text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(intermediate_channel_sizes[-1], config.d_model, kernel_size=1),
nn.GroupNorm(32, config.d_model),
)
]
) | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if not config.two_stage:
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2)
self.encoder = DetaEncoder(config)
self.decoder = DetaDecoder(config)
self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
if ... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def freeze_backbone(self):
for name, param in self.backbone.model.named_parameters():
param.requires_grad_(False)
def unfreeze_backbone(self):
for name, param in self.backbone... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
num_pos_feats = self.config.d_model // 2
temperature = 10000
scale = 2 * math.pi
dim_t = torch.arange(num_pos_feats, dtype=torch.int64, device=proposals.device).float()
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
# batch_size, num_quer... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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 (Tensor[num_feature_levels, 2]): Spatial shapes of the feature maps. | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
grid_y, grid_x = meshgrid(
torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device),
torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device),
indexing="ij",
)
grid = torch.cat([grid_x.unsqueeze... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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_heigth = torch.ones_like(grid) * 0.05 * (2.0**level)
proposal = torch.cat((grid, width_heigth), -1).vi... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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))
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
@add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=DetaModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large-o365")
>>> model = DetaModel.from_pretrained("jozhang97/deta-swin-large-o365", two_st... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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:
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Prepare encoder inputs (by flattening)
spatial_shapes = [(source.shape[2:]) for source in sources]
source_flatten = [source.flatten(2).transpose(1, 2) for source in sources]
mask_flatten = [mask.flatten(1) for mask in masks]
lvl_pos_embed_flatten = []
for level, pos_embed in e... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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, dtype=torch.long, device=source_flatten.device)
level_start_index = torch.cat((spatial_shap... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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,
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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,
) | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Fifth, prepare decoder inputs
batch_size, _, num_channels = encoder_outputs[0].shape
enc_outputs_class = None
enc_outputs_coord_logits = None
output_proposals = None
if self.config.two_stage:
object_query_embedding, output_proposals, level_ids = self.gen_encoder_out... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# only keep top scoring `config.two_stage_num_proposals` proposals
topk = self.two_stage_num_proposals
proposal_logit = enc_outputs_class[..., 0]
if self.assign_first_stage:
proposal_boxes = center_to_corners_format(enc_outputs_coord_logits.sigmoid().float()).clamp(0... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# nms on topk indices
post_nms_inds = batched_nms(
prop_boxes_b[pre_nms_inds], prop_logits_b[pre_nms_inds], level_ids[pre_nms_inds], 0.9
)
keep_inds = pre_nms_inds[post_nms_inds]
if len(keep_inds) < self.two_stage_n... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# keep top Q/L indices for L levels
q_per_l = topk // len(spatial_shapes)
is_level_ordered = (
level_ids[keep_inds][None]
== torch.arange(len(spatial_shapes), device=level_ids.device)[:, None]
)
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
keep_inds_topk = keep_inds[keep_inds_mask]
topk_proposals.append(keep_inds_topk)
topk_proposals = torch.stack(topk_proposals)
else:
topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
topk_coords_logits = torch.gather(
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
topk_feats = torch.stack(
[object_query_embedding[b][topk_proposals[b]] for b in range(batch_size)]
).detach()
target = target + self.pix_trans_norm(self.pix_trans(topk_feats))
else:
query_embed, target = torch.split(query_embeds, num_channels, dim=1)
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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,
leve... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
return DetaModelOutput(
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,
... | 10,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaForObjectDetection(DetaPreTrainedModel):
# When using clones, all layers > 0 will be clones, but layer 0 *is* required
_tied_weights_keys = [r"bbox_embed\.\d+", r"class_embed\.\d+"]
# We can't initialize the model on meta device as some weights are modified during the initialization
_no_split_... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
prior_prob = 0.01
bias_value = -math.log((1 - prior_prob) / prior_prob)
self.class_embed.bias.data = torch.ones(config.num_labels) * bias_value
nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
self.model.decoder.class_embed = self.class_embed
for box_embed in self.bbox_embed:
nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Initialize weights and apply final processing
self.post_init()
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict h... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
@add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=DetaObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
pixel_mask: Optional[torch.LongTensor] = None,
decoder_attention_mask:... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, DetaForObjectDetection
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
>>> # 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
... ]
... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Detected couch with confidence 0.584 at location [0.03, 0.99, 640.02, 474.93]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# 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,
... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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])
... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
outputs_class = torch.stack(outputs_classes, dim=1)
outputs_coord = torch.stack(outputs_coords, dim=1) | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
logits = outputs_class[:, -1]
pred_boxes = outputs_coord[:, -1] | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
# First: create the matcher
matcher = DetaHungarianMatcher(
class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
)
# Second:... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
outputs_loss["pred_boxes"] = pred_boxes
outputs_loss["init_reference"] = init_reference
if self.config.auxiliary_loss:
auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord)
outputs_loss["auxiliary_outputs"] = auxiliary_outputs
if self.co... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
loss_dict = criterion(outputs_loss, labels)
# Fourth: compute total loss, as a weighted sum of the various losses
weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient}
weight_dict["loss_giou"] = self.config.giou_loss_coefficient
if self.config.auxil... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
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
re... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
dict_outputs = DetaObjectDetectionOutput(
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_hidd... | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
output_proposals=outputs.output_proposals,
) | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
return dict_outputs | 10,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaLoss(nn.Module):
"""
This class computes the losses for `DetaForObjectDetection`. The process happens in two steps: 1) we compute
hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
ground-truth / prediction (supervised class and bo... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def __init__(
self,
matcher,
num_classes,
focal_alpha,
losses,
num_queries,
assign_first_stage=False,
assign_second_stage=False,
):
super().__init__()
self.matcher = matcher
self.num_classes = num_classes
self.focal_alph... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def loss_labels(self, outputs, targets, indices, num_boxes):
"""
Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
of dim [nb_target_boxes]
"""
if "logits" not in outputs:
raise KeyError("No logits were found... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
target_classes_onehot = torch.zeros(
[source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
dtype=source_logits.dtype,
layout=source_logits.layout,
device=source_logits.device,
)
target_classes_onehot.scatter_(2, target_classes.unsqu... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
"""
logits = outputs["logits"]
device = logits.device
target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device)
# Count the number of predictions ... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
if "pred_boxes" not in outputs:
raise KeyError("No predicted boxes found in outputs")
... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def _get_source_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)])
source_idx = torch.cat([source for (source, _) in indices])
return batch_idx, source_idx
def _get_targ... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(self, outputs, targets):
"""
This performs the loss computation.
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_boxes = sum(len(t["class_labels"]) for t in targets)
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
# Check that we have initialized the distrib... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "auxiliary_outputs" in outputs:
for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
if not self.assign_second_stage:
indices = self.matcher(auxili... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
if "enc_outputs" in outputs:
enc_outputs = outputs["enc_outputs"]
bin_targets = copy.deepcopy(targets)
for bt in bin_targets:
bt["class_labels"] = torch.zeros_like(bt["class_labels"])
if self.assign_first_stage:
indices = self.stg1_assigner... | 10,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaMLPPredictionHead(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... | 10,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaHungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matchi... | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
raise ValueError("All costs of the Matcher can't be 0") | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
@torch.no_grad()
def forward(self, outputs, targets):
"""
Args:
outputs (`dict`):
A dictionary that contains at least these entries:
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
* "pred_box... | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Returns:
`List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds: ... | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
# Compute the classification cost.
alpha = 0.25
gamma = 2.0
neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
class_cost = pos_cost_class[:, target_ids] - neg_cost_clas... | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
sizes = [len(v["boxes"]) for v in targets]
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] | 10,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaMatcher:
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will
have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements.
The matching is determined by the MxN match_quality_matr... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def __init__(self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False):
"""
Args:
thresholds (`list[float]`):
A list of thresholds used to stratify predictions into levels.
labels (`list[int`):
A list of values to l... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
For example,
thresholds = [0.3, 0.5] labels = [0, -1, 1] All predictions with iou < 0.3 will be marked with 0 and
thus will be considered as false positives while training. All predictions with 0.3 <= iou < 0.5 will
be marked with -1 and thus will be ignored. All predicti... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
raise ValueError("All labels should be either -1, 0 or 1")
if len(labels) != len(thresholds) - 1:
raise ValueError("Number of labels should be equal to number of thresholds - 1")
self.thresholds = thresholds
self.labels = labels
self.allow_low_quality_matches = allow_low_qual... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def __call__(self, match_quality_matrix):
"""
Args:
match_quality_matrix (Tensor[float]): an MxN tensor, containing the
pairwise quality between M ground-truth elements and N predicted elements. All elements must be >= 0
(due to the us of `torch.nonzero` for s... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Returns:
matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
ground-truth index in [0, M)
match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
whether a prediction is a true or false positive or ignored
... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
assert torch.all(match_quality_matrix >= 0)
# match_quality_matrix is M (gt) x N (predicted)
# Max over gt elements (dim 0) to find best gt candidate for each prediction
matched_vals, matches = match_quality_matrix.max(dim=0)
match_labels = matches.new_full(matches.size(), 1, dtype=tor... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def set_low_quality_matches_(self, match_labels, match_quality_matrix):
"""
Produce additional matches for predictions that have only low-quality matches. Specifically, for each
ground-truth G find the set of predictions that have maximum overlap with it (including ties); for each
predic... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
This function implements the RPN assignment case (i) in Sec. 3.1.2 of :paper:`Faster R-CNN`.
