# coding=utf-8 # Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Dict, List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...utils import auto_docstring, logging from ..sam.configuration_sam import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig from ..sam.modeling_sam import ( SamFeedForward, SamImageSegmentationOutput, SamLayerNorm, SamModel, SamPreTrainedModel, SamTwoWayTransformer, SamVisionEncoder, SamVisionEncoderOutput, SamVisionModel, ) logger = logging.get_logger(__name__) class SamHQPromptEncoderConfig(SamPromptEncoderConfig): r""" This is the configuration class to store the configuration of a [`SamHQPromptEncoderModel`].The [`SamHQPromptEncoderModel`] module is used to encode the input 2D points and bounding boxes. Instantiating a configuration defaults will yield a similar configuration to that of the SAM_HQ model. The configuration is used to store the configuration of the model. [Uminosachi/sam-hq](https://huggingface.co/Uminosachi/sam-hq) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model's output.Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 256): Dimensionality of the hidden states. image_size (`int`, *optional*, defaults to 1024): The expected output resolution of the image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. mask_input_channels (`int`, *optional*, defaults to 16): The number of channels to be fed to the `MaskDecoder` module. num_point_embeddings (`int`, *optional*, defaults to 4): The number of point embeddings to be used. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function in the encoder and pooler. """ pass class SamHQVisionConfig(SamVisionConfig): pass class SamHQMaskDecoderConfig(SamMaskDecoderConfig): r""" vit_dim (`int`, *optional*, defaults to 768): Dimensionality of the Vision Transformer (ViT) used in the `SamHQMaskDecoder` module. """ def __init__( self, vit_dim=768, **super_kwargs, ): super().__init__(**super_kwargs) self.vit_dim = vit_dim class SamHQConfig(SamConfig): r""" [`SamHQConfig`] is the configuration class to store the configuration of a [`SamHQModel`]. It is used to instantiate a SAM-HQ model according to the specified arguments, defining the vision model, prompt-encoder model and mask decoder configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the SAM-HQ-ViT-H [sushmanth/sam_hq_vit_h](https://huggingface.co/sushmanth/sam_hq_vit_h) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (Union[`dict`, `SamHQVisionConfig`], *optional*): Dictionary of configuration options used to initialize [`SamHQVisionConfig`]. prompt_encoder_config (Union[`dict`, `SamHQPromptEncoderConfig`], *optional*): Dictionary of configuration options used to initialize [`SamHQPromptEncoderConfig`]. mask_decoder_config (Union[`dict`, `SamHQMaskDecoderConfig`], *optional*): Dictionary of configuration options used to initialize [`SamHQMaskDecoderConfig`]. kwargs (*optional*): Dictionary of keyword arguments. """ pass @dataclass class SamHQVisionEncoderOutput(SamVisionEncoderOutput): """ intermediate_embeddings (`list(torch.FloatTensor)`, *optional*): A list of intermediate embeddings collected from certain blocks within the model, typically those without windowed attention. Each element in the list is of shape `(batch_size, sequence_length, hidden_size)`. This is specific to SAM-HQ and not present in base SAM. """ intermediate_embeddings: Optional[List[torch.FloatTensor]] = None @dataclass class SamHQImageSegmentationOutput(SamImageSegmentationOutput): pass class SamHQVisionEncoder(SamVisionEncoder): def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SamHQVisionEncoderOutput]: 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 pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.patch_embed(pixel_values) if self.pos_embed is not None: hidden_states = hidden_states + self.pos_embed all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None intermediate_embeddings = [] for i, layer_module in enumerate(self.layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, ) else: layer_outputs = layer_module(hidden_states, output_attentions=output_attentions) hidden_states = layer_outputs[0] # Collect embeddings from non-windowed blocks if hasattr(layer_module, "window_size") and layer_module.window_size == 0: intermediate_embeddings.append(hidden_states) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) hidden_states = self.neck(hidden_states) if not return_dict: outputs = (hidden_states, intermediate_embeddings) if output_hidden_states: outputs = outputs + (all_hidden_states,) if output_attentions: outputs = outputs + (all_self_attentions,) return outputs return SamHQVisionEncoderOutput( last_hidden_state=hidden_states, intermediate_embeddings=intermediate_embeddings, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class SamHQLayerNorm(SamLayerNorm): pass class SamHQTwoWayTransformer(SamTwoWayTransformer): pass class SamHQFeedForward(SamFeedForward): pass class SamHQMaskDecoder(nn.Module): def __init__(self, config: SamHQMaskDecoderConfig): super().