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Functions470 in github.com/Lizhe1228/MambaDFuse

⨍ Functions 470◇ Types & classes 81
↓ 34 callersFunctionconv
(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True, mode='CBR', negative_slope=0.
models/basicblock.py:61
↓ 13 callersMethod__init__
(self, channel=64, reduction=4, bias=True)
models/basicblock.py:272
↓ 11 callersMethod__init__
(self, dim)
models/network.py:163
↓ 8 callersMethod__init__
(self)
models/loss_gt.py:28
↓ 8 callersMethod__init__
(self, n_feat, out_channel)
models/refine.py:81
↓ 7 callersMethodsave
(self, label)
models/model_base.py:29
↓ 5 callersMethod__init__
(self)
models/loss_med.py:30
↓ 5 callersMethod__init__
(self)
models/loss_vif.py:31
↓ 5 callersMethodget_bare_model
Get bare model, especially under wrapping with DistributedDataParallel or DataParallel.
models/model_base.py:89
↓ 5 callersFunctionis_supported_instance
(module)
utils/utils_modelsummary.py:261
↓ 5 callersMethodload
(self)
models/model_base.py:26
↓ 5 callersFunctionssim
(img1, img2, window_size=11, size_average=True)
models/loss_ssim.py:90
↓ 4 callersMethod__init__
Total variation loss https://github.com/jxgu1016/Total_Variation_Loss.pytorch Args: tv_loss_weight (int):
models/loss.py:137
↓ 4 callersFunctioncalculate_weights_indices
(in_length, out_length, scale, kernel, kernel_width, antialiasing)
utils/utils_image.py:820
↓ 4 callersFunctioncreate_window
(window_size, channel)
models/loss_ssim.py:20
↓ 4 callersFunctionis_supported_instance_for_activation
(module)
utils/utils_modelsummary.py:421
↓ 4 callersMethodstep
(self, hidden_states, conv_state, ssm_state)
mamba_simple.py:447
↓ 3 callersFunctionanisotropic_Gaussian
generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1
utils/utils_sisr.py:35
↓ 3 callersFunctionbicubic_degradation
Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image
utils/utils_sisr.py:223
↓ 3 callersFunctionget_world_size
()
utils/utils_dist.py:90
↓ 3 callersFunctionsequential
Advanced nn.Sequential. Args: nn.Sequential, nn.Module Returns: nn.Sequential
models/basicblock.py:15
↓ 3 callersFunctionssim
(img1, img2)
utils/utils_image.py:700
↓ 2 callersFunctionBlockMM
myfun = @(block_struct) reshape(block_struct.data,m,1); x1 = blockproc(x1,[nr nc],myfun); x1 = reshape(x1,m,Nb); x1 = sum(x1,2);
utils/utils_sisr.py:624
↓ 2 callersMethod__init__
( self, d_model, d_state=16, d_conv=4, expand=2, dt_rank="auto
mamba_simple.py:35
↓ 2 callersFunction_get_paths_from_images
(path)
utils/utils_image.py:78
↓ 2 callersFunction_ssim
(img1, img2, window, window_size, channel, size_average=True)
models/loss_ssim.py:27
↓ 2 callersFunctionadd_flops_counting_methods
(net_main_module)
utils/utils_modelsummary.py:165
↓ 2 callersMethodbackward
(ctx, grad_output)
models/op/upfirdn2d.py:127
↓ 2 callersFunctionblockproc
(im, blocksize, fun)
utils/utils_sisr.py:600
↓ 2 callersFunctioncmul
complex multiplication t1: NxCxHxWx2 output: NxCxHxWx2
utils/utils_sisr.py:387
↓ 2 callersFunctioncubic
(x)
utils/utils_image.py:812
↓ 2 callersFunctiondefine_Dataset
(dataset_opt)
data/select_dataset.py:12
↓ 2 callersFunctiondefine_G
(opt)
models/select_network.py:16
↓ 2 callersFunctiondescribe_model
(model)
utils/utils_model.py:278
↓ 2 callersMethoddescribe_network
(self, network)
models/model_base.py:114
↓ 2 callersFunctiondescribe_params
(model)
utils/utils_model.py:291
↓ 2 callersMethoddescribe_params
(self, network)
models/model_base.py:125
↓ 2 callersMethodfeed_data
(self, data)
models/model_base.py:48
↓ 2 callersFunctionflops_to_string
(flops, units='GMac', precision=2)
utils/utils_modelsummary.py:92
↓ 2 callersFunctionget_dist_info
()
utils/utils_dist.py:66
↓ 2 callersFunctionimfilter
x: image, NxcxHxW k: kernel, cx1xhxw
utils/utils_sisr.py:539
↓ 2 callersFunctionimfilter_np
x: image, NxcxHxW k: kernel, cx1xhxw
utils/utils_sisr.py:764
↓ 2 callersFunctionimread_uint
(path, n_channels=1)
utils/utils_image.py:190
↓ 2 callersFunctioninit_weights
# Kai Zhang, https://github.com/cszn/KAIR # # Args: # init_type: # default, none: pass init_weights # normal; n
models/select_network.py:146
↓ 2 callersMethodload_network
(self, load_path, network, strict=True, param_key='params')
models/model_base.py:157
↓ 2 callersFunctionmerge_bn
Kai Zhang, 11/Jan/2019. merge all 'Conv+BN' (or 'TConv+BN') into 'Conv' (or 'TConv') based on https://github.com/pytorch/pytorch/pull/901
utils/utils_bnorm.py:32
↓ 2 callersFunctionmkdir
(path)
utils/utils_image.py:158
↓ 2 callersMethodnetG_forward
(self, phase='test')
models/model_plain.py:175
↓ 2 callersFunctionprintLayer
(layer, layer_name)
utils/utils_receptivefield.py:39
↓ 2 callersFunctionprint_model_with_flops
(model, units='GMac', precision=3)
utils/utils_modelsummary.py:122
↓ 2 callersMethodsave_network
(self, save_dir, network, network_label, iter_label)
models/model_base.py:145
↓ 2 callersFunctionsizeof_fmt
Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Formated f
utils/utils_googledownload.py:12
↓ 2 callersFunctionsplits
a: tensor NxCxWxHx2 sf: scale factor out: tensor NxCx(W/sf)x(H/sf)x2x(sf^2)
utils/utils_sisr.py:353
↓ 2 callersMethodtensor_size
(t)
models/loss.py:158
↓ 2 callersFunctiontest_mode
# --------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -----------------------------
utils/utils_model.py:49
↓ 2 callersFunctiontest_pad
(model, L, modulo=16, sf=1)
utils/utils_model.py:108
↓ 2 callersFunctiontest_split_fn
Args: model: trained model L: input Low-quality image refield: effective receptive filed of the network, 32 is enough
utils/utils_model.py:125
↓ 2 callersFunctiontidy_sequential
Kai Zhang, 11/Jan/2019.
