SYMBOL INDEX (268 symbols across 26 files)

FILE: d2/converter.py
  function parse_args (line 12) | def parse_args():
  function main (line 20) | def main():

FILE: d2/detr/config.py
  function add_detr_config (line 6) | def add_detr_config(cfg):

FILE: d2/detr/dataset_mapper.py
  function build_transform_gen (line 15) | def build_transform_gen(cfg, is_train):
  class DetrDatasetMapper (line 42) | class DetrDatasetMapper:
    method __init__ (line 55) | def __init__(self, cfg, is_train=True):
    method __call__ (line 73) | def __call__(self, dataset_dict):

FILE: d2/detr/detr.py
  class MaskedBackbone (line 31) | class MaskedBackbone(nn.Module):
    method __init__ (line 34) | def __init__(self, cfg):
    method forward (line 41) | def forward(self, images):
    method mask_out_padding (line 53) | def mask_out_padding(self, feature_shapes, image_sizes, device):
  class Detr (line 70) | class Detr(nn.Module):
    method __init__ (line 75) | def __init__(self, cfg):
    method forward (line 158) | def forward(self, batched_inputs):
    method prepare_targets (line 202) | def prepare_targets(self, targets):
    method inference (line 217) | def inference(self, box_cls, box_pred, mask_pred, image_sizes):
    method preprocess_image (line 255) | def preprocess_image(self, batched_inputs):

FILE: d2/train_net.py
  class Trainer (line 31) | class Trainer(DefaultTrainer):
    method build_evaluator (line 37) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
    method build_train_loader (line 49) | def build_train_loader(cls, cfg):
    method build_optimizer (line 57) | def build_optimizer(cls, cfg, model):
  function setup (line 106) | def setup(args):
  function main (line 119) | def main(args):

FILE: datasets/__init__.py
  function get_coco_api_from_dataset (line 8) | def get_coco_api_from_dataset(dataset):
  function build_dataset (line 18) | def build_dataset(image_set, args):

FILE: datasets/coco.py
  class CocoDetection (line 17) | class CocoDetection(torchvision.datasets.CocoDetection):
    method __init__ (line 18) | def __init__(self, img_folder, ann_file, transforms, return_masks):
    method __getitem__ (line 23) | def __getitem__(self, idx):
  function convert_coco_poly_to_mask (line 33) | def convert_coco_poly_to_mask(segmentations, height, width):
  class ConvertCocoPolysToMask (line 50) | class ConvertCocoPolysToMask(object):
    method __init__ (line 51) | def __init__(self, return_masks=False):
    method __call__ (line 54) | def __call__(self, image, target):
  function make_coco_transforms (line 115) | def make_coco_transforms(image_set):
  function build (line 147) | def build(image_set, args):

FILE: datasets/coco_eval.py
  class CocoEvaluator (line 22) | class CocoEvaluator(object):
    method __init__ (line 23) | def __init__(self, coco_gt, iou_types):
    method update (line 36) | def update(self, predictions):
    method synchronize_between_processes (line 55) | def synchronize_between_processes(self):
    method accumulate (line 60) | def accumulate(self):
    method summarize (line 64) | def summarize(self):
    method prepare (line 69) | def prepare(self, predictions, iou_type):
    method prepare_for_coco_detection (line 79) | def prepare_for_coco_detection(self, predictions):
    method prepare_for_coco_segmentation (line 103) | def prepare_for_coco_segmentation(self, predictions):
    method prepare_for_coco_keypoint (line 138) | def prepare_for_coco_keypoint(self, predictions):
  function convert_to_xywh (line 165) | def convert_to_xywh(boxes):
  function merge (line 170) | def merge(img_ids, eval_imgs):
  function create_common_coco_eval (line 192) | def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
  function evaluate (line 208) | def evaluate(self):

FILE: datasets/coco_panoptic.py
  class CocoPanoptic (line 15) | class CocoPanoptic:
    method __init__ (line 16) | def __init__(self, img_folder, ann_folder, ann_file, transforms=None, ...
    method __getitem__ (line 34) | def __getitem__(self, idx):
    method __len__ (line 70) | def __len__(self):
    method get_height_and_width (line 73) | def get_height_and_width(self, idx):
  function build (line 80) | def build(image_set, args):

FILE: datasets/panoptic_eval.py
  class PanopticEvaluator (line 13) | class PanopticEvaluator(object):
    method __init__ (line 14) | def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"):
    method update (line 23) | def update(self, predictions):
    method synchronize_between_processes (line 30) | def synchronize_between_processes(self):
    method summarize (line 37) | def summarize(self):

