def main():
"""Main training program."""
print('Generate Samples')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
_ = Timers()
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# get the tokenizer
tokenizer = prepare_tokenizer(args)
# Model, optimizer, and learning rate.
model = setup_model(args)
# setting default batch size to 1
args.batch_size = 1
# generate samples
generate_samples(model, tokenizer, args)
def main():
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# get the tokenizer
tokenizer = prepare_tokenizer(args)
tokenizer.save_pretrained(args.export_huggingface)
# Model
_ = setup_model(args)
import sys
import os
import os.path as osp
import pickle
from PIL import Image
from dataset.bdd_dataset import BDD_Train_DataSet, BDD_Valid_DataSet, BDD_Test_DataSet
import matplotlib.pyplot as plt
import random
import timeit
from src.train import TrainNetwork
from src.test import TestNetwork
from models.enet_model import ENetModel
from src.arguments import get_args
from src.utils import enet_weighing
args = get_args()
#Select the GPU to run the network on
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu_select
def train_main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
crop_size = (int(h),int(w))
# Create the directory to store the checkpoints and verbose text files
directory = 'saved_models/' + args.run_name + '/'
if not os.path.exists(directory):
os.makedirs(directory)
def main():
"""Main training program."""
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
# if args.load_huggingface:
# args.make_vocab_size_divisible_by = 1
# Pytorch distributed.
initialize_distributed(args)
if torch.distributed.get_rank() == 0:
print('Pretrain GPT3 model')
print_args(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# Data stuff.
train_data, val_data, test_data, args.vocab_size, args.eod_token, tokenizer = get_train_val_test_data(args)
# Model, optimizer, and learning rate.
model, optimizer, lr_scheduler = setup_model_and_optimizer(args)
# Resume data loader if necessary.
if args.resume_dataloader:
if train_data is not None:
train_data.batch_sampler.start_iter = args.iteration % len(train_data)
print_rank_0(f"Resume train set from iteration {train_data.batch_sampler.start_iter}")
if val_data is not None:
start_iter_val = (args.train_iters // args.save_interval) * args.eval_interval
val_data.batch_sampler.start_iter = start_iter_val % len(val_data)
if train_data is not None:
train_data_iterator = iter(train_data)
else:
train_data_iterator = None
iteration = 0
if args.train_iters > 0:
if args.do_train:
iteration, skipped = train(model, optimizer,
lr_scheduler,
train_data_iterator,
val_data,
timers,
args,
tokenizer)
if args.do_valid:
prefix = 'the end of training for val data'
# val_loss, val_ppl
_ = evaluate_and_print_results(prefix, iter(val_data) if val_data else None,
model, args, timers, False)
if args.save and iteration != 0:
save_checkpoint(iteration, model, optimizer, lr_scheduler, args, deepspeed=DEEPSPEED_WRAP and args.deepspeed)
if args.do_test:
# Run on test data.
prefix = 'the end of training for test data'
evaluate_and_print_results(prefix, iter(test_data) if test_data else None,
model, args, timers, True)