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class ViTHybridPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTHybridConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_c... | 10,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half`... | 10,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
).to(module.position_embeddings.dtype) | 10,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
module.cls_token.data = nn.init.trunc_normal_(
module.cls_token.data.to(torch.float32),
mean=0.0,
std=self.config.initializer_range,
).to(module.cls_token.dtype) | 10,397 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridModel(ViTHybridPreTrainedModel):
def __init__(self, config: ViTHybridConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = ViTHybridEmbeddings(config, use_mask_token=use_mask_token)
self.... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.l... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
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 els... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_head... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = sel... | 10,398 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridPooler(nn.Module):
def __init__(self, config: ViTHybridConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state ... | 10,399 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class ViTHybridForImageClassification(ViTHybridPreTrainedModel):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.vit = ViTHybridModel(config, add_pooling_layer=False)
# Classifier head
self.classifier = ... | 10,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
@add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pi... | 10,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
outputs = self.vit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = ... | 10,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,400 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | 10,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
@staticmethod
@once_differentiable
def backward(context, grad_output):
(
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
) = context.saved_tensors
grad_value, grad_sampling_loc, grad_... | 10,401 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaDecoderOutput(ModelOutput):
"""
Base class for outputs of the DetaDecoder. 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 refer... | 10,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)... | 10,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tu... | 10,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
used to compute the weighted average in the cross-attention heads.
""" | 10,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
last_hidden_state: torch.FloatTensor = None
intermediate_hidden_states: torch.FloatTensor = None
intermediate_reference_points: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[tor... | 10,402 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaModelOutput(ModelOutput):
"""
Base class for outputs of the Deformable DETR encoder-decoder model. | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Args:
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 o... | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTen... | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_queries, num_heads, 4, 4)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
enc_outputs_class (`torch.FloatTensor` of shape `(bat... | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
output_proposals (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
Logits of proposal bounding boxes coordinates in the gen_encoder_output_proposals.
""" | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
init_reference_points: torch.FloatTensor = None
last_hidden_state: torch.FloatTensor = None
intermediate_hidden_states: torch.FloatTensor = None
intermediate_reference_points: torch.FloatTensor = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple... | 10,403 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaObjectDetectionOutput(ModelOutput):
"""
Output type of [`DetaForObjectDetection`]. | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
possible padding). You can use [`~DetaProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (`list[Dict]`, *optional*):
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (`tuple(torch.FloatTe... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
4)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average
in the self-attention heads.
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
Stacked intermediate hidden st... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 `... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
auxiliary_outputs: Optional[List[Dict]] = None
init_reference_points: Optional[torch.FloatTensor] = None
last_hidden_state: Optional[torch.FloatTensor] ... | 10,404 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaFrozenBatchNorm2d(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__(s... | 10,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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.res... | 10,405 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaBackboneWithPositionalEncodings(nn.Module):
"""
Backbone model with positional embeddings.
nn.BatchNorm2d layers are replaced by DetaFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
backbone = load_backbone(config)
with torch... | 10,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
"""
Outputs feature maps of latter stages C_3 through C_5 in ResNet if `config.num_feature_levels > 1`, otherwise
outputs feature maps of C_5.
