replicate demo

cog.yaml

@@ -0,0 +1,24 @@
+build:
+  cuda: "11.3"
+  gpu: true
+  python_version: "3.9"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "numpy==1.21.1"
+    - "ipython==7.21.0"
+    - "addict==2.4.0"
+    - "future==0.18.2"
+    - "lmdb==1.3.0"
+    - "opencv-python==4.5.5.64"
+    - "Pillow==9.1.0"
+    - "pyyaml==6.0"
+    - "torch==1.11.0"
+    - "torchvision==0.12.0"
+    - "tqdm==4.64.0"
+    - "scipy==1.8.0"
+    - "scikit-image==0.19.2"
+    - "matplotlib==3.5.1"
+
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,137 @@
+import torch
+import numpy as np
+import cv2
+import tempfile
+import matplotlib.pyplot as plt
+from cog import BasePredictor, Path, Input, BaseModel
+
+from basicsr.models import create_model
+from basicsr.utils import img2tensor as _img2tensor, tensor2img, imwrite
+from basicsr.utils.options import parse
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+        opt_path_denoise = "options/test/SIDD/NAFNet-width64.yml"
+        opt_denoise = parse(opt_path_denoise, is_train=False)
+        opt_denoise["dist"] = False
+
+        opt_path_deblur = "options/test/GoPro/NAFNet-width64.yml"
+        opt_deblur = parse(opt_path_deblur, is_train=False)
+        opt_deblur["dist"] = False
+
+        opt_path_stereo = "options/test/NAFSSR/NAFSSR-L_4x.yml"
+        opt_stereo = parse(opt_path_stereo, is_train=False)
+        opt_stereo["dist"] = False
+
+        self.models = {
+            "Image Denoising": create_model(opt_denoise),
+            "Image Debluring": create_model(opt_deblur),
+            "Stereo Image Super-Resolution": create_model(opt_stereo),
+        }
+
+    def predict(
+        self,
+        task_type: str = Input(
+            choices=[
+                "Image Denoising",
+                "Image Debluring",
+                "Stereo Image Super-Resolution",
+            ],
+            default="Image Debluring",
+            description="Choose task type.",
+        ),
+        image: Path = Input(
+            description="Input image. Stereo Image Super-Resolution, upload the left image here.",
+        ),
+        image_r: Path = Input(
+            default=None,
+            description="Right Input image for Stereo Image Super-Resolution. Optional, only valid for Stereo"
+            " Image Super-Resolution task.",
+        ),
+    ) -> Path:
+
+        out_path = Path(tempfile.mkdtemp()) / "output.png"
+
+        model = self.models[task_type]
+        if task_type == "Stereo Image Super-Resolution":
+            assert image_r is not None, (
+                "Please provide both left and right input image for "
+                "Stereo Image Super-Resolution task."
+            )
+
+            img_l = imread(str(image))
+            inp_l = img2tensor(img_l)
+            img_r = imread(str(image_r))
+            inp_r = img2tensor(img_r)
+            stereo_image_inference(model, inp_l, inp_r, str(out_path))
+
+        else:
+
+            img_input = imread(str(image))
+            inp = img2tensor(img_input)
+            out_path = Path(tempfile.mkdtemp()) / "output.png"
+            single_image_inference(model, inp, str(out_path))
+
+        return out_path
+
+
+def imread(img_path):
+    img = cv2.imread(img_path)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return img
+
+
+def img2tensor(img, bgr2rgb=False, float32=True):
+    img = img.astype(np.float32) / 255.0
+    return _img2tensor(img, bgr2rgb=bgr2rgb, float32=float32)
+
+
+def single_image_inference(model, img, save_path):
+    model.feed_data(data={"lq": img.unsqueeze(dim=0)})
+
+    if model.opt["val"].get("grids", False):
+        model.grids()
+
+    model.test()
+
+    if model.opt["val"].get("grids", False):
+        model.grids_inverse()
+
+    visuals = model.get_current_visuals()
+    sr_img = tensor2img([visuals["result"]])
+    imwrite(sr_img, save_path)
+
+
+def stereo_image_inference(model, img_l, img_r, out_path):
+    img = torch.cat([img_l, img_r], dim=0)
+    model.feed_data(data={"lq": img.unsqueeze(dim=0)})
+
+    if model.opt["val"].get("grids", False):
+        model.grids()
+
+    model.test()
+
+    if model.opt["val"].get("grids", False):
+        model.grids_inverse()
+
+    visuals = model.get_current_visuals()
+    img_L = visuals["result"][:, :3]
+    img_R = visuals["result"][:, 3:]
+    img_L, img_R = tensor2img([img_L, img_R], rgb2bgr=False)
+
+    # save_stereo_image
+    h, w = img_L.shape[:2]
+    fig = plt.figure(figsize=(w // 40, h // 40))
+    ax1 = fig.add_subplot(2, 1, 1)
+    plt.title("NAFSSR output (Left)", fontsize=14)
+    ax1.axis("off")
+    ax1.imshow(img_L)
+
+    ax2 = fig.add_subplot(2, 1, 2)
+    plt.title("NAFSSR output (Right)", fontsize=14)
+    ax2.axis("off")
+    ax2.imshow(img_R)
+
+    plt.subplots_adjust(hspace=0.08)
+    plt.savefig(str(out_path), bbox_inches="tight", dpi=600)

readme.md

@@ -49,6 +49,8 @@ python setup.py develop --no_cuda_ext
 * Image Deblur Colab Demo: [<a href="https://colab.research.google.com/drive/1yR2ClVuMefisH12d_srXMhHnHwwA1YmU?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/drive/1yR2ClVuMefisH12d_srXMhHnHwwA1YmU?usp=sharing)
 * Stereo Image Super-Resolution Colab Demo: [<a href="https://colab.research.google.com/drive/1PkLog2imf7jCOPKq1G32SOISz0eLLJaO?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/drive/1PkLog2imf7jCOPKq1G32SOISz0eLLJaO?usp=sharing)
 
+Try the web demo with all three tasks here: [![Replicate](https://replicate.com/megvii-research/nafnet/badge)](https://replicate.com/megvii-research/nafnet)
+
 * Single Image Inference Demo:
     * Image Denoise:
     ```