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Functions115 in github.com/LintaoPeng/SS-UIE

⨍ Functions 115◇ Types & classes 22
↓ 8 callersFunction_t
(data)
utils/ptcolor.py:34
↓ 7 callersMethod__init__
(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)
net/blocks.py:262
↓ 5 callersMethod__init__
(self, in_channels, channels, inplace=True)
net/model.py:29
↓ 4 callersFunction_mul
(coeffs, image)
utils/ptcolor.py:40
↓ 4 callersMethoddt_init
(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4,
net/blocks.py:111
↓ 4 callersFunctionrgb2lab
sRGB to Lab conversion.
utils/ptcolor.py:242
↓ 3 callersFunctioncreate_window
(window_size, channel)
pytorch_ssim/__init__.py:11
↓ 3 callersFunctioncreate_window
(window_size, channel)
pytorch-ssim-loss/pytorch_ssim/__init__.py:11
↓ 3 callersFunctioneme
(ch,blocksize=8)
data/U45/nevaluate.py:97
↓ 3 callersFunctionsoftquant
(x, vmin, vmax, bins)
utils/Relu_Softmax_LAB.py:8
↓ 2 callersMethodHist_2_Dist_AB
(self,img,tab,alpha)
utils/LAB.py:22
↓ 2 callersFunction_ssim
(img1, img2, window, window_size, channel, size_average = True)
pytorch_ssim/__init__.py:17
↓ 2 callersFunction_ssim
(img1, img2, window, window_size, channel, size_average = True)
pytorch-ssim-loss/pytorch_ssim/__init__.py:17
↓ 2 callersMethodadd
Add a new value to the series.
utils/ptutils.py:88
↓ 2 callersFunctionestimate_aggd_params
(x)
niqe.py:12
↓ 2 callersMethodhue_to_distribution
(self,h, levels, eps=0.0)
utils/LCH.py:15
↓ 2 callersFunctionmake_plots
(data, height, width, dpi=100.0, rgbonly=False)
utils/plots.py:8
↓ 2 callersMethodtensor2freq
(self, x)
utils/FDL.py:24
↓ 1 callersMethodA_log_init
(d_state, d_inner, copies=1, device=None, merge=True)
net/blocks.py:139
↓ 1 callersMethodD_init
(d_inner, copies=1, device=None, merge=True)
net/blocks.py:156
↓ 1 callersMethod_apply
(self, x, coeffs)
utils/splines.py:60
↓ 1 callersMethod_coefficients
(self, y)
utils/splines.py:49
↓ 1 callersFunction_demo
()
utils/ptutils.py:111
↓ 1 callersFunction_demo
()
utils/splines.py:89
↓ 1 callersFunction_inv_lab_f
(x)
utils/ptcolor.py:216
↓ 1 callersFunction_lab_f
(x)
utils/ptcolor.py:190
↓ 1 callersFunction_main
()
utils/plots.py:61
↓ 1 callersMethod_precalc
(self, n)
utils/splines.py:32
↓ 1 callersMethod_update_steps_s
(self, step)
utils/ptutils.py:55
↓ 1 callersMethod_update_var
(self, k, v)
utils/ptutils.py:50
↓ 1 callersFunctionapply_gamma
Linear to gamma rgb. Assume that rgb values are in the [0, 1] range (but values outside are tolerated). gamma can be "srgb", a real-valued e
utils/ptcolor.py:86
↓ 1 callersFunctionbatch_PSNR
(img, imclean, data_range)
utils/utils.py:21
↓ 1 callersFunctioncompute_nss_features
(img_norm)
niqe.py:31
↓ 1 callersFunctiondeltaE
Delta E (CIE 1976). lab1: Bx3xHxW lab2: Bx3xHxW return: Bx1xHxW >>> lab1 = torch.tensor([100., 75., 50.]).view(1, 3, 1, 1) >>> l
utils/ptcolor.py:278
↓ 1 callersMethoddisp
Print on stdout the given information according the the format of the display.
