File size: 6,674 Bytes
df9f13e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
"""
The main training script for training on synthetic data
"""
import torch
import torch.utils.data
import torch.nn as nn
import argparse
import json
import os
import multiprocessing
import time
import numpy as np
import src.utils as utils
from src.training.tain_val import train_epoch, test_epoch
import shutil
import sys
import wandb
VAL_SEED = 0
CURRENT_EPOCH = 0
def seed_from_epoch(seed):
global CURRENT_EPOCH
utils.seed_all(seed + CURRENT_EPOCH)
def print_metrics(metrics: list):
input_sisdr = np.array([x['input_si_sdr'] for x in metrics])
sisdr = np.array([x['si_sdr'] for x in metrics])
print("Average Input SI-SDR: {:03f}, Average Output SI-SDR: {:03f}, Average SI-SDRi: {:03f}".format(np.mean(input_sisdr), np.mean(sisdr), np.mean(sisdr - input_sisdr)))
def train(args: argparse.Namespace):
"""
Resolve the network to be trained
"""
# Fix random seeds
utils.seed_all(args.seed)
os.environ["CUBLAS_WORKSPACE_CONFIG"]=":4096:8"
# Turn on deterministic algorithms if specified (Note: slower training).
if torch.cuda.is_available():
if args.use_nondeterministic_cudnn:
torch.backends.cudnn.deterministic = False
else:
torch.backends.cudnn.deterministic = True
# Load experiment description
with open(args.config, 'rb') as f:
params = json.load(f)
# Initialize datasets
data_train = utils.import_attr(params['train_dataset'])(**params['train_data_args'], split='train')
data_val = utils.import_attr(params['val_dataset'])(**params['val_data_args'], split='val')
# Set up the device and workers
use_cuda = True
device = torch.device('cuda' if use_cuda else 'cpu')
print("Using device {}".format('cuda' if use_cuda else 'cpu'))
# Set multiprocessing params
num_workers = min(multiprocessing.cpu_count(), params['num_workers'])
kwargs = {
'num_workers': num_workers,
'worker_init_fn': lambda x: seed_from_epoch(args.seed),
'pin_memory': False
} if use_cuda else {}
# Set up data loaders
train_loader = torch.utils.data.DataLoader(data_train,
batch_size=params['batch_size'],
shuffle=True,
**kwargs)
kwargs['worker_init_fn'] = lambda x: utils.seed_all(VAL_SEED)
test_loader = torch.utils.data.DataLoader(data_val,
batch_size=params['eval_batch_size'],
**kwargs)
# Initialize HL module
hl_module = utils.import_attr(params['pl_module'])(**params['pl_module_args'])
hl_module.model.to(device)
# Get run name from run dir
run_name = os.path.basename(args.run_dir.rstrip('/'))
checkpoints_dir = os.path.join(args.run_dir, 'checkpoints')
# Set up checkpoints
if not os.path.exists(checkpoints_dir):
os.makedirs(checkpoints_dir)
# Copy json
shutil.copyfile(args.config, os.path.join(args.run_dir, 'config.json'))
# Check if a model state path exists for this model, if it does, load it
best_path = os.path.join(checkpoints_dir, 'best.pt')
state_path = os.path.join(checkpoints_dir, 'last.pt')
if args.best and os.path.exists(best_path):
print("load best state path .....")
hl_module.load_state(best_path)
elif os.path.exists(state_path):
print("load state path .....")
hl_module.load_state(state_path)
start_epoch = hl_module.epoch
if "project_name" in params.keys():
project_name = params["project_name"]
else:
project_name = "AcousticBubble"
# Initialize wandb
# print(project_name)
wandb_run = wandb.init(
project=project_name,
name=run_name,
notes='Example of a note',
tags=['speech', 'audio', 'embedded-systems']
)
# Training loop
try:
# Go over remaining epochs
for epoch in range(start_epoch, params['epochs']):
global CURRENT_EPOCH, VAL_SEED
CURRENT_EPOCH = epoch
seed_from_epoch(args.seed)
hl_module.on_epoch_start()
current_lr = hl_module.get_current_lr()
print("CURRENT learning rate: {:0.08f}".format(current_lr))
print("[TRAINING]")
# Run testing step
t1 = time.time()
train_loss = train_epoch(hl_module, train_loader, device)
t2 = time.time()
print(f"Train epoch time: {t2 - t1:02f}s")
print("\nTrain set: Average Loss: {:.4f}\n".format(train_loss))
print()
if np.isnan(train_loss):
raise ValueError("Got NAN in training")
utils.seed_all(VAL_SEED)
# Run testing step
print("[TESTING]")
test_loss = test_epoch(hl_module, test_loader, device)
print("\nTest set: Average Loss: {:.4f}\n".format(test_loss))
hl_module.on_epoch_end(best_path, wandb_run)
hl_module.dump_state(state_path)
print()
print("=" * 25, "FINISHED EPOCH", epoch, "=" * 25)
print()
except KeyboardInterrupt:
print("Interrupted")
except Exception as _:
import traceback
traceback.print_exc()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Experiment Params
parser.add_argument('--config', type=str,
help='Path to experiment config')
parser.add_argument('--run_dir', type=str,
help='Path to experiment directory')
parser.add_argument('--best', action='store_true',
help="load from best checkpoint instead of last checkpoint")
# Randomization Params
parser.add_argument('--seed', type=int, default=10,
help='Random seed for reproducibility')
parser.add_argument('--use_nondeterministic_cudnn',
action='store_true',
help="If using cuda, chooses whether or not to use \
non-deterministic cudDNN algorithms. Training will be\
faster, but the final results may differ slighty.")
# wandb params
parser.add_argument('--project_name',
type=str,
default='AcousticBubble',
help='Project name that shows up on wandb')
train(parser.parse_args())
|