# coding=utf-8 # Copyright 2022 SenseTime 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 Deformable DETR model.""" import copy import math import warnings from dataclasses import dataclass from typing import Optional, Union import torch import torch.nn.functional as F from torch import Tensor, nn from ...activations import ACT2FN from ...integrations import use_kernel_forward_from_hub from ...modeling_attn_mask_utils import _prepare_4d_attention_mask from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import meshgrid from ...utils import ( ModelOutput, auto_docstring, is_timm_available, logging, requires_backends, ) from ...utils.backbone_utils import load_backbone from .configuration_deformable_detr import DeformableDetrConfig logger = logging.get_logger(__name__) if is_timm_available(): from timm import create_model logger = logging.get_logger(__name__) @use_kernel_forward_from_hub("MultiScaleDeformableAttention") class MultiScaleDeformableAttention(nn.Module): def forward( self, value: Tensor, value_spatial_shapes: Tensor, value_spatial_shapes_list: list[tuple], level_start_index: Tensor, sampling_locations: Tensor, attention_weights: Tensor, im2col_step: int, ): batch_size, _, num_heads, hidden_dim = value.shape _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1) sampling_grids = 2 * sampling_locations - 1 sampling_value_list = [] for level_id, (height, width) in enumerate(value_spatial_shapes_list): # batch_size, height*width, num_heads, hidden_dim # -> batch_size, height*width, num_heads*hidden_dim # -> batch_size, num_heads*hidden_dim, height*width # -> batch_size*num_heads, hidden_dim, height, width value_l_ = ( value_list[level_id] .flatten(2) .transpose(1, 2) .reshape(batch_size * num_heads, hidden_dim, height, width) ) # batch_size, num_queries, num_heads, num_points, 2 # -> batch_size, num_heads, num_queries, num_points, 2 # -> batch_size*num_heads, num_queries, num_points, 2 sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) # batch_size*num_heads, hidden_dim, num_queries, num_points sampling_value_l_ = nn.functional.grid_sample( value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False, ) sampling_value_list.append(sampling_value_l_) # (batch_size, num_queries, num_heads, num_levels, num_points) # -> (batch_size, num_heads, num_queries, num_levels, num_points) # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) attention_weights = attention_weights.transpose(1, 2).reshape( batch_size * num_heads, 1, num_queries, num_levels * num_points ) output = ( (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) .sum(-1) .view(batch_size, num_heads * hidden_dim, num_queries) ) return output.transpose(1, 2).contiguous() @dataclass @auto_docstring( custom_intro=""" Base class for outputs of the DeformableDetrDecoder. This class adds two attributes to BaseModelOutputWithCrossAttentions, namely: - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) - a stacked tensor of intermediate reference points. """ ) class DeformableDetrDecoderOutput(ModelOutput): r""" intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): Stacked intermediate reference points (reference points of each layer of the decoder). cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=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 of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: Optional[torch.FloatTensor] = None intermediate_hidden_states: Optional[torch.FloatTensor] = None intermediate_reference_points: Optional[torch.FloatTensor] = None hidden_states: Optional[tuple[torch.FloatTensor]] = None attentions: Optional[tuple[torch.FloatTensor]] = None cross_attentions: Optional[tuple[torch.FloatTensor]] = None @dataclass @auto_docstring( custom_intro=""" Base class for outputs of the Deformable DETR encoder-decoder model. """ ) class DeformableDetrModelOutput(ModelOutput): r""" init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Initial reference points sent through the Transformer decoder. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked intermediate reference points (reference points of each layer of the decoder). enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background). enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Logits of predicted bounding boxes coordinates in the first stage. """ init_reference_points: Optional[torch.FloatTensor] = None last_hidden_state: Optional[torch.FloatTensor] = None intermediate_hidden_states: Optional[torch.FloatTensor] = None intermediate_reference_points: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None decoder_attentions: Optional[tuple[torch.FloatTensor]] = None cross_attentions: Optional[tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None encoder_attentions: Optional[tuple[torch.FloatTensor]] = None enc_outputs_class: Optional[torch.FloatTensor] = None enc_outputs_coord_logits: Optional[torch.FloatTensor] = None @dataclass @auto_docstring( custom_intro=""" Output type of [`DeformableDetrForObjectDetection`]. """ ) class DeformableDetrObjectDetectionOutput(ModelOutput): r""" 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 [`~DeformableDetrProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxiliary 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. init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Initial reference points sent through the Transformer decoder. