text stringlengths 1 93.6k |
|---|
print(f"args{args['arg_num']}")
|
in_channels = 3 if args["dataset"].lower() == "cifar" else 1
|
unet = UNetModel(
|
args['img_size'][0], args['base_channels'], channel_mults=args['channel_mults'], in_channels=in_channels
|
)
|
ema = UNetModel(
|
args['img_size'][0], args['base_channels'], channel_mults=args['channel_mults'], in_channels=in_channels
|
)
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betas = get_beta_schedule(args['T'], args['beta_schedule'])
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diff = GaussianDiffusionModel(
|
args['img_size'], betas, loss_weight=args['loss_weight'],
|
loss_type=args['loss-type'], noise=args["noise_fn"]
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)
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ema.load_state_dict(output["ema"])
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ema.to(device)
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ema.eval()
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unet.load_state_dict(output["model_state_dict"])
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unet.to(device)
|
unet.eval()
|
_, testing_dataset = dataset.init_datasets("./", args)
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testing_dataset_loader = dataset.init_dataset_loader(testing_dataset, args)
|
testing(testing_dataset_loader, diff, args, ema, unet)
|
if __name__ == '__main__':
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import dataset
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import os
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import matplotlib.animation as animation
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import numpy as np
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from GaussianDiffusion import GaussianDiffusionModel, get_beta_schedule
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from UNet import UNetModel
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from detection import load_parameters
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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main()
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# <FILESEP>
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import os
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import torch
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import numpy as np
|
import random
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import logging
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import time
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from absl import flags
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from torch.utils import tensorboard
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from torch_geometric.loader import DataLoader, DenseDataLoader
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import pickle
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from models import pgsn
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import losses
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import sampling
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from models import utils as mutils
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from models.ema import ExponentialMovingAverage
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import datasets
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from evaluation import get_stats_eval, get_nn_eval
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import sde_lib
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import visualize
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from utils import *
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FLAGS = flags.FLAGS
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def set_random_seed(config):
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seed = config.seed
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os.environ['PYTHONHASHSEED'] = str(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
|
torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
|
torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def sde_train(config, workdir):
|
"""Runs the training pipeline.
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Args:
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config: Configuration to use.
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workdir: Working directory for checkpoints and TF summaries.
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If this contains checkpoint training will be resumed from the latest checkpoint.
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"""
|
# Create directories for experimental logs
|
sample_dir = os.path.join(workdir, "samples")
|
if not os.path.exists(sample_dir):
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os.makedirs(sample_dir)
|
tb_dir = os.path.join(workdir, "tensorboard")
|
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