"""
# first, send pixel_values through the backbone to get list ... | 10,406 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaSinePositionEmbedding(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):
... | 10,407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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=torch.float32)
x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
if self.normalize:
eps = 1e-6
y_embed... | 10,407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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((p... | 10,407 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaLearnedPositionEmbedding(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, e... | 10,408 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: DetaConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not None
if is_... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 &... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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_m... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def _reset_parameters(self):
nn.init.constant_(self.sampling_offsets.weight.data, 0.0)
default_dtype = torch.get_default_dtype()
thetas = torch.arange(self.n_heads, dtype=torch.int64).to(default_dtype) * (2.0 * math.pi / self.n_heads)
grid_init = torch.stack([thetas.cos(), thetas.sin()],... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
nn.init.xavier_uniform_(self.output_proj.weight.data)
nn.init.constant_(self.output_proj.bias.data, 0.0) | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
batch_size, num_queries, _ = hidden_states.shape
batch_size, sequence_length, _ = encoder_hidden_states.shape
if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
raise ValueError(
"Make sure to align the spatial shapes with the sequence length of the en... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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)
samplin... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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:
... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if self.disable_custom_kernels:
# PyTorch implementation
output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights)
else:
try:
# custom kernel
output = MultiScaleDeformableAttentionFunction.apply(
... | 10,409 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaMultiheadAttention(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,
... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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_si... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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_emb... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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
... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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()}"
)
... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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_weigh... | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 | 10,410 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaEncoderLayer(nn.Module):
def __init__(self, config: DetaConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetaMultiscaleDeformableAttention(
config,
num_heads=config.encoder_attention_heads,
n_points=config.encoder_n_po... | 10,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: torch.Tensor = None,
reference_points=None,
spatial_shapes=None,
level_start_index=None,
output_attentions: bool = False,
):
"""
Args:
... | 10,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 | 10,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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,
... | 10,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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)... | 10,411 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaDecoderLayer(nn.Module):
def __init__(self, config: DetaConfig):
super().__init__()
self.embed_dim = config.d_model
# self-attention
self.self_attn = DetaMultiheadAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
# cross-attention
self.encoder_attn = DetaMultiscaleDeformableAttention(
config,
num_heads=config.decoder_attention_heads,
n_points=config.decoder_n_points,
)
self.encoder_attn_layer_norm = nn.La... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes=None,
level_start_index=None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[t... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by ver... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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_mas... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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.dro... | 10,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaPreTrainedModel(PreTrainedModel):
config_class = DetaConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [r"DetaBackboneWithPositionalEncodings", r"DetaEncoderLayer", r"DetaDecoderLayer"]
supports_gradient_checkpointing = True
def _init_weights(self... | 10,413 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if isinstance(module, DetaLearnedPositionEmbedding):
nn.init.uniform_(module.row_embeddings.weight)
nn.init.uniform_(module.column_embeddings.weight)
elif isinstance(module, DetaMultiscaleDeformableAttention):
module._reset_parameters()
elif isinstance(module, (nn.Lin... | 10,413 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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) | 10,413 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaEncoder(DetaPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`DetaEncoderLayer`].
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
Args:
config: De... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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,... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(
self,
inputs_embeds=None,
attention_mask=None,
position_embeddings=None,
spatial_shapes=None,
level_start_index=None,
valid_ratios=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 ... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 = out... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
hidden_states = inputs_embeds
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device)
encoder_states = () if output_hidden_states else None
all_a... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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)
... | 10,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaDecoder(DetaPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetaDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some tweaks for Deformable DETR:
- `position_emb... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Initialize weights and apply final processing
self.post_init() | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(
self,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
position_embeddings=None,
reference_points=None,
spatial_shapes=None,
level_start_index=None,
valid_ratios=None,
output_attentions=None,
... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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` o... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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. | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 a... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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 = ()
... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
position_embeddings,
reference_points_input,
spatial_shapes,
... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
output_attentions=output_attentions,
) | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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)
if reference_points.shape[-1] == 4:
new_reference_points = tmp + inverse_sig... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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],)
# Kee... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
intermediate,
intermediate_reference_points,
all_hidden_states,
all_self_attns,
all_cross_atte... | 10,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaModel(DetaPreTrainedModel):
def __init__(self, config: DetaConfig):
super().__init__(config)
if config.two_stage:
requires_backends(self, ["torchvision"])
# Create backbone with positional encoding
self.backbone = DetaBackboneWithPositionalEncodings(config)
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Create input projection layers
if config.num_feature_levels > 1:
num_backbone_outs = len(intermediate_channel_sizes)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = intermediate_channel_sizes[_]
input_proj_list.append(
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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