def load_model(directory):
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
model = create_model(parse_config(directory)['model'])
model.load(directory)
model.train()
model.to(device)
return model
def dataloader_debug():
import sys
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
args = init_args()
assert os.path.exists(args.config_file)
config = anyconfig.load(open(args.config_file, 'rb'))
# print('===config:', config)
if 'base' in config:
config = parse_config(config)
print('===config:', config)
print('==torch.cuda.device_count():', torch.cuda.device_count())
if torch.cuda.device_count() > 1:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
world_size=torch.cuda.device_count(),
rank=args.local_rank)
config['distributed'] = True
else:
config['distributed'] = False
config['local_rank'] = args.local_rank
train_loader = get_trainloader(dataset.ICDAR2015Dataset, config)
# eval_loader = get_evalloader(dataset.ICDAR2015Dataset, config)
output_path = './查看图片_dataloader'
if not os.path.exists(output_path):
os.mkdir(output_path)
epochs = 1
for epoch in range(epochs):
for i, data_info in enumerate(tqdm(train_loader)):
# if i < 1:
print('===data_info:', data_info.keys())
batch_img = data_info['img']
shrink_map = data_info['shrink_map']
# threshold_label = data_info['threshold_map']
batch_gt = data_info['gt']
print('== batch_img.shape', batch_img.shape)
print('===shrink_map.shape', shrink_map.shape)
# print(batch_img.shape, threshold_label.shape, threshold_label.shape, batch_gt.shape, data_shape)
for j in range(batch_img.shape[0]):
img = batch_img[j].numpy().transpose(1, 2, 0)
gt = batch_gt[j].numpy() * 255.
# print('===img.shape:', img.shape)
# shrink_label = shrink_map[j].numpy()*255.
gt = np.expand_dims(gt, axis=-1)
img = (img * np.array([0.229, 0.224, 0.225]) +
np.array([0.485, 0.456, 0.406])) * 255.
img = np.clip(gt + img, 0, 255)
cv2.imwrite(
os.path.join(output_path,
str(i) + '_' + str(j) + '.jpg'),
img[..., ::-1])
def main_entrance():
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
args = init_args()
config = anyconfig.load(open(args.config_file, 'rb'))
# print('===config:', config)
if 'base' in config:
config = parse_config(config)
print('===config:', config)
if torch.cuda.device_count() > 1:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://", world_size=torch.cuda.device_count(),
rank=args.local_rank)
config['distributed'] = True
else:
config['distributed'] = False
config['local_rank'] = args.local_rank
logging.info(config['dataset']['train'])
model = build_model(config['arch']['type'], **config['arch'])
criterion = build_loss(config['loss'].pop('type'), **config['loss']).cuda()
post_process = get_post_processing(config['post_processing'])
train_loader = get_trainloader(dataset.ICDAR2015Dataset, config)
eval_loader = get_evalloader(dataset.ICDAR2015Dataset, config)
model = model.cuda()
if config['distributed']:
model = nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank, broadcast_buffers=False,
find_unused_parameters=True)
checkpoint_path = config['train']['resume_checkpoint']
output_path = config['train']['output_path']
optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.00005)
# load_weights(model, optimizer, config['distributed'], checkpoint_path='/red_detection/DBNet/code_pretrