modeling_yolos.py

# coding=utf-8
# Copyright 2022 School of EIC, Huazhong University of Science & Technology and The 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.
""" PyTorch YOLOS model."""


import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import Tensor, nn

from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_scipy_available,
    is_vision_available,
    logging,
    replace_return_docstrings,
    requires_backends,
)
from .configuration_yolos import YolosConfig


if is_scipy_available():
    from scipy.optimize import linear_sum_assignment

if is_vision_available():
    from transformers.models.detr.feature_extraction_detr import center_to_corners_format


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "YolosConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor"

# Base docstring
_CHECKPOINT_FOR_DOC = "google/yolos-base-patch16-224-in21k"
_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]


YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST = [
    # TODO update nielsr to organization
    "nielsr/yolos-s",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
]


@dataclass
class YolosObjectDetectionOutput(ModelOutput):
    """
    Output type of [`DetrForObjectDetection`].

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
            Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
            bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
            scale-invariant IoU loss.
        loss_dict (`Dict`, *optional*):
            A dictionary containing the individual losses. Useful for logging.
        logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
            Classification logits (including no-object) for all queries.
        pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
            Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
            values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
            possible padding). You can use [`~DetrFeatureExtractor.post_process`] to retrieve the unnormalized bounding
            boxes.
        auxiliary_outputs (`list[Dict]`, *optional*):
            Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
            and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
            `pred_boxes`) for each decoder layer.
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the decoder of the model.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
            the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
            the self-attention heads.
    """

    loss: Optional[torch.FloatTensor] = None
    loss_dict: Optional[Dict] = None
    logits: torch.FloatTensor = None
    pred_boxes: torch.FloatTensor = None
    auxiliary_outputs: Optional[List[Dict]] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


# copied from transformers.models.vit.modeling_vit.to_2tuple
def to_2tuple(x):
    if isinstance(x, collections.abc.Iterable):
        return x
    return (x, x)


class YolosEmbeddings(nn.Module):
    """
    Construct the CLS token, detection token, position and patch embeddings.

    """

    def __init__(self, config: YolosConfig) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.det_token = nn.Parameter(torch.zeros(1, config.num_detection_tokens, config.hidden_size))
        self.patch_embeddings = PatchEmbeddings(
            image_size=config.image_size,
            patch_size=config.patch_size,
            num_channels=config.num_channels,
            embed_dim=config.hidden_size,
        )
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(
            torch.zeros(1, num_patches + config.num_detection_tokens + 1, config.hidden_size)
        )

        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.interpolation = InterpolateInitialPositionEmbeddings(config)
        self.config = config

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values)

        batch_size, seq_len, _ = embeddings.size()

        # add the [CLS] token to the embedded patch tokens
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        det_tokens = self.det_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings, det_tokens), dim=1)

        # add positional encoding to each token
        # this might require interpolation of the existing position embeddings
        position_embeddings = self.interpolation(self.position_embeddings, (height, width))

        embeddings = embeddings + position_embeddings

        embeddings = self.dropout(embeddings)

        return embeddings


class InterpolateInitialPositionEmbeddings(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.config = config

    def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
        cls_pos_embed = pos_embed[:, 0, :]
        cls_pos_embed = cls_pos_embed[:, None]
        det_pos_embed = pos_embed[:, -self.config.num_detection_tokens :, :]
        patch_pos_embed = pos_embed[:, 1 : -self.config.num_detection_tokens, :]
        patch_pos_embed = patch_pos_embed.transpose(1, 2)
        B, E, Q = patch_pos_embed.shape

        patch_height, patch_width = (
            self.config.image_size[0] // self.config.patch_size,
            self.config.image_size[1] // self.config.patch_size,
        )
        patch_pos_embed = patch_pos_embed.view(B, E, patch_height, patch_width)

