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Functions193 in github.com/NVlabs/edm

Method__init__
(self, beta_d=19.9, beta_min=0.1, epsilon_t=1e-5)
training/loss.py:21
Method__init__
(self, sigma_min=0.02, sigma_max=100)
training/loss.py:47
Method__init__
(self, P_mean=-1.2, P_std=1.2, sigma_data=0.5)
training/loss.py:67
Method__init__
(self, path, # Path to directory or zip. resolution = None, # Ensure sp
training/dataset.py:167
Method__init__
(self, in_channels, out_channels, kernel, bias=True, up=False, down=False, resample_filter=[1,
training/networks.py:50
Method__init__
(self, num_channels, num_groups=32, min_channels_per_group=4, eps=1e-5)
training/networks.py:97
Method__init__
(self, in_channels, out_channels, emb_channels, up=False, down=False, attention=False, num_hea
training/networks.py:135
Method__init__
(self, num_channels, max_positions=10000, endpoint=False)
training/networks.py:194
Method__init__
(self, num_channels, scale=16)
training/networks.py:213
Method__init__
(self, img_resolution, # Image resolution at input/output. in_channels,
training/networks.py:230
Method__init__
(self, img_resolution, # Image resolution at input/output. in_channels,
training/networks.py:373
Method__init__
(self, img_resolution, # Image resolution. img_channels, # N
training/networks.py:470
Method__init__
(self, img_resolution, # Image resolution. img_channels, # N
training/networks.py:529
Method__init__
(self, img_resolution, # Image resolution. img_channels,
training/networks.py:573
Method__init__
(self, img_resolution, # Image resolution. img_channels,
training/networks.py:633
Method__init__
(self, p=1, xflip=0, yflip=0, rotate_int=0, translate_int=0, translate_int_max=0.125, scale=0,
training/augment.py:116
Method__init__
(self, file_name: Optional[str] = None, file_mode: str = "w", should_flush: bool = True)
dnnlib/util.py:58
Method__iter__
(self)
torch_utils/misc.py:124
Method__len__
(self)
training/dataset.py:87
Method__setattr__
(self, name: str, value: Any)
dnnlib/util.py:48
Method_load_raw_labels
(self)
training/dataset.py:236
Function_reconstruct_persistent_obj
r"""Hook that is called internally by the `pickle` module to unpickle a persistent object.
torch_utils/persistence.py:185
Functionablation_sampler
( net, latents, class_labels=None, randn_like=torch.randn_like, num_steps=18, sigma_min=None, sigma_ma
generate.py:66
Functionask_yes_no
Ask the user the question until the user inputs a valid answer.
dnnlib/util.py:166
Functionassert_shape
(tensor, ref_shape)
torch_utils/misc.py:81
Functioncalc
Calculate FID for a given set of images.
fid.py:120
Functioncenter_crop
(width, height, img)
dataset_tool.py:219
Functioncenter_crop_wide
(width, height, img)
dataset_tool.py:228
Functioncheck_ddp_consistency
(module, ignore_regex=None)
torch_utils/misc.py:180
Methodclose
(self)
training/dataset.py:214
Functionconstant
(value, shape=None, dtype=None, device=None, memory_format=None)
torch_utils/misc.py:21
Functionconstruct_class_by_name
Finds the python class with the given name and constructs it with the given arguments.
dnnlib/util.py:300
Functioncopy_files_and_create_dirs
Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories.
dnnlib/util.py:361
Functioncopy_params_and_buffers
(src_module, dst_module, require_all=False)
torch_utils/misc.py:155
Functionddp_sync
(module, sync)
torch_utils/misc.py:169
Functiondecorator
(*args, **kwargs)
torch_utils/misc.py:100
Functionedm_sampler
( net, latents, class_labels=None, randn_like=torch.randn_like, num_steps=18, sigma_min=0.002, sigma_m
generate.py:25
Functionfolder_write_bytes
(fname: str, data: Union[bytes, str])
dataset_tool.py:300
Functionformat_time
Convert the seconds to human readable string with days, hours, minutes and seconds.
dnnlib/util.py:138
Functionformat_time_brief
Convert the seconds to human readable string with days, hours, minutes and seconds.
