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params.py
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params.py
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import os
class Parameters():
def __init__(self):
self.n_processors = 8
# Path
self.data_dir = '/nfs/nas12.ethz.ch/fs1201/infk_ivc_students/cvg-students/chsiao/KITTI/'
self.image_dir = self.data_dir + '/images/'
self.pose_dir = self.data_dir + '/pose_GT/'
self.train_video = ['00', '01', '02', '05', '08', '09']
self.valid_video = ['04', '06', '07', '10']
self.partition = None # partition videos in 'train_video' to train / valid dataset #0.8
# Data Preprocessing
self.resize_mode = 'rescale' # choice: 'crop' 'rescale' None
self.img_w = 608 # original size is about 1226
self.img_h = 184 # original size is about 370
self.img_means = (0.19007764876619865, 0.15170388157131237, 0.10659445665650864)
self.img_stds = (0.2610784009469139, 0.25729316928935814, 0.25163823815039915)
self.minus_point_5 = True
self.seq_len = (5, 7)
self.sample_times = 3
# Data info path
self.train_data_info_path = 'datainfo/train_df_t{}_v{}_p{}_seq{}x{}_sample{}.pickle'.format(''.join(self.train_video), ''.join(self.valid_video), self.partition, self.seq_len[0], self.seq_len[1], self.sample_times)
self.valid_data_info_path = 'datainfo/valid_df_t{}_v{}_p{}_seq{}x{}_sample{}.pickle'.format(''.join(self.train_video), ''.join(self.valid_video), self.partition, self.seq_len[0], self.seq_len[1], self.sample_times)
# Model
self.rnn_hidden_size = 1000
self.conv_dropout = (0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5)
self.rnn_dropout_out = 0.5
self.rnn_dropout_between = 0 # 0: no dropout
self.clip = None
self.batch_norm = True
# Training
self.epochs = 250
self.batch_size = 8
self.pin_mem = True
self.optim = {'opt': 'Adagrad', 'lr': 0.0005}
# Choice:
# {'opt': 'Adagrad', 'lr': 0.001}
# {'opt': 'Adam'}
# {'opt': 'Cosine', 'T': 100 , 'lr': 0.001}
# Pretrain, Resume training
self.pretrained_flownet = None
# Choice:
# None
# './pretrained/flownets_bn_EPE2.459.pth.tar'
# './pretrained/flownets_EPE1.951.pth.tar'
self.resume = True # resume training
self.resume_t_or_v = '.train'
self.load_model_path = 'models/t{}_v{}_im{}x{}_s{}x{}_b{}_rnn{}_{}.model{}'.format(''.join(self.train_video), ''.join(self.valid_video), self.img_h, self.img_w, self.seq_len[0], self.seq_len[1], self.batch_size, self.rnn_hidden_size, '_'.join([k+str(v) for k, v in self.optim.items()]), self.resume_t_or_v)
self.load_optimizer_path = 'models/t{}_v{}_im{}x{}_s{}x{}_b{}_rnn{}_{}.optimizer{}'.format(''.join(self.train_video), ''.join(self.valid_video), self.img_h, self.img_w, self.seq_len[0], self.seq_len[1], self.batch_size, self.rnn_hidden_size, '_'.join([k+str(v) for k, v in self.optim.items()]), self.resume_t_or_v)
self.record_path = 'records/t{}_v{}_im{}x{}_s{}x{}_b{}_rnn{}_{}.txt'.format(''.join(self.train_video), ''.join(self.valid_video), self.img_h, self.img_w, self.seq_len[0], self.seq_len[1], self.batch_size, self.rnn_hidden_size, '_'.join([k+str(v) for k, v in self.optim.items()]))
self.save_model_path = 'models/t{}_v{}_im{}x{}_s{}x{}_b{}_rnn{}_{}.model'.format(''.join(self.train_video), ''.join(self.valid_video), self.img_h, self.img_w, self.seq_len[0], self.seq_len[1], self.batch_size, self.rnn_hidden_size, '_'.join([k+str(v) for k, v in self.optim.items()]))
self.save_optimzer_path = 'models/t{}_v{}_im{}x{}_s{}x{}_b{}_rnn{}_{}.optimizer'.format(''.join(self.train_video), ''.join(self.valid_video), self.img_h, self.img_w, self.seq_len[0], self.seq_len[1], self.batch_size, self.rnn_hidden_size, '_'.join([k+str(v) for k, v in self.optim.items()]))
if not os.path.isdir(os.path.dirname(self.record_path)):
os.makedirs(os.path.dirname(self.record_path))
if not os.path.isdir(os.path.dirname(self.save_model_path)):
os.makedirs(os.path.dirname(self.save_model_path))
if not os.path.isdir(os.path.dirname(self.save_optimzer_path)):
os.makedirs(os.path.dirname(self.save_optimzer_path))
if not os.path.isdir(os.path.dirname(self.train_data_info_path)):
os.makedirs(os.path.dirname(self.train_data_info_path))
par = Parameters()