Functions — github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP

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Functions234 in github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP

⨍ Functions 234◇ Types & classes 71

↓ 24 callersMethodupdate

(self, val, n=1)

train/utils.py:23

↓ 16 callersFunctionnormalize

(x, centralize=False, normalize=False, val_range=255.0)

data/utils.py:118

↓ 8 callersMethod__init__

(self, in_chs, growth_rate, num_layer, num_blocks, activation='relu')

model/arches.py:174

↓ 8 callersMethodregister

(self, name, epoch, value)

utils/logger.py:52

↓ 7 callersMethod__init__

(self, n_feat, kernel_size, reduction, bias, act)

model/RNN-MBP.py:31

↓ 7 callersMethod__init__

(self, para)

train/loss.py:158

↓ 5 callersFunctioncupy_kernel

(strFunction, objectVariables)

model/correlation.py:241

↓ 5 callersFunctioncupy_launch

(strFunction, strKernel)

model/correlation.py:283

↓ 5 callersFunctionloss_parse

parse loss parameters

train/loss.py:239

↓ 5 callersFunctionnormalize_reverse

(x, centralize=False, normalize=False, val_range=255.0)

data/utils.py:127

↓ 4 callersMethod__init__

(self, device='cuda')

model/flow_pwc.py:134

↓ 4 callersMethodbackward

(self, gradOutput)

model/correlation.py:348

↓ 4 callersFunctionconv

(in_channels, out_channels, kernel_size, bias=False, stride = 1)

model/arches.py:27

↓ 4 callersFunctionconv3x3

(in_channels, out_channels, stride=1)

model/arches.py:12

↓ 4 callersMethodget_lr

(self)

train/optimizer.py:31

↓ 4 callersFunctionreduce_tensor

reduce tensor from multiple gpus

train/utils.py:31

↓ 4 callersMethodreport

(self, items, state, epoch)

utils/logger.py:65

↓ 4 callersMethodstep

(self)

train/optimizer.py:34

↓ 3 callersMethod__init__

(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False)

model/attention.py:95

↓ 3 callersFunctionactFunc

(act, *args, **kwargs)

model/arches.py:67

↓ 3 callersFunctionimg2video

generate video

train/utils.py:48

↓ 3 callersFunctionpsnr_calculate

(x, y, val_range=255.0)

train/metrics.py:27

↓ 3 callersMethodsave

(self, state, epoch, filename='checkpoint.pth.tar')

utils/logger.py:84

↓ 3 callersFunctionssim_calculate

(img1, img2, sd=1.5, C1=0.01**2, C2=0.03**2)

train/metrics.py:44

↓ 3 callersMethodzero_grad

(self)

train/optimizer.py:37

↓ 2 callersMethod__init__

(self, num_feat=64, num_block=30, spynet_path=None)

model/basicvsr.py:22

↓ 2 callersMethod_forward_single

(self, x, y, valid_flag=False)

train/loss.py:268

↓ 2 callersMethod_quantize

(self, img)

train/metrics.py:92

↓ 2 callersFunctionconv1x1

(in_channels, out_channels, stride=1)

model/arches.py:8

↓ 2 callersMethodforward

(self, x, y, valid_flag=False)

train/loss.py:167

↓ 2 callersFunctionget_confirm_token

(response)

data/download_reds.py:6

↓ 2 callersMethodlr_schedule

(self)

train/optimizer.py:40

↓ 2 callersMethodrun

(self)

train/trainer.py:18

↓ 2 callersFunctionsave_response_content

(response, destination)

data/download_reds.py:13

↓ 2 callersMethodwarp

warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow

model/flow_pwc.py:56

↓ 1 callersFunctionBackward

(tensorInput, tensorFlow, device='cuda')

model/flow_pwc.py:102

↓ 1 callersFunction_augment

(img)

model/utils.py:102

↓ 1 callersMethod_forward_list

(self, x, y, valid_flag=False)

train/loss.py:289

↓ 1 callersMethod_generate_samples

(self, dataset_path, data_format)

data/BSD.py:38

↓ 1 callersMethod_generate_samples

(self, dataset_path)

data/reds.py:37

↓ 1 callersMethod_generate_samples

(self, dataset_path)