FILE: datasets/transforms.py
  function crop (line 16) | def crop(image, target, region):
  function hflip (line 59) | def hflip(image, target):
  function resize (line 76) | def resize(image, target, size, max_size=None):
  function pad (line 135) | def pad(image, target, padding):
  class RandomCrop (line 148) | class RandomCrop(object):
    method __init__ (line 149) | def __init__(self, size):
    method __call__ (line 152) | def __call__(self, img, target):
  class RandomSizeCrop (line 157) | class RandomSizeCrop(object):
    method __init__ (line 158) | def __init__(self, min_size: int, max_size: int):
    method __call__ (line 162) | def __call__(self, img: PIL.Image.Image, target: dict):
  class CenterCrop (line 169) | class CenterCrop(object):
    method __init__ (line 170) | def __init__(self, size):
    method __call__ (line 173) | def __call__(self, img, target):
  class RandomHorizontalFlip (line 181) | class RandomHorizontalFlip(object):
    method __init__ (line 182) | def __init__(self, p=0.5):
    method __call__ (line 185) | def __call__(self, img, target):
  class RandomResize (line 191) | class RandomResize(object):
    method __init__ (line 192) | def __init__(self, sizes, max_size=None):
    method __call__ (line 197) | def __call__(self, img, target=None):
  class RandomPad (line 202) | class RandomPad(object):
    method __init__ (line 203) | def __init__(self, max_pad):
    method __call__ (line 206) | def __call__(self, img, target):
  class RandomSelect (line 212) | class RandomSelect(object):
    method __init__ (line 217) | def __init__(self, transforms1, transforms2, p=0.5):
    method __call__ (line 222) | def __call__(self, img, target):
  class ToTensor (line 228) | class ToTensor(object):
    method __call__ (line 229) | def __call__(self, img, target):
  class RandomErasing (line 233) | class RandomErasing(object):
    method __init__ (line 235) | def __init__(self, *args, **kwargs):
    method __call__ (line 238) | def __call__(self, img, target):
  class Normalize (line 242) | class Normalize(object):
    method __init__ (line 243) | def __init__(self, mean, std):
    method __call__ (line 247) | def __call__(self, image, target=None):
  class Compose (line 261) | class Compose(object):
    method __init__ (line 262) | def __init__(self, transforms):
    method __call__ (line 265) | def __call__(self, image, target):
    method __repr__ (line 270) | def __repr__(self):

FILE: engine.py
  function train_one_epoch (line 17) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 68) | def evaluate(model, criterion, postprocessors, data_loader, base_ds, dev...

FILE: hubconf.py
  function _make_detr (line 13) | def _make_detr(backbone_name: str, dilation=False, num_classes=91, mask=...
  function detr_resnet50 (line 26) | def detr_resnet50(pretrained=False, num_classes=91, return_postprocessor...
  function detr_resnet50_dc5 (line 43) | def detr_resnet50_dc5(pretrained=False, num_classes=91, return_postproce...
  function detr_resnet101 (line 62) | def detr_resnet101(pretrained=False, num_classes=91, return_postprocesso...
  function detr_resnet101_dc5 (line 79) | def detr_resnet101_dc5(pretrained=False, num_classes=91, return_postproc...
  function detr_resnet50_panoptic (line 98) | def detr_resnet50_panoptic(
  function detr_resnet50_dc5_panoptic (line 121) | def detr_resnet50_dc5_panoptic(
  function detr_resnet101_panoptic (line 147) | def detr_resnet101_panoptic(

FILE: main.py
  function get_args_parser (line 20) | def get_args_parser():
  function main (line 105) | def main(args):

FILE: models/__init__.py
  function build_model (line 5) | def build_model(args):

FILE: models/backbone.py
  class FrozenBatchNorm2d (line 19) | class FrozenBatchNorm2d(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, n):
    method _load_from_state_dict (line 35) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
    method forward (line 45) | def forward(self, x):
  class BackboneBase (line 58) | class BackboneBase(nn.Module):
    method __init__ (line 60) | def __init__(self, backbone: nn.Module, train_backbone: bool, num_chan...
    method forward (line 72) | def forward(self, tensor_list: NestedTensor):
  class Backbone (line 83) | class Backbone(BackboneBase):
    method __init__ (line 85) | def __init__(self, name: str,
  class Joiner (line 96) | class Joiner(nn.Sequential):
    method __init__ (line 97) | def __init__(self, backbone, position_embedding):
    method forward (line 100) | def forward(self, tensor_list: NestedTensor):
  function build_backbone (line 112) | def build_backbone(args):

