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Functions1,075 in github.com/TonyXuQAQ/RNGDetPlusPlus

⨍ Functions 1,075◇ Types & classes 166
MethodaddEdgeToOneExistedNode
(self, nid1,lat1,lon1,nid2, reverse=False, nodeScore1 = 0, edgeScore = 0)
cityscale/metrics/topo/graph.py:134
MethodaddEdgeToOneExistedNode
(self, nid1,lat1,lon1,nid2, reverse=False, nodeScore1 = 0, edgeScore = 0)
spacenet/metrics/topo/graph.py:134
Methodadd_batch_cpu
(self, pnt_lst, maps_np, CROP_SZ)
cityscale/utils/utils.py:98
Methodadd_batch_cpu
(self, pnt_lst, maps_np, CROP_SZ)
spacenet/utils/utils.py:98
Methodadd_batch_gpu
(self, pnt_lst, maps_cuda, CROP_SZ)
cityscale/utils/utils.py:93
Methodadd_batch_gpu
(self, pnt_lst, maps_cuda, CROP_SZ)
spacenet/utils/utils.py:93
Methodadd_meter
(self, name, meter)
cityscale/utils/misc.py:190
Methodadd_meter
(self, name, meter)
cityscale/models/util/misc.py:191
Methodadd_meter
(self, name, meter)
spacenet/utils/misc.py:190
Methodadd_meter
(self, name, meter)
spacenet/models/util/misc.py:191
Functionall_gather
Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]
cityscale/utils/misc.py:88
Functionall_gather
Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]
cityscale/models/util/misc.py:89
Functionall_gather
Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]
spacenet/utils/misc.py:88
Functionall_gather
Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]
spacenet/models/util/misc.py:89
Methodangle_to
(self, other)
cityscale/lib/geom.py:26
Methodangle_to
(self, other)
cityscale/lib/geom.py:81
Methodangle_to
(self, other)
spacenet/lib/geom.py:26
Methodangle_to
(self, other)
spacenet/lib/geom.py:81
Methodappend_next_starting_pos
(curr_rs, target_poses, curr_pnt, curr_vertex, explored_points, potential_rs_list)
cityscale/utils/model_utils.py:578
Methodappend_next_starting_pos
(curr_rs, target_poses, curr_pnt, curr_vertex, explored_points, potential_rs_list)
spacenet/utils/model_utils.py:578
Methodavg
(self)
cityscale/utils/misc.py:63
Methodavg
(self)
cityscale/models/util/misc.py:64
Methodavg
(self)
spacenet/utils/misc.py:63
Methodavg
(self)
spacenet/models/util/misc.py:64
Methodbounds
(self)
cityscale/lib/graph.py:207
Methodbounds
(self)
cityscale/lib/geom.py:156
Methodbounds
(self)
spacenet/lib/graph.py:207
Methodbounds
(self)
spacenet/lib/geom.py:156
Functionbox_cxcywh_to_xyxy
(x)
cityscale/models/util/box_ops.py:9
Functionbox_cxcywh_to_xyxy
(x)
spacenet/models/util/box_ops.py:9
Methodclone
(self)
cityscale/lib/graph.py:549
Methodclone
(self)
cityscale/lib/graph.py:681
Methodclone
(self)
spacenet/lib/graph.py:549
Methodclone
(self)
spacenet/lib/graph.py:681
Methodclose
(self)
cityscale/utils/utils.py:102
Methodclose
(self)
spacenet/utils/utils.py:102
Methodclosest_pos
(self, point, graph)
cityscale/lib/graph.py:508
Methodclosest_pos
(self, point, graph)
spacenet/lib/graph.py:508
Functioncollate_fn
(batch)
cityscale/utils/misc.py:268
Functioncollate_fn
(batch)
cityscale/models/util/misc.py:269
Functioncollate_fn
(batch)
spacenet/utils/misc.py:268
Functioncollate_fn
(batch)
spacenet/models/util/misc.py:269
Functionconvert_gt
()
spacenet/convert.py:13
Functionconvert_pred
()
spacenet/convert.py:48
Methodconvert_rs_to_wkt
(self)
cityscale/lib/graph.py:264
Methodconvert_rs_to_wkt
(self)
spacenet/lib/graph.py:264
Methodconvert_to_networkx
(self)
cityscale/lib/graph.py:235
Methodconvert_to_networkx
(self)
spacenet/lib/graph.py:235
Methoddecompose
(self)
cityscale/utils/misc.py:299
Methoddecompose
(self)
spacenet/utils/misc.py:299
Functiondice_loss
Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred
cityscale/models/detr/segmentation_raw.py:312
Functiondice_loss
Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred
cityscale/models/detr/segmentation.py:388
Functiondice_loss
Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred
spacenet/models/detr/segmentation_raw.py:312
Functiondice_loss
Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred
spacenet/models/detr/segmentation.py:392
Methoddistance
(self, other)
cityscale/lib/geom.py:64
Methoddistance
(self, point)
cityscale/lib/geom.py:128
Methoddistance
(self, other)
spacenet/lib/geom.py:64
