/ECCV2022_DIS_Chinese.pdf).
-
-<br> 
+[[**Project Page**]](https://xuebinqin.github.io/dis/index.html), [[**Arxiv**]](https://arxiv.org/pdf/2203.03041.pdf), [[**中文**]](https://github.com/xuebinqin/xuebinqin.github.io/blob/main/ECCV2022_DIS_Chinese.pdf).
+[![asd](https://replicate.com/arielreplicate/dichotomous_image_segmentation/badge)](https://replicate.com/arielreplicate/dichotomous_image_segmentation)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/doevent/dis-background-removal)
 
 # PLEASE STAY TUNED FOR OUR DIS V2.0 (Jul. 30th, 2022) 
 ![disv2-peacock](figures/peacock.jpg)