def main(args):
# load train data into ram
# data_path = '/mntlong/lanl_comp/data/'
file_dir = os.path.dirname(__file__)
data_path = os.path.abspath(os.path.join(file_dir, os.path.pardir, 'data'))
train_info_path = os.path.join(data_path, 'train_info.csv')
train_data_path = os.path.join(data_path, 'train_compressed.npz')
train_info = pd.read_csv(train_info_path, index_col='Unnamed: 0')
train_info['exp_len'] = train_info['indx_end'] - train_info['indx_start']
train_signal = np.load(train_data_path)['signal']
train_quaketime = np.load(train_data_path)['quake_time']
# В валидацию берем 2 последних волны (части эксперимента)
val_start_idx = train_info.iloc[-2, :]['indx_start']
val_signal = train_signal[val_start_idx:]
val_quaketime = train_quaketime[val_start_idx:]
train_signal = train_signal[:val_start_idx]
train_quaketime = train_quaketime[:val_start_idx]
# training params
large_ws = 1500000
overlap_size = int(large_ws * 0.5)
small_ws = 150000
num_bins = 17
cpc_meta_model = models.CPCv1(out_size=num_bins - 1)
# logs_path = '/mntlong/scripts/logs/'
logs_path = os.path.abspath(os.path.join(file_dir, os.path.pardir, 'logs'))
current_datetime = datetime.today().strftime('%b-%d_%H-%M-%S')
log_writer_path = os.path.join(logs_path, 'runs',
current_datetime + '_' + args.model_name)
train_dataset = data.SignalCPCDataset(
train_signal,
train_quaketime,
num_bins=num_bins,
idxs_wave_end=train_info['indx_end'].values,
large_ws=large_ws,
overlap_size=overlap_size,
small_ws=small_ws)
val_dataset = data.SignalCPCDataset(
val_signal,
val_quaketime,
num_bins=num_bins,
idxs_wave_end=train_info['indx_end'].values,
large_ws=large_ws,
overlap_size=overlap_size,
small_ws=small_ws)
print('x_t size:', train_dataset[0][0].size())
train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
val_loader = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=5,
pin_memory=True)
if args.find_lr:
from lr_finder import LRFinder
optimizer = optim.Adam(cpc_meta_model.parameters(), lr=1e-6)
lr_find = LRFinder(cpc_meta_model,
optimizer,
criterion=None,
is_cpc=True,
device='cuda')
lr_find.range_test(train_loader,
end_lr=2,
num_iter=75,
step_mode='exp')
best_lr = lr_find.get_best_lr()
lr_find.plot()
lr_find.reset()
print('best lr found: {:.2e}'.format(best_lr))
else:
best_lr = 3e-4
# sys.exit()
# model_path = os.path.join(logs_path, 'cpc_no_target_head_cont_last_state.pth')
# cpc_meta_model.load_state_dict(torch.load(model_path)['model_state_dict'])
# cpc_meta_model.to(torch.device('cuda'))
optimizer = optim.Adam(cpc_meta_model.parameters(), lr=best_lr)
# optimizer.load_state_dict(torch.load(model_path)['optimizer_state_dict'])
lr_sched = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.5,
patience=3,
threshold=0.005)
log_writer = SummaryWriter(log_writer_path)
utils.train_cpc_model(cpc_meta_model=cpc_meta_model,
optimizer=optimizer,
num_bins=num_bins,
lr_scheduler=lr_sched,
train_loader=train_loader,
val_loader=val_loader,
num_epochs=args.num_epochs,
model_name=args.model_name,
logs_path=logs_path,
log_writer=log_writer)
# else tensor.size(self.ch_dim) # for i in range(2)] # pad = torch.empty(padding_size, dtype=tensor.dtype).fill_(self.fill_value) # tensor = torch.cat((tensor, pad), dim=self.len_dim) # import os # os.environ['MKL_NUM_THREADS'] = '1' # import numpy as np # import utils # # # def main(): # inpt = np.random.randint(-10, 10, 150000) # # for i in range(1000): # out = utils.spectrogram(inpt, 256) # # # if __name__ == '__main__': # main() from lr_finder import LRFinder criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=1e-5) lr_finder = LRFinder(model, optimizer, criterion, device="cuda") lr_finder.range_test(train_loader, end_lr=1, num_iter=50, step_mode="exp") lr_finder.get_best_lr() # lr_finder.plot() # lr_finder.history
def main(args):
# load train data into ram
# data_path = '/mntlong/lanl_comp/data/'
file_dir = os.path.dirname(__file__)
data_path = os.path.abspath(os.path.join(file_dir, os.path.pardir, 'data'))
train_info_path = os.path.join(data_path, 'train_info.csv')
train_data_path = os.path.join(data_path, 'train_compressed.npz')
train_info = pd.read_csv(train_info_path, index_col='Unnamed: 0')
train_info['exp_len'] = train_info['indx_end'] - train_info['indx_start']
train_signal = np.load(train_data_path)['signal']
train_quaketime = np.load(train_data_path)['quake_time']
# В валидацию берем 2 последних волны (части эксперимента)
val_start_idx = train_info.iloc[-2, :]['indx_start']
val_signal = train_signal[val_start_idx:]
val_quaketime = train_quaketime[val_start_idx:]
train_signal = train_signal[:val_start_idx]
train_quaketime = train_quaketime[:val_start_idx]
# training params
window_size = 150000
overlap_size = int(window_size * 0.5)
num_bins = 17
model = models.BaselineNetRawSignalCnnRnnV1(out_size=num_bins-1)
loss_fn = nn.CrossEntropyLoss() # L1Loss() SmoothL1Loss() MSELoss()
# logs_path = '/mntlong/scripts/logs/'
logs_path = os.path.abspath(os.path.join(file_dir, os.path.pardir, 'logs'))
current_datetime = datetime.today().strftime('%b-%d_%H-%M-%S')
log_writer_path = os.path.join(logs_path, 'runs',
current_datetime + '_' + args.model_name)
train_dataset = data.SignalDataset(train_signal, train_quaketime,
num_bins=num_bins,
idxs_wave_end=train_info['indx_end'].values,
window_size=window_size,
overlap_size=overlap_size)
val_dataset = data.SignalDataset(val_signal, val_quaketime,
num_bins=num_bins,
idxs_wave_end=train_info['indx_end'].values,
window_size=window_size,
overlap_size=overlap_size)
print('wave size:', train_dataset[0][0].size())
train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=True)
val_loader = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=5,
pin_memory=True)
if args.find_lr:
from lr_finder import LRFinder
optimizer = optim.Adam(model.parameters(), lr=1e-6)
lr_find = LRFinder(model, optimizer, loss_fn, device='cuda')
lr_find.range_test(train_loader, end_lr=1, num_iter=50, step_mode='exp')
best_lr = lr_find.get_best_lr()
lr_find.plot()
lr_find.reset()
print('best lr found: {:.2e}'.format(best_lr))
else:
best_lr = 3e-4
optimizer = optim.Adam(model.parameters(), lr=best_lr) # weight_decay=0.1
lr_sched = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.5,
patience=3,
threshold=0.005)
log_writer = SummaryWriter(log_writer_path)
utils.train_clf_model(model=model, optimizer=optimizer, lr_scheduler=lr_sched,
train_loader=train_loader, val_loader=val_loader,
num_epochs=args.num_epochs, model_name=args.model_name,
logs_path=logs_path, log_writer=log_writer,
loss_fn=loss_fn, num_bins=num_bins)
