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Functions470 in github.com/Lizhe1228/MambaDFuse

⨍ Functions 470◇ Types & classes 81
↓ 1 callersFunctionfused_leaky_relu
(input, bias, negative_slope=0.2, scale=2 ** 0.5)
models/op/fused_act.py:87
↓ 1 callersFunctiongaussian
(window_size, sigma)
models/loss_ssim.py:15
↓ 1 callersFunctionget_confirm_token
(response)
utils/utils_googledownload.py:58
↓ 1 callersFunctionget_image_paths
(dataroot)
utils/utils_image.py:67
↓ 1 callersFunctionget_model_parameters_number
(model)
utils/utils_modelsummary.py:160
↓ 1 callersFunctionget_pca_matrix
Args: x: 225x10000 matrix dim_pca: 15 Returns: pca_matrix: 15x225
utils/utils_sisr.py:76
↓ 1 callersMethodget_target_label
(self, input, target_is_real)
models/loss.py:119
↓ 1 callersFunctionget_timestamp
()
utils/utils_image.py:33
↓ 1 callersFunctionget_timestamp
()
utils/utils_option.py:19
↓ 1 callersFunctiongm_blur_kernel
(mean, cov, size=15)
utils/utils_sisr.py:56
↓ 1 callersFunctionimssave
imgs: list, N images of size WxHxC
utils/utils_image.py:116
↓ 1 callersFunctioninit_dist
(launcher, backend='nccl', **kwargs)
utils/utils_dist.py:13
↓ 1 callersMethodinit_train
(self)
models/model_base.py:23
↓ 1 callersFunctionis_image_file
(filename)
utils/utils_image.py:29
↓ 1 callersMethodload
(self)
models/model_plain.py:62
↓ 1 callersMethodload_optimizer
(self, load_path, optimizer)
models/model_base.py:185
↓ 1 callersMethodload_optimizers
(self)
models/model_plain.py:80
↓ 1 callersFunctionloadmat
this function should be called instead of direct spio.loadmat as it cures the problem of not properly recovering python dictionaries from
utils/utils_mat.py:7
↓ 1 callersFunctionmain
()
test_MambaDFuse.py:19
↓ 1 callersFunctionmain
# ---------------------------------------- # Step--1 (prepare opt) # ----------------------------------------
train_MambaDFuse.py:37
↓ 1 callersFunctionmodcrop_np
Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image
utils/utils_sisr.py:297
↓ 1 callersMethodmodel_to_device
Model to device. It also warps models with DistributedDataParallel or DataParallel. Args: network (nn.Module)
models/model_base.py:97
↓ 1 callersMethodoptimize_parameters
(self)
models/model_base.py:51
↓ 1 callersFunctionoutFromIn
(conv, layerIn)
utils/utils_receptivefield.py:20
↓ 1 callersFunctionpad_circular
Arguments :param input: tensor of shape :math:`(N, C_{\text{in}}, H, [W, D]))` :param padding: (tuple): m-elem tuple where m is the degre
utils/utils_sisr.py:514
↓ 1 callersFunctionparams_to_string
(params_num)
utils/utils_modelsummary.py:113
↓ 1 callersFunctionpatches_from_image
(img, p_size=512, p_overlap=64, p_max=800)
utils/utils_image.py:97
↓ 1 callersFunctionpixel_unshuffle
r"""Rearranges elements in a Tensor of shape :math:`(C, rH, rW)` to a tensor of shape :math:`(*, r^2C, H, W)`. Authors: Zhaoyi Yan, h
models/basicblock.py:104
↓ 1 callersFunctionprint_model
(model)
utils/utils_model.py:232
↓ 1 callersFunctionprint_params
(model)
utils/utils_model.py:240
↓ 1 callersMethodsave_optimizer
(self, save_dir, optimizer, optimizer_label, iter_label)
models/model_base.py:177
↓ 1 callersFunctionsave_response_content
(response, destination, file_size=None,
utils/utils_googledownload.py:65
↓ 1 callersFunctionsetup
(args)
test_MambaDFuse.py:99
↓ 1 callersMethodshift
(self, x_real, x_imag)
models/refine.py:114
↓ 1 callersMethodshift_feat
(self, x, shiftPixel, g)
models/refine.py:181
↓ 1 callersFunctionshifted_anisotropic_Gaussian
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gau
utils/utils_sisr.py:130
↓ 1 callersFunctionsrmd_degradation
blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsam
utils/utils_sisr.py:235
↓ 1 callersMethodstep
(self, hidden_states, conv_state, ssm_state)
mamba_simple.py:923
↓ 1 callersFunctiontest
(img_a, img_b, model, args, window_size)
test_MambaDFuse.py:121
↓ 1 callersFunctiontest
(model, L)
utils/utils_model.py:96
↓ 1 callersMethodtest
(self)
models/model_plain.py:271
↓ 1 callersFunctiontest_split
(model, L, refield=32, min_size=256, sf=1, modulo=1)
utils/utils_model.py:172
↓ 1 callersFunctiontest_split_x8
(model, L, refield=32, min_size=256, sf=1, modulo=1)
utils/utils_model.py:203
↓ 1 callersFunctiontest_x8
(model, L, modulo=1, sf=1)
utils/utils_model.py:184
↓ 1 callersFunctionto_3d
(x)
models/network.py:52
↓ 1 callersFunctionto_4d
