text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
query_position_embeddings = self.conditional_detr.model.query_position_embeddings.weight.unsqueeze(0).repeat(
batch_size, 1, 1
)
queries = torch.zeros_like(query_position_... | 4,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
# Sixth, compute logits, pred_boxes and pred_masks
logits = self.conditional_detr.class_labels_classifier(sequence_output)
pred_boxes = self.conditional_detr.bbox_predictor(sequence_output).sigmoid()
memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, widt... | 4,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
pred_masks = seg_masks.view(
batch_size, self.conditional_detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1]
)
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
outputs_class, outputs_coord = None, None
if self.config... | 4,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
if not return_dict:
if auxiliary_outputs is not None:
output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs
else:
output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs
return ((loss, l... | 4,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
return ConditionalDetrSegmentationOutput(
loss=loss,
loss_dict=loss_dict,
logits=logits,
pred_boxes=pred_boxes,
pred_masks=pred_masks,
auxiliary_outputs=auxiliary_outputs,
last_hidden_state=decoder_outputs.last_hidden_state,
... | 4,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrMaskHeadSmallConv(nn.Module):
"""
Simple convolutional head, using group norm. Upsampling is done using a FPN approach
"""
def __init__(self, dim, fpn_dims, context_dim):
super().__init__()
if dim % 8 != 0:
raise ValueError(
"The hidden_... | 4,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
self.lay1 = nn.Conv2d(dim, dim, 3, padding=1)
self.gn1 = nn.GroupNorm(8, dim)
self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1)
self.gn2 = nn.GroupNorm(min(8, inter_dims[1]), inter_dims[1])
self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1)
self.gn3 = nn.Grou... | 4,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=1)
nn.init.constant_(m.bias, 0)
def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]):
# here we concatenate x, the projected feature map, of shape (batch_siz... | 4,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
cur_fpn = self.adapter1(fpns[0])
if cur_fpn.size(0) != x.size(0):
cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0))
x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest")
x = self.lay3(x)
x = self.gn3(x)
x = nn.functional.relu(x)
... | 4,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
x = self.out_lay(x)
return x | 4,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrMHAttentionMap(nn.Module):
"""This is a 2D attention module, which only returns the attention softmax (no multiplication by value)"""
def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None):
super().__init__()
self.num_heads = num_heads
... | 4,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
def forward(self, q, k, mask: Optional[Tensor] = None):
q = self.q_linear(q)
k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias)
queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads)
keys_per_hea... | 4,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py |
class ConditionalDetrImageProcessor(BaseImageProcessor):
r"""
Constructs a Conditional Detr image processor. | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `si... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping t... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
do_normalize:
Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
`preprocess` method.
image_mean ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Controls whether to convert the annotations to the format expected by the DETR model. Converts the
bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
do_pad ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
""" | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
model_input_names = ["pixel_values", "pixel_mask"]
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.__init__
def __init__(
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
)
max_size = kwargs.pop("max_size")
else:
max_size = None if ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
super().__init__(**kwargs)
self.format = format
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.do_convert_annotations = do_convert... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
"format",
"return_tensors",
"data_format",
"input_data_format",
] | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
@classmethod
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->ConditionalDetr
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure parameters are updated if im... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->ConditionalDetr
def prepare_annotation(
self,
image: np.ndarray,
target: Dict,
format: Optional[AnnotationFormat] = None,
return_segmentation_masks: bool = None,
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if format == AnnotationFormat.COCO_DETECTION:
return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
target = prepare_coco_detection_annotation(
image, target, return_segmentation_masks, input_data_format=input_data_format
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[ChannelDimension] = None,
input_data_for... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the image's `(height, width)` dimensions after resizing. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(heigh... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension fo... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if "shortest_edge" in size and "longest_edge" in size:
new_size = get_resize_output_image_size(
image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
)
elif "max_height" in size and "max_width" in size:
new_size = get_image_si... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation
def resize_annotation(
self,
annotation,
orig_size,
size,
resample: PILImageResampling = PILImageResampling.NEAREST,
) -> Dict:
"""
Resize the annotation to m... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
image (`np.ndarray`):
Image to rescale.