data/gopro.py:37

↓ 1 callersMethod_load_sample

(self, sample_dict, sample)

data/BSD.py:73

↓ 1 callersMethod_load_sample

(self, sample_dict, sample)

data/reds.py:72

↓ 1 callersMethod_load_sample

(self, sample_dict, sample)

data/gopro.py:74

↓ 1 callersFunction_main

(path, ds_path)

train/test_isp.py:22

↓ 1 callersFunction_np2Tensor

(img)

model/utils.py:86

↓ 1 callersFunction_postprocess

(img, rgb_coefficient, ycbcr_flag, device)

model/utils.py:116

↓ 1 callersFunction_test_lmdb

(para, logger, model, ds_type)

train/test.py:142

↓ 1 callersFunction_test_torch

(para, logger, model, ds_type)

train/test.py:45

↓ 1 callersMethodcheck_dir

(self, file_path)

utils/logger.py:28

↓ 1 callersMethodcomp_flow

Compute optical flow using SPyNet for feature warping. Args: lrs (tensor): LR frames, the shape is (n, t, c, h, w) Retur

model/basicvsr.py:51

↓ 1 callersMethodcontentFunc

(self)

train/loss.py:116

↓ 1 callersFunctionconv5x5

(in_channels, out_channels, stride=1)

model/arches.py:16

↓ 1 callersFunctioncount_param

(model)

train/dp.py:21

↓ 1 callersFunctiondist_train

(train_loader, model, criterion, metrics, opt, epoch, para, logger)

train/ddp.py:125

↓ 1 callersFunctiondist_valid

(valid_loader, model, criterion, metrics, epoch, para, logger)

train/ddp.py:209

↓ 1 callersFunctiondownload_file_from_server

(server, subset, destination)

data/download_reds.py:34

↓ 1 callersMethodestimate_flow

(self, tensorFirst, tensorSecond)

model/flow_pwc.py:33

↓ 1 callersMethodextract_args

(self)

para/parameter.py:8

↓ 1 callersMethodget_img

(self, seq_idx, frame_idx, sample)

data/reds_lmdb.py:69

↓ 1 callersMethodget_img

(self, seq_idx, frame_idx, sample)

data/gopro_ds_lmdb.py:74

↓ 1 callersMethodget_img

(self, seq_idx, frame_idx, sample)

data/rbvd_lmdb.py:74

↓ 1 callersMethodget_img

(self, seq_idx, frame_idx, sample)

data/DeepVideoDeblurring_lmdb.py:74

↓ 1 callersMethodis_best

(self, epoch)

utils/logger.py:80

↓ 1 callersFunctionlogsumexp_2d

(tensor)

model/attention.py:68

↓ 1 callersFunctionprocess

data parallel training

train/dp.py:29

↓ 1 callersMethodprofile

(self)

model/model.py:18

↓ 1 callersFunctionraw2rgb

(img_raw)

train/test_isp.py:11

↓ 1 callersMethodrecord_para

(self)

utils/logger.py:23

↓ 1 callersMethodreset

(self)

train/utils.py:17

↓ 1 callersFunctiontest

test code

train/test.py:18

↓ 1 callersFunctiontrain

(train_loader, model, criterion, metrics, opt, epoch, para, logger)

train/dp.py:117

↓ 1 callersFunctionvalid

(valid_loader, model, criterion, metrics, epoch, para, logger)

train/dp.py:194

FunctionFunctionCorrelation

(tensorFirst, tensorSecond)

model/correlation.py:410

(self, *args, verbose=True, prefix='', timestamp=True)

(self, fakeIm, realIm)

data/gopro_ds_lmdb.py:42

data/DeepVideoDeblurring_lmdb.py:42

(self, in_feat, out_feat=64, num_block=30)

model/basicvsr.py:128

Method__init__

(self, in_feat, out_feat, scale_factor)

model/basicvsr.py:144

Method__init__

(self, in_channels, s_factor)

model/arches.py:34

Method__init__

(self, in_channels, s_factor)

model/arches.py:45

Method__init__

(self, in_channels, s_factor)

model/arches.py:56

Method__init__

(self, in_chs, activation='relu', batch_norm=False)

model/arches.py:114

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