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Functions470 in github.com/Lizhe1228/MambaDFuse

Functionmodcrop
(img_in, scale)
utils/utils_image.py:524
Methodno_weight_decay
(self)
models/network.py:400
Methodno_weight_decay_keywords
(self)
models/network.py:404
Methodoptimize_parameters
(self, current_step)
models/model_plain.py:185
Functionp2o
Args: psf: NxCxhxw shape: [H,W] Returns: otf: NxCxHxWx2
utils/utils_sisr.py:425
Functionparse
(opt_path, is_train=True)
utils/utils_option.py:23
Functionpool_flops_counter_hook
(module, input, output)
utils/utils_modelsummary.py:456
Methodprint_network
(self)
models/model_base.py:77
Methodprint_network
(self)
models/model_plain.py:325
Methodprint_params
(self)
models/model_base.py:83
Methodprint_params
(self)
models/model_plain.py:332
Functionpsf2otf
Convert point-spread function to optical transfer function. Compute the Fast Fourier Transform (FFT) of the point-spread function (PSF) a
utils/utils_sisr.py:661
Functionr1_penalty
R1 regularization for discriminator. The core idea is to penalize the gradient on real data alone: when the generator distribution pro
models/loss.py:179
Functionr2c
(x)
utils/utils_sisr.py:368
Functionread_img
(path)
utils/utils_image.py:225
Functionreal2complex
(x)
utils/utils_sisr.py:474
Functionreduce_loss_dict
(loss_dict)
utils/utils_dist.py:178
Functionreduce_sum
(tensor)
utils/utils_dist.py:118
Functionregularizer_clip
# ---------------------------------------- # usage: net.apply(regularizer_clip) # ----------------------------------------
utils/utils_regularizers.py:74
Functionregularizer_orth
# ---------------------------------------- # SVD Orthogonal Regularization # ---------------------------------------- # Applies regul
utils/utils_regularizers.py:16
Functionregularizer_orth2
# ---------------------------------------- # Applies regularization to the training by performing the # orthogonalization technique descr
utils/utils_regularizers.py:47
Functionrelu_flops_counter_hook
(module, input, output)
utils/utils_modelsummary.py:298
Functionremove_activation_counter_hook_function
(module)
utils/utils_modelsummary.py:408
Functionremove_flops_counter_hook_function
(module)
utils/utils_modelsummary.py:248
Methodrequires_grad
(self, model, flag=True)
models/model_base.py:67
Functionreset_activation_count
A method that will be available after add_activation_counting_methods() is called on a desired net object. Resets statistics computed so
utils/utils_modelsummary.py:387
Functionreset_flops_count
A method that will be available after add_flops_counting_methods() is called on a desired net object. Resets statistics computed so far.
utils/utils_modelsummary.py:219
Functionrfft
(t)
utils/utils_sisr.py:409
Functionrgb2gray_net
(net, only_input=True)
utils/utils_params.py:64
Functionrgb2ycbcr
same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]
utils/utils_image.py:559
Functionsave
(opt)
utils/utils_option.py:193
Methodsave
(self, iter_label)
models/model_plain.py:89
Functionsave_model
(network, save_path)
utils/utils_matconvnet.py:68
Functionshave
(img_in, border=0)
utils/utils_image.py:540
Functionshift_pixel
shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH, image or kernel sf: scale factor uppe
utils/utils_sisr.py:317
Functionshow_kv
(net)
utils/utils_params.py:7
Functionshow_pca
x: PCA projection matrix, e.g., 15x225
utils/utils_sisr.py:91
Functionsingle2tensor3
(img)
utils/utils_image.py:307
Functionsingle2tensor4
(img)
utils/utils_image.py:312
Functionsingle2tensor5
(img)
utils/utils_image.py:334
Functionsingle2uint
(img)
utils/utils_image.py:259
Functionsingle2uint16
(img)
utils/utils_image.py:269
Functionsingle32tensor5
(img)
utils/utils_image.py:338
Functionsingle42tensor4
(img)
utils/utils_image.py:342
Methodsoftplusgan_loss
(input, target)
models/loss.py:111
Functionsplit_imageset
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; o
utils/utils_image.py:129
Functionstart_activation_count
A method that will be available after add_activation_counting_methods() is called on a desired net object. Activates the computation of
utils/utils_modelsummary.py:363
Functionstart_flops_count
A method that will be available after add_flops_counting_methods() is called on a desired net object. Activates the computation of mean
utils/utils_modelsummary.py:195
Functionstop_activation_count
A method that will be available after add_activation_counting_methods() is called on a desired net object. Stops computing the mean acti
utils/utils_modelsummary.py:375
Functionstop_flops_count
A method that will be available after add_flops_counting_methods() is called on a desired net object. Stops computing the mean flops con
utils/utils_modelsummary.py:207
Functionsurf
(Z, cmap='rainbow', figsize=None)
utils/utils_image.py:47
Functiontensor2img
Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel
utils/utils_image.py:347
Functiontensor2single
(img)
utils/utils_image.py:317
Functiontensor2single3
(img)
utils/utils_image.py:325
Functiontensor2uint
(img)
utils/utils_image.py:294
Methodtestx8
(self)
models/model_plain.py:280
Functionuint162single
(img)
utils/utils_image.py:264
Functionuint2single
(img)
utils/utils_image.py:254
Functionuint2tensor3
(img)
utils/utils_image.py:287
Functionuint2tensor4
(img)
utils/utils_image.py:280
Functionupfirdn2d
(input, kernel, up=1, down=1, pad=(0, 0))
models/op/upfirdn2d.py:145
Functionupfirdn2d
models/op/upfirdn2d.cpp:12
Functionupfirdn2d_native
( input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 )
models/op/upfirdn2d.py:153
Functionupsample_convtranspose
(in_channels=64, out_channels=3, kernel_size=2, stride=2, padding=0, bias=True, mode='2R', negative_slope=0.2)
models/basicblock.py:471
Functionupsample_flops_counter_hook
(module, input, output)
utils/utils_modelsummary.py:447
Functionupsample_pixelshuffle
(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=True, mode='2R', negative_slope=0.2)
models/basicblock.py:446
Functionupsample_upconv
(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=True, mode='2R', negative_slope=0.2)
models/basicblock.py:455
Methodwgan_loss
(input, target)
models/loss.py:105
Functionwrapper
(*args, **kwargs)
utils/utils_dist.py:103
Functionycbcr2rgb
same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1]
utils/utils_image.py:583
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