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked intermediate reference points (reference points of each layer of the decoder). enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background). enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Logits of predicted bounding boxes coordinates in the first stage. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[dict] = None logits: Optional[torch.FloatTensor] = None pred_boxes: Optional[torch.FloatTensor] = None auxiliary_outputs: Optional[list[dict]] = None init_reference_points: Optional[torch.FloatTensor] = None last_hidden_state: Optional[torch.FloatTensor] = None intermediate_hidden_states: Optional[torch.FloatTensor] = None intermediate_reference_points: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None decoder_attentions: Optional[tuple[torch.FloatTensor]] = None cross_attentions: Optional[tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None encoder_attentions: Optional[tuple[torch.FloatTensor]] = None enc_outputs_class: Optional = None enc_outputs_coord_logits: Optional = None def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def inverse_sigmoid(x, eps=1e-5): x = x.clamp(min=0, max=1) x1 = x.clamp(min=eps) x2 = (1 - x).clamp(min=eps) return torch.log(x1 / x2) # Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->DeformableDetr class DeformableDetrFrozenBatchNorm2d(nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super().__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): num_batches_tracked_key = prefix + "num_batches_tracked" if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def forward(self, x): # move reshapes to the beginning # to make it user-friendly weight = self.weight.reshape(1, -1, 1, 1) bias = self.bias.reshape(1, -1, 1, 1) running_var = self.running_var.reshape(1, -1, 1, 1) running_mean = self.running_mean.reshape(1, -1, 1, 1) epsilon = 1e-5 scale = weight * (running_var + epsilon).rsqrt() bias = bias - running_mean * scale return x * scale + bias # Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->DeformableDetr def replace_batch_norm(model): r""" Recursively replace all `torch.nn.BatchNorm2d` with `DeformableDetrFrozenBatchNorm2d`. Args: model (torch.nn.Module): input model """ for name, module in model.named_children(): if isinstance(module, nn.BatchNorm2d): new_module = DeformableDetrFrozenBatchNorm2d(module.num_features) if module.weight.device != torch.device("meta"): new_module.weight.data.copy_(module.weight) new_module.bias.data.copy_(module.bias) new_module.running_mean.data.copy_(module.running_mean) new_module.running_var.data.copy_(module.running_var) model._modules[name] = new_module if len(list(module.children())) > 0: replace_batch_norm(module) class DeformableDetrConvEncoder(nn.Module): """ Convolutional backbone, using either the AutoBackbone API or one from the timm library. nn.BatchNorm2d layers are replaced by DeformableDetrFrozenBatchNorm2d as defined above. """ def __init__(self, config): super().__init__() self.config = config # For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API if config.use_timm_backbone: # We default to values which were previously hard-coded. This enables configurability from the config # using backbone arguments, while keeping the default behavior the same. requires_backends(self, ["timm"]) kwargs = getattr(config, "backbone_kwargs", {}) kwargs = {} if kwargs is None else kwargs.copy() out_indices = kwargs.pop("out_indices", (2, 3, 4) if config.num_feature_levels > 1 else (4,)) num_channels = kwargs.pop("in_chans", config.num_channels) if config.dilation: kwargs["output_stride"] = kwargs.get("output_stride", 16) backbone = create_model( config.backbone, pretrained=config.use_pretrained_backbone, features_only=True, out_indices=out_indices, in_chans=num_channels, **kwargs, ) else: backbone = load_backbone(config) # replace batch norm by frozen batch norm with torch.no_grad(): replace_batch_norm(backbone) self.model = backbone self.intermediate_channel_sizes = ( self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels ) backbone_model_type = None if config.backbone is not None: backbone_model_type = config.backbone elif config.backbone_config is not None: backbone_model_type = config.backbone_config.model_type else: raise ValueError("Either `backbone` or `backbone_config` should be provided in the config") if "resnet" in backbone_model_type: for name, parameter in self.model.named_parameters(): if config.use_timm_backbone: if "layer2" not in name and "layer3" not in name and "layer4" not in name: parameter.requires_grad_(False) else: if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name: parameter.requires_grad_(False) # Copied from transformers.models.detr.modeling_detr.DetrConvEncoder.forward with Detr->DeformableDetr def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): # send pixel_values through the model to get list of feature maps features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps out = [] for feature_map in features: # downsample pixel_mask to match shape of corresponding feature_map mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] out.append((feature_map, mask)) return out # Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->DeformableDetr class DeformableDetrConvModel(nn.Module): """ This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. """ def __init__(self, conv_encoder, position_embedding): super().__init__() self.conv_encoder = conv_encoder self.position_embedding = position_embedding def forward(self, pixel_values, pixel_mask): # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples out = self.conv_encoder(pixel_values, pixel_mask) pos = [] for feature_map, mask in out: # position encoding pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) return out, pos class DeformableDetrSinePositionEmbedding(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): super().