feat: ugly training image logging

Former-commit-id: 16a25008320f436069cff9f44bf013c1c2d0f890 [formerly 683afc2cb6322ce3f1d98797b947cca8c6af09a4]
Former-commit-id: a5dae735e10107b514f028e84084ce7a303216ef

src/train.py

@@ -4,7 +4,6 @@ import pytorch_lightning as pl
 import torch
 from pytorch_lightning.callbacks import RichProgressBar
 from pytorch_lightning.loggers import WandbLogger
-from torch.utils.data import DataLoader
 
 import wandb
 from unet import UNet
@@ -13,7 +12,7 @@ CONFIG = {
     "DIR_TRAIN_IMG": "/home/lilian/data_disk/lfainsin/train/",
     "DIR_VALID_IMG": "/home/lilian/data_disk/lfainsin/val/",
     "DIR_TEST_IMG": "/home/lilian/data_disk/lfainsin/test/",
-    "DIR_SPHERE": "/home/lilian/data_disk/lfainsin/spheres_prod/",
+    "DIR_SPHERE": "/home/lilian/data_disk/lfainsin/spheres/",
     "FEATURES": [8, 16, 32, 64],
     "N_CHANNELS": 3,
     "N_CLASSES": 1,

src/unet/blocks.py

@@ -1,4 +1,4 @@
-""" Parts of the U-Net model """
+"""Parts of the U-Net model."""
 
 import torch
 import torch.nn as nn

src/unet/model.py

@@ -130,6 +130,46 @@ class UNet(pl.LightningModule):
             },
         )
 
+        if batch_idx == 22000:
+            rows = []
+            columns = ["ID", "image", "ground truth", "prediction", "dice", "dice_bin"]
+            for i, (img, mask, pred, pred_bin) in enumerate(
+                zip(
+                    images.cpu(),
+                    masks_true.cpu(),
+                    masks_pred.cpu(),
+                    masks_pred_bin.cpu().squeeze(1).int().numpy(),
+                )
+            ):
+                rows.append(
+                    [
+                        i,
+                        wandb.Image(img),
+                        wandb.Image(mask),
+                        wandb.Image(
+                            pred,
+                            masks={
+                                "predictions": {
+                                    "mask_data": pred_bin,
+                                    "class_labels": class_labels,
+                                },
+                            },
+                        ),
+                        dice,
+                        dice_bin,
+                    ]
+                )
+
+            # logging
+            try:  # required by autofinding, logger replaced by dummy
+                self.logger.log_table(
+                    key="train/predictions",
+                    columns=columns,
+                    data=rows,
+                )
+            except:
+                pass
+
         return dict(
             accuracy=accuracy,
             loss=dice,
@@ -155,7 +195,7 @@ class UNet(pl.LightningModule):
         accuracy = (masks_true == masks_pred_bin).float().mean()
 
         rows = []
-        if batch_idx % 50 == 0:
+        if batch_idx % 50 == 0 or dice < 0.1:
             for i, (img, mask, pred, pred_bin) in enumerate(
                 zip(
                     images.cpu(),