Regarding error while computing the SemanticSegmentationTask with num_classes=2 at loss_ fun

model =SemanticSegmentationTask( segmentation_model=“unet”, encoder_name=“resnet18”, encoder_weights=None, in_channels=10, num_classes=2, num_filters=64, loss=“jaccard”, ignore_zeros=False, learning_rate=0.1, learning_rate_schedule_patience=5, )

trainer = pl.Trainer(

              gpus=1,
              max_epochs=10,
              precision=16,
              log_every_n_steps=1,
              max_steps=2,
           )

trainer.fit(model,dl)

---

Using 16bit native Automatic Mixed Precision (AMP) GPU available: True, used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs /usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/configuration_validator.py:133: UserWarning: You defined a validation_step but have no val_dataloader. Skipping val loop. rank_zero_warn(“You defined a validation_step but have no val_dataloader. Skipping val loop.”) Missing logger folder: /content/lightning_logs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

| Name | Type | Params

0 | model | Unet | 14.4 M 1 | loss | JaccardLoss | 0
2 | train_metrics | MetricCollection | 0
3 | val_metrics | MetricCollection | 0
4 | test_metrics | MetricCollection | 0

14.4 M Trainable params 0 Non-trainable params 14.4 M Total params 28.701 Total estimated model params size (MB) Epoch 0: 0% 0/4 [00:00<?, ?it/s]

ValueError Traceback (most recent call last) <ipython-input-15-c68a00784715> in <module>() 21 ) 22 —> 23 trainer.fit(model,dl)

36 frames /usr/local/lib/python3.7/dist-packages/torchmetrics/utilities/checks.py in _check_shape_and_type_consistency(preds, target) 113 else: 114 raise ValueError( –> 115 “Either preds and target both should have the (same) shape (N, …), or target should be (N, …)” 116 " and preds should be (N, C, …)." 117 )

ValueError: Either preds and target both should have the (same) shape (N, …), or target should be (N, …) and preds should be (N, C, …).

[kethan52]

Thank you soo much @calebrob6 i think it worked well

[calebrob6]

I think you need to change your dataloader to return masks of size (batch size, height, width)