pano-seg-0912/mask2former.py

import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Optional, Tuple, Union
from transformers.models.mask2former.modeling_mask2former import (
     Mask2FormerMaskedAttentionDecoderOutput, Mask2FormerModelOutput,
     Mask2FormerForUniversalSegmentationOutput, Mask2FormerMLPPredictionHead,
     sample_point, pair_wise_sigmoid_cross_entropy_loss, pair_wise_dice_loss,
     sigmoid_cross_entropy_loss, dice_loss)
from torch import Tensor
import torch.nn.functional as F

from transformers.file_utils import is_scipy_available

if is_scipy_available():
    from scipy.optimize import linear_sum_assignment


def get_classification_logits(x, text_classifier, logit_scale):
    # x in shape of [B, *, C]
    # text_classifier in shape of [num_classes, C]
    # logit_scale is a learnable scalar https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/model.py#L201
    # return: [B, *, num_classes]
    x = F.normalize(x, dim=-1)
    text_classifier = F.normalize(text_classifier, dim=-1)
    logit_scale = torch.clamp(logit_scale.exp(), max=100)
    pred_logits = logit_scale * x @ text_classifier.T # B, *, N + 1
    return pred_logits


def _post_init(self):
    self.class_embed = Mask2FormerMLPPredictionHead(self.config.hidden_dim, self.config.hidden_dim, self.config.hidden_dim, 3)
    self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))


def ov_class_predictor(self, x, text_classifier):
    x = self.class_embed(x)
    all_pred_logits = []
    for per_x, per_text_classifier in zip(x, text_classifier):
        per_pred_logits = get_classification_logits(per_x.unsqueeze(0), per_text_classifier, self.logit_scale)
        all_pred_logits.append(per_pred_logits.squeeze(0))

    return all_pred_logits



def Mask2FormerLoss_loss_labels(
        self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array]
    ) -> Dict[str, Tensor]:
        batch_size = len(class_queries_logits)
        num_queries = class_queries_logits[0].shape[0]
        all_ce_loss = []
        for i in range(batch_size):
            num_labels_plus1 = class_queries_logits[i].shape[-1]
            empty_weight = torch.ones(num_labels_plus1)
            empty_weight[-1] = self.eos_coef
            empty_weight = empty_weight.to(class_queries_logits[i].device).to(class_queries_logits[i].dtype)
            criterion = nn.CrossEntropyLoss(weight=empty_weight, reduction='none')
            target_classes_o = class_labels[i][indices[i][1]]
            target_classes = torch.full(
                 (num_queries, ), fill_value=num_labels_plus1-1, dtype=torch.int64, device=class_queries_logits[i].device)
            target_classes[indices[i][0]] = target_classes_o.to(class_queries_logits[i].device)
            target_classes = target_classes.unsqueeze(0)
            pred_logits = class_queries_logits[i].unsqueeze(0).transpose(1, 2)
            loss_ce = criterion(pred_logits, target_classes)
            all_ce_loss.append(loss_ce)
        losses = {"loss_cross_entropy": torch.cat(all_ce_loss, dim=-1).mean()}
        return losses

def Mask2FormerLoss_loss_masks(
        self, 
        masks_queries_logits: torch.Tensor,
        mask_labels: List[torch.Tensor],
        indices: Tuple[np.array],
        num_masks: int
    ) -> Dict[str, torch.Tensor]:
        src_idx = self._get_predictions_permutation_indices(indices)
        tgt_idx = self._get_targets_permutation_indices(indices)
        # shape (batch_size * num_queries, height, width)
        pred_masks = masks_queries_logits[src_idx]
        # shape (batch_size, num_queries, height, width)
        # pad all and stack the targets to the num_labels dimension
        target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
        target_masks = target_masks[tgt_idx]

        # No need to upsample predictions as we are using normalized coordinates
        pred_masks = pred_masks[:, None]
        target_masks = target_masks[:, None]

