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642 lines
27 KiB
642 lines
27 KiB
# Copyright 2022 School of EIC, Huazhong University of Science & Technology and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch YOLOS model."""
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import collections.abc
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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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from torch import nn
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, logging
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from ...utils.generic import can_return_tuple, check_model_inputs
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from .configuration_yolos import YolosConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring(
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custom_intro="""
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Output type of [`YolosForObjectDetection`].
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"""
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)
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class YolosObjectDetectionOutput(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
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Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
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bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
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scale-invariant IoU loss.
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loss_dict (`Dict`, *optional*):
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A dictionary containing the individual losses. Useful for logging.
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logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
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Classification logits (including no-object) for all queries.
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pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
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Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
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values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
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possible padding). You can use [`~YolosImageProcessor.post_process`] to retrieve the unnormalized bounding
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boxes.
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auxiliary_outputs (`list[Dict]`, *optional*):
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Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
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and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
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`pred_boxes`) for each decoder layer.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Sequence of hidden-states at the output of the last layer of the decoder of the model.
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"""
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loss: torch.FloatTensor | None = None
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loss_dict: dict | None = None
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logits: torch.FloatTensor | None = None
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pred_boxes: torch.FloatTensor | None = None
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auxiliary_outputs: list[dict] | None = None
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last_hidden_state: torch.FloatTensor | None = None
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hidden_states: tuple[torch.FloatTensor] | None = None
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attentions: tuple[torch.FloatTensor] | None = None
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class YolosEmbeddings(nn.Module):
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"""
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Construct the CLS token, detection tokens, position and patch embeddings.
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"""
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.detection_tokens = nn.Parameter(torch.zeros(1, config.num_detection_tokens, config.hidden_size))
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self.patch_embeddings = YolosPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(
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torch.zeros(1, num_patches + config.num_detection_tokens + 1, config.hidden_size)
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)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.interpolation = InterpolateInitialPositionEmbeddings(config)
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self.config = config
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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embeddings = self.patch_embeddings(pixel_values)
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batch_size, seq_len, _ = embeddings.size()
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# add the [CLS] and detection tokens to the embedded patch tokens
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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detection_tokens = self.detection_tokens.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings, detection_tokens), dim=1)
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# add positional encoding to each token
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# this might require interpolation of the existing position embeddings
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position_embeddings = self.interpolation(self.position_embeddings, (height, width))
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embeddings = embeddings + position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class InterpolateInitialPositionEmbeddings(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.config = config
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def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
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cls_pos_embed = pos_embed[:, 0, :]
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cls_pos_embed = cls_pos_embed[:, None]
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det_pos_embed = pos_embed[:, -self.config.num_detection_tokens :, :]
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patch_pos_embed = pos_embed[:, 1 : -self.config.num_detection_tokens, :]
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patch_pos_embed = patch_pos_embed.transpose(1, 2)
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batch_size, hidden_size, seq_len = patch_pos_embed.shape
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patch_height, patch_width = (
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self.config.image_size[0] // self.config.patch_size,
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self.config.image_size[1] // self.config.patch_size,
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)
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patch_pos_embed = patch_pos_embed.view(batch_size, hidden_size, patch_height, patch_width)
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height, width = img_size
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new_patch_height, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed, size=(new_patch_height, new_patch_width), mode="bicubic", align_corners=False
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)
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patch_pos_embed = patch_pos_embed.flatten(2).transpose(1, 2)
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scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=1)
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return scale_pos_embed
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class InterpolateMidPositionEmbeddings(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.config = config
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def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
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cls_pos_embed = pos_embed[:, :, 0, :]
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cls_pos_embed = cls_pos_embed[:, None]
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det_pos_embed = pos_embed[:, :, -self.config.num_detection_tokens :, :]
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patch_pos_embed = pos_embed[:, :, 1 : -self.config.num_detection_tokens, :]
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patch_pos_embed = patch_pos_embed.transpose(2, 3)
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depth, batch_size, hidden_size, seq_len = patch_pos_embed.shape
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patch_height, patch_width = (
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self.config.image_size[0] // self.config.patch_size,
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self.config.image_size[1] // self.config.patch_size,
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)
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patch_pos_embed = patch_pos_embed.view(depth * batch_size, hidden_size, patch_height, patch_width)
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height, width = img_size
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new_patch_height, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed, size=(new_patch_height, new_patch_width), mode="bicubic", align_corners=False
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)
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patch_pos_embed = (
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patch_pos_embed.flatten(2)
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.transpose(1, 2)
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.contiguous()
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.view(depth, batch_size, new_patch_height * new_patch_width, hidden_size)
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)
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scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=2)
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return scale_pos_embed
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class YolosPatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return embeddings
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# Copied from transformers.models.bert.modeling_bert.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float | None = None,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Yolos
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class YolosSelfAttention(nn.Module):
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def __init__(self, config: YolosConfig):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.dropout_prob = config.attention_probs_dropout_prob
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self.scaling = self.attention_head_size**-0.5
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self.is_causal = False
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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batch_size = hidden_states.shape[0]
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new_shape = batch_size, -1, self.num_attention_heads, self.attention_head_size
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key_layer = self.key(hidden_states).view(*new_shape).transpose(1, 2)