dnnlib/util.py:152
Methodforward
(self, x)
training/networks.py:39
Methodforward
(self, x)
training/networks.py:68
Methodforward
(self, x)
training/networks.py:104
Methodforward
(ctx, q, k)
training/networks.py:115
Methodforward
(self, x, emb)
training/networks.py:166
Methodforward
(self, x)
training/networks.py:200
Methodforward
(self, x)
training/networks.py:217
Methodforward
(self, x, noise_labels, class_labels, augment_labels=None)
training/networks.py:320
Methodforward
(self, x, noise_labels, class_labels, augment_labels=None)
training/networks.py:435
Methodforward
(self, x, sigma, class_labels=None, force_fp32=False, **model_kwargs)
training/networks.py:495
Methodforward
(self, x, sigma, class_labels=None, force_fp32=False, **model_kwargs)
training/networks.py:548
Methodforward
(self, x, sigma, class_labels=None, force_fp32=False, **model_kwargs)
training/networks.py:601
Methodforward
(self, x, sigma, class_labels=None, force_fp32=False, **model_kwargs)
training/networks.py:654
Methodget_details
(self, idx)
training/dataset.py:113
Functionget_dtype_and_ctype
Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.
dnnlib/util.py:200
Functionget_module_dir_by_obj_name
Get the directory path of the module containing the given object name.
dnnlib/util.py:305
Functionget_top_level_function_name
Return the fully-qualified name of a top-level function.
dnnlib/util.py:316
Methodhas_labels
(self)
training/dataset.py:155
Methodhas_onehot_labels
(self)
training/dataset.py:159
Methodimage_shape
(self)
training/dataset.py:125
Functionimport_hook
r"""Register an import hook that is called whenever a persistent object is being unpickled. A typical use case is to patch the pickled source
torch_utils/persistence.py:153
Functioninit
()
torch_utils/distributed.py:14
Methodinit_args
(self)
torch_utils/persistence.py:114
Methodinit_kwargs
(self)
torch_utils/persistence.py:119
Functioninit_multiprocessing
r"""Initializes `torch_utils.training_stats` for collecting statistics across multiple processes. This function must be called after `tor
torch_utils/training_stats.py:33
Functionis_pickleable
(obj: Any)
dnnlib/util.py:223
Methodlabel_dim
(self)
training/dataset.py:150
Methodlabel_shape
(self)
training/dataset.py:140
Functionlist_dir_recursively_with_ignore
List all files recursively in a given directory while ignoring given file and directory names. Returns list of tuples containing both absolute and
dnnlib/util.py:328
Methodname
(self)
training/dataset.py:121
Methodnum_channels
(self)
training/dataset.py:129
Functionopen_url
Download the given URL and return a binary-mode file object to access the data.
dnnlib/util.py:395
Functionparams_and_buffers
(module)
torch_utils/misc.py:146
Functionparse_int_list
(s)
train.py:27
Functionparse_int_list
(s)
generate.py:202
Functionparse_tuple
(s: str)
dataset_tool.py:31
Functionpost_hook
(mod, _inputs, outputs)
torch_utils/misc.py:206
Functionpre_hook
(_mod, _inputs)
torch_utils/misc.py:204
Functionprint0
(*args, **kwargs)
torch_utils/distributed.py:55
Functionprint_module_summary
(module, inputs, max_nesting=3, skip_redundant=True)
torch_utils/misc.py:196
Functionprofiled_function
(fn)
torch_utils/misc.py:99
Functionref
Calculate dataset reference statistics needed by 'calc'.
fid.py:145
Functionreport0
r"""Broadcasts the given set of scalars by the first process (`rank = 0`), but ignores any scalars provided by the other processes. See `repor
torch_utils/training_stats.py:102
Methodresolution
(self)
training/dataset.py:134
Methodround_sigma
(self, sigma)
training/networks.py:564
Methodround_sigma
(self, sigma)
training/networks.py:670
Functionscale
(width, height, img)
dataset_tool.py:208
Functionset_cache_dir
(path: str)
dnnlib/util.py:119
Functionshould_stop
()
torch_utils/distributed.py:45
Functiontraining_loop
( run_dir = '.', # Output directory. dataset_kwargs = {}, # Options for tr
training/training_loop.py:25
Functiontuple_product
Calculate the product of the tuple elements.
dnnlib/util.py:176
Functionupdate_progress
(cur, total)
torch_utils/distributed.py:50
Functionzip_write_bytes
(fname: str, data: Union[bytes, str])
dataset_tool.py:285
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