__init__() self.hidden_size = config.hidden_size self.num_multimask_outputs = config.num_multimask_outputs self.num_mask_tokens = config.num_multimask_outputs + 1 self.iou_token = nn.Embedding(1, self.hidden_size) self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) self.transformer = SamHQTwoWayTransformer(config) self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) self.upscale_layer_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") self.activation = nn.GELU() mlps_list = [] for _ in range(self.num_mask_tokens): mlps_list += [SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) self.iou_prediction_head = SamHQFeedForward( self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth ) self.hq_token = nn.Embedding(1, self.hidden_size) self.hq_mask_mlp = SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3) self.num_mask_tokens = self.num_mask_tokens + 1 # Compress ViT features self.compress_vit_conv1 = nn.ConvTranspose2d(config.vit_dim, self.hidden_size, kernel_size=2, stride=2) self.compress_vit_norm = SamHQLayerNorm(self.hidden_size, data_format="channels_first") self.compress_vit_conv2 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 8, kernel_size=2, stride=2) # Embedding encoder self.encoder_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) self.encoder_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") self.encoder_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) # Embedding mask feature self.mask_conv1 = nn.Conv2d(self.hidden_size // 8, self.hidden_size // 4, kernel_size=3, stride=1, padding=1) self.mask_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") self.mask_conv2 = nn.Conv2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=3, stride=1, padding=1) def forward( self, image_embeddings: torch.Tensor, image_positional_embeddings: torch.Tensor, sparse_prompt_embeddings: torch.Tensor, dense_prompt_embeddings: torch.Tensor, multimask_output: bool, hq_token_only: bool, intermediate_embeddings: Optional[List[torch.Tensor]] = None, output_attentions: Optional[bool] = None, attention_similarity: Optional[torch.Tensor] = None, target_embedding: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Predict high-quality masks given image and prompt embeddings. Args: image_embeddings (`torch.Tensor`): The embeddings from the image encoder. image_positional_embedding (`torch.Tensor`): Positional encoding with the shape of image_embeddings. sparse_prompt_embeddings (`torch.Tensor`): The embeddings of the points and boxes. dense_prompt_embeddings (`torch.Tensor`): The embeddings of the mask inputs. multimask_output (bool): Whether to return multiple masks or a single mask. hq_token_only (bool): Whether to use only the high-quality token output or combine with SAM output. intermediate_embeddings (`torch.Tensor`): Intermediate embeddings from the vision encoder for feature fusion. output_attentions (bool, *optional*): Whether or not to return the attentions tensors of all attention layers. attention_similarity (`torch.Tensor`, *optional*): Optional tensor for attention similarity computation. target_embedding (`torch.Tensor`, *optional*): Optional target embedding for transformer processing. Returns: `Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`: A tuple of tensors containing: - A tensor of shape `(batch_size, num_prompts, num_masks, height, width)` containing the output masks. - A tensor of shape `(batch_size, num_prompts, num_masks)` containing the iou predictions for each mask. - (Optional) A tuple containing attention tensors if output_attentions is True. """ batch_size, num_channels, height, width = image_embeddings.shape point_batch_size = sparse_prompt_embeddings.shape[1] has_intermediate = intermediate_embeddings is not None and len(intermediate_embeddings) > 0 if has_intermediate: vit_features = intermediate_embeddings[0].permute(0, 3, 1, 2).contiguous() embed_encode = self.encoder_conv1(image_embeddings) embed_encode = self.activation(self.encoder_norm(embed_encode)) embed_encode = self.encoder_conv2(embed_encode) if has_intermediate: compressed_vit_features = self.compress_vit_conv1(vit_features) compressed_vit_features = self.activation(self.compress_vit_norm(compressed_vit_features)) compressed_vit_features = self.compress_vit_conv2(compressed_vit_features) hq_features = embed_encode + compressed_vit_features else: hq_features = embed_encode output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight, self.hq_token.weight], dim=0) output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) if torch.any(sparse_prompt_embeddings != 0): tokens = torch.cat([output_tokens, sparse_prompt_embeddings], dim=2) else: tokens = output_tokens point_embeddings = tokens.to(self.iou_token.weight.dtype) image_embeddings = image_embeddings + dense_prompt_embeddings image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0) image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) point_embedding, image_embeddings, attentions = self.transformer( point_embeddings=point_embeddings, image_embeddings=image_embeddings, image_positional_embeddings=image_positional_embeddings, attention_similarity=attention_similarity, target_embedding=target_embedding, output_attentions=output_attentions, ) iou_token_out = point_embedding[:, :, 0, :] mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :] image_embeddings = image_embeddings.transpose(2, 3).reshape( batch_size * point_batch_size, num_channels, height, width ) upscaled_embedding = self.upscale_conv1(image_embeddings) upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding)) upscaled_embedding_hq = self.mask_conv1(upscaled_embedding) upscaled_embedding_hq = self.activation(self.mask_norm(upscaled_embedding_hq)) upscaled_embedding_hq = self.mask_conv2(upscaled_embedding_hq) if hq_features.shape[0] == 1: hq_features = hq_features.repeat(batch_size * point_batch_size, 1, 1, 1) elif hq_features.shape[0] == batch_size and