utils/ptutils.py:46
↓ 1 callersMethodforward_core
(self, x: torch.Tensor)
net/blocks.py:167
↓ 1 callersFunctiongaussian
(window_size, sigma)
pytorch_ssim/__init__.py:7
↓ 1 callersFunctiongaussian
(window_size, sigma)
pytorch-ssim-loss/pytorch_ssim/__init__.py:7
↓ 1 callersFunctionlab2lch
Lab to LCH conversion.
utils/ptcolor.py:252
↓ 1 callersFunctionlab2rgb
Lab to sRGB conversion.
utils/ptcolor.py:248
↓ 1 callersFunctionlab2xyz
lab to XYZ conversion. lab: Bx3xHxW return: Bx3xHxW >>> lab2xyz(torch.tensor([0., 0., 0.]).view(1, 3, 1, 1)).view(-1) tensor([0., 0
utils/ptcolor.py:221
↓ 1 callersFunctionlogamee
(ch,blocksize=8)
data/U45/nevaluate.py:145
↓ 1 callersMethodloss_ab
(self,img,gt,alpha,tab,levels)
utils/LAB.py:27
↓ 1 callersMethodloss_formulation
(self, recon_freq, real_freq, matrix=None)
utils/FDL.py:41
↓ 1 callersFunctionmain
()
data/U45/nevaluate.py:183
↓ 1 callersMethodmessage
Compose a message with the given information.
utils/ptutils.py:40
↓ 1 callersFunctionnmetrics
(a)
data/U45/nevaluate.py:18
↓ 1 callersFunctionnorm
(img, sigma=7 / 6)
niqe.py:45
↓ 1 callersFunctionplipmult
(c,j,gamma=1026)
data/U45/nevaluate.py:142
↓ 1 callersFunctionplipsub
(i,j,k=1026)
data/U45/nevaluate.py:139
↓ 1 callersFunctionplipsum
(i,j,gamma=1026)
data/U45/nevaluate.py:136
↓ 1 callersFunctionquantAB
(bins, vmax,vmin)
utils/Qnt.py:25
↓ 1 callersFunctionremove_gamma
Gamma to linear rgb. Assume that rgb values are in the [0, 1] range (but values outside are tolerated). gamma can be "srgb", a real-valued e
utils/ptcolor.py:108
↓ 1 callersMethodreshape_output
(self,x)
net/blocks.py:339
↓ 1 callersMethodrfft
(self, t)
utils/FDL.py:19
↓ 1 callersFunctionrgb2xyz
sRGB to XYZ conversion. rgb: Bx3xHxW return: Bx3xHxW >>> rgb2xyz(torch.tensor([0., 0., 0.]).view(1, 3, 1, 1)).view(-1) tensor([0.,
utils/ptcolor.py:133
↓ 1 callersFunctionsample_images
Saves a generated sample from the validation set
train.py:38
↓ 1 callersFunctionsofthist_AB
(lab, vmax, bins)
utils/Relu_Softmax_LAB.py:20
↓ 1 callersFunctionsofthist_L
(x, vmin, vmax, bins)
utils/Relu_Softmax_LAB.py:14
↓ 1 callersFunctionsquared_deltaE94
Squared Delta E (CIE 1994). Default parameters for the 'Graphic Art' version. lab1: Bx3xHxW (reference color) lab2: Bx3xHxW (other c
utils/ptcolor.py:294
↓ 1 callersFunctionxyz2lab
XYZ to Lab conversion. xyz: Bx3xHxW return: Bx3xHxW >>> xyz2lab(torch.tensor([0., 0., 0.]).view(1, 3, 1, 1)).view(-1) tensor([0., 0
utils/ptcolor.py:195
↓ 1 callersFunctionxyz2rgb
XYZ to sRGB conversion. rgb: Bx3xHxW return: Bx3xHxW >>> xyz2rgb(torch.tensor([0., 0., 0.]).view(1, 3, 1, 1)).view(-1) tensor([0.,
utils/ptcolor.py:164
MethodHist_2_Dist_L
(self,img, tab,alpha)
utils/LAB.py:17
Method__format__
Format the last added value.
utils/ptutils.py:105
Method__init__
(self, window_size = 11, size_average = True)
pytorch_ssim/__init__.py:40
Method__init__
(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False)
utils/FDL.py:10
Method__init__
(self, weightC=1,weightH=1,levels=4,eps=0.01,weight=None)
utils/LCH.py:6
Method__init__
Create the display object. The format string encodes how information should be displayed.
utils/ptutils.py:29
Method__init__
Initialize the object. Remembers up to history_len values.
utils/ptutils.py:78
Method__init__
(self, alpha=1,weight=1,levels=7,vmin=-80,vmax=80)
utils/LAB.py:9
Method__init__
Create the object. Parameters ---------- nodes : int number of nodes. dtype type of internal
utils/splines.py:18
Method__init__
(self,seq_length=256, embedding_dim=512)
net/blocks.py:25
Method__init__
( self, d_model, d_state=16, d_conv=3, expand=2.,
net/blocks.py:38
Method__init__
(self, input_dim, output_dim, d_state = 16, d_conv = 3, expand = 2, drop_rate=0.0)
net/blocks.py:221
Method__init__
(self, dim, h=14, w=8)
net/blocks.py:280
Method__init__
(self, dim, mlp_ratio=4., drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, h=14, w=8)
net/blocks.py:308
Method__init__
(self,in_Channel,h,w,dropout_rate=0.0,mlp_ratio=4.,norm_layer=nn.LayerNorm)
net/blocks.py:323
Method__init__
FWSA and Mamba_Block
net/blocks.py:384
Method__init__
(self, in_channels, channels, inplace=True)
net/model.py:14
Method__init__
(self, in_channels, channels, drop_rate, H, W, inplace=True)
net/model.py:22
Method__init__
(self, channels, drop_rate, H, W)
net/model.py:38
Method__init__
(self, channels, num_resblock, num_memblock, drop_rate, H, W)
net/model.py:53
Method__init__
(self, in_channels=3, channels=16, num_memblock=6, num_resblock=6, drop_rate=0.0, H=256, W=256)
net/model.py:88
Method__init__
(self, window_size = 11, size_average = True)
pytorch-ssim-loss/pytorch_ssim/__init__.py:40
Function_check_conversion
Verify the conversions on the RGB cube. >>> _check_conversion(white_point='d65', gamma_correction='srgb', clip_rgb=False, space='srgb') True
utils/ptcolor.py:352
Function_check_gradients
Verify some borderline gradient computation >>> a = torch.zeros(1, 3, 1, 1, requires_grad=True) >>> b = torch.zeros(1, 3, 1, 1, requires_grad
utils/ptcolor.py:375
Functiondata_augmentation
(image, mode)
utils/utils.py:29
FunctiondeltaE94
Delta E (CIE 1994). Default parameters for the 'Graphic Art' version. lab1: Bx3xHxW (reference color) lab2: Bx3xHxW (other color)
utils/ptcolor.py:317
Methodforward
(self, img1, img2)
pytorch_ssim/__init__.py:47
Methodforward
(self, pred, target, matrix=None, **kwargs)
utils/FDL.py:69
Methodforward
(self,img,gt)
utils/LCH.py:27
Methodforward
(self,img,gt)
utils/LAB.py:37
Methodforward
Interpolate values using splines. Parameters ---------- y : tensor (b, n) y coordinates for the nodes (one set fo
utils/splines.py:71
Methodforward
(self, x, position_ids=None)
net/blocks.py:31
Methodforward
(self, x: torch.Tensor, **kwargs)
net/blocks.py:200
Methodforward
(self, x)
net/blocks.py:237
Methodforward
(self, x)
net/blocks.py:271
Methodforward
(self, x, spatial_size=None)
net/blocks.py:286
Methodforward
(self, x)
net/blocks.py:317
Methodforward
(self, x)
net/blocks.py:354
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