utils/utils_bnorm.py:84
↓ 2 callersMethodupdate_E
(self, decay=0.999)
models/model_base.py:188
↓ 2 callersFunctionweights2tensor
Modified version of https://github.com/albanie/pytorch-mcn Adjust memory layout and load weights as torch tensor Args: x (ndaray): a n
utils/utils_matconvnet.py:20
↓ 2 callersMethodwrite
(self, message)
utils/utils_logger.py:61
↓ 1 callersMethod__getitem__
(self, index)
data/dataloder.py:28
↓ 1 callersMethod__init__
(self, eps=1e-8)
utils/utils_color.py:10
↓ 1 callersMethod__len__
(self)
data/dataloder.py:55
↓ 1 callersMethod__repr__
(self)
models/basicblock.py:184
↓ 1 callersFunction_augment
(img)
utils/utils_image.py:505
↓ 1 callersFunction_blocking_effect_factor
(im)
utils/utils_image.py:723
↓ 1 callersFunction_check_keys
checks if entries in dictionary are mat-objects. If yes todict is called to change them to nested dictionaries
utils/utils_mat.py:17
↓ 1 callersMethod_get_states_from_cache
(self, inference_params, batch_size, initialize_states=False)
mamba_simple.py:507
↓ 1 callersMethod_get_states_from_cache
(self, inference_params, batch_size, initialize_states=False)
mamba_simple.py:983
↓ 1 callersFunction_init_dist_pytorch
(backend, **kwargs)
utils/utils_dist.py:24
↓ 1 callersFunction_init_dist_slurm
Initialize slurm distributed training environment. If argument ``port`` is not specified, then the master port will be system environment vari
utils/utils_dist.py:31
↓ 1 callersFunction_todict
A recursive function which constructs from matobjects nested dictionaries
utils/utils_mat.py:27
↓ 1 callersFunctionadd_activation_counting_methods
(net_main_module)
utils/utils_modelsummary.py:332
↓ 1 callersMethodallocate_inference_cache
(self, batch_size, max_seqlen, dtype=None, **kwargs)
mamba_simple.py:494
↓ 1 callersFunctionaugment_img
Kai Zhang (github: https://github.com/cszn)
utils/utils_image.py:385
↓ 1 callersFunctionbgr2ycbcr
bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]
utils/utils_image.py:604
↓ 1 callersFunctioncdiv
(x, y)
utils/utils_sisr.py:372
↓ 1 callersFunctionclassical_degradation
blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: down
utils/utils_sisr.py:280
↓ 1 callersFunctioncsum
(x, y)
utils/utils_sisr.py:379
↓ 1 callersMethodcurrent_learning_rate
(self)
models/model_base.py:64
↓ 1 callersMethodcurrent_log
(self)
models/model_plain.py:289
↓ 1 callersMethodcurrent_visuals
(self)
models/model_base.py:54
↓ 1 callersFunctiondefine_Model
(opt)
models/select_model.py:9
↓ 1 callersMethoddefine_loss
(self)
models/model_plain.py:99
↓ 1 callersFunctiondefine_model
(args)
test_MambaDFuse.py:86
↓ 1 callersMethoddefine_optimizer
(self)
models/model_base.py:35
↓ 1 callersMethoddefine_optimizer
(self)
models/model_plain.py:139
↓ 1 callersMethoddefine_scheduler
(self)
models/model_base.py:38
↓ 1 callersMethoddefine_scheduler
(self)
models/model_plain.py:151
↓ 1 callersFunctiondict_to_nonedict
(opt)
utils/utils_mat.py:41
↓ 1 callersFunctiondim_pad_circular
(input, padding, dimension)
utils/utils_sisr.py:530
↓ 1 callersFunctiondownload_file_from_google_drive
Download files from google drive. Ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 Ar
utils/utils_googledownload.py:27
↓ 1 callersFunctiondownsample
(x, sf=3, center=False)
utils/utils_sisr.py:498
↓ 1 callersFunctiondownsample_np
(x, sf=3, center=False)
utils/utils_sisr.py:759
↓ 1 callersFunctiondpsr_degradation
bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsam
utils/utils_sisr.py:257
↓ 1 callersMethoddual_level_feature_extraction
(self, x, y)
models/network.py:407
↓ 1 callersMethoddual_phase_feature_fusion
(self, x, x_residual, y, y_residual, h, w)
models/network.py:434
↓ 1 callersFunctionfun_mul
(a, b)
utils/utils_sisr.py:620
↓ 1 callersMethodfused_img_recon
(self, x)
models/network.py:466
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