batch_size * point_batch_size != batch_size: hq_features = hq_features.repeat_interleave(point_batch_size, 0) upscaled_embedding_hq = upscaled_embedding_hq + hq_features hyper_in_list = [] for mask_token_index in range(self.num_mask_tokens): if mask_token_index < self.num_mask_tokens - 1: current_mlp = self.output_hypernetworks_mlps[mask_token_index] else: current_mlp = self.hq_mask_mlp hyper_in_list += [current_mlp(mask_tokens_out[:, :, mask_token_index, :])] hyper_in = torch.stack(hyper_in_list, dim=2) _, num_channels, height, width = upscaled_embedding.shape upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width) upscaled_embedding_hq = upscaled_embedding_hq.reshape( batch_size, point_batch_size, num_channels, height * width ) masks_sam = (hyper_in[:, :, : self.num_mask_tokens - 1] @ upscaled_embedding).reshape( batch_size, point_batch_size, -1, height, width ) masks_hq = (hyper_in[:, :, self.num_mask_tokens - 1 :] @ upscaled_embedding_hq).reshape( batch_size, point_batch_size, -1, height, width ) masks = torch.cat([masks_sam, masks_hq], dim=2) iou_pred = self.iou_prediction_head(iou_token_out) if multimask_output: mask_slice = slice(1, self.num_mask_tokens - 1) iou_pred = iou_pred[:, :, mask_slice] # Sort the IoU scores in descending order and get indices iou_pred_sorted, sort_indices = torch.sort(iou_pred, dim=2, descending=True) # Reorder the masks according to sorted scores masks_sam = masks[:, :, mask_slice, :, :] masks_sam = torch.gather( masks_sam, 2, sort_indices[..., None, None].expand(-1, -1, -1, masks_sam.shape[3], masks_sam.shape[4]), ) # Update iou_pred with sorted scores iou_pred = iou_pred_sorted else: mask_slice = slice(0, 1) iou_pred = iou_pred[:, :, mask_slice] masks_sam = masks[:, :, mask_slice, :, :] masks_hq = masks[:, :, slice(self.num_mask_tokens - 1, self.num_mask_tokens), :, :] if hq_token_only: masks = masks_hq else: masks = masks_sam + masks_hq outputs = (masks, iou_pred) if output_attentions: outputs = outputs + (attentions,) else: outputs = outputs + (None,) return outputs class SamHQPreTrainedModel(SamPreTrainedModel): def _init_weights(self, module): super()._init_weights(module) if isinstance(module, SamHQVisionEncoder): if module.pos_embed is not None: module.pos_embed.data.zero_() class SamHQVisionModel(SamVisionModel): pass @auto_docstring( custom_intro=""" Segment Anything Model HQ (SAM-HQ) for generating masks, given an input image and optional 2D location and bounding boxes. """ ) class SamHQModel(SamModel): _tied_weights_keys = ["prompt_encoder.shared_embedding.positional_embedding"] _keys_to_ignore_on_load_missing = ["prompt_encoder.shared_embedding.positional_embedding"] def __init__(self, config): super().__init__(config) self.vision_encoder = SamHQVisionEncoder(config.vision_config) self.mask_decoder = SamHQMaskDecoder(config.mask_decoder_config) self.post_init() @torch.no_grad() def get_image_embeddings( self, pixel_values, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" Returns the image embeddings by passing the pixel values through the vision encoder. Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Input pixel values output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ vision_output = self.vision_encoder( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeddings = vision_output[0] intermediate_embeddings = vision_output[1] return image_embeddings, intermediate_embeddings def forward( self, pixel_values: Optional[torch.FloatTensor] = None, input_points: Optional[torch.FloatTensor] = None, input_labels: Optional[torch.LongTensor] = None, input_boxes: Optional[torch.FloatTensor] = None, input_masks: Optional[torch.LongTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, multimask_output: bool = True, hq_token_only: bool = False, attention_similarity: Optional[torch.FloatTensor] = None, target_embedding: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, intermediate_embeddings: Optional[List[torch.FloatTensor]] = None, **kwargs, ) -> List[Dict[str, torch.Tensor]]: r""" input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much better results. The points can be obtained by passing a list of list of list to the processor that will create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict per input point), the third dimension is the number of points per segmentation mask (it is possible to pass multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) coordinates of the point. If a different number of points is passed either for each image, or for each mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the computation of the embedding will be skipped for these points using the labels. input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the official implementation, there are 3 types of labels - `1`: the point is a point that contains the object of interest - `0`: the point is a point that does not contain the object of interest - `-1`: the point corresponds to the background We added the label: - `-10`: the point is a padding point, thus should be ignored by the prompt encoder The padding labels should be automatically done by the processor. input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. In the order (`x1`, `y1`, `x2`, `y2`): - `x1`: the x coordinate of the top left point of the input box - `y1`: the y coordinate of the top left point of the input box - `x2`: the x coordinate of the bottom right point of the input box - `y2`: the y coordinate of the bottom right point of