__init__() self.embedding_dim = embedding_dim self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, pixel_values, pixel_mask): if pixel_mask is None: raise ValueError("No pixel mask provided") y_embed = pixel_mask.cumsum(1, dtype=pixel_values.dtype) x_embed = pixel_mask.cumsum(2, dtype=pixel_values.dtype) if self.normalize: eps = 1e-6 y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale dim_t = torch.arange(self.embedding_dim, dtype=pixel_values.dtype, device=pixel_values.device) dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos # Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding class DeformableDetrLearnedPositionEmbedding(nn.Module): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, embedding_dim=256): super().__init__() self.row_embeddings = nn.Embedding(50, embedding_dim) self.column_embeddings = nn.Embedding(50, embedding_dim) def forward(self, pixel_values, pixel_mask=None): height, width = pixel_values.shape[-2:] width_values = torch.arange(width, device=pixel_values.device) height_values = torch.arange(height, device=pixel_values.device) x_emb = self.column_embeddings(width_values) y_emb = self.row_embeddings(height_values) pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) pos = pos.permute(2, 0, 1) pos = pos.unsqueeze(0) pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) return pos # Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->DeformableDetr def build_position_encoding(config): n_steps = config.d_model // 2 if config.position_embedding_type == "sine": # TODO find a better way of exposing other arguments position_embedding = DeformableDetrSinePositionEmbedding(n_steps, normalize=True) elif config.position_embedding_type == "learned": position_embedding = DeformableDetrLearnedPositionEmbedding(n_steps) else: raise ValueError(f"Not supported {config.position_embedding_type}") return position_embedding class DeformableDetrMultiscaleDeformableAttention(nn.Module): """ Multiscale deformable attention as proposed in Deformable DETR. """ def __init__(self, config: DeformableDetrConfig, num_heads: int, n_points: int): super().__init__() self.attn = MultiScaleDeformableAttention() if config.d_model % num_heads != 0: raise ValueError( f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" ) dim_per_head = config.d_model // num_heads # check if dim_per_head is power of 2 if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): warnings.warn( "You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the" " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" " implementation." ) self.im2col_step = 64 self.d_model = config.d_model self.n_levels = config.num_feature_levels self.n_heads = num_heads self.n_points = n_points self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) self.value_proj = nn.Linear(config.d_model, config.d_model) self.output_proj = nn.Linear(config.d_model, config.d_model) self.disable_custom_kernels = config.disable_custom_kernels def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings: Optional[torch.Tensor] = None, reference_points=None, spatial_shapes=None, spatial_shapes_list=None, level_start_index=None, output_attentions: bool = False, ): # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states = self.with_pos_embed(hidden_states, position_embeddings) batch_size, num_queries, _ = hidden_states.shape batch_size, sequence_length, _ = encoder_hidden_states.shape total_elements = sum(height * width for height, width in spatial_shapes_list) if total_elements != sequence_length: raise ValueError( "Make sure to align the spatial shapes with the sequence length of the encoder hidden states" ) value = self.value_proj(encoder_hidden_states) if attention_mask is not None: # we invert the attention_mask value = value.masked_fill(~attention_mask[..., None], float(0)) value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) sampling_offsets = self.sampling_offsets(hidden_states).view( batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 ) attention_weights = self.attention_weights(hidden_states).view( batch_size, num_queries, self.n_heads, self.n_levels * self.n_points ) attention_weights = F.softmax(attention_weights, -1).view( batch_size, num_queries, self.n_heads, self.n_levels, self.n_points ) # batch_size, num_queries, n_heads, n_levels, n_points, 2 num_coordinates = reference_points.shape[-1] if num_coordinates == 2: offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) sampling_locations = ( reference_points[:, :, None, :, None, :] + sampling_offsets / offset_normalizer[None, None, None, :, None, :] ) elif num_coordinates == 4: sampling_locations = ( reference_points[:, :, None, :, None, :2] + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 ) else: raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") output = self.attn( value, spatial_shapes, spatial_shapes_list, level_start_index, sampling_locations, attention_weights, self.im2col_step, ) output = self.output_proj(output) return output, attention_weights class DeformableDetrMultiheadAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and keys (as explained in the Deformable DETR paper). """ def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if self.head_dim * num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, position_embeddings) # get queries, keys and values query_states = self.q_proj(hidden_states) * self.scaling key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) proj_shape = (batch_size * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) source_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): raise ValueError( f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" f" {attn_weights.size()}" ) # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, target_len, source_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" f" {attention_mask.size()}" ) attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != ( batch_size * self.num_heads, target_len, self.head_dim, ): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, target_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped class DeformableDetrEncoderLayer(GradientCheckpointingLayer): def __init__(self, config: DeformableDetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DeformableDetrMultiscaleDeformableAttention( config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, position_embeddings: Optional[torch.Tensor] = None, reference_points=None, spatial_shapes=None, spatial_shapes_list=None, level_start_index=None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Input to the layer. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Attention mask. position_embeddings (`torch.FloatTensor`, *optional*): Position embeddings, to be added to `hidden_states`. reference_points (`torch.FloatTensor`, *optional*): Reference points. spatial_shapes (`torch.LongTensor`, *optional*): Spatial shapes of the backbone feature maps. level_start_index (`torch.LongTensor`, *optional*): Level start index. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps. hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, spatial_shapes_list=spatial_shapes_list, level_start_index=level_start_index, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if self.training: if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class DeformableDetrDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: DeformableDetrConfig): super().__init__() self.embed_dim = config.d_model # self-attention self.self_attn = DeformableDetrMultiheadAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) # cross-attention self.encoder_attn = DeformableDetrMultiscaleDeformableAttention( config, num_heads=config.decoder_attention_heads, n_points=config.decoder_n_points, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) # feedforward neural networks self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[torch.Tensor] = None, reference_points=None, spatial_shapes=None, spatial_shapes_list=None, level_start_index=None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ): """ Args: hidden_states (`torch.FloatTensor`): Input to the layer of shape `(seq_len, batch, embed_dim)`. position_embeddings (`torch.FloatTensor`, *optional*): Position embeddings that are added to the queries and keys in the self-attention layer. reference_points (`torch.FloatTensor`, *optional*): Reference points. spatial_shapes (`torch.LongTensor`, *optional*): Spatial shapes. level_start_index (`torch.LongTensor`, *optional*): Level start index. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) second_residual = hidden_states # Cross-Attention cross_attn_weights = None hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, spatial_shapes_list=spatial_shapes_list, level_start_index=level_start_index, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = second_residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs @auto_docstring class DeformableDetrPreTrainedModel(PreTrainedModel): config: DeformableDetrConfig base_model_prefix = "model" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = [ r"DeformableDetrConvEncoder", r"DeformableDetrEncoderLayer", r"DeformableDetrDecoderLayer", ] def _init_weights(self, module): std = self.config.init_std if isinstance(module, DeformableDetrLearnedPositionEmbedding): nn.init.uniform_(module.row_embeddings.weight) nn.init.uniform_(module.column_embeddings.weight) elif isinstance(module, DeformableDetrMultiscaleDeformableAttention): nn.init.constant_(module.sampling_offsets.weight.data, 0.0) default_dtype = torch.get_default_dtype() thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( 2.0 * math.pi / module.n_heads ) grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) grid_init = ( (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) .view(module.n_heads, 1, 1, 2) .repeat(1, module.n_levels, module.n_points, 1) ) for i in range(module.n_points): grid_init[:, :, i, :] *= i + 1 with torch.no_grad(): module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) nn.init.constant_(module.attention_weights.weight.data, 0.0) nn.init.constant_(module.attention_weights.bias.data, 0.0) nn.init.xavier_uniform_(module.value_proj.weight.data) nn.init.constant_(module.value_proj.bias.data, 0.0) nn.init.xavier_uniform_(module.output_proj.weight.data) nn.init.constant_(module.output_proj.bias.data, 0.0) elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): # 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=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() if hasattr(module, "reference_points") and not self.config.two_stage: nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0) nn.init.constant_(module.reference_points.bias.data, 0.0) if hasattr(module, "level_embed"): nn.init.normal_(module.level_embed) class DeformableDetrEncoder(DeformableDetrPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a [`DeformableDetrEncoderLayer`]. The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers. Args: config: DeformableDetrConfig """ def __init__(self, config: DeformableDetrConfig): super().