Merge branch 'onnx' into pl

Former-commit-id: 7f28adc38d86319181dea3e311976162ae20b6d2 [formerly 2b8801777106b1a17a9cef2c3a7d65e6ad780f3f]
Former-commit-id: 6d658bfd379005fcfdb75f97509be8dc920a8ef5

.vscode/launch.json

@@ -12,11 +12,13 @@
       "console": "integratedTerminal",
       "args": [
         "--input",
-        "images/SM.png",
+        "images/test.png",
         "--output",
-        "output.png",
+        "output_onnx.png",
+        "--model",
+        "good.onnx",
       ],
       "justMyCode": true
     }
   ]
-}
+}

pyproject.toml

@@ -8,14 +8,16 @@ version = "0.1.0"
 albumentations = "^1.2.0"
 matplotlib = "^3.5.2"
 numpy = "^1.23.0"
+onnx = "^1.12.0"
+onnxruntime = "^1.11.1"
 python = ">=3.8,<3.11"
 pytorch-lightning = "^1.6.4"
+rich = "^12.4.4"
 scipy = "^1.8.1"
 torch = "^1.11.0"
 torchvision = "^0.12.0"
 tqdm = "^4.64.0"
 wandb = "^0.12.19"
-rich = "^12.4.4"
 
 [tool.poetry.dev-dependencies]
 black = "^22.3.0"

src/predict.py

@@ -3,12 +3,12 @@ import logging
 
 import albumentations as A
 import numpy as np
+import onnx
+import onnxruntime
 import torch
 from albumentations.pytorch import ToTensorV2
 from PIL import Image
 
-from unet import UNet
-
 
 def get_args():
     parser = argparse.ArgumentParser(
@@ -38,23 +38,23 @@ def get_args():
     return parser.parse_args()
 
 
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
 if __name__ == "__main__":
     args = get_args()
 
     logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
 
-    net = UNet(n_channels=3, n_classes=1)
+    onnx_model = onnx.load(args.model)
+    onnx.checker.check_model(onnx_model)
 
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    ort_session = onnxruntime.InferenceSession(args.model)
-    logging.info(f"Using device {device}")
 
-    logging.info("Transfering model to device")
+    def to_numpy(tensor):
-    net.to(device=device)
+        return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
 
-    logging.info(f"Loading model {args.model}")
-    net.load_state_dict(torch.load(args.model, map_location=device))
-
-    logging.info(f"Loading image {args.input}")
     img = Image.open(args.input).convert("RGB")
 
     logging.info(f"Preprocessing image {args.input}")
@@ -68,17 +68,14 @@ if __name__ == "__main__":
     img = aug["image"]
 
     logging.info(f"Predicting image {args.input}")
-    img = img.unsqueeze(0).to(device=device, dtype=torch.float32)
+    img = img.unsqueeze(0)
 
-    net.eval()
+    # compute ONNX Runtime output prediction
-    with torch.inference_mode():
+    ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(img)}
-        mask = net(img)
+    ort_outs = ort_session.run(None, ort_inputs)
-        mask = torch.sigmoid(mask)[0]
-        mask = mask.cpu()
-        mask = mask.squeeze()
-        mask = mask > 0.5
-        mask = np.asarray(mask)
 
-    logging.info(f"Saving prediction to {args.output}")
+    img_out_y = ort_outs[0]
-    mask = Image.fromarray(mask)
+
-    mask.save(args.output)
+    img_out_y = Image.fromarray(np.uint8((img_out_y[0] * 255.0).clip(0, 255)[0]), mode="L")
+
+    img_out_y.save(args.output)

src/utils/paste.py

@@ -24,7 +24,7 @@ class RandomPaste(A.DualTransform):
         self,
         nb,
         image_dir,
-        scale_range=(0.1, 0.2),
+        scale_range=(0.05, 0.25),
         always_apply=True,
         p=1.0,
     ):