Methoddistance
(self, point)
spacenet/lib/geom.py:128
Methoddot
(self, point)
cityscale/lib/geom.py:95
Methoddot
(self, point)
spacenet/lib/geom.py:95
Functiondraw_line
(start, end, lengths)
cityscale/lib/geom.py:223
Functiondraw_line
(start, end, lengths)
spacenet/lib/geom.py:223
FunctionedgeIntersection
(baseX, baseY, dX, dY, n1X, n1Y, n2X, n2Y)
cityscale/metrics/topo/graph.py:751
FunctionedgeIntersection
(baseX, baseY, dX, dY, n1X, n1Y, n2X, n2Y)
spacenet/metrics/topo/graph.py:751
Methodexplore
(node_cur, node_prev, dist)
cityscale/metrics/topo/graph.py:209
Methodexplore
(node_cur, node_prev, dist)
spacenet/metrics/topo/graph.py:209
Methodfilter_edges
(self, filter_edges)
cityscale/lib/graph.py:323
Methodfilter_edges
(self, filter_edges)
spacenet/lib/graph.py:323
Methodforward
Performs the matching Params: outputs: This is a dict that contains at least these entries: "pred_logits": Tens
cityscale/models/detr/matcher.py:35
Methodforward
(self, samples: NestedTensor, history_samples: NestedTensor, gt_labels=None)
cityscale/models/detr/segmentation_raw.py:27
Methodforward
(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor])
cityscale/models/detr/segmentation_raw.py:106
Methodforward
(self, q, k, mask: Optional[Tensor] = None)
cityscale/models/detr/segmentation_raw.py:164
Methodforward
(self, x)
cityscale/models/detr/segmentation_raw.py:199
Methodforward
(self,fpns)
cityscale/models/detr/segmentation_raw.py:271
Methodforward
(self, src, mask, query_embed, pos_embed)
cityscale/models/detr/transformer.py:47
Methodforward
(self, src, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tens
cityscale/models/detr/transformer.py:73
Methodforward
(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[T
cityscale/models/detr/transformer.py:98
Methodforward
(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[
cityscale/models/detr/transformer.py:181
Methodforward
(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[T
cityscale/models/detr/transformer.py:261
Methodforward
(self, samples: NestedTensor, history_samples: NestedTensor)
cityscale/models/detr/segmentation.py:45
Methodforward
(self, q, k, mask: Optional[Tensor] = None)
cityscale/models/detr/segmentation.py:97
Methodforward
(self, features, features2, query_embed_weight, pos)
cityscale/models/detr/segmentation.py:157
Methodforward
(self,fpns)
cityscale/models/detr/segmentation.py:267
Methodforward
(self,fpns)
cityscale/models/detr/segmentation.py:346
Methodforward
 The forward expects a NestedTensor, which consists of: - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
cityscale/models/detr/detr.py:54
Methodforward
This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the form
cityscale/models/detr/detr.py:241
Methodforward
(self, x)
cityscale/models/detr/detr.py:276
Methodforward
(self, x)
cityscale/models/detr/backbone.py:42
Methodforward
(self, tensor_list: NestedTensor)
cityscale/models/detr/backbone.py:63
Methodforward
(self, tensor_list: NestedTensor)
cityscale/models/detr/backbone.py:92
Methodforward
(self, tensor_list: NestedTensor)
cityscale/models/detr/position_encoding.py:28
Methodforward
(self, tensor_list: NestedTensor)
cityscale/models/detr/position_encoding.py:65
Methodforward
Performs the matching Params: outputs: This is a dict that contains at least these entries: "pred_logits": Tens
spacenet/models/detr/matcher.py:35
Methodforward
(self, samples: NestedTensor, history_samples: NestedTensor, gt_labels=None)
spacenet/models/detr/segmentation_raw.py:27
Methodforward
(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor])
spacenet/models/detr/segmentation_raw.py:106
Methodforward
(self, q, k, mask: Optional[Tensor] = None)
spacenet/models/detr/segmentation_raw.py:164
Methodforward
(self, x)
spacenet/models/detr/segmentation_raw.py:199
Methodforward
(self,fpns)
spacenet/models/detr/segmentation_raw.py:271
Methodforward
(self, src, mask, query_embed, pos_embed)
spacenet/models/detr/transformer.py:47
Methodforward
(self, src, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tens
spacenet/models/detr/transformer.py:73
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