rescale_factor (`float`):
The value to use for rescaling.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dime... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
"""
return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format) | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation
def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
"""
Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
for key, value in annotation.items():
if key == "masks":
masks = value
masks = pad(
masks,
padding,
mode=PaddingMode.CONSTANT,
constant_values=0,
input_data_format=ChannelDimen... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
new_annotation["size"] = output_image_size
else:
new_annotation[key] = value
return new_annotation | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
annotation: Optional[Dict[str, Any]] = None,
constant_values: Union[float, Iterable[float]] = 0,
data_form... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=dat... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
def pad(
self,
images: List[np.ndarray],
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
images (List[`np.ndarray`]):
Images to pad.
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
Annotations to transform according to the padding that is applied to the images.
constant_values (`float` or `Iterable[float]`, *opt... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *o... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
"""
pad_size = pad_size if pad_size is not None else self.pad_size
if pad_size is not None:
padded_size = (pad_size["height"], pad_size["widt... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
annotation_list = annotations if annotations is not None else [None] * len(images)
padded_images = []
padded_annotations = []
for image, annotation in zip(images, annotation_list):
padded_image, padded_annotation = self._pad_image(
image,
padded_size,
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
if annotations is not None:
encoded_inputs["labels"] = [
BatchFeature(annotation, tensor_type=return_tensors) for annotation in padded_annotations
]
return encoded_inputs | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.preprocess
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: bool = None,
masks_path: Optional[Union[s... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
pad_size: Optional[Dict[str, int]] = None,
**kwargs,
) -> BatchFeature:
"""
Preprocess an image or a batch of images so that it can be used by... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
annotations (`AnnotationType` or `List... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
- "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.
- "file_name" (`str`): The file name of the image.
return_segmentation_masks (`bool`, *optional*, def... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Rescale factor to use when rescaling the image.
do_normalize (`bool`, *optional*, defaults to self.do_normalize):
Whether to normalize the image.
do_convert_annotations (`bool`, *optional*, defaults to self.do_convert_annotations):
Whether to convert the annotatio... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Whether to pad the image. If `True`, padding will be applied to the bottom and right of
the image with zeros. If `pad_size` is provided, the image will be padded to the specified
dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
for... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
height and width in the batch.
"""
if "pad_and_return_pixel_mask" in kwargs:
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead."
)
do_pad = kwar... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
max_size = None
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size") | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
do_resize = self.do_resize if do_resize is None else do_resize
size = self.size if size is None else size
size = get_size_dict(size=size, max_size=max_size, default_to_square=False)
resample = self.resample if resample is None else resample
do_rescale = self.do_rescale if do_rescale is N... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), v... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if annotations is not None and len(images) != len(annotations):
raise ValueError(
f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
)
format = AnnotationFormat(format)
if annotations is not None:
validate_annot... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if inpu... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
if annotations is not None:
prepared_images = []
prepared_annotations = []
for image, target in zip(images, annotations):
target = self.prepare_annotation(
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# transformations
if do_resize:
if annotations is not None:
resized_images, resized_annotations = [], []
for image, target in zip(images, annotations):
orig_size = get_image_size(image, input_data_format)
resized_image = self.re... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
] | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if do_rescale:
images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
if do_normalize:
images = [
self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
]
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if do_pad:
# Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
encoded_inputs = self.pad(
images,
annotations=annotations,
return_pixel_mask=True,
data_format=data_format,
input_data_format... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
return encoded_inputs
# POSTPROCESSING METHODS - TODO: add support for other frameworks
def post_process(self, outputs, target_sizes):
"""
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the Pascal VOC format (xmin, ymin, xmax, ymax).
