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import argparse
import torch
parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
"----------------------------- General options -----------------------------"
parser.add_argument('--expID', default='default', type=str,
help='Experiment ID')
parser.add_argument('--dataset', default='coco', type=str,
help='Dataset choice: mpii | coco')
parser.add_argument('--nThreads', default=30, type=int,
help='Number of data loading threads')
parser.add_argument('--debug', default=False, type=bool,
help='Print the debug information')
parser.add_argument('--snapshot', default=1, type=int,
help='How often to take a snapshot of the model (0 = never)')
"----------------------------- AlphaPose options -----------------------------"
parser.add_argument('--addDPG', default=False, type=bool,
help='Train with data augmentation')
"----------------------------- Model options -----------------------------"
parser.add_argument('--netType', default='hgPRM', type=str,
help='Options: hgPRM | resnext')
parser.add_argument('--loadModel', default=None, type=str,
help='Provide full path to a previously trained model')
parser.add_argument('--Continue', default=False, type=bool,
help='Pick up where an experiment left off')
parser.add_argument('--nFeats', default=256, type=int,
help='Number of features in the hourglass')
parser.add_argument('--nClasses', default=17, type=int,
help='Number of output channel')
parser.add_argument('--nStack', default=8, type=int,
help='Number of hourglasses to stack')
"----------------------------- Hyperparameter options -----------------------------"
parser.add_argument('--LR', default=2.5e-4, type=float,
help='Learning rate')
parser.add_argument('--momentum', default=0, type=float,
help='Momentum')
parser.add_argument('--weightDecay', default=0, type=float,
help='Weight decay')
parser.add_argument('--crit', default='MSE', type=str,
help='Criterion type')
parser.add_argument('--optMethod', default='rmsprop', type=str,
help='Optimization method: rmsprop | sgd | nag | adadelta')
"----------------------------- Training options -----------------------------"
parser.add_argument('--nEpochs', default=50, type=int,
help='Number of hourglasses to stack')
parser.add_argument('--epoch', default=0, type=int,
help='Current epoch')
parser.add_argument('--trainBatch', default=40, type=int,
help='Train-batch size')
parser.add_argument('--validBatch', default=20, type=int,
help='Valid-batch size')
parser.add_argument('--trainIters', default=0, type=int,
help='Total train iters')
parser.add_argument('--valIters', default=0, type=int,
help='Total valid iters')
parser.add_argument('--init', default=None, type=str,
help='Initialization')
"----------------------------- Data options -----------------------------"
parser.add_argument('--inputResH', default=384, type=int,
help='Input image height')
parser.add_argument('--inputResW', default=320, type=int,
help='Input image width')
parser.add_argument('--outputResH', default=96, type=int,
help='Output heatmap height')
parser.add_argument('--outputResW', default=80, type=int,
help='Output heatmap width')
parser.add_argument('--scale', default=0.25, type=float,
help='Degree of scale augmentation')
parser.add_argument('--rotate', default=30, type=float,
help='Degree of rotation augmentation')
parser.add_argument('--hmGauss', default=1, type=int,
help='Heatmap gaussian size')
"----------------------------- PyraNet options -----------------------------"
parser.add_argument('--baseWidth', default=9, type=int,
help='Heatmap gaussian size')
parser.add_argument('--cardinality', default=5, type=int,
help='Heatmap gaussian size')
parser.add_argument('--nResidual', default=1, type=int,
help='Number of residual modules at each location in the pyranet')
"----------------------------- Distribution options -----------------------------"
parser.add_argument('--dist', dest='dist', type=int, default=1,
help='distributed training or not')
parser.add_argument('--backend', dest='backend', type=str, default='gloo',
help='backend for distributed training')
parser.add_argument('--port', dest='port',
help='port of server')
opt = parser.parse_args()
if opt.Continue:
opt = torch.load("../exp/{}/{}/option.pkl".format(opt.dataset, opt.expID), map_location=torch.device('cpu'))
opt.Continue = True
opt.nEpochs = 50
print("--- Continue ---")
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