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Functions296 in github.com/RolandGao/RegSeg

Method__init__
(self, inplanes, planes, stride=1, downsample=None, no_relu=False)
competitors_models/DDRNet_Reimplementation.py:48
Method__init__
(self, inplanes, planes, stride=1, downsample=None, no_relu=True)
competitors_models/DDRNet_Reimplementation.py:82
Method__init__
(self, inplanes, interplanes, outplanes)
competitors_models/DDRNet_Reimplementation.py:233
Method__init__
(self, block, layers, num_classes=19, planes=64, spp_planes=128, head_planes=128, augment=False)
competitors_models/DDRNet_Reimplementation.py:258
Method__init__
(self,root,image_set,transforms,download=False)
datasets/voc12.py:9
Method__init__
(self,root,image_set,transforms)
datasets/camvid.py:10
Method__init__
(self, root, image_set, transforms, categories=None)
datasets/coco.py:75
Method__init__
( self, root: str, split: str = "train", mode: str = "fine",
datasets/cityscapes.py:68
Method__init__
(self,root,image_set,transforms,reduced,version="v1.2")
datasets/mapillary.py:25
Method__len__
(self)
datasets/voc12.py:44
Method__len__
(self)
datasets/camvid.py:48
Method__len__
(self)
datasets/coco.py:100
Method__len__
(self)
datasets/cityscapes.py:151
Method__len__
(self)
datasets/mapillary.py:55
Method__repr__
(self)
transforms.py:24
Method__str__
(self)
train.py:42
Functionbenchmark_memory
(models,batch_size,crop_size,mixed_precision,num_classes=19)
benchmark.py:211
Functionbenchmark_multiple
(configs)
train.py:329
Functionblock_speed_test
()
model.py:104
Functionbuild_centroids
The first step of uniform sampling is to decide sampling centers. The idea is to divide each image into tiles and within each tile, we co
datasets/class_uniform_sampling.py:193
Functionbuild_epoch
Generate an epoch of crops using uniform sampling. Needs to be called every epoch. Will not apply uniform sampling if not train or class
datasets/class_uniform_sampling.py:222
Functioncalculate_flops
()
model.py:114
Functioncalculate_params
(model)
model.py:124
Functioncamvid_speed_test
()
model.py:143
Methodchannels
(self)
blocks.py:352
Methodchannels
(self)
blocks.py:407
Functioncheck_support
()
augment.py:118
Functioncollate_fn
(batch)
data_utils.py:14
Functioncompute_time_full
(model,data_loader,warmup_iter,num_iter,device,crop_size,val_input_size,batch_size,num_classes,mixed_precision
benchmark.py:158
Functioncompute_train_time2
(model,x,target,warmup_iter,num_iter,mixed_precision)
benchmark.py:45
Functioncount_class_nums
(data_loader,num_classes)
data.py:185
Functiondilation_speed_test
()
model.py:94
Functiondispay_mapillary
()
show.py:263
Functiondisplay_camvid
()
show.py:229
Functiondisplay_cityscapes
()
show.py:206
Functiondisplay_coco
()
show.py:247
Methodforward
(self, x)
blocks.py:21
Methodforward
(self, x)
blocks.py:38
Methodforward
(self, x)
blocks.py:55
Methodforward
(self,x)
blocks.py:75
Methodforward
(self,x)
blocks.py:87
Methodforward
(self, x)
blocks.py:112
Methodforward
(self, x)
blocks.py:155
Methodforward
(self, x)
blocks.py:184
Methodforward
(self, x)
blocks.py:202
Methodforward
(self, x)
blocks.py:222
Methodforward
(self, x)
blocks.py:242
Methodforward
(self, x)
blocks.py:262
Methodforward
(self, x)
blocks.py:284
Methodforward
(self, x)
blocks.py:309
Methodforward
(self,x)
blocks.py:346
Methodforward
(self,x)
blocks.py:376
Methodforward
(self,x)
blocks.py:402
Methodforward
(self, x)
competitor_blocks.py:27
Methodforward
(self, x)
competitor_blocks.py:44
Methodforward
(self, x, y)
competitor_blocks.py:79
Methodforward
(self, x)
competitor_blocks.py:95
Methodforward
(self, x)
competitor_blocks.py:111
Methodforward
(self,x)
competitor_blocks.py:130
Methodforward
(self, feat_l, feat_s)
competitor_blocks.py:146
Methodforward
(self, x)
competitor_blocks.py:164
Methodforward
(self, x)
competitor_blocks.py:194
Methodforward
(self, x)
competitor_blocks.py:216
Methodforward
(self, logits, labels)
losses.py:13
Methodforward
(self,x)
model.py:74
Methodforward
(self, x)
competitors_models/hardnet.py:19
Methodforward
(self, x)
competitors_models/hardnet.py:138
Methodforward
(self, x)
competitors_models/hardnet.py:215
Methodforward
(self, x, skip, concat=True)
competitors_models/hardnet.py:244
Methodforward
(self, x)
competitors_models/hardnet.py:336
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:24
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:38
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:59
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:97
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:202
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:242
Methodforward
(self, x)
competitors_models/DDRNet_Reimplementation.py:359
Functionget_colors_cityscapes_labelid
()
show.py:30
Functionget_config_and_check_files
(config_filename)
train.py:102
Methodget_out_ch
(self)
competitors_models/hardnet.py:53
Functionget_pascal_voc
(root, batch_size, train_min_size, train_max_size, train_crop_size, val_input_size,val_label_size, aug_mode, n
data.py:175
Functionnum_classes_speed_test
()
model.py:82
Functionpanoptic_auto_aug
(im, mask,fill=(128,128,128),ignore_value=255)
augment.py:180
Functionrand_augment
Applies random augmentation to an image.
augment.py:173
Methodreset
(self)
train.py:29
Functionrgb_to_mask2
(rgb, color_to_class)
datasets/camvid.py:51
Functionsave_results_main
()
train.py:395
Functionshow_cityscapes_failure_modes
()
show.py:353
Functionshow_cityscapes_mask
(images)
show.py:80
Functionshow_cityscapes_test
()
show.py:299
Functionshow_mapillary_model
()
show.py:277
Functiontrain_3runs
()
train.py:383
Functionunpooled_class_centroids_all
Calculate class centroids for all classes for all images for all tiles. items: list of (image_fn, label_fn) tile size: size of tile r
datasets/class_uniform_sampling.py:142
Methodv2_transform
(self, trt=False)
competitors_models/hardnet.py:324
Functionvalidate_multiple
(configs)
train.py:267
Functionworker_init_fn
(worker_id)
data_utils.py:20
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