"""
# For each gt, find the prediction with which it has highest quality
highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
# Find the highest quality match available, even if it is ... | 10,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaStage2Assigner(nn.Module):
def __init__(self, num_queries, max_k=4):
super().__init__()
self.positive_fraction = 0.25
self.bg_label = 400 # number > 91 to filter out later
self.batch_size_per_image = num_queries
self.proposal_matcher = DetaMatcher(thresholds=[0.6],... | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Returns:
Tensor: a vector of indices of sampled proposals. Each is in [0, N). Tensor: a vector of the same length,
the classification label for
each sampled proposal. Each sample is labeled as either a category in [0, num_classes) or the
background (num_classes).
... | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0)
return sampled_idxs, gt_classes[sampled_idxs]
def forward(self, outputs, targets, return_cost_matrix=False):
# COCO categories are from 1 to 90. They set num_classes=91 and apply sigmoid. | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
bs = len(targets)
indices = []
ious = []
for b in range(bs):
iou, _ = box_iou(
center_to_corners_format(targets[b]["boxes"]),
center_to_corners_format(outputs["init_reference"][b].detach()),
)
matched_idxs, matched_labels = self... | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
ious.append(iou)
if return_cost_matrix:
return indices, ious
return indices | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def postprocess_indices(self, pr_inds, gt_inds, iou):
return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) | 10,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaStage1Assigner(nn.Module):
def __init__(self, t_low=0.3, t_high=0.7, max_k=4):
super().__init__()
self.positive_fraction = 0.5
self.batch_size_per_image = 256
self.k = max_k
self.t_low = t_low
self.t_high = t_high
self.anchor_matcher = DetaMatcher(
... | 10,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
Args:
labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned.
"""
pos_idx, neg_idx = subsample_labels(label, self.batch_size_per_image, self.positive_fraction, 0)
# Fill with the ignore label (-1), then set positive and negative labels
label.fill_(-1)
... | 10,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
def forward(self, outputs, targets):
bs = len(targets)
indices = []
for b in range(bs):
anchors = outputs["anchors"][b]
if len(targets[b]["boxes"]) == 0:
indices.append(
(
torch.tensor([], dtype=torch.long, devic... | 10,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
all_pr_inds = torch.arange(len(anchors), device=matched_labels.device)
pos_pr_inds = all_pr_inds[matched_labels == 1]
pos_gt_inds = matched_idxs[pos_pr_inds]
pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou)
pos_pr_inds, pos_gt_inds = pos_... | 10,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py |
class DetaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DetaModel`]. It is used to instantiate a DETA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
The configuration of the backbone model.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
num_queries (`int`, *optional*, defaults to 900):
Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetaModel`] can
detect in a single image. In case `two_stage` i... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_funct... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map modu... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2):
Relative wei... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (`float`, *optional*, defaults to 0.1):
Relative classification weight of the 'no-object' class in the object detection loss.
num_feature_levels (`int`, *optional*, defaults to 5):
The numbe... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
two_stage_num_proposals (`int`, *optional*, defaults to 300):
The number of region proposals to be generated, in case `two_stage` is set to `True`.
with_box_refine (`bool`, *optional*, defaults to `True`):
Whether to apply iterative bounding box refinement, where each decoder layer refin... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
kernels are not supported by PyTorch ONNX export. | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
Examples:
```python
>>> from transformers import DetaConfig, DetaModel
>>> # Initializing a DETA SenseTime/deformable-detr style configuration
>>> configuration = DetaConfig()
>>> # Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
>>> model = D... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
def __init__(
self,
backbone_config=None,
backbone=None,
use_pretrained_backbone=False,
use_timm_backbone=False,
backbone_kwargs=None,
num_queries=900,
max_position_embeddings=2048,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_at... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
assign_second_stage=True,
class_cost=1,
bbox_cost=5,
giou_cost=2,
mask_loss_coefficient=1,
dice_loss_coefficient=1,
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
eos_coefficient=0.1,
focal_alpha=0.25,
disable_custom_kernels=True,
... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
if backbone_config is not None and backbone is not None:
raise ValueError("You can't specify both `backbone` and `backbone_config`.")
if backbone_config is None and backbone is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
self.backbone_config = backbone_config
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
self.num_queries = num_queries
self.max_position_embeddings = max_posit... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
# deformable attributes
self.num_feature_levels = num_feature_levels
self.encoder_n_points = encoder_n_points
self.decoder_n_points = decoder_n_points
self.two_stage = two_stage
... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
self.bbox_loss_coefficient = bbox_loss_coefficient
self.giou_loss_coefficient = giou_loss_coefficient
self.eos_coefficient = eos_coefficient
self.focal_alpha = focal_alpha
self.disable_custom_kernels = disable_custom_kernels
super().__init__(is_encoder_decoder=is_encoder_decoder,... | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model | 10,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py |
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