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Functions296 in github.com/RolandGao/RegSeg

⨍ Functions 296◇ Types & classes 63
↓ 16 callersMethod__init__
(self, in_channels, out_channels, dilation,group_width, stride)
blocks.py:94
↓ 10 callersFunctionget_dataset_loaders
(config)
train_utils.py:80
↓ 9 callersMethod__init__
(self, in_channels, out_channels=128, sizes=(1, 2, 3, 6))
competitor_blocks.py:12
↓ 9 callersMethodchannels
(self)
blocks.py:381
↓ 8 callersMethod_make_layer
(self, block, inplanes, planes, blocks, stride=1)
competitors_models/DDRNet_Reimplementation.py:337
↓ 7 callersFunctionactivation
()
blocks.py:5
↓ 7 callersFunctionbuild_val_transform
(val_input_size,val_label_size)
data.py:9
↓ 7 callersFunctionnorm2d
(out_channels)
blocks.py:8
↓ 6 callersMethod__init__
(self, inplanes, branch_planes, outplanes)
competitors_models/DDRNet_Reimplementation.py:121
↓ 6 callersFunctionaverage
(v)
benchmark.py:59
↓ 6 callersFunctionget_dataloader_val
(dataset_test,num_workers=4)
data_utils.py:33
↓ 5 callersMethod__init__
(self, in_channels)
competitors_models/hardnet.py:24
↓ 5 callersFunctionbenchmark_eval
(models,x,mixed_precision)
benchmark.py:187
↓ 5 callersFunctionget_dataloader_train
(dataset,batch_size,num_workers=4)
data_utils.py:25
↓ 4 callersFunctionaffine_warp
Applies affine transform to image.
augment.py:20
↓ 4 callersFunctionapply_op
Apply the selected op to image with given probability and magnitude.
augment.py:129
↓ 4 callersFunctionbuild_train_transform
(train_min_size, train_max_size, train_crop_size, aug_mode,ignore_value)
data.py:78
↓ 4 callersFunctioncalc_miou
(ious)
precise_iou.py:5
↓ 4 callersFunctiondisplay
(data_loader,show_mask,num_images=5,skip=4,images_per_line=6)
show.py:97
↓ 4 callersFunctionf
(log_dir)
precise_iou.py:39
↓ 4 callersFunctionget_ddrnet_23
(num_classes=19)
competitors_models/DDRNet_Reimplementation.py:406
↓ 4 callersFunctionget_model
(config)
train_utils.py:147
↓ 4 callersFunctionprint_dict
(dic)
precise_iou.py:32
↓ 4 callersFunctionsetup_env
(config)
train.py:144
↓ 4 callersFunctionshow_mask
(images)
show.py:75
↓ 4 callersMethodupdate
(self, a, b)
train.py:20
↓ 3 callersFunctioncalc_miou_reduced
(ious)
precise_iou.py:16
↓ 3 callersMethodcompute
(self)
train.py:32
↓ 3 callersFunctioncompute_loader_time
(data_loader,warmup_iter,num_iter)
benchmark.py:89
↓ 3 callersFunctioncompute_memory_usage
(model,device,crop_size,batch_size,num_classes,mixed_precision, loss_fun)
benchmark.py:129
↓ 3 callersFunctioncompute_train_time
(model,warmup_iter,num_iter,train_crop_size,batch_size,num_classes,mixed_precision, loss_fun)
benchmark.py:61
↓ 3 callersFunctionfind_cityscapes_filenames
(images_dir,targets_dir,split,target_suffix)
datasets/cityscapes.py:8
↓ 3 callersFunctiongenerate_stage
(num,block_fun)
blocks.py:323
↓ 3 callersFunctionmean_and_std
(v)
precise_iou.py:116
↓ 3 callersFunctionshow
(model,data_loader,device,show_mask,num_images=5,skip=4,images_per_line=2,mixed_precision=False)
show.py:122
↓ 3 callersFunctionshow_image
Imshow for Tensor.
show.py:57
↓ 3 callersMethodtransform
(self, blk, trt=False)
competitors_models/hardnet.py:86
↓ 2 callersFunctionapply_op_both
Apply the selected op to image with given probability and magnitude.
augment.py:141
↓ 2 callersFunctionbenchmark_one
(config)
train.py:332
↓ 2 callersFunctioncat_list
(images, fill_value=0)
data_utils.py:6
↓ 2 callersFunctioncheck_config_files
(config)
train.py:106
↓ 2 callersFunctioncompute_eval_time
(model,device,warmup_iter,num_iter,val_input_size,mixed_precision)
benchmark.py:10
↓ 2 callersFunctioncompute_precise_bn_stats
Computes precise BN stats on training data.