        # Sample point coordinates
        with torch.no_grad():
            point_coordinates = self.sample_points_using_uncertainty(
                pred_masks,
                lambda logits: self.calculate_uncertainty(logits),
                self.num_points,
                self.oversample_ratio,
                self.importance_sample_ratio,
            )
            point_labels = sample_point(target_masks.to(torch.bfloat16), point_coordinates.to(torch.bfloat16), align_corners=False).squeeze(1)
        
        point_logits = sample_point(pred_masks, point_coordinates.to(pred_masks.dtype), align_corners=False).squeeze(1)

        losses = {
            "loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks),
            "loss_dice": dice_loss(point_logits, point_labels, num_masks),
        }

        del pred_masks
        del target_masks
        return losses

def Mask2FormerLoss_sample_points_using_uncertainty(
        self,
        logits: torch.Tensor,
        uncertainty_function,
        num_points: int,
        oversample_ratio: int,
        importance_sample_ratio: float,
    ) -> torch.Tensor:
        
        num_boxes = logits.shape[0]
        num_points_sampled = int(num_points * oversample_ratio)

        # Get random point coordinates
        point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device)
        # Get sampled prediction value for the point coordinates
        point_logits = sample_point(logits, point_coordinates.to(logits.dtype), align_corners=False)
        # Calculate the uncertainties based on the sampled prediction values of the points
        point_uncertainties = uncertainty_function(point_logits)

        num_uncertain_points = int(importance_sample_ratio * num_points)
        num_random_points = num_points - num_uncertain_points

        idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
        shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device)
        idx += shift[:, None]
        point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2)

        if num_random_points > 0:
            point_coordinates = torch.cat(
                [point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)],
                dim=1,
            )
        return point_coordinates



@torch.no_grad()
def Mask2FormerHungarianMatcher_forward(
        self,
        masks_queries_logits: torch.Tensor,
        class_queries_logits: torch.Tensor,
        mask_labels: torch.Tensor,
        class_labels: torch.Tensor,
    ) -> List[Tuple[Tensor]]:
        indices: List[Tuple[np.array]] = []

        # iterate through batch size
        batch_size = masks_queries_logits.shape[0]
        for i in range(batch_size):
            pred_probs = class_queries_logits[i].softmax(-1)
            pred_mask = masks_queries_logits[i]

            # Compute the classification cost. Contrary to the loss, we don't use the NLL, but approximate it in 1 - proba[target class]. The 1 is a constant that doesn't change the matching, it can be ommitted.
            cost_class = -pred_probs[:, class_labels[i]]
            target_mask = mask_labels[i].to(pred_mask)
            target_mask = target_mask[:, None]
            pred_mask = pred_mask[:, None]

            # Sample ground truth and predicted masks
            point_coordinates = torch.rand(1, self.num_points, 2, device=pred_mask.device)

            target_coordinates = point_coordinates.repeat(target_mask.shape[0], 1, 1).to(target_mask.dtype)
            target_mask = sample_point(target_mask, target_coordinates, align_corners=False).squeeze(1)

            pred_coordinates = point_coordinates.repeat(pred_mask.shape[0], 1, 1).to(pred_mask.dtype)
            pred_mask = sample_point(pred_mask, pred_coordinates, align_corners=False).squeeze(1)

            # compute the cross entropy loss between each mask pairs -> shape (num_queries, num_labels)
            cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask)
            # Compute the dice loss betwen each mask pairs -> shape (num_queries, num_labels)
            cost_dice = pair_wise_dice_loss(pred_mask, target_mask)
            # final cost matrix
            cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
            # eliminate infinite values in cost_matrix to avoid the error ``ValueError: cost matrix is infeasible``
            cost_matrix = torch.minimum(cost_matrix, torch.tensor(1e10))
            cost_matrix = torch.maximum(cost_matrix, torch.tensor(-1e10))
            cost_matrix = torch.nan_to_num(cost_matrix, 0)
            # do the assigmented using the hungarian algorithm in scipy
            assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.to(torch.float32).cpu())
            indices.append(assigned_indices)