FILE: models/detr.py
  class DETR (line 21) | class DETR(nn.Module):
    method __init__ (line 23) | def __init__(self, backbone, transformer, num_classes, num_queries, au...
    method forward (line 44) | def forward(self, samples: NestedTensor):
    method _set_aux_loss (line 75) | def _set_aux_loss(self, outputs_class, outputs_coord):
  class SetCriterion (line 83) | class SetCriterion(nn.Module):
    method __init__ (line 89) | def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses):
    method loss_labels (line 108) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
    method loss_cardinality (line 130) | def loss_cardinality(self, outputs, targets, indices, num_boxes):
    method loss_boxes (line 143) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method loss_masks (line 164) | def loss_masks(self, outputs, targets, indices, num_boxes):
    method _get_src_permutation_idx (line 193) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 199) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 205) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
    method forward (line 215) | def forward(self, outputs, targets):
  class PostProcess (line 258) | class PostProcess(nn.Module):
    method forward (line 261) | def forward(self, outputs, target_sizes):
  class MLP (line 289) | class MLP(nn.Module):
    method __init__ (line 292) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 298) | def forward(self, x):
  function build (line 304) | def build(args):

FILE: models/matcher.py
  class HungarianMatcher (line 12) | class HungarianMatcher(nn.Module):
    method __init__ (line 20) | def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_g...
    method forward (line 35) | def forward(self, outputs, targets):
  function build_matcher (line 85) | def build_matcher(args):

FILE: models/position_encoding.py
  class PositionEmbeddingSine (line 12) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 17) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 28) | def forward(self, tensor_list: NestedTensor):
  class PositionEmbeddingLearned (line 51) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 55) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 61) | def reset_parameters(self):
    method forward (line 65) | def forward(self, tensor_list: NestedTensor):
  function build_position_encoding (line 79) | def build_position_encoding(args):

FILE: models/segmentation.py
  class DETRsegm (line 24) | class DETRsegm(nn.Module):
    method __init__ (line 25) | def __init__(self, detr, freeze_detr=False):
    method forward (line 37) | def forward(self, samples: NestedTensor):
  function _expand (line 65) | def _expand(tensor, length: int):
  class MaskHeadSmallConv (line 69) | class MaskHeadSmallConv(nn.Module):
    method __init__ (line 75) | def __init__(self, dim, fpn_dims, context_dim):
    method forward (line 102) | def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]):
  class MHAttentionMap (line 140) | class MHAttentionMap(nn.Module):
    method __init__ (line 143) | def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias...
    method forward (line 158) | def forward(self, q, k, mask: Optional[Tensor] = None):
  function dice_loss (line 172) | def dice_loss(inputs, targets, num_boxes):
  function sigmoid_focal_loss (line 190) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
  class PostProcessSegm (line 218) | class PostProcessSegm(nn.Module):
    method __init__ (line 219) | def __init__(self, threshold=0.5):
    method forward (line 224) | def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
  class PostProcessPanoptic (line 241) | class PostProcessPanoptic(nn.Module):
    method __init__ (line 245) | def __init__(self, is_thing_map, threshold=0.85):
    method forward (line 256) | def forward(self, outputs, processed_sizes, target_sizes=None):

FILE: models/transformer.py
  class Transformer (line 18) | class Transformer(nn.Module):
    method __init__ (line 20) | def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
    method _reset_parameters (line 42) | def _reset_parameters(self):
    method forward (line 47) | def forward(self, src, mask, query_embed, pos_embed):
  class TransformerEncoder (line 62) | class TransformerEncoder(nn.Module):
    method __init__ (line 64) | def __init__(self, encoder_layer, num_layers, norm=None):
    method forward (line 70) | def forward(self, src,
  class TransformerDecoder (line 86) | class TransformerDecoder(nn.Module):
    method __init__ (line 88) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
    method forward (line 95) | def forward(self, tgt, memory,
  class TransformerEncoderLayer (line 127) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 129) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
    method with_pos_embed (line 146) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 149) | def forward_post(self,
    method forward_pre (line 164) | def forward_pre(self, src,
    method forward (line 178) | def forward(self, src,
  class TransformerDecoderLayer (line 187) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 189) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
    method with_pos_embed (line 209) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 212) | def forward_post(self, tgt, memory,
    method forward_pre (line 235) | def forward_pre(self, tgt, memory,
    method forward (line 258) | def forward(self, tgt, memory,
  function _get_clones (line 272) | def _get_clones(module, N):
  function build_transformer (line 276) | def build_transformer(args):
  function _get_activation_fn (line 289) | def _get_activation_fn(activation):

