From dnnlib import easydict
WebEasyDictallows to access dict values as attributes (works recursively). A Javascript-like properties dot notation for python dicts. USAGE >>> from easydict import EasyDict as … Webimport os import copy import numpy as np import torch import pickle import dnnlib import legacy def extract_conv_names (model, model_res): model_names = list (name for name,weight in model.named_parameters()) return model_names def blend_models (low, high, model_res, resolution, level, blend_width = None):
From dnnlib import easydict
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Webimport dnnlib from dnnlib import EasyDict import argparse import glob import sys import loader # Conditional set: if property is not None, then assign d [name] := prop # for every d in a set of dictionaries def cset ( dicts, name, prop ): if not isinstance ( dicts, list ): dicts = [ dicts] if prop is not None: for d in dicts: d [ name] = prop Webimport click: import os: import classifier_lib: import torch: import numpy as np: import dnnlib: from guided_diffusion.image_datasets import load_data_latent: ... opts = dnnlib.EasyDict(kwargs) savedir = os.getcwd() + opts.savedir: os.makedirs(savedir, exist_ok=True) ## Prepare fake/real data:
Webfrom .. import util from ..util import EasyDict from . import internal class SubmitTarget (Enum): """The target where the function should be run. LOCAL: Run it locally. """ LOCAL = 1 class PathType (Enum): """Determines in which format should a path be formatted. WINDOWS: Format with Windows style. LINUX: Format with Linux/Posix style. Websched = EasyDict () # Options for TrainingSchedule. grid = EasyDict ( size='4k', layout='random') #8k # Options for setup_snapshot_image_grid (). sc = dnnlib. SubmitConfig () # Options for dnnlib.submit_run (). tf_config = { 'rnd.np_random_seed': 1000 } # Options for tflib.init_tf (). train. data_dir = data_dir train. total_kimg = total_kimg
WebContribute to T-jatesada/Stylegan development by creating an account on GitHub. WebDec 7, 2024 · import os import pickle import numpy as np import PIL.Image import dnnlib import dnnlib.tflib as tflib import config def main (): # Initialize TensorFlow. …
Webr"""Fused bias and activation function. Adds bias `b` to activation tensor `x`, evaluates activation function `act`, and scales the result by `gain`. Each of the steps is optional. In most cases, the fused op is considerably more efficient than performing the same calculation. using standard TensorFlow ops.
WebEasyDict permite acessar valores dict como atributos (funciona recursivamente). Ou seja, pode ser convenientemente aplicado .para acessar o valor do dict. pip install easydict Três, use. exemplo; from easydict import EasyDict test = { 'foo': 3, 'bar': { 'x': 1, 'y': 2}} e_dict = EasyDict (test) print (e_dict. foo) # 3 print (e_dict. bar. x ... pip install gym 0.10.5WebMay 30, 2024 · from easydict import EasyDict as edict easy = edict(d = {'foo':3, 'bar':{'x':1, 'y':2}}) # 将普通的字典传入到edict () print(easy['foo']) # 这是传统的方法 print(easy.foo) # … pip install gym 0.19.0WebA tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. pip install glove pythonWebMay 1, 2024 · Try the following command in terminal : sudo pip2 install easydict. This worked for me well . pip install glob windowsWebimport copy: import dnnlib: from dnnlib import EasyDict: import os ##### #DESCRIPTION LABEL TO RESULT FOLDER AND DICTIONARIES TO STORE TRAINING AND SCHEDULING OPTIONS: desc = 'FedGIMP' train = EasyDict (run_func_name = 'training.training_loop.training_loop') sched = EasyDict submit_config … pip install graphicsWeb'linear': dnnlib.EasyDict (func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), 'relu': dnnlib.EasyDict (func=lambda x, **_: torch.nn.functional.relu (x), def_alpha=0, def_gain=np.sqrt (2), cuda_idx=2, ref='y', has_2nd_grad=False), pip install graphiteWebMar 18, 2024 · self. _results += [dnnlib. EasyDict (value = value, suffix = suffix, fmt = fmt)] def _report_progress (self, pcur, pmax, status_str = ''): if self. _progress_lo is None or self. _progress_hi is None or self. _progress_max is None: return: t = time. time if self. _progress_sec is not None and self. _progress_time is not None and t < self ... pip install gym -e