precise_bn.py:7
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
competitors_models/DDRNet_Reimplementation.py:9
↓ 2 callersFunctionevaluate
(model, data_loader, device, confmat,mixed_precision,print_every,max_eval)
train.py:53
↓ 2 callersFunctionextract_ious
(filename)
precise_iou.py:84
↓ 2 callersFunctiongenerate_stage2
(ds,block_fun)
blocks.py:355
↓ 2 callersFunctionget_centroid_crop_params
(img,output_size,centroid)
transforms.py:192
↓ 2 callersFunctionget_ddrnet_23slim
(num_classes=19)
competitors_models/DDRNet_Reimplementation.py:410
↓ 2 callersFunctionget_edge_aware_crop_param
(img,output_size)
transforms.py:174
↓ 2 callersFunctionget_loss_fun
(config)
train_utils.py:27
↓ 2 callersMethodget_out_ch
(self)
competitors_models/hardnet.py:191
↓ 2 callersFunctionmemory_test_helper
(model,device,train_crop_size,batch_size,num_classes,mixed_precision,loss_fun)
benchmark.py:109
↓ 2 callersFunctionrandom_sampling
Randomly sample num items from the list alist: list of centroids to sample from num: can be larger than the list and if so, then wrap aro
datasets/class_uniform_sampling.py:175
↓ 2 callersFunctionsave
(model,optimizer,scheduler,epoch,path,best_mIU,scaler,run)
train.py:90
↓ 2 callersFunctiontrain_one
(config)
train.py:167
↓ 2 callersFunctionvalidate_one
(config)
train.py:273
↓ 2 callersFunctionz
(log_dir)
precise_iou.py:72
↓ 1 callersMethod_get_target_suffix
(self, mode: str, target_type: str)
datasets/cityscapes.py:154
↓ 1 callersFunction_has_valid_annotation
(anno)
datasets/coco.py:35
↓ 1 callersFunction_open
(filename)
show.py:65
↓ 1 callersFunctionbackbone_ablation_studies
()
precise_iou.py:159
↓ 1 callersFunctionbenchmark_main
()
train.py:358
↓ 1 callersFunctionbenchmark_train
(models,batch_size,crop_size,mixed_precision,num_classes=19)
benchmark.py:199
↓ 1 callersMethodbuild_epoch
(self)
datasets/camvid.py:36
↓ 1 callersMethodbuild_epoch
For class uniform sampling ... every epoch, we want to recompute which tiles from which images we want to sample from, so that the
datasets/cityscapes.py:112
↓ 1 callersFunctionbuild_train_transform2
(train_min_size, train_max_size, train_crop_size, aug_mode,ignore_value)
data.py:22
↓ 1 callersFunctioncalc_miou_last
(ious)
precise_iou.py:12
↓ 1 callersFunctioncalc_miou_reduced_last
(ious)
precise_iou.py:25
↓ 1 callersFunctioncalc_tile_locations
Divide an image into tiles to help us cover classes that are spread out. tile_size: size of tile to distribute image_size: original image
datasets/class_uniform_sampling.py:56
↓ 1 callersFunctioncamvid_5runs
()
precise_iou.py:150
↓ 1 callersFunctioncamvid_log
()
precise_iou.py:155
↓ 1 callersFunctioncityscapes_speed_test
()
model.py:133
↓ 1 callersFunctionclass_centroids_all
intermediate function to call pooled_class_centroid
datasets/class_uniform_sampling.py:164
↓ 1 callersFunctionclass_centroids_image
For one image, calculate centroids for all classes present in image. item: image, image_name tile_size: num_classes: id2trainid:
datasets/class_uniform_sampling.py:73
↓ 1 callersFunctioncomparison_against_ddrnet23
()
precise_iou.py:122
↓ 1 callersFunctioncomparison_against_ddrnet23_log
()
precise_iou.py:167
↓ 1 callersFunctioncompute_eval_time2
(model,x,warmup_iter,num_iter,mixed_precision)
benchmark.py:30
↓ 1 callersFunctioncompute_time_no_loader
(model,warmup_iter,num_iter,device,crop_size,val_input_size,batch_size,num_classes,mixed_precision,loss_fun)
benchmark.py:142
↓ 1 callersFunctionconvert_annotations_to_mask
(annotations, h, w)
datasets/coco.py:62
↓ 1 callersFunctionconvert_polygons_to_mask
(polygons, height, width)
datasets/coco.py:55
↓ 1 callersFunctioncosine_lr_scheduler
(current_iter, total_iters,warmup_iters,warmup_factor,min_lr=0)
lr_schedulers.py:18
↓ 1 callersFunctiondecoder_ablation_studies
()
precise_iou.py:163
↓ 1 callersFunctiondownload_extract
(url, root, filename, md5)
datasets/voc12.py:47
↓ 1 callersFunctionevaluate_cityscapes
(pred_dir,gt_dir)
evaluator.py:19
↓ 1 callersFunctionexp_lr_scheduler
(current_iter, total_iters,warmup_iters,warmup_factor,beta)
lr_schedulers.py:11
↓ 1 callersFunctionextract_ious_runs
(filename)
precise_iou.py:96
↓ 1 callersFunctionfilter_and_remap_records
(records, categories,filter_records=True)
datasets/coco.py:34
↓ 1 callersFunctionfind_failure_modes
()
experiments.py:10
↓ 1 callersMethodflow_warp
(self, input, flow, size)
competitor_blocks.py:57
↓ 1 callersMethodforward
(self, x)
competitors_models/hardnet.py:29
↓ 1 callersFunctiong
(log_dir)
precise_iou.py:56
↓ 1 callersFunctionget_camvid
(root, batch_size, train_min_size, train_max_size, train_crop_size, val_input_size,val_label_size, aug_mode,tr
data.py:148
↓ 1 callersFunctionget_cityscapes
(root, batch_size, train_min_size, train_max_size, train_crop_size, val_input_size,val_label_size, aug_mode,cl
data.py:136
↓ 1 callersFunctionget_coco
(root, batch_size, train_min_size, train_max_size, train_crop_size, val_input_size, val_label_size, aug_mode,
data.py:157
↓ 1 callersFunctionget_colors
()
show.py:18
↓ 1 callersFunctionget_colors_camvid
(color_to_class)
show.py:50
↓ 1 callersFunctionget_colors_cityscapes
()
show.py:23
↓ 1 callersFunctionget_colors_mapillary
()
show.py:36
↓ 1 callersFunctionget_colors_mapillary_reduced
()
show.py:41
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