FILE: run_with_submitit.py
  function parse_args (line 14) | def parse_args():
  function get_shared_folder (line 24) | def get_shared_folder() -> Path:
  function get_init_file (line 33) | def get_init_file():
  class Trainer (line 42) | class Trainer(object):
    method __init__ (line 43) | def __init__(self, args):
    method __call__ (line 46) | def __call__(self):
    method checkpoint (line 52) | def checkpoint(self):
    method _setup_gpu_args (line 65) | def _setup_gpu_args(self):
  function main (line 77) | def main():

FILE: test_all.py
  class Tester (line 23) | class Tester(unittest.TestCase):
    method test_box_cxcywh_to_xyxy (line 25) | def test_box_cxcywh_to_xyxy(self):
    method indices_torch2python (line 31) | def indices_torch2python(indices):
    method test_hungarian (line 34) | def test_hungarian(self):
    method test_position_encoding_script (line 63) | def test_position_encoding_script(self):
    method test_backbone_script (line 67) | def test_backbone_script(self):
    method test_model_script_detection (line 71) | def test_model_script_detection(self):
    method test_model_script_panoptic (line 80) | def test_model_script_panoptic(self):
    method test_model_detection_different_inputs (line 90) | def test_model_detection_different_inputs(self):
    method test_warpped_model_script_detection (line 105) | def test_warpped_model_script_detection(self):
  class ONNXExporterTester (line 127) | class ONNXExporterTester(unittest.TestCase):
    method setUpClass (line 129) | def setUpClass(cls):
    method run_model (line 132) | def run_model(self, model, inputs_list, tolerate_small_mismatch=False,...
    method ort_validate (line 151) | def ort_validate(self, onnx_io, inputs, outputs, tolerate_small_mismat...
    method test_model_onnx_detection (line 178) | def test_model_onnx_detection(self):
    method test_model_onnx_detection_panoptic (line 193) | def test_model_onnx_detection_panoptic(self):

FILE: util/box_ops.py
  function box_cxcywh_to_xyxy (line 9) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 16) | def box_xyxy_to_cxcywh(x):
  function box_iou (line 24) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 40) | def generalized_box_iou(boxes1, boxes2):
  function masks_to_boxes (line 64) | def masks_to_boxes(masks):

FILE: util/misc.py
  class SmoothedValue (line 27) | class SmoothedValue(object):
    method __init__ (line 32) | def __init__(self, window_size=20, fmt=None):
    method update (line 40) | def update(self, value, n=1):
    method synchronize_between_processes (line 45) | def synchronize_between_processes(self):
    method median (line 59) | def median(self):
    method avg (line 64) | def avg(self):
    method global_avg (line 69) | def global_avg(self):
    method max (line 73) | def max(self):
    method value (line 77) | def value(self):
    method __str__ (line 80) | def __str__(self):
  function all_gather (line 89) | def all_gather(data):
  function reduce_dict (line 132) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 159) | class MetricLogger(object):
    method __init__ (line 160) | def __init__(self, delimiter="\t"):
    method update (line 164) | def update(self, **kwargs):
    method __getattr__ (line 171) | def __getattr__(self, attr):
    method __str__ (line 179) | def __str__(self):
    method synchronize_between_processes (line 187) | def synchronize_between_processes(self):
    method add_meter (line 191) | def add_meter(self, name, meter):
    method log_every (line 194) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 249) | def get_sha():
  function collate_fn (line 269) | def collate_fn(batch):
  function _max_by_axis (line 275) | def _max_by_axis(the_list):
  class NestedTensor (line 284) | class NestedTensor(object):
    method __init__ (line 285) | def __init__(self, tensors, mask: Optional[Tensor]):
    method to (line 289) | def to(self, device):
    method decompose (line 300) | def decompose(self):
    method __repr__ (line 303) | def __repr__(self):
  function nested_tensor_from_tensor_list (line 307) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
  function _onnx_nested_tensor_from_tensor_list (line 335) | def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> N...
  function setup_for_distributed (line 363) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 378) | def is_dist_avail_and_initialized():
  function get_world_size (line 386) | def get_world_size():
  function get_rank (line 392) | def get_rank():
  function is_main_process (line 398) | def is_main_process():
  function save_on_master (line 402) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 407) | def init_distributed_mode(args):
  function accuracy (line 433) | def accuracy(output, target, topk=(1,)):
  function interpolate (line 451) | def interpolate(input, size=None, scale_factor=None, mode="nearest", ali...

FILE: util/plot_utils.py
  function plot_logs (line 13) | def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'),...
  function plot_precision_recall (line 76) | def plot_precision_recall(files, naming_scheme='iter'):