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import os | |
import torch | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # sets device for model and PyTorch tensors | |
# Model parameters | |
image_w = 112 | |
image_h = 112 | |
channel = 3 | |
emb_size = 512 | |
# Training parameters | |
num_workers = 1 # for data-loading; right now, only 1 works with h5py | |
grad_clip = 5. # clip gradients at an absolute value of | |
print_freq = 100 # print training/validation stats every __ batches | |
checkpoint = None # path to checkpoint, None if none | |
# Data parameters | |
num_classes = 93431 | |
num_samples = 5179510 | |
DATA_DIR = 'data' | |
# faces_ms1m_folder = 'data/faces_ms1m_112x112' | |
faces_ms1m_folder = 'data/ms1m-retinaface-t1' | |
path_imgidx = os.path.join(faces_ms1m_folder, 'train.idx') | |
path_imgrec = os.path.join(faces_ms1m_folder, 'train.rec') | |
IMG_DIR = 'data/images' | |
pickle_file = 'data/faces_ms1m_112x112.pickle' | |