        # It could be stacked in one tensor
        matched_indices = [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
        ]
        return matched_indices




def Mask2FormerMaskedAttentionDecoder_forward_first3layers(
        self,
        inputs_embeds: torch.Tensor = None,
        multi_stage_positional_embeddings: torch.Tensor = None,
        pixel_embeddings: torch.Tensor = None,
        encoder_hidden_states: torch.Tensor = None,
        query_position_embeddings: torch.Tensor = None,
        feature_size_list: List = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
                The query embeddings that are passed into the decoder.
            multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
                Position embeddings that are added to the keys in each cross(masked)-attention layer.
            pixel_embeddings (`torch.FloatTensor`):
                Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
                Decoder.
            query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the
                cross(masked)-attention of the decoder.
            feature_size_list (`List[torch.Size]`):
                This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is not None:
            hidden_states = inputs_embeds

        # intermediate hidden states with layernorm applied - required for predicting class logits
        intermediate = ()

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        attentions = () if output_attentions else None

        # intermediate mask predictions from transformer decoder layers
        intermediate_mask_predictions = ()

        intermediate_hidden_states = self.layernorm(inputs_embeds)
        intermediate += (intermediate_hidden_states,)

        predicted_mask, attention_mask = self.mask_predictor(
            intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
        )
        intermediate_mask_predictions += (predicted_mask,)

        for idx, decoder_layer in enumerate(self.layers[:3]):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            dropout_probability = torch.rand([])

            if self.training and (dropout_probability < self.layerdrop):
                continue

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    None,
                    None,
                    output_attentions,
                )

            else:
                level_index = idx % self.num_feature_levels

                where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
                # Multiply the attention mask instead of indexing to avoid issue in torch.export.
                attention_mask = attention_mask * where.unsqueeze(-1)

                layer_outputs = decoder_layer(
                    hidden_states,
                    level_index=level_index,
                    position_embeddings=multi_stage_positional_embeddings,
                    query_position_embeddings=query_position_embeddings,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=attention_mask,
                    output_attentions=output_attentions,
                )

                intermediate_hidden_states = self.layernorm(layer_outputs[0])

                predicted_mask, attention_mask = self.mask_predictor(
                    intermediate_hidden_states,
                    pixel_embeddings,
                    feature_size_list[(idx + 1) % self.num_feature_levels],
                )

                intermediate_mask_predictions += (predicted_mask,)

                # add intermediate hidden states with layer norm applied which will be used for predicting class logits
                intermediate += (intermediate_hidden_states,)

            hidden_states = layer_outputs[0]

            if output_attentions:
                attentions += (layer_outputs[1],)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        hidden_states = hidden_states.transpose(1, 0)
        if not return_dict:
            outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
            return tuple(v for v in outputs if v is not None)

        return Mask2FormerMaskedAttentionDecoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=attentions,
            intermediate_hidden_states=intermediate,
            masks_queries_logits=intermediate_mask_predictions,
        )


def Mask2FormerMaskedAttentionDecoder_forward_last3layers(
        self,
        inputs_embeds: torch.Tensor = None,
        multi_stage_positional_embeddings: torch.Tensor = None,
        pixel_embeddings: torch.Tensor = None,
        encoder_hidden_states: torch.Tensor = None,
        query_position_embeddings: torch.Tensor = None,
        feature_size_list: List = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
                The query embeddings that are passed into the decoder.
            multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
                Position embeddings that are added to the keys in each cross(masked)-attention layer.
            pixel_embeddings (`torch.FloatTensor`):
                Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
                Decoder.
            query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
                , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the
                cross(masked)-attention of the decoder.
            feature_size_list (`List[torch.Size]`):
                This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is not None:
            hidden_states = inputs_embeds

        # intermediate hidden states with layernorm applied - required for predicting class logits
        intermediate = ()

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        attentions = () if output_attentions else None

        # intermediate mask predictions from transformer decoder layers
        intermediate_mask_predictions = ()

        intermediate_hidden_states = self.layernorm(inputs_embeds)
        intermediate += (intermediate_hidden_states,)

        predicted_mask, attention_mask = self.mask_predictor(
            intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
        )
        intermediate_mask_predictions += (predicted_mask,)

        for _idx, decoder_layer in enumerate(self.layers[3:]):
            idx = _idx + 3
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            dropout_probability = torch.rand([])

            if self.training and (dropout_probability < self.layerdrop):
                continue

