Datasets:
jblitzar
/
github-python-corpus

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(ite_step, ite_epoch+1, num_epoch, sum_improved_PSNR/num_patch_count))
file_object.flush()
if __name__ == '__main__':
### Settings
CHANNEL = 1 # use only Y
ratio_small = 0.01
lr_ori = 1e-4
epoch_step1 = 20
epoch_step2 = 60
parser = argparse.ArgumentParser()
parser.add_argument('-hf', '--height', type=int, help="HEIGHT of frame")
parser.add_argument('-wf', '--width', type=int, help="WIDTH of frame")
parser.add_argument('-bs', '--batch_size', type=int)
parser.add_argument('-gpu', '--gpu', type=str, help="GPU")
parser.add_argument('-qp', '--qp', type=str, help="QP")
args = parser.parse_args()
BATCH_SIZE = args.batch_size
QP = args.qp
WIDTH = args.width
HEIGHT = args.height
dir_stack = "/home/x/SCI_1/MFQEv2.0/Database/Training_stack/QP" + QP
dir_model = "./model_QP" + QP
record_FileName = "./record_train_QP" + QP + ".txt"
file_object = open(record_FileName, 'w')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # only show error and warning
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
config = tf.ConfigProto(allow_soft_placement = True) # if GPU is not usable, then turn to CPU automatically
main_train()
print("##### Training completes! #####")
file_object.write("##### Training completes! #####\n")
file_object.close()
# <FILESEP>
import argparse
import pycls.core.config as config
import pycls.core.builders as builders
from pycls.datasets.transforms import create_test_transform
import os
import numpy as np
from tqdm import tqdm
from collections import defaultdict
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR100
from torchvision.transforms import Compose
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='configs/resnet/r-56_c100.yaml')
parser.add_argument('--ckpt', type=str, default='work_dirs/r-56_c100/model.pyth')
args = parser.parse_args()
save_dir = 'temp'
os.makedirs(save_dir, exist_ok=True)
config.load_cfg(args.cfg)
transforms = create_test_transform()
transform = Compose(transforms)
dataset = CIFAR100(root='data', train=True, transform=transform, download=True)
loader = DataLoader(dataset, batch_size=200)
model = builders.build_model()
model.load_state_dict(torch.load(args.ckpt, map_location='cpu')['model_state'])
model.cuda()
feat_dict = defaultdict(list)
for img, _ in tqdm(loader):
img = img.cuda()
with torch.no_grad():
model(img)
for i, feat in enumerate(model.features):
N, _, H, W = feat.shape
feat = feat.cpu()
feat_dict[f'layer_{i}'].append(feat)
for k in feat_dict:
feat_dict[k] = torch.cat(feat_dict[k], dim=0).numpy()
cfg_name = os.path.splitext(os.path.basename(args.cfg))[0]
np.savez(os.path.join(save_dir, f'{cfg_name}.npz'), **feat_dict)
if __name__ == '__main__':