        H, W = img_size
        new_patch_heigth, new_patch_width = H // self.config.patch_size, W // self.config.patch_size
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed, size=(new_patch_heigth, new_patch_width), mode="bicubic", align_corners=False
        )
        patch_pos_embed = patch_pos_embed.flatten(2).transpose(1, 2)
        scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=1)
        return scale_pos_embed


class InterpolateMidPositionEmbeddings(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.config = config

    def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
        cls_pos_embed = pos_embed[:, :, 0, :]
        cls_pos_embed = cls_pos_embed[:, None]
        det_pos_embed = pos_embed[:, :, -self.config.num_detection_tokens :, :]
        patch_pos_embed = pos_embed[:, :, 1 : -self.config.num_detection_tokens, :]
        patch_pos_embed = patch_pos_embed.transpose(2, 3)
        D, B, E, Q = patch_pos_embed.shape

        patch_height, patch_width = (
            self.config.image_size[0] // self.config.patch_size,
            self.config.image_size[1] // self.config.patch_size,
        )
        patch_pos_embed = patch_pos_embed.view(D * B, E, patch_height, patch_width)
        height, width = img_size
        new_patch_height, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed, size=(new_patch_height, new_patch_width), mode="bicubic", align_corners=False
        )
        patch_pos_embed = (
            patch_pos_embed.flatten(2).transpose(1, 2).contiguous().view(D, B, new_patch_height * new_patch_width, E)
        )
        scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=2)
        return scale_pos_embed


# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class PatchEmbeddings(nn.Module):
    """
    Image to Patch Embedding.

    """

    def __init__(
        self,
        image_size: int = 224,
        patch_size: Union[int, Tuple[int, int]] = 16,
        num_channels: int = 3,
        embed_dim: int = 768,
    ):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings


# copied from transformers.models.vit.modeling_vit.ViTSelfAttention
class YolosSelfAttention(nn.Module):
    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        mixed_query_layer = self.query(hidden_states)

        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


# copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Yolos
class YolosSelfOutput(nn.Module):
    """
    The residual connection is defined in YolosLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:

        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        return hidden_states


# copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Yolos
class YolosAttention(nn.Module):
    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.attention = YolosSelfAttention(config)
        self.output = YolosSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: Set[int]) -> None:
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_outputs = self.attention(hidden_states, head_mask, output_attentions)

        attention_output = self.output(self_outputs[0], hidden_states)

        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


# copied from transformers.models.vit.modeling_vit.ViTIntermediate
class YolosIntermediate(nn.Module):
    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:

        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


# copied from transformers.models.vit.modeling_vit.ViTOutput
class YolosOutput(nn.Module):
    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        hidden_states = hidden_states + input_tensor

        return hidden_states


# copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->Yolos
class YolosLayer(nn.Module):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = YolosAttention(config)
        self.intermediate = YolosIntermediate(config)
        self.output = YolosOutput(config)
        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        self_attention_outputs = self.attention(
            self.layernorm_before(hidden_states),  # in YOLOS, layernorm is applied before self-attention
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in YOLOS, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.intermediate(layer_output)

        # second residual connection is done here
        layer_output = self.output(layer_output, hidden_states)

        outputs = (layer_output,) + outputs

        return outputs


class YolosEncoder(nn.Module):
    def __init__(self, config: YolosConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([YolosLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

        self.mid_position_embeddings = (
            nn.Parameter(
                torch.zeros(
                    config.num_hidden_layers - 1,
                    1,
                    1
                    + (config.image_size[0] * config.image_size[1] // config.patch_size**2)
                    + config.num_detection_tokens,
                    config.hidden_size,
                )
            )
            if config.use_mid_position_embeddings
            else None
        )

        self.interpolation = InterpolateMidPositionEmbeddings(config) if config.use_mid_position_embeddings else None

    def forward(
        self,
        hidden_states: torch.Tensor,
        height,
        width,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if self.config.use_mid_position_embeddings:
            interpolated_mid_position_embeddings = self.interpolation(self.mid_position_embeddings, (height, width))

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    layer_head_mask,
                )
            else:
                layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)

            hidden_states = layer_outputs[0]

            if self.config.use_mid_position_embeddings:
                if i < (self.config.num_hidden_layers - 1):
                    hidden_states = hidden_states + interpolated_mid_position_embeddings[i]

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

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class YolosPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = YolosConfig
    base_model_prefix = "vit"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def _set_gradient_checkpointing(self, module: YolosEncoder, value: bool = False) -> None:
        if isinstance(module, YolosEncoder):
            module.gradient_checkpointing = value


YOLOS_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`YolosConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

YOLOS_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
            [`AutoFeatureExtractor.__call__`] for details.