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    None,
                    None,
                    output_attentions,
                )

            else:
                level_index = idx % self.num_feature_levels

                where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
                # Multiply the attention mask instead of indexing to avoid issue in torch.export.
                attention_mask = attention_mask * where.unsqueeze(-1)

                layer_outputs = decoder_layer(
                    hidden_states,
                    level_index=level_index,
                    position_embeddings=multi_stage_positional_embeddings,
                    query_position_embeddings=query_position_embeddings,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=attention_mask,
                    output_attentions=output_attentions,
                )

                intermediate_hidden_states = self.layernorm(layer_outputs[0])

                predicted_mask, attention_mask = self.mask_predictor(
                    intermediate_hidden_states,
                    pixel_embeddings,
                    feature_size_list[(idx + 1) % self.num_feature_levels],
                )

                intermediate_mask_predictions += (predicted_mask,)

                # add intermediate hidden states with layer norm applied which will be used for predicting class logits
                intermediate += (intermediate_hidden_states,)

            hidden_states = layer_outputs[0]

            if output_attentions:
                attentions += (layer_outputs[1],)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        hidden_states = hidden_states.transpose(1, 0)
        if not return_dict:
            outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
            return tuple(v for v in outputs if v is not None)

        return Mask2FormerMaskedAttentionDecoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=attentions,
            intermediate_hidden_states=intermediate,
            masks_queries_logits=intermediate_mask_predictions,
        )


def Mask2FormerTransformerModule_forward_first_part(
        self,
        multi_scale_features: List[Tensor],
        mask_features: Tensor,
        output_hidden_states: bool = False,
        output_attentions: bool = False,
    ) -> Mask2FormerMaskedAttentionDecoderOutput:
        multi_stage_features = []
        multi_stage_positional_embeddings = []
        size_list = []

        for i in range(self.num_feature_levels):
            size_list.append(multi_scale_features[i].shape[-2:])
            multi_stage_positional_embeddings.append(self.position_embedder(multi_scale_features[i], None).flatten(2))
            multi_stage_features.append(
                self.input_projections[i](multi_scale_features[i]).flatten(2)
                + self.level_embed.weight[i][None, :, None]
            )

            # Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
            multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
            multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)

        _, batch_size, _ = multi_stage_features[0].shape

        # [num_queries, batch_size, num_channels]
        query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
        query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)

        decoder_output = self.decoder.Mask2FormerMaskedAttentionDecoder_forward_first3layers(
            inputs_embeds=query_features,
            multi_stage_positional_embeddings=multi_stage_positional_embeddings,
            pixel_embeddings=mask_features,
            encoder_hidden_states=multi_stage_features,
            query_position_embeddings=query_embeddings,
            feature_size_list=size_list,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=True,
        )

        return decoder_output


def Mask2FormerTransformerModule_forward_second_part(
        self,
        query_features: Tensor,
        query_embeddings: Tensor,
        multi_scale_features: List[Tensor],
        mask_features: Tensor,
        output_hidden_states: bool = False,
        output_attentions: bool = False,
    ) -> Mask2FormerMaskedAttentionDecoderOutput:
        multi_stage_features = []
        multi_stage_positional_embeddings = []
        size_list = []

        for i in range(self.num_feature_levels):
            size_list.append(multi_scale_features[i].shape[-2:])
            multi_stage_positional_embeddings.append(self.position_embedder(multi_scale_features[i], None).flatten(2))
            multi_stage_features.append(
                self.input_projections[i](multi_scale_features[i]).flatten(2)
                + self.level_embed.weight[i][None, :, None]
            )

            # Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
            multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
            multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)