__init__(config) self.gradient_checkpointing = False self.dropout = config.dropout self.layers = nn.ModuleList([DeformableDetrEncoderLayer(config) for _ in range(config.encoder_layers)]) # Initialize weights and apply final processing self.post_init() @staticmethod def get_reference_points(spatial_shapes, valid_ratios, device): """ Get reference points for each feature map. Used in decoder. Args: spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of each feature map. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): Valid ratios of each feature map. device (`torch.device`): Device on which to create the tensors. Returns: `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)` """ reference_points_list = [] for level, (height, width) in enumerate(spatial_shapes): ref_y, ref_x = meshgrid( torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device), torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device), indexing="ij", ) # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36 ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height) ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width) ref = torch.stack((ref_x, ref_y), -1) reference_points_list.append(ref) reference_points = torch.cat(reference_points_list, 1) reference_points = reference_points[:, :, None] * valid_ratios[:, None] return reference_points def forward( self, inputs_embeds=None, attention_mask=None, position_embeddings=None, spatial_shapes=None, spatial_shapes_list=None, level_start_index=None, valid_ratios=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: - 1 for pixel features that are real (i.e. **not masked**), - 0 for pixel features that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Position embeddings that are added to the queries and keys in each self-attention layer. spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of each feature map. level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`): Starting index of each feature map. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): Ratio of valid area in each feature level. 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 [`~file_utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) spatial_shapes_tuple = tuple(spatial_shapes_list) reference_points = self.get_reference_points(spatial_shapes_tuple, valid_ratios, device=inputs_embeds.device) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) layer_outputs = encoder_layer( hidden_states, attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, spatial_shapes_list=spatial_shapes_list, level_start_index=level_start_index, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions, ) class DeformableDetrDecoder(DeformableDetrPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DeformableDetrDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some tweaks for Deformable DETR: - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass. - it also returns a stack of intermediate outputs and reference points from all decoding layers. Args: config: DeformableDetrConfig """ def __init__(self, config: DeformableDetrConfig): super().__init__(config) self.dropout = config.dropout self.layers = nn.ModuleList([DeformableDetrDecoderLayer(config) for _ in range(config.decoder_layers)]) self.gradient_checkpointing = False # hack implementation for iterative bounding box refinement and two-stage Deformable DETR self.bbox_embed = None self.class_embed = None # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings=None, reference_points=None, spatial_shapes=None, spatial_shapes_list=None, level_start_index=None, valid_ratios=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): The query embeddings that are passed into the decoder. encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of the feature maps. level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): Indexes for the start of each feature level. In range `[0, sequence_length]`. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*): Ratio of valid area in each feature level. 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 [`~file_utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None: hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None intermediate = () intermediate_reference_points = () for idx, decoder_layer in enumerate(self.layers): num_coordinates = reference_points.shape[-1] if num_coordinates == 4: reference_points_input = ( reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None] ) elif reference_points.shape[-1] == 2: reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] else: raise ValueError("Reference points' last dimension must be of size 2") if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, position_embeddings, reference_points_input, spatial_shapes, spatial_shapes_list, level_start_index, encoder_hidden_states, # as a positional argument for gradient checkpointing encoder_attention_mask, output_attentions, ) hidden_states = layer_outputs[0] # hack implementation for iterative bounding box refinement if self.bbox_embed is not None: tmp = self.bbox_embed[idx](hidden_states) num_coordinates = reference_points.shape[-1] if num_coordinates == 4: new_reference_points = tmp + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() elif num_coordinates == 2: new_reference_points = tmp