feat: better image logging

Former-commit-id: 5ce04a5534cef72a3815be13cd79731800b7419f [formerly 47ab70b94c3f4a696b4e5a131e087660dba0b8ba]
Former-commit-id: 81858cf5970cdecdbd0a151e3547a25857e3e958

src/utils/callback.py

@@ -7,16 +7,54 @@ columns = [
 ]
 class_labels = {
     1: "sphere",
+    2: "sphere_gt",
 }
 
 
 class TableLog(Callback):
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        if batch_idx == 0:
+            rows = []
+
+            # unpacking
+            images, targets = batch
+
+            for i, (image, target) in enumerate(
+                zip(
+                    images,
+                    targets,
+                )
+            ):
+                rows.append(
+                    [
+                        i,
+                        wandb.Image(
+                            image.cpu(),
+                            masks={
+                                "ground_truth": {
+                                    "mask_data": (target["masks"].cpu().sum(dim=0) > 0.5).int().numpy() * 2,
+                                    "class_labels": class_labels,
+                                },
+                            },
+                        ),
+                    ]
+                )
+
+            wandb.log(
+                {
+                    "train/predictions": wandb.Table(
+                        columns=columns,
+                        data=rows,
+                    )
+                }
+            )
+
     def on_validation_epoch_start(self, trainer, pl_module):
         self.rows = []
 
     def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
-        # unpacking
         if batch_idx == 2:
+            # unpacking
             images, targets = batch
 
             for i, (image, target, pred) in enumerate(
@@ -26,6 +64,37 @@ class TableLog(Callback):
                     outputs,
                 )
             ):
+                box_data_gt = [
+                    {
+                        "position": {
+                            "minX": int(target["boxes"][j][0]),
+                            "minY": int(target["boxes"][j][1]),
+                            "maxX": int(target["boxes"][j][2]),
+                            "maxY": int(target["boxes"][j][3]),
+                        },
+                        "domain": "pixel",
+                        "class_id": 2,
+                        "class_labels": class_labels,
+                    }
+                    for j in range(len(target["labels"]))
+                ]
+
+                box_data = [
+                    {
+                        "position": {
+                            "minX": int(pred["boxes"][j][0]),
+                            "minY": int(pred["boxes"][j][1]),
+                            "maxX": int(pred["boxes"][j][2]),
+                            "maxY": int(pred["boxes"][j][3]),
+                        },
+                        "domain": "pixel",
+                        "class_id": 1,
+                        "box_caption": f"{pred['scores'][j]:0.3f}",
+                        "class_labels": class_labels,
+                    }
+                    for j in range(len(pred["labels"]))
+                ]
+
                 self.rows.append(
                     [
                         i,
@@ -33,14 +102,18 @@ class TableLog(Callback):
                             image.cpu(),
                             masks={
                                 "ground_truth": {
-                                    "mask_data": (target["masks"].cpu().sum(dim=0) > 0.5).int().numpy(),
+                                    "mask_data": target["masks"].cpu().sum(dim=0).int().numpy() * 2,
                                     "class_labels": class_labels,
                                 },
                                 "predictions": {
-                                    "mask_data": (pred["masks"].cpu().sum(dim=0) > 0.5).int().numpy(),
+                                    "mask_data": pred["masks"].cpu().sum(dim=0).int().numpy(),
                                     "class_labels": class_labels,
                                 },
                             },
+                            boxes={
+                                "ground_truth": {"box_data": box_data_gt},
+                                "predictions": {"box_data": box_data},
+                            },
                         ),
                     ]
                 )