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value_layer = self.value(hidden_states).view(*new_shape).transpose(1, 2)
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query_layer = self.query(hidden_states).view(*new_shape).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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context_layer, attention_probs = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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None,
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is_causal=self.is_causal,
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scaling=self.scaling,
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dropout=0.0 if not self.training else self.dropout_prob,
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)
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.reshape(new_context_layer_shape)
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return context_layer, attention_probs
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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Yolos
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class YolosSelfOutput(nn.Module):
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"""
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The residual connection is defined in YolosLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: YolosConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Yolos
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class YolosAttention(nn.Module):
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def __init__(self, config: YolosConfig):
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super().__init__()
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self.attention = YolosSelfAttention(config)
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self.output = YolosSelfOutput(config)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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self_attn_output, _ = self.attention(hidden_states)
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output = self.output(self_attn_output, hidden_states)
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return output
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# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->Yolos
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class YolosIntermediate(nn.Module):
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def __init__(self, config: YolosConfig):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->Yolos
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class YolosOutput(nn.Module):
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def __init__(self, config: YolosConfig):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->Yolos,VIT->YOLOS
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class YolosLayer(GradientCheckpointingLayer):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: YolosConfig):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = YolosAttention(config)
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self.intermediate = YolosIntermediate(config)
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self.output = YolosOutput(config)
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self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states_norm = self.layernorm_before(hidden_states)
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attention_output = self.attention(hidden_states_norm)
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# first residual connection
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hidden_states = attention_output + hidden_states
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# in Yolos, layernorm is also applied after self-attention
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.intermediate(layer_output)
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# second residual connection is done here
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layer_output = self.output(layer_output, hidden_states)
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return layer_output
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class YolosEncoder(nn.Module):
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def __init__(self, config: YolosConfig) -> None:
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([YolosLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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seq_length = (
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1 + (config.image_size[0] * config.image_size[1] // config.patch_size**2) + config.num_detection_tokens
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)
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self.mid_position_embeddings = (
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nn.Parameter(
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torch.zeros(
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config.num_hidden_layers - 1,
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1,
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seq_length,
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config.hidden_size,
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)
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)
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if config.use_mid_position_embeddings
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else None
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)
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self.interpolation = InterpolateMidPositionEmbeddings(config) if config.use_mid_position_embeddings else None
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def forward(
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self,
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hidden_states: torch.Tensor,
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height: int,
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width: int,
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) -> BaseModelOutput:
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if self.config.use_mid_position_embeddings:
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interpolated_mid_position_embeddings = self.interpolation(self.mid_position_embeddings, (height, width))
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for i, layer_module in enumerate(self.layer):
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hidden_states = layer_module(hidden_states)
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if self.config.use_mid_position_embeddings:
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if i < (self.config.num_hidden_layers - 1):
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hidden_states = hidden_states + interpolated_mid_position_embeddings[i]
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return BaseModelOutput(last_hidden_state=hidden_states)
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@auto_docstring
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class YolosPreTrainedModel(PreTrainedModel):
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config: YolosConfig
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base_model_prefix = "vit"
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main_input_name = "pixel_values"
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input_modalities = ("image",)
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supports_gradient_checkpointing = True
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|
_no_split_modules = []
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|
_supports_sdpa = True
|
|
_supports_flash_attn = True
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|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": YolosLayer,
|
|
"attentions": YolosSelfAttention,
|
|
}
|
|
|
|
|
|
@auto_docstring
|
|
class YolosModel(YolosPreTrainedModel):
|
|
def __init__(self, config: YolosConfig, add_pooling_layer: bool = True):
|
|
r"""
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|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
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|
self.config = config
|
|
|
|
self.embeddings = YolosEmbeddings(config)
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|
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) -> YolosPatchEmbeddings:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
@check_model_inputs(tie_last_hidden_states=False)
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPooling:
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
height, width = pixel_values.shape[-2:]
|
|
encoder_outputs: BaseModelOutput = self.encoder(embedding_output, height=height, width=width)
|
|
sequence_output = encoder_outputs.last_hidden_state
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
return BaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output)
|
|
|
|
|
|
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: torch.Tensor) -> torch.Tensor:
|
|
# 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
|
|
|
|
|
|
# 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.
|
|
|
|
"""
|
|
|
|
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
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection.
|
|
"""
|
|
)
|
|
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
|
|
def _set_aux_loss(self, outputs_class, outputs_coord):
|
|
return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
labels: list[dict] | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> YolosObjectDetectionOutput:
|
|
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)`.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, AutoModelForObjectDetection
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny")
|
|
>>> model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
|
|
>>> target_sizes = torch.tensor([image.size[::-1]])
|
|
>>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
|
|
... 0
|
|
... ]
|
|
|
|
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
|
|
... box = [round(i, 2) for i in box.tolist()]
|
|
... print(
|
|
... f"Detected {model.config.id2label[label.item()]} with confidence "
|
|
... f"{round(score.item(), 3)} at location {box}"
|
|
... )
|
|
Detected remote with confidence 0.991 at location [46.48, 72.78, 178.98, 119.3]
|
|
Detected remote with confidence 0.908 at location [336.48, 79.27, 368.23, 192.36]
|
|
Detected cat with confidence 0.934 at location [337.18, 18.06, 638.14, 373.09]
|
|
Detected cat with confidence 0.979 at location [10.93, 53.74, 313.41, 470.67]
|
|
Detected remote with confidence 0.974 at location [41.63, 72.23, 178.09, 119.99]
|
|
```"""
|
|
|
|
# First, sent images through YOLOS base model to obtain hidden states
|
|
outputs: BaseModelOutputWithPooling = self.vit(pixel_values, **kwargs)
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
# 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:
|
|
outputs_class, outputs_coord = None, None
|
|
if self.config.auxiliary_loss:
|
|
intermediate = outputs.hidden_states
|
|
outputs_class = self.class_labels_classifier(intermediate)
|
|
outputs_coord = self.bbox_predictor(intermediate).sigmoid()
|
|
loss, loss_dict, auxiliary_outputs = self.loss_function(
|
|
logits, labels, self.device, pred_boxes, self.config, outputs_class, outputs_coord
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
__all__ = ["YolosForObjectDetection", "YolosModel", "YolosPreTrainedModel"]
|