new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() else: raise ValueError( f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}" ) reference_points = new_reference_points.detach() intermediate += (hidden_states,) intermediate_reference_points += (reference_points,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # Keep batch_size as first dimension intermediate = torch.stack(intermediate, dim=1) intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, intermediate, intermediate_reference_points, all_hidden_states, all_self_attns, all_cross_attentions, ] if v is not None ) return DeformableDetrDecoderOutput( last_hidden_state=hidden_states, intermediate_hidden_states=intermediate, intermediate_reference_points=intermediate_reference_points, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @auto_docstring( custom_intro=""" The bare Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. """ ) class DeformableDetrModel(DeformableDetrPreTrainedModel): def __init__(self, config: DeformableDetrConfig): super().__init__(config) # Create backbone + positional encoding backbone = DeformableDetrConvEncoder(config) position_embeddings = build_position_encoding(config) self.backbone = DeformableDetrConvModel(backbone, position_embeddings) # Create input projection layers if config.num_feature_levels > 1: num_backbone_outs = len(backbone.intermediate_channel_sizes) input_proj_list = [] for _ in range(num_backbone_outs): in_channels = backbone.intermediate_channel_sizes[_] input_proj_list.append( nn.Sequential( nn.Conv2d(in_channels, config.d_model, kernel_size=1), nn.GroupNorm(32, config.d_model), ) ) for _ in range(config.num_feature_levels - num_backbone_outs): input_proj_list.append( nn.Sequential( nn.Conv2d( in_channels, config.d_model, kernel_size=3, stride=2, padding=1, ), nn.GroupNorm(32, config.d_model), ) ) in_channels = config.d_model self.input_proj = nn.ModuleList(input_proj_list) else: self.input_proj = nn.ModuleList( [ nn.Sequential( nn.Conv2d( backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1, ), nn.GroupNorm(32, config.d_model), ) ] ) if not config.two_stage: self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2) self.encoder = DeformableDetrEncoder(config) self.decoder = DeformableDetrDecoder(config) self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model)) if config.two_stage: self.enc_output = nn.Linear(config.d_model, config.d_model) self.enc_output_norm = nn.LayerNorm(config.d_model) self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2) self.pos_trans_norm = nn.LayerNorm(config.d_model * 2) else: self.reference_points = nn.Linear(config.d_model, 2) self.post_init() def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def freeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(False) def unfreeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(True) def get_valid_ratio(self, mask, dtype=torch.float32): """Get the valid ratio of all feature maps.""" _, height, width = mask.shape valid_height = torch.sum(mask[:, :, 0], 1) valid_width = torch.sum(mask[:, 0, :], 1) valid_ratio_height = valid_height.to(dtype) / height valid_ratio_width = valid_width.to(dtype) / width valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1) return valid_ratio def get_proposal_pos_embed(self, proposals): """Get the position embedding of the proposals.""" num_pos_feats = self.config.d_model // 2 temperature = 10000 scale = 2 * math.pi dim_t = torch.arange(num_pos_feats, dtype=proposals.dtype, device=proposals.device) dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) # batch_size, num_queries, 4 proposals = proposals.sigmoid() * scale # batch_size, num_queries, 4, 128 pos = proposals[:, :, :, None] / dim_t # batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512 pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) return pos def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes): """Generate the encoder output proposals from encoded enc_output. Args: enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder. padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`. spatial_shapes (list[tuple[int, int]]): Spatial shapes of the feature maps. Returns: `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction. - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to directly predict a bounding box. (without the need of a decoder) - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse sigmoid. """ batch_size = enc_output.shape[0] proposals = [] _cur = 0 for level, (height, width) in enumerate(spatial_shapes): mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1) valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1) valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1) grid_y, grid_x = meshgrid( torch.linspace( 0, height - 1, height, dtype=enc_output.dtype, device=enc_output.device, ), torch.linspace( 0, width - 1, width, dtype=enc_output.dtype, device=enc_output.device, ), indexing="ij", ) grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2) grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale width_height = torch.ones_like(grid) * 0.05 * (2.0**level) proposal = torch.cat((grid, width_height), -1).view(batch_size, -1, 4) proposals.append(proposal) _cur += height * width output_proposals = torch.cat(proposals, 1) output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) output_proposals = torch.log(output_proposals / (1 - output_proposals)) # inverse sigmoid output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf")) output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) # assign each pixel as an object query object_query = enc_output object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0)) object_query = object_query.masked_fill(~output_proposals_valid, float(0)) object_query = self.enc_output_norm(self.enc_output(object_query)) return object_query, output_proposals @auto_docstring def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.FloatTensor] = None, encoder_outputs: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple[torch.FloatTensor], DeformableDetrModelOutput]: r""" decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): Not used by default. Can be used to mask object queries. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. Examples: ```python >>> from transformers import AutoImageProcessor, DeformableDetrModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") >>> model = DeformableDetrModel.from_pretrained("SenseTime/deformable-detr") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 300, 256] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device) # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper) # First, sent pixel_values + pixel_mask through Backbone to obtain the features # which is a list of tuples features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) sources = [] masks = [] for level, (source, mask) in enumerate(features): sources.append(self.input_proj[level](source)) masks.append(mask) if mask is None: raise ValueError("No attention mask was provided") # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage if self.config.num_feature_levels > len(sources): _len_sources = len(sources) for level in range(_len_sources, self.config.num_feature_levels): if level == _len_sources: source = self.input_proj[level](features[-1][0]) else: source = self.input_proj[level](sources[-1]) mask = nn.functional.interpolate(pixel_mask[None].to(pixel_values.dtype), size=source.shape[-2:]).to( torch.bool )[0] pos_l = self.backbone.position_embedding(source, mask).to(source.dtype) sources.append(source) masks.append(mask) position_embeddings_list.append(pos_l) # Create queries query_embeds = None if not self.config.two_stage: query_embeds = self.query_position_embeddings.weight # Prepare encoder inputs (by flattening) source_flatten = [] mask_flatten = [] lvl_pos_embed_flatten = [] spatial_shapes_list = [] for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)): batch_size, num_channels, height, width = source.shape spatial_shape = (height, width) spatial_shapes_list.append(spatial_shape) source = source.flatten(2).transpose(1, 2) mask = mask.flatten(1) pos_embed = pos_embed.flatten(2).transpose(1, 2) lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1) lvl_pos_embed_flatten.append(lvl_pos_embed) source_flatten.append(source) mask_flatten.append(mask) source_flatten = torch.cat(source_flatten, 1) mask_flatten = torch.cat(mask_flatten, 1) lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=source_flatten.device) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) valid_ratios = torch.stack([self.get_valid_ratio(m, dtype=source_flatten.dtype) for m in masks], 1) # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder # Also provide spatial_shapes, level_start_index and valid_ratios if encoder_outputs is None: encoder_outputs = self.encoder( inputs_embeds=source_flatten, attention_mask=mask_flatten, position_embeddings=lvl_pos_embed_flatten, spatial_shapes=spatial_shapes, spatial_shapes_list=spatial_shapes_list, level_start_index=level_start_index, valid_ratios=valid_ratios, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, prepare decoder inputs batch_size, _, num_channels = encoder_outputs[0].shape enc_outputs_class = None enc_outputs_coord_logits = None if self.config.two_stage: object_query_embedding, output_proposals = self.gen_encoder_output_proposals( encoder_outputs[0], ~mask_flatten, spatial_shapes_list ) # hack implementation for two-stage Deformable DETR # apply a detection head to each pixel (A.4 in paper) # linear projection for bounding box binary classification (i.e. foreground and background) enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding) # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch) delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding) enc_outputs_coord_logits = delta_bbox + output_proposals # only keep top scoring `config.two_stage_num_proposals` proposals topk = self.config.two_stage_num_proposals topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] topk_coords_logits = torch.gather( enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4), ) topk_coords_logits = topk_coords_logits.detach() reference_points = topk_coords_logits.sigmoid() init_reference_points = reference_points pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits))) query_embed, target = torch.split(pos_trans_out, num_channels, dim=2) else: query_embed, target = torch.split(query_embeds, num_channels, dim=1) query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1) target = target.unsqueeze(0).expand(batch_size, -1, -1) reference_points = self.reference_points(query_embed).sigmoid() init_reference_points = reference_points decoder_outputs = self.decoder( inputs_embeds=target, position_embeddings=query_embed, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=mask_flatten, reference_points=reference_points, spatial_shapes=spatial_shapes, spatial_shapes_list=spatial_shapes_list, level_start_index=level_start_index, valid_ratios=valid_ratios, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None) tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs