Fixed main train issues

src/transformers/models/seggpt/modeling_seggpt.py

@@ -753,11 +753,15 @@ def forward(
         bool_masked_pos: Optional[torch.BoolTensor] = None,
         feature_ensemble: Optional[bool] = None,
         embedding_type: Optional[str] = None,
+        labels: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, SegGptEncoderOutput]:
         r"""
+        labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, `optional`):
+            Ground truth mask for input images.
+
         Returns:
 
         Examples:
@@ -799,10 +803,21 @@ def forward(
 
         # Prepare inputs
         pixel_values = torch.cat((prompt_pixel_values, pixel_values), dim=2)
-        prompt_pixel_values = torch.cat((prompt_masks, prompt_masks), dim=2)
+        prompt_pixel_values = (
+            torch.cat((prompt_masks, prompt_masks), dim=2)
+            if labels is None
+            else torch.cat((prompt_pixel_values, labels), dim=2)
+        )
+
+        if bool_masked_pos is None and labels is not None:
+            logger.warning_once(
+                "Labels were provided, but bool_masked_pos is not. It will be set to default value. If you're training the model, make sure to provide a bool_masked_pos."
+            )
 
         # We concat on height axis so SegGPT can handle as a single image, hence we need to mask the portion
-        # of the prompt pixels that will be destinated to the prediction as they don't add any information.
+        # of the mask prompt pixels that will be destinated to the prediction as they don't add any information.
+        # This is only the case for inference. In training, the model concat of prompt mask and label is masked
+        # and reconstructed together (In-Context Painting).
         if bool_masked_pos is None:
             num_patches = self.embeddings.patch_embeddings.num_patches
             bool_masked_pos = torch.zeros(num_patches, dtype=torch.bool).to(pixel_values.device)
@@ -840,7 +855,9 @@ def unpatchify(tensor: torch.Tensor, patch_height: int, patch_width: int) -> tor
     batch_size = tensor.shape[0]
     patch_size = int((tensor.shape[-1] / 3) ** 0.5)
     if patch_height * patch_width != tensor.shape[1]:
-        raise ValueError(f"Number of patches {tensor.shape[1]} does not match patch height and width.")
+        raise ValueError(
+            f"Number of patches {tensor.shape[1]} does not match patch height ({patch_height}) and width ({patch_width})."
+        )
 
     tensor = tensor.reshape(shape=(batch_size, patch_height, patch_width, patch_size, patch_size, 3))
     tensor = tensor.permute(0, 5, 1, 3, 2, 4)
@@ -857,20 +874,16 @@ def __init__(self, config):
 
     def forward(
         self,
-        pixel_values: torch.FloatTensor,
-        prompt_pixel_values: torch.FloatTensor,
+        prompt_masks: torch.FloatTensor,
         pred_masks: torch.FloatTensor,
         labels: torch.FloatTensor,
         bool_masked_pos: torch.BoolTensor,
     ):
         """Computes the L1 loss between the predicted masks and the ground truth masks.
 
         Args:
-            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, 2*height, width)`):
-                Concatenated pixel values from prompt and input images.
-
-            prompt_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, 2*height, width)`):
-                Concatenated pixel values from mask prompt.
+            prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+                Pixel values from mask prompt.
 
             pred_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, 2*height, width)`):
                 Predicted masks.
@@ -884,12 +897,12 @@ def forward(
         Returns:
             `torch.FloatTensor`: The mean L1 loss between the predicted masks and the ground truth masks.
         """
+        ground_truth = torch.cat((prompt_masks, labels), dim=2)
+
         mask = bool_masked_pos[:, :, None].repeat(1, 1, self.patch_size**2 * 3)
-        mask = unpatchify(mask, pixel_values.shape[1] // self.patch_size, pixel_values.shape[2] // self.patch_size)
-        # Changing dummy mask in prompt_pixel_values to labels values
-        prompt_pixel_values = prompt_pixel_values.clone()
-        prompt_pixel_values[:, :, prompt_pixel_values.shape[2] // 2 :, :] = labels
-        loss = F.smooth_l1_loss(pred_masks, prompt_pixel_values, reduction="none", beta=self.beta)
+        mask = unpatchify(mask, ground_truth.shape[2] // self.patch_size, ground_truth.shape[3] // self.patch_size)
+
+        loss = F.smooth_l1_loss(pred_masks, ground_truth, reduction="none", beta=self.beta)
         loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
 
         return loss
@@ -988,7 +1001,7 @@ def forward(
         loss = None
         if labels is not None:
             loss_fn = SegGptLoss(self.config)
-            loss = loss_fn(pixel_values, prompt_pixel_values, pred_masks, labels, bool_masked_pos)
+            loss = loss_fn(prompt_masks, pred_masks, labels, bool_masked_pos)
 
         if not return_dict:
             output = (pred_masks,)