        _, batch_size, _ = multi_stage_features[0].shape

        # [num_queries, batch_size, num_channels]
        # query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
        # query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)

        decoder_output = self.decoder.Mask2FormerMaskedAttentionDecoder_forward_last3layers(
            inputs_embeds=query_features,
            multi_stage_positional_embeddings=multi_stage_positional_embeddings,
            pixel_embeddings=mask_features,
            encoder_hidden_states=multi_stage_features,
            query_position_embeddings=query_embeddings,
            feature_size_list=size_list,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=True,
        )

        return decoder_output


def Mask2FormerModel_forward_first_part(
        self,
        pixel_values: Tensor,
        pixel_mask: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Mask2FormerModelOutput:
        r"""
        Returns:
            `Mask2FormerModelOutput`

        Examples:
        ```python
        >>> import torch
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoImageProcessor, Mask2FormerModel

        >>> # load image
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # load image preprocessor and Mask2FormerModel trained on COCO instance segmentation dataset
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
        >>> model = Mask2FormerModel.from_pretrained("facebook/mask2former-swin-small-coco-instance")
        >>> inputs = image_processor(image, return_tensors="pt")

        >>> # forward pass
        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> # model outputs last hidden states of shape (batch_size, num_queries, hidden_size)
        >>> print(outputs.transformer_decoder_last_hidden_state.shape)
        torch.Size([1, 100, 256])
        ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        batch_size, _, height, width = pixel_values.shape

        if pixel_mask is None:
            pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)

        pixel_level_module_output = self.pixel_level_module(
            pixel_values=pixel_values, output_hidden_states=output_hidden_states
        )

        transformer_module_output = self.transformer_module.Mask2FormerTransformerModule_forward_first_part(
            multi_scale_features=pixel_level_module_output.decoder_hidden_states,
            mask_features=pixel_level_module_output.decoder_last_hidden_state,
            output_hidden_states=True,
            output_attentions=output_attentions,
        )

        query_features = transformer_module_output.last_hidden_state
        return query_features, pixel_level_module_output


def Mask2FormerModel_forward_second_part(
        self,
        query_features: Tensor,
        query_embeddings: Tensor,
        pixel_level_module_output,
        pixel_values: Tensor,
        pixel_mask: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Mask2FormerModelOutput:
        r"""
        Returns:
            `Mask2FormerModelOutput`

        Examples:
        ```python
        >>> import torch
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoImageProcessor, Mask2FormerModel

        >>> # load image
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # load image preprocessor and Mask2FormerModel trained on COCO instance segmentation dataset
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
        >>> model = Mask2FormerModel.from_pretrained("facebook/mask2former-swin-small-coco-instance")
        >>> inputs = image_processor(image, return_tensors="pt")

        >>> # forward pass
        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> # model outputs last hidden states of shape (batch_size, num_queries, hidden_size)
        >>> print(outputs.transformer_decoder_last_hidden_state.shape)
        torch.Size([1, 100, 256])
        ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        batch_size, _, height, width = pixel_values.shape

        if pixel_mask is None:
            pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)

        transformer_module_output = self.transformer_module.Mask2FormerTransformerModule_forward_second_part(
            query_features=query_features,
            query_embeddings=query_embeddings,
            multi_scale_features=pixel_level_module_output.decoder_hidden_states,
            mask_features=pixel_level_module_output.decoder_last_hidden_state,
            output_hidden_states=True,
            output_attentions=output_attentions,
        )

        encoder_hidden_states = None
        pixel_decoder_hidden_states = None
        transformer_decoder_hidden_states = None
        transformer_decoder_intermediate_states = None

        if output_hidden_states:
            encoder_hidden_states = pixel_level_module_output.encoder_hidden_states
            pixel_decoder_hidden_states = pixel_level_module_output.decoder_hidden_states
            transformer_decoder_hidden_states = transformer_module_output.hidden_states
            transformer_decoder_intermediate_states = transformer_module_output.intermediate_hidden_states