return tuple_outputs return DeformableDetrModelOutput( init_reference_points=init_reference_points, last_hidden_state=decoder_outputs.last_hidden_state, intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, intermediate_reference_points=decoder_outputs.intermediate_reference_points, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, enc_outputs_class=enc_outputs_class, enc_outputs_coord_logits=enc_outputs_coord_logits, ) # Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead class DeformableDetrMLPPredictionHead(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 @auto_docstring( custom_intro=""" Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. """ ) class DeformableDetrForObjectDetection(DeformableDetrPreTrainedModel): # When using clones, all layers > 0 will be clones, but layer 0 *is* required _tied_weights_keys = [r"bbox_embed\.[1-9]\d*", r"class_embed\.[1-9]\d*"] # We can't initialize the model on meta device as some weights are modified during the initialization _no_split_modules = None def __init__(self, config: DeformableDetrConfig): super().__init__(config) # Deformable DETR encoder-decoder model self.model = DeformableDetrModel(config) # Detection heads on top self.class_embed = nn.Linear(config.d_model, config.num_labels) self.bbox_embed = DeformableDetrMLPPredictionHead( input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3, ) # if two-stage, the last class_embed and bbox_embed is for region proposal generation num_pred = (config.decoder_layers + 1) if config.two_stage else config.decoder_layers if config.with_box_refine: self.class_embed = _get_clones(self.class_embed, num_pred) self.bbox_embed = _get_clones(self.bbox_embed, num_pred) # hack implementation for iterative bounding box refinement self.model.decoder.bbox_embed = self.bbox_embed else: self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) self.model.decoder.bbox_embed = None if config.two_stage: # hack implementation for two-stage self.model.decoder.class_embed = self.class_embed # Initialize weights and apply final processing self.post_init() @auto_docstring def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.FloatTensor] = None, encoder_outputs: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[list[dict]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple[torch.FloatTensor], DeformableDetrObjectDetectionOutput]: r""" decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): Not used by default. Can be used to mask object queries. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. 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, DeformableDetrForObjectDetection >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") >>> model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr") >>> 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=0.5, 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 cat with confidence 0.8 at location [16.5, 52.84, 318.25, 470.78] Detected cat with confidence 0.789 at location [342.19, 24.3, 640.02, 372.25] Detected remote with confidence 0.633 at location [40.79, 72.78, 176.76, 117.25] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # First, sent images through DETR base model to obtain encoder + decoder outputs outputs = self.model( pixel_values, pixel_mask=pixel_mask, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2] init_reference = outputs.init_reference_points if return_dict else outputs[0] inter_references = outputs.intermediate_reference_points if return_dict else outputs[3] # class logits + predicted bounding boxes outputs_classes = [] outputs_coords = [] for level in range(hidden_states.shape[1]): if level == 0: reference = init_reference else: reference = inter_references[:, level - 1] reference = inverse_sigmoid(reference) outputs_class = self.class_embed[level](hidden_states[:, level]) delta_bbox = self.bbox_embed[level](hidden_states[:, level]) if reference.shape[-1] == 4: outputs_coord_logits = delta_bbox + reference elif reference.shape[-1] == 2: delta_bbox[..., :2] += reference outputs_coord_logits = delta_bbox else: raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}") outputs_coord = outputs_coord_logits.sigmoid() outputs_classes.append(outputs_class) outputs_coords.append(outputs_coord) outputs_class = torch.stack(outputs_classes) outputs_coord = torch.stack(outputs_coords) logits = outputs_class[-1] pred_boxes = outputs_coord[-1] loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: loss, loss_dict, auxiliary_outputs = self.loss_function( logits, labels, self.device, pred_boxes, self.config, outputs_class, outputs_coord, ) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes) + auxiliary_outputs + outputs else: output = (logits, pred_boxes) + outputs tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output return tuple_outputs dict_outputs = DeformableDetrObjectDetectionOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, auxiliary_outputs=auxiliary_outputs, last_hidden_state=outputs.last_hidden_state, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, intermediate_hidden_states=outputs.intermediate_hidden_states, intermediate_reference_points=outputs.intermediate_reference_points, init_reference_points=outputs.init_reference_points, enc_outputs_class=outputs.enc_outputs_class, enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, ) return dict_outputs __all__ = [ "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ]