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Functions100 in github.com/MCG-NJU/EMA-VFI

⨍ Functions 100◇ Types & classes 21
↓ 20 callersMethodpad
(self, *inputs)
benchmark/utils/padder.py:14
↓ 17 callersMethoddevice
(self)
Trainer.py:31
↓ 11 callersMethodeval
(self)
Trainer.py:28
↓ 10 callersMethodload_model
(self, name=None, rank=0)
Trainer.py:34
↓ 8 callersFunctionwarp
(tenInput, tenFlow)
model/warplayer.py:6
↓ 7 callersMethod__init__
(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)
model/feature_extractor.py:73
↓ 7 callersMethodunpad
(self,x)
benchmark/utils/padder.py:17
↓ 6 callersMethodinference
(self, img0, img1, TTA = False, timestep = 0.5, fast_TTA = False)
Trainer.py:82
↓ 6 callersMethodread
(self, offset_frame=None)
benchmark/utils/yuv_frame_io.py:29
↓ 4 callersFunctiondeconv
(in_planes, out_planes, kernel_size=4, stride=2, padding=1)
model/refine.py:15
↓ 4 callersFunctionssim_matlab
(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None)
benchmark/utils/pytorch_msssim.py:81
↓ 4 callersFunctionwindow_partition
(x, window_size)
model/feature_extractor.py:6
↓ 3 callersMethodcalculate_flow
(self, imgs, timestep, af=None, mf=None)
model/flow_estimation.py:67
↓ 3 callersFunctionconv
(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1)
model/flow_estimation.py:8
↓ 3 callersFunctioncreate_window
(window_size, channel=1)
benchmark/utils/pytorch_msssim.py:13
↓ 3 callersMethodhr_inference
Infer with down_scale flow Noting: return BxCxHxW
Trainer.py:51
↓ 2 callersFunctionconv
(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1)
model/refine.py:8
↓ 2 callersFunctionconv_gauss
(img, kernel)
model/loss.py:31
↓ 2 callersMethodcoraseWarp_and_Refine
(self, imgs, af, flow, mask)
model/flow_estimation.py:95
↓ 2 callersFunctiondepad_if_needed
(x, size, window_size)
model/feature_extractor.py:63
↓ 2 callersFunctiongauss_kernel
(channels=3)
model/loss.py:8
↓ 2 callersFunctiongaussian
(window_size, sigma)
benchmark/utils/pytorch_msssim.py:8
↓ 2 callersFunctionlaplacian_pyramid
(img, kernel, max_levels=3)
model/loss.py:36
↓ 2 callersMethodmulti_inference
Run backbone once, get multi frames at different timesteps Noting: return a list of [CxHxW]
Trainer.py:101
↓ 2 callersFunctionpad_if_needed
(x, size, window_size)
model/feature_extractor.py:26
↓ 2 callersMethodrgb2gray
(self, rgb)
model/loss.py:75
↓ 2 callersFunctionssim
(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None)
benchmark/utils/pytorch_msssim.py:27
↓ 2 callersMethodtransform
(self, img)
model/loss.py:69
↓ 2 callersMethodupdate
(self, imgs, gt, learning_rate=0, training=True)
Trainer.py:143
↓ 2 callersMethodwarp_features
(self, xs, flow)
model/flow_estimation.py:57
↓ 2 callersFunctionwindow_reverse
(windows, window_size, H, W)
model/feature_extractor.py:15
↓ 1 callersMethod__init__
(self, max_levels=5, channels=3)
model/loss.py:49
↓ 1 callersMethod__init__
(self, in_planes, scale, c, in_else=17)
model/flow_estimation.py:17
↓ 1 callersMethod__init__
(self, c, out=3)
model/refine.py:33
↓ 1 callersMethod__init__
(self, window_size=11, size_average=True, val_range=None)
benchmark/utils/pytorch_msssim.py:168
↓ 1 callersMethod__len__
(self)
dataset.py:28
↓ 1 callersMethodaug
(self, img0, gt, img1, h, w)
dataset.py:37
↓ 1 callersMethodclose
(self)
benchmark/utils/yuv_frame_io.py:67
↓ 1 callersFunctioncreate_window_3d
(window_size, channel=1)
benchmark/utils/pytorch_msssim.py:19
↓ 1 callersFunctiondownsample
(x)
model/loss.py:19
↓ 1 callersFunctionevaluate
(model, val_data, nr_eval, local_rank)
train.py:67
↓ 1 callersFunctiongetXVFI
make [I0,I1,It,t,scene_folder]
benchmark/XTest_8X.py:52
↓ 1 callersMethodget_cor
(self, shape, device)
model/feature_extractor.py:457
↓ 1 callersFunctionget_learning_rate
(step)
train.py:21
↓ 1 callersMethodgetimg
(self, index)
dataset.py:46
↓ 1 callersMethodhamming
(self, t1, t2)
model/loss.py:80
↓ 1 callersFunctioninit_model_config
This function should not be modified
config.py:8
↓ 1 callersMethodload_data
(self)
dataset.py:31
↓ 1 callersFunctionmsssim
(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False)
benchmark/utils/pytorch_msssim.py:137
↓ 1 callersMethodsave_model
(self, rank=0)
Trainer.py:46
↓ 1 callersFunctiontrain
(model, local_rank, batch_size, data_path)
train.py:29
↓ 1 callersMethodtrain
(self)
Trainer.py:25
↓ 1 callersFunctionupsample
(x)
model/loss.py:22
↓ 1 callersMethodvalid_mask
(self, t, padding)
model/loss.py:85
Method__getitem__
(self, index)
dataset.py:55
Method__init__
(self, local_rank)
Trainer.py:12
Method__init__
(self, dataset_name, path, batch_size=32, model="RIFE")
dataset.py:12
Method__init__
(self, device)
model/loss.py:60
Method__init__
(self, backbone, **kargs)
model/flow_estimation.py:46
Method__init__
(self, dim, motion_dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)
model/feature_extractor.py:111
Method__init__
(self, dim, motion_dim, num_heads, window_size=0, shift_size=0, mlp_ratio=4., bidirectional=True, qkv_bias=Fal
model/feature_extractor.py:175
Method__init__
(self, in_dim, out_dim, depths=2,act_layer=nn.PReLU)
model/feature_extractor.py:283
Method__init__
(self, patch_size=7, stride=4, in_chans=3, embed_dim=768)
model/feature_extractor.py:310
Method__init__
(self, in_dims=[16,32,64], embed_dim=768)
model/feature_extractor.py:346
Method__init__
(self, in_chans=3, embed_dims=[32, 64, 128, 256, 512], motion_dims=64, num_heads=[8, 16], ml
model/feature_extractor.py:391
Method__init__
(self, dim)
model/feature_extractor.py:500
Method__init__
(self, in_planes, out_planes, stride=2)
model/refine.py:22
Method__init__
(self, dims, divisor = 16)
benchmark/utils/padder.py:8
Method__init__
(self, filepath, h, w, format='yuv420', toRGB=True)
benchmark/utils/yuv_frame_io.py:14
Method__init__
(self, filepath, fromRGB=True)
benchmark/utils/yuv_frame_io.py:72
Method__init__
(self, window_size=11, size_average=True, channel=3)
benchmark/utils/pytorch_msssim.py:193
Method_init_weights
(self, m)
model/feature_extractor.py:85
Method_init_weights
(self, m)
model/feature_extractor.py:130
Method_init_weights
(self, m)
model/feature_extractor.py:197
Method_init_weights
(self, m)
model/feature_extractor.py:296
Method_init_weights
(self, m)
model/feature_extractor.py:321
Method_init_weights
(self, m)
model/feature_extractor.py:360
Method_init_weights
(self, m)
model/feature_extractor.py:442
Method_init_weights
(self, m)
model/refine.py:46
Methodclose
(self)
benchmark/utils/yuv_frame_io.py:123
Methodconvert
(param)
Trainer.py:35
Functionfeature_extractor
(**kargs)
model/feature_extractor.py:513
Methodforward
(self, input, target)
model/loss.py:54
Methodforward
(self, img0, img1)
model/loss.py:91
Methodforward
(self, motion_feature, x, flow)
model/flow_estimation.py:27
Methodforward
(self, x, timestep=0.5)
model/flow_estimation.py:109
Methodforward
(self, x, H, W)
model/feature_extractor.py:100
Methodforward
(self, x1, x2, cor, H, W, mask=None)
model/feature_extractor.py:145
Methodforward
(self, x, cor, H, W, B)
model/feature_extractor.py:212
Methodforward
(self, x)
model/feature_extractor.py:304
Methodforward
(self, x)
model/feature_extractor.py:336
Methodforward
(self, xs)
model/feature_extractor.py:375
Methodforward
(self, x1, x2)
model/feature_extractor.py:467
Methodforward
(self, x, H, W)
model/feature_extractor.py:504
Methodforward
(self, x)
model/refine.py:27
Methodforward
(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
model/refine.py:61
Methodforward
(self, img1, img2)
benchmark/utils/pytorch_msssim.py:178
Methodforward
(self, img1, img2)
benchmark/utils/pytorch_msssim.py:199
Methodinfer
(imgs)
Trainer.py:56
Methodwrite
(self, Frame)
benchmark/utils/yuv_frame_io.py:79