        output = Mask2FormerModelOutput(
            encoder_last_hidden_state=pixel_level_module_output.encoder_last_hidden_state,
            pixel_decoder_last_hidden_state=pixel_level_module_output.decoder_last_hidden_state,
            transformer_decoder_last_hidden_state=transformer_module_output.last_hidden_state,
            encoder_hidden_states=encoder_hidden_states,
            pixel_decoder_hidden_states=pixel_decoder_hidden_states,
            transformer_decoder_hidden_states=transformer_decoder_hidden_states,
            transformer_decoder_intermediate_states=transformer_decoder_intermediate_states,
            attentions=transformer_module_output.attentions,
            masks_queries_logits=transformer_module_output.masks_queries_logits,
        )

        if not return_dict:
            output = tuple(v for v in output.values() if v is not None)

        return output


def Mask2FormerForUniversalSegmentation_forward_first_part(
        self,
        pixel_values: Tensor,
        mask_labels: Optional[List[Tensor]] = None,
        class_labels: Optional[List[Tensor]] = None,
        pixel_mask: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_auxiliary_logits: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Mask2FormerForUniversalSegmentationOutput:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        query_features, pixel_level_module_output = self.model.Mask2FormerModel_forward_first_part(
            pixel_values=pixel_values,
            pixel_mask=pixel_mask,
            output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
            output_attentions=output_attentions,
            return_dict=True,
        )

        return query_features, pixel_level_module_output


def Mask2FormerForUniversalSegmentation_forward_second_part(
        self,
        query_features,
        query_embeddings,
        pixel_level_module_output,
        text_classifier,
        pixel_values: Tensor,
        mask_labels: Optional[List[Tensor]] = None,
        class_labels: Optional[List[Tensor]] = None,
        pixel_mask: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_auxiliary_logits: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Mask2FormerForUniversalSegmentationOutput:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.model.Mask2FormerModel_forward_second_part(
            query_features=query_features,
            query_embeddings=query_embeddings,
            pixel_level_module_output=pixel_level_module_output,
            pixel_values=pixel_values,
            pixel_mask=pixel_mask,
            output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
            output_attentions=output_attentions,
            return_dict=True,
        )

        loss, loss_dict, auxiliary_logits = None, None, None
        class_queries_logits = ()

        for decoder_output in outputs.transformer_decoder_intermediate_states:
            class_prediction = self.ov_class_predictor(decoder_output.transpose(0, 1), text_classifier)
            # class_prediction = self.class_predictor(decoder_output.transpose(0, 1))
            class_queries_logits += (class_prediction,)

        masks_queries_logits = outputs.masks_queries_logits

        auxiliary_logits = self.get_auxiliary_logits(class_queries_logits, masks_queries_logits)

        if mask_labels is not None and class_labels is not None:
            loss_dict = self.get_loss_dict(
                masks_queries_logits=masks_queries_logits[-1],
                class_queries_logits=class_queries_logits[-1],
                mask_labels=mask_labels,
                class_labels=class_labels,
                auxiliary_predictions=auxiliary_logits,
            )
            loss = self.get_loss(loss_dict)

        encoder_hidden_states = None
        pixel_decoder_hidden_states = None
        transformer_decoder_hidden_states = None

        if output_hidden_states:
            encoder_hidden_states = outputs.encoder_hidden_states
            pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states
            transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states

        output_auxiliary_logits = (
            self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
        )
        if not output_auxiliary_logits:
            auxiliary_logits = None

        output = Mask2FormerForUniversalSegmentationOutput(
            loss=loss,
            class_queries_logits=class_queries_logits[-1],
            masks_queries_logits=masks_queries_logits[-1],
            auxiliary_logits=auxiliary_logits,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            pixel_decoder_last_hidden_state=outputs.pixel_decoder_last_hidden_state,
            transformer_decoder_last_hidden_state=outputs.transformer_decoder_last_hidden_state,
            encoder_hidden_states=encoder_hidden_states,
            pixel_decoder_hidden_states=pixel_decoder_hidden_states,
            transformer_decoder_hidden_states=transformer_decoder_hidden_states,
            attentions=outputs.attentions,
        )

        if not return_dict:
            output = tuple(v for v in output.values() if v is not None)
            if loss is not None:
                output = (loss) + output
        return output