        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        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.
"""


@add_start_docstrings(
    "The bare YOLOS Model transformer outputting raw hidden-states without any specific head on top.",
    YOLOS_START_DOCSTRING,
)
class YolosModel(YolosPreTrainedModel):
    def __init__(self, config: YolosConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.config = config

        self.embeddings = YolosEmbeddings(config)
        self.encoder = YolosEncoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pooler = YolosPooler(config) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> PatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(YOLOS_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        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")

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(pixel_values)

        encoder_outputs = self.encoder(
            embedding_output,
            height=pixel_values.shape[-2],
            width=pixel_values.shape[-1],
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class YolosPooler(nn.Module):
    def __init__(self, config: YolosConfig):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


@add_start_docstrings(
    """
    YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection.
    """,
    YOLOS_START_DOCSTRING,
)
class YolosForObjectDetection(YolosPreTrainedModel):
    def __init__(self, config: YolosConfig):
        super().__init__(config)

        # YOLOS (ViT) encoder model
        self.vit = YolosModel(config, add_pooling_layer=False)

        # Object detection heads
        # We add one for the "no object" class
        self.class_labels_classifier = YolosMLPPredictionHead(
            input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=config.num_labels + 1, num_layers=3
        )
        self.bbox_predictor = YolosMLPPredictionHead(
            input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=4, num_layers=3
        )

        # Initialize weights and apply final processing
        self.post_init()

    # taken from https://github.com/facebookresearch/detr/blob/master/models/detr.py
    @torch.jit.unused
    def _set_aux_loss(self, outputs_class, outputs_coord):
        # this is a workaround to make torchscript happy, as torchscript
        # doesn't support dictionary with non-homogeneous values, such
        # as a dict having both a Tensor and a list.
        return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]

    @add_start_docstrings_to_model_forward(YOLOS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=YolosObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        pixel_values,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (`List[Dict]` of len `(batch_size,)`, *optional*):
            Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
            following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
            respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
            in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoFeatureExtractor, YolosForObjectDetection
        >>> from PIL import Image
        >>> import requests

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

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
        >>> model = YolosForObjectDetection.from_pretrained("facebook/detr-resnet-50")

        >>> inputs = feature_extractor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> # model predicts bounding boxes and corresponding COCO classes
        >>> logits = outputs.logits
        >>> bboxes = outputs.pred_boxes
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # First, sent images through YOLOS base model to obtain hidden states
        outputs = self.vit(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        # Take the final hidden states of the detection tokens
        sequence_output = sequence_output[:, -self.config.num_detection_tokens :, :]

        # Class logits + predicted bounding boxes
        logits = self.class_labels_classifier(sequence_output)
        pred_boxes = self.bbox_predictor(sequence_output).sigmoid()

        loss, loss_dict, auxiliary_outputs = None, None, None
        if labels is not None:
            # First: create the matcher
            matcher = YolosHungarianMatcher(
                class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
            )
            # Second: create the criterion
            losses = ["labels", "boxes", "cardinality"]
            criterion = YolosLoss(
                matcher=matcher,
                num_classes=self.config.num_labels,
                eos_coef=self.config.eos_coefficient,
                losses=losses,
            )
            criterion.to(self.device)
            # Third: compute the losses, based on outputs and labels
            outputs_loss = {}
            outputs_loss["logits"] = logits
            outputs_loss["pred_boxes"] = pred_boxes
            if self.config.auxiliary_loss:
                intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4]
                outputs_class = self.class_labels_classifier(intermediate)
                outputs_coord = self.bbox_predictor(intermediate).sigmoid()
                auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
                outputs_loss["auxiliary_outputs"] = auxiliary_outputs

            loss_dict = criterion(outputs_loss, labels)
            # Fourth: compute total loss, as a weighted sum of the various losses
            weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
            weight_dict["loss_giou"] = self.config.giou_loss_coefficient
            if self.config.auxiliary_loss:
                aux_weight_dict = {}
                for i in range(self.config.decoder_layers - 1):
                    aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
                weight_dict.update(aux_weight_dict)
            loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)

        if not return_dict:
            if auxiliary_outputs is not None:
                output = (logits, pred_boxes) + auxiliary_outputs + outputs
            else:
                output = (logits, pred_boxes) + outputs
            return ((loss, loss_dict) + output) if loss is not None else output

        return YolosObjectDetectionOutput(
            loss=loss,
            loss_dict=loss_dict,
            logits=logits,
            pred_boxes=pred_boxes,
            auxiliary_outputs=auxiliary_outputs,
            last_hidden_state=outputs.last_hidden_state,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


# copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->Yolos
class YolosLoss(nn.Module):
    """
    This class computes the losses for YolosForObjectDetection. The process happens in two steps: 1) we compute
    hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
    ground-truth / prediction (supervise class and box)
    """

    def __init__(self, matcher, num_classes, eos_coef, losses):
        """
        Parameters:
        Create the criterion. A note on the num_classes parameter (copied from original repo in detr.py): "the naming
        of the `num_classes` parameter of the criterion is somewhat misleading. it indeed corresponds to `max_obj_id +
        1`, where max_obj_id is the maximum id for a class in your dataset. For example, COCO has a max_obj_id of 90,
        so we pass `num_classes` to be 91. As another example, for a dataset that has a single class with id 1, you
        should pass `num_classes` to be 2 (max_obj_id + 1). For more details on this, check the following discussion
        https://github.com/facebookresearch/detr/issues/108#issuecomment-650269223"
            matcher: module able to compute a matching between targets and proposals. num_classes: number of object
            categories, omitting the special no-object category. weight_dict: dict containing as key the names of the
            losses and as values their relative weight. eos_coef: relative classification weight applied to the
            no-object category. losses: list of all the losses to be applied. See get_loss for list of available
            losses.
        """
        super().__init__()
        self.num_classes = num_classes
        self.matcher = matcher
        self.eos_coef = eos_coef
        self.losses = losses
        empty_weight = torch.ones(self.num_classes + 1)
        empty_weight[-1] = self.eos_coef
        self.register_buffer("empty_weight", empty_weight)

    # removed logging parameter, which was part of the original implementation
    def loss_labels(self, outputs, targets, indices, num_boxes):
        """
        Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim
        [nb_target_boxes]
        """
        if "logits" not in outputs:
            raise KeyError("No logits were found in the outputs")
        src_logits = outputs["logits"]

        idx = self._get_src_permutation_idx(indices)
        target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
        target_classes = torch.full(
            src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
        )
        target_classes[idx] = target_classes_o

        loss_ce = nn.functional.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)
        losses = {"loss_ce": loss_ce}

        return losses

    @torch.no_grad()
    def loss_cardinality(self, outputs, targets, indices, num_boxes):
        """
        Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. This is not
        really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
        """
        logits = outputs["logits"]
        device = logits.device
        tgt_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
        # Count the number of predictions that are NOT "no-object" (which is the last class)
        card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1)
        card_err = nn.functional.l1_loss(card_pred.float(), tgt_lengths.float())
        losses = {"cardinality_error": card_err}
        return losses

    def loss_boxes(self, outputs, targets, indices, num_boxes):
        """
        Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. Targets dicts must
        contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes are expected in
        format (center_x, center_y, w, h), normalized by the image size.
        """
        if "pred_boxes" not in outputs:
            raise KeyError("No predicted boxes found in outputs")
        idx = self._get_src_permutation_idx(indices)
        src_boxes = outputs["pred_boxes"][idx]
        target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)

        loss_bbox = nn.functional.l1_loss(src_boxes, target_boxes, reduction="none")

        losses = {}
        losses["loss_bbox"] = loss_bbox.sum() / num_boxes

        loss_giou = 1 - torch.diag(
            generalized_box_iou(center_to_corners_format(src_boxes), center_to_corners_format(target_boxes))
        )
        losses["loss_giou"] = loss_giou.sum() / num_boxes
        return losses

    def _get_src_permutation_idx(self, indices):
        # permute predictions following indices
        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
        src_idx = torch.cat([src for (src, _) in indices])
        return batch_idx, src_idx

    def _get_tgt_permutation_idx(self, indices):
        # permute targets following indices
        batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
        tgt_idx = torch.cat([tgt for (_, tgt) in indices])
        return batch_idx, tgt_idx

    def get_loss(self, loss, outputs, targets, indices, num_boxes):
        loss_map = {
            "labels": self.loss_labels,
            "cardinality": self.loss_cardinality,
            "boxes": self.loss_boxes,
        }
        if loss not in loss_map:
            raise ValueError(f"Loss {loss} not supported")
        return loss_map[loss](outputs, targets, indices, num_boxes)

    def forward(self, outputs, targets):
        """
        Parameters:
        This performs the loss computation.
             outputs: dict of tensors, see the output specification of the model for the format targets: list of dicts,
             such that len(targets) == batch_size.
                      The expected keys in each dict depends on the losses applied, see each loss' doc
        """
        outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"}