Only suppor... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
outputs ([`ConditionalDetrObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
im... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if len(out_logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
return results
# Copied from transformers.models.deformable_detr.image_processing_deformable_detr.DeformableDetrImageProcessor.post_process_object_detection with DeformableDetr->ConditionalDetr
def post_proces... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score threshold to keep object detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if target_sizes is not None:
if len(out_logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
prob = out_logits.sigmoid()
prob = prob.view(out_logits.shape[... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# and from relative [0, 1] to absolute [0, height] coordinates
if target_sizes is not None:
if isinstance(target_sizes, List):
img_h = torch.Tensor([i[0] for i in target_sizes])
img_w = torch.Tensor([i[1] for i in target_sizes])
else:
img_h... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation with Detr->ConditionalDetr
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*):
A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the
batch. If unset, ... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Remove the null class `[..., :-1]`
masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Semantic segmentation logits of shape (batch_size, num_classes, height, width)
segmentation = to... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
semantic_segmentation = []
for idx in range(batch_size):
resized_logits = nn.functional.interpolate(
segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_instance_segmentation with Detr->ConditionalDetr
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshol... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns:
`List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
- **segmentation** -- A tensor of shape `(height, width)` whe... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width] | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
batch_size = class_queries_logits.shape[0]
num_labels = class_queries_logits.shape[-1] - 1
mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
# Predicted label and score of each query (batch_size, num_queries)
pred_scores, pred_labels = nn.functiona... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# No mask found
if mask_probs_item.shape[0] <= 0:
height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
segmentation = torch.zeros((height, width)) - 1
results.append({"segmentation": segmentation, "segments_info": []})
... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Return segmentation map in run-length encoding (RLE) format
if return_coco_annotation:
segmentation = convert_segmentation_to_rle(segmentation)
results.append({"segmentation": segmentation, "segments_info": segments})
return results
# Copied from transformers.mode... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
Args:
outputs ([`ConditionalDetrForSegmentation`]):
The outputs from [`ConditionalDetrForSegmentation`].
threshold (`float`, *optional*, defaults to 0.5):
The probability score threshold to keep predicted instance masks.
mask_threshold (`float`, *optio... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
target_sizes (`List[Tuple]`, *optional*):
List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
Returns:
`List[Dict]`: A li... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
- **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
Multiple instances of the same class / label were fused and assigned a single `segment_id`.
- **score** -- Prediction score of segment with `segment_id`.
""" | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
if label_ids_to_fuse is None:
logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
label_ids_to_fuse = set()
class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1]
masks_queries_logits = outputs.pred_masks # [batch_size, num_qu... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
for i in range(batch_size):
mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
)
# No mask found
if mask_probs_item.shape[0] <= 0:
height, w... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
# Get segmentation map and segment information of batch item
target_size = target_sizes[i] if target_sizes is not None else None
segmentation, segments = compute_segments(
mask_probs=mask_probs_item,
pred_scores=pred_scores_item,
pred_labels=pred_l... | 4,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py |
class ConditionalDetrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate
a Conditional DETR model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the d... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
Args:
use_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the back... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`,... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
use_pretrained_backbone (`bool`, *optional*, defaults to `True`):
Whether to use pretrained weights for the backbone.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cann... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
mask_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (`float`, *optional*, defaults to 1):
... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
Examples:
```python
>>> from transformers import ConditionalDetrConfig, ConditionalDetrModel
>>> # Initializing a Conditional DETR microsoft/conditional-detr-resnet-50 style configuration
>>> configuration = ConditionalDetrConfig()
>>> # Initializing a model (with random weights) from the microso... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
def __init__(
self,
use_timm_backbone=True,
backbone_config=None,
num_channels=3,
num_queries=300,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=8,... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
giou_loss_coefficient=2,
focal_alpha=0.25,
**kwargs,
):
# We default to values which were previously hard-coded in the model. This enables configurability of the config
# while keeping the default behavior the same.
if use_timm_backbone and backbone_kwargs is None:
... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config) | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
) | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
self.use_timm_backbone = use_timm_backbone
self.backbone_config = backbone_config
self.num_channels = num_channels
self.num_queries = num_queries
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attent... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
self.position_embedding_type = position_embedding_type
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.backbone_kwargs = backbone_kwargs
self.dilation = dilation
# Hungarian matcher
self.class_cost = class_cost
self.bbox_cost =... | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
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