feat: got positive loss values !

Former-commit-id: 84e2a715b843ecee2e12e4878fcee4a52bb0a4cb [formerly 1a5fc82bc099885853b7b4deff81b779dafd0168]
Former-commit-id: c82cd66d6c432555a126e506631dfa2fd756437e

.gitignore

@@ -5,6 +5,7 @@ __pycache__/
 wandb/
 images/
 
+checkpoints/
 *.pth
 *.onnx
 

.vscode/launch.json

@@ -19,4 +19,4 @@
       "justMyCode": true
     }
   ]
-}
+}

src/predict.py

@@ -38,19 +38,6 @@ def get_args():
     return parser.parse_args()
 
 
-def predict_img(net, img, device):
-    img = img.unsqueeze(0)
-    img = img.to(device=device, dtype=torch.float32)
-
-    net.eval()
-    with torch.inference_mode():
-        output = net(img)
-        # preds = torch.sigmoid(output)[0]
-        # full_mask = output.squeeze(0).cpu()
-
-    return np.asarray(output.squeeze().cpu())
-
-
 if __name__ == "__main__":
     args = get_args()
 
@@ -81,8 +68,17 @@ if __name__ == "__main__":
     img = aug["image"]
 
     logging.info(f"Predicting image {args.input}")
-    mask = predict_img(net=net, img=img, device=device)
+    img = img.unsqueeze(0).to(device=device, dtype=torch.float32)
+
+    net.eval()
+    with torch.inference_mode():
+        mask = net(img)
+        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}")
-    mask = Image.fromarray(mask, "L")
+    mask = Image.fromarray(mask)
     mask.save(args.output)

src/train.py

@@ -26,7 +26,7 @@ def main():
             DIR_VALID_IMG="/home/lilian/data_disk/lfainsin/smoltrain2017/",
             DIR_SPHERE_IMG="/home/lilian/data_disk/lfainsin/spheres/Images/",
             DIR_SPHERE_MASK="/home/lilian/data_disk/lfainsin/spheres/Masks/",
-            FEATURES=[64, 128, 256, 512],
+            FEATURES=[16, 32, 64, 128],
             N_CHANNELS=3,
             N_CLASSES=1,
             AMP=True,
@@ -35,8 +35,8 @@ def main():
             DEVICE="cuda",
             WORKERS=8,
             EPOCHS=5,
-            BATCH_SIZE=16,
-            LEARNING_RATE=1e-5,
+            BATCH_SIZE=64,
+            LEARNING_RATE=1e-4,
             IMG_SIZE=512,
             SPHERES=5,
         ),
@@ -50,7 +50,8 @@ def main():
 
     # 0. Create network
     net = UNet(n_channels=wandb.config.N_CHANNELS, n_classes=wandb.config.N_CLASSES, features=wandb.config.FEATURES)
-    wandb.config.parameters = sum(p.numel() for p in net.parameters() if p.requires_grad)
+    wandb.config.PARAMETERS = sum(p.numel() for p in net.parameters() if p.requires_grad)
+    wandb.watch(net, log_freq=100)
 
     # transfer network to device
     net.to(device=device)
@@ -80,6 +81,11 @@ def main():
     # 2. Create datasets
     ds_train = SphereDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=tf_train)
     ds_valid = SphereDataset(image_dir=wandb.config.DIR_VALID_IMG, transform=tf_valid)
+    # ds_train_bg20k = SphereDataset(image_dir="/home/lilian/data_disk/lfainsin/BG-20k/train/", transform=tf_train)
+    # ds_valid_bg20k = SphereDataset(image_dir="/home/lilian/data_disk/lfainsin/BG-20k/testval/", transform=tf_valid)
+
+    # ds_train = torch.utils.data.ChainDataset([ds_train_coco, ds_train_bg20k])
+    # ds_valid = torch.utils.data.ChainDataset([ds_valid_coco, ds_valid_bg20k]) # TODO: modifier la classe SphereDataset pour prendre plusieurs dossiers
 
     # 3. Create data loaders
     train_loader = DataLoader(
@@ -99,24 +105,24 @@ def main():
     )
 
     # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for amp
-    optimizer = torch.optim.RMSprop(net.parameters(), lr=wandb.config.LEARNING_RATE, weight_decay=1e-8, momentum=0.9)
+    optimizer = torch.optim.Adam(net.parameters(), lr=wandb.config.LEARNING_RATE)
     scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, "max", patience=2)
     grad_scaler = torch.cuda.amp.GradScaler(enabled=wandb.config.AMP)
     criterion = torch.nn.BCEWithLogitsLoss()
 
     # save model.pth
-    wandb.watch(net, log_freq=100)
+    torch.save(net.state_dict(), "checkpoints/model-0.pth")
     artifact = wandb.Artifact("pth", type="model")
-    artifact.add_file("model.pth")
+    artifact.add_file("checkpoints/model-0.pth")
     wandb.run.log_artifact(artifact)
 
     # save model.onxx
     dummy_input = torch.randn(
         1, wandb.config.N_CHANNELS, wandb.config.IMG_SIZE, wandb.config.IMG_SIZE, requires_grad=True
     ).to(device)
-    torch.onnx.export(net, dummy_input, "model.onnx")
+    torch.onnx.export(net, dummy_input, "checkpoints/model-0.onnx")
     artifact = wandb.Artifact("onnx", type="model")
-    artifact.add_file("model.onnx")
+    artifact.add_file("checkpoints/model-0.onnx")
     wandb.run.log_artifact(artifact)
 
     # print the config
@@ -145,7 +151,7 @@ def main():
                     # forward
                     with torch.cuda.amp.autocast(enabled=wandb.config.AMP):
                         pred_masks = net(images)
-                        train_loss = criterion(pred_masks, true_masks)
+                        train_loss = criterion(true_masks, pred_masks)
 
                     # backward
                     optimizer.zero_grad(set_to_none=True)
@@ -167,7 +173,7 @@ def main():
 
                 # Evaluation round
                 val_score = evaluate(net, val_loader, device)
-                scheduler.step(val_score)
+                # scheduler.step(val_score)
 
                 # log validation metrics
                 wandb.log(
@@ -177,18 +183,19 @@ def main():
                 )
 
             # save weights when epoch end
-            torch.save(net.state_dict(), "model.pth")
+            torch.save(net.state_dict(), f"checkpoints/model-{epoch}.pth")
             artifact = wandb.Artifact("pth", type="model")
-            artifact.add_file("model.pth")
+            artifact.add_file(f"checkpoints/model-{epoch}.pth")
             wandb.run.log_artifact(artifact)
 
             # export model to onnx format
             dummy_input = torch.randn(1, 3, 512, 512, requires_grad=True).to(device)
-            torch.onnx.export(net, dummy_input, "model.onnx")
+            torch.onnx.export(net, dummy_input, f"checkpoints/model-{epoch}.onnx")
             artifact = wandb.Artifact("onnx", type="model")
-            artifact.add_file("model.onnx")
+            artifact.add_file(f"checkpoints/model-{epoch}.onnx")
             wandb.run.log_artifact(artifact)
 
+        # stop wandb
         wandb.run.finish()
 
     except KeyboardInterrupt:

src/unet/blocks.py

@@ -70,7 +70,10 @@ class OutConv(nn.Module):
     def __init__(self, in_channels, out_channels):
         super(OutConv, self).__init__()
 
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=1),
+            nn.Sigmoid(),
+        )
 
     def forward(self, x):
         return self.conv(x)