        # Retrieve the matching between the outputs of the last layer and the targets
        indices = self.matcher(outputs_without_aux, targets)

        # Compute the average number of target boxes accross all nodes, for normalization purposes
        num_boxes = sum(len(t["class_labels"]) for t in targets)
        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
        # (Niels): comment out function below, distributed training to be added
        # if is_dist_avail_and_initialized():
        #     torch.distributed.all_reduce(num_boxes)
        # (Niels) in original implementation, num_boxes is divided by get_world_size()
        num_boxes = torch.clamp(num_boxes, min=1).item()

        # Compute all the requested losses
        losses = {}
        for loss in self.losses:
            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))

        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
        if "auxiliary_outputs" in outputs:
            for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
                indices = self.matcher(auxiliary_outputs, targets)
                for loss in self.losses:
                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes)
                    l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
                    losses.update(l_dict)

        return losses


# copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->Yolos
class YolosMLPPredictionHead(nn.Module):
    """
    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
    height and width of a bounding box w.r.t. an image. Copied from
    https://github.com/facebookresearch/detr/blob/master/models/detr.py
    """

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x


# copied from transformers.models.detr.modeling_detr.DetrHungarianMatcher with Detr->Yolos
class YolosHungarianMatcher(nn.Module):
    """
    This class computes an assignment between the targets and the predictions of the network. For efficiency reasons,
    the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In
    this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as
    non-objects).
    """

    def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1):
        """
        Params:
        Creates the matcher.
            class_cost: This is the relative weight of the classification error in the matching cost bbox_cost:
                This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
            giou_cost: This is the relative weight of the giou loss of the bounding box in the matching cost
        """
        super().__init__()

        requires_backends(self, ["scipy"])

        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        if class_cost == 0 or bbox_cost == 0 or giou_cost == 0:
            raise ValueError("All costs of the Matcher can't be 0")

    @torch.no_grad()
    def forward(self, outputs, targets):
        """
        Params:
        Performs the matching.
            outputs: This is a dict that contains at least these entries:
                 "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
                 "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
                 objects in the target) containing the class labels "boxes": Tensor of dim [num_target_boxes, 4]
                 containing the target box coordinates
        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        bs, num_queries = outputs["logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        out_prob = outputs["logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        tgt_ids = torch.cat([v["class_labels"] for v in targets])
        tgt_bbox = torch.cat([v["boxes"] for v in targets])

        # 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.
        class_cost = -out_prob[:, tgt_ids]

        # Compute the L1 cost between boxes
        bbox_cost = torch.cdist(out_bbox, tgt_bbox, p=1)

        # Compute the giou cost between boxes
        giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(tgt_bbox))

        # Final cost matrix
        cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost
        cost_matrix = cost_matrix.view(bs, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]


# copied from transformers.models.detr.modeling_detr._upcast
def _upcast(t: Tensor) -> Tensor:
    # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
    if t.is_floating_point():
        return t if t.dtype in (torch.float32, torch.float64) else t.float()
    else:
        return t if t.dtype in (torch.int32, torch.int64) else t.int()


# copied from transformers.models.detr.modeling_detr.box_area
def box_area(boxes: Tensor) -> Tensor:
    """
    Args:
    Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
        boxes (Tensor[N, 4]): boxes for which the area will be computed. They
            are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`.
    Returns:
        area (Tensor[N]): area for each box
    """
    boxes = _upcast(boxes)
    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])


# copied from transformers.models.detr.modeling_detr.box_iou
def box_iou(boxes1, boxes2):
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]

    union = area1[:, None] + area2 - inter

    iou = inter / union
    return iou, union


# copied from transformers.models.detr.modeling_detr.generalized_box_iou
def generalized_box_iou(boxes1, boxes2):
    """
    Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns:
        a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
    """
    # degenerate boxes gives inf / nan results
    # so do an early check
    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
    iou, union = box_iou(boxes1, boxes2)

    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    wh = (rb - lt).clamp(min=0)  # [N,M,2]
    area = wh[:, :, 0] * wh[:, :, 1]

    return iou - (area - union) / area