the input box input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): SAM_HQ model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): Image embeddings, this is used by the mask decder to generate masks and iou scores. For more memory efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` method, and then feed them to the `forward` method instead of feeding the `pixel_values`. multimask_output (`bool`, *optional*): In the original implementation and paper, the model always outputs 3 masks per image (or per point / per bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the "best" mask, by specifying `multimask_output=False`. hq_token_only (`bool`, *optional*, defaults to `False`): Whether to use only the HQ token path for mask generation. When False, combines both standard and HQ paths. This is specific to SAM-HQ's architecture. attention_similarity (`torch.FloatTensor`, *optional*): Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the model is used for personalization as introduced in [PerSAM](https://arxiv.org/abs/2305.03048). target_embedding (`torch.FloatTensor`, *optional*): Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case the model is used for personalization as introduced in [PerSAM](https://arxiv.org/abs/2305.03048). intermediate_embeddings (`List[torch.FloatTensor]`, *optional*): Intermediate embeddings from vision encoder's non-windowed blocks, used by SAM-HQ for enhanced mask quality. Required when providing pre-computed image_embeddings instead of pixel_values. Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoModel, AutoProcessor >>> model = AutoModel.from_pretrained("sushmanth/sam_hq_vit_b") >>> processor = AutoProcessor.from_pretrained("sushmanth/sam_hq_vit_b") >>> img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") >>> input_points = [[[400, 650]]] # 2D location of a window on the car >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt") >>> # Get high-quality segmentation mask >>> outputs = model(**inputs) >>> # For high-quality mask only >>> outputs = model(**inputs, hq_token_only=True) >>> # Postprocess masks >>> masks = processor.post_process_masks( ... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"] ... ) ``` """ 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 pixel_values is None and image_embeddings is None: raise ValueError("Either pixel_values or image_embeddings must be provided.") if pixel_values is not None and image_embeddings is not None: raise ValueError("Only one of pixel_values and image_embeddings can be provided.") if input_points is not None and len(input_points.shape) != 4: raise ValueError( "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`." f" got {input_points.shape}." ) if input_boxes is not None and len(input_boxes.shape) != 3: raise ValueError( "The input_boxes must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`." f" got {input_boxes.shape}." ) # Add validation for point and box batch sizes if input_points is not None and input_boxes is not None: point_batch_size = input_points.shape[1] box_batch_size = input_boxes.shape[1] if point_batch_size != box_batch_size: raise ValueError( "You should provide as many bounding boxes as input points per box. Got {} and {}.".format( point_batch_size, box_batch_size ) ) image_positional_embeddings = self.get_image_wide_positional_embeddings() # repeat with batch size batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0] image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) vision_attentions = None vision_hidden_states = None if pixel_values is not None: vision_outputs = self.vision_encoder( pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if return_dict: image_embeddings = vision_outputs.last_hidden_state intermediate_embeddings = vision_outputs.intermediate_embeddings if output_hidden_states: vision_hidden_states = vision_outputs.hidden_states if output_attentions: vision_attentions = vision_outputs.attentions else: image_embeddings = vision_outputs[0] intermediate_embeddings = vision_outputs[1] if output_hidden_states: vision_hidden_states = vision_outputs[2] if output_attentions: vision_attentions = vision_outputs[-1] if input_points is not None and input_labels is None: input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) sparse_embeddings, dense_embeddings = self.prompt_encoder( input_points=input_points, input_labels=input_labels, input_boxes=input_boxes, input_masks=input_masks, ) # Predict masks low_res_masks, iou_predictions, mask_decoder_attentions = self.mask_decoder( image_embeddings=image_embeddings, image_positional_embeddings=image_positional_embeddings, sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, hq_token_only=hq_token_only, intermediate_embeddings=intermediate_embeddings, attention_similarity=attention_similarity, target_embedding=target_embedding, output_attentions=output_attentions, ) if not return_dict: output = (iou_predictions, low_res_masks) if output_hidden_states: output = output + (vision_hidden_states,) if output_attentions: output = output + (vision_attentions, mask_decoder_attentions) return output return SamHQImageSegmentationOutput( iou_scores=iou_predictions, pred_masks=low_res_masks, vision_hidden_states=vision_hidden_states, vision_attentions=vision_attentions, mask_decoder_attentions=mask_decoder_attentions, ) __all__ = [ "SamHQVisionConfig", "SamHQMaskDecoderConfig", "SamHQPromptEncoderConfig", "SamHQConfig", "SamHQModel", "SamHQPreTrainedModel", "SamHQVisionModel", ]