(x, h, w)
models/network.py:55
↓ 1 callersMethodupdate_learning_rate
(self, n)
models/model_base.py:60
↓ 1 callersFunctionupsample
x: tensor image, NxCxWxH
utils/utils_sisr.py:488
↓ 1 callersFunctionupsample_np
(x, sf=3, center=False)
utils/utils_sisr.py:752
↓ 1 callersFunctionzero_pad
Extends image to a certain size with zeros Parameters ---------- image: real 2d `numpy.ndarray` Input image shape: tuple
utils/utils_sisr.py:710
FunctionContrast
(img1, img2, window_size=11, channel=1)
models/loss_ssim.py:48
FunctionG
x: image, NxcxHxW k: kernel, cx1xhxw sf: scale factor center: the first one or the moddle one Matlab function: tmp = imfilte
utils/utils_sisr.py:549
FunctionG_np
x: image, NxcxHxW k: kernel, cx1xhxw Matlab function: tmp = imfilter(x,h,'circular'); y = downsample2(tmp,K);
utils/utils_sisr.py:773
FunctionGt
x: image, NxcxHxW k: kernel, cx1xhxw sf: scale factor center: the first one or the moddle one Matlab function: tmp = upsampl
utils/utils_sisr.py:564
FunctionGt_np
x: image, NxcxHxW k: kernel, cx1xhxw Matlab function: tmp = upsample2(x,K); y = imfilter(tmp,h,'circular');
utils/utils_sisr.py:786
MethodHSV2RGB
(self, hsv)
utils/utils_color.py:35
FunctionINVLS
x1 = FB.*FR; FBR = BlockMM(nr,nc,Nb,m,x1); invW = BlockMM(nr,nc,Nb,m,F2B); invWBR = FBR./(invW + tau*Nb); fun = @(block_struct) b
utils/utils_sisr.py:640
FunctionINVLS_pytorch
FB: NxCxWxHx2 F2B: NxCxWxHx2 x1 = FB.*FR; FBR = BlockMM(nr,nc,Nb,m,x1); invW = BlockMM(nr,nc,Nb,m,F2B); invWBR = FBR./(invW
utils/utils_sisr.py:450
FunctionL1_norm
(source_en_a, source_en_b)
models/network.py:11
FunctionPYBIND11_MODULE
models/op/fused_bias_act.cpp:19
FunctionPYBIND11_MODULE
models/op/upfirdn2d.cpp:21
MethodRGB2HSV
(self, img)
utils/utils_color.py:14
MethodRGB2YCrCb
(self, input_im)
utils/utils_color.py:104
MethodYCrCb2RGB
(self, input_im)
utils/utils_color.py:93
Method__getitem__
(self, index)
data/dataset_wogt.py:34
Method__init__
Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection" This Block has a slightly different structure co
mamba_simple.py:537
Method__init__
( self, d_model, d_state=16, d_conv=4, expand=2, dt_rank="auto
mamba_simple.py:597
Method__init__
(self, log_path="default.log")
utils/utils_logger.py:57
Method__init__
(self, in_channels=3, out_channels=3)
utils/utils_model.py:306
Method__init__
(self)
utils/utils_color.py:90
Method__init__
(self, root_A, root_B, in_channels)
data/dataloder.py:18
Method__init__
(self, opt)
data/dataset_wogt.py:18
Method__init__
(self, feature_layer=[2,7,16,25,34], use_input_norm=True, use_range_norm=False)
models/loss.py:13
Method__init__
(self, feature_layer=[2,7,16,25,34], weights=[0.1,0.1,1.0,1.0,1.0], lossfn_type='l1', use_input_norm=True, use
models/loss.py:61
Method__init__
(self, gan_type, real_label_val=1.0, fake_label_val=0.0)
models/loss.py:94
Method__init__
(self, eps=1e-9)
models/loss.py:168
Method__init__
(self, normalized_shape)
models/network.py:59
Method__init__
(self, p=2)
models/network.py:75
Method__init__
(self, normalized_shape)
models/network.py:95
Method__init__
(self, dim, LayerNorm_type)
models/network.py:113
Method__init__
(self, dim)
models/network.py:128
Method__init__
(self, dim)
models/network.py:140
Method__init__
(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)
models/network.py:195
Method__init__
(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)
models/network.py:238
Method__init__
(self, scale, num_feat)
models/network.py:269
Method__init__
(self, scale, num_feat, num_out_ch, input_resolution=None)
models/network.py:293
Method__init__
(self, img_size=64, patch_size=1, in_chans=1, embed_dim=96, Ex_depths=[4], Fusion_depths=[2,
models/network.py:309
Method__init__
(self, opt)
models/model_base.py:9
Method__init__
(self, opt)
models/model_plain.py:21
Method__init__
(self)
models/loss_gt.py:12
Method__init__
(self)
models/loss_gt.py:41
Method__init__
(self)
models/loss_gt.py:57
Method__init__
(self)
models/loss_gt.py:74
Method__init__
(self)
models/loss_gt.py:83
Method__init__
(self,patch_size,mean_val)
models/loss_gt.py:127
Method__init__
(self)
models/loss_gt.py:143
Method__init__
(self)
models/loss_gt.py:177
Method__init__
(self)
models/loss_med.py:13
Method__init__
(self)
models/loss_med.py:39
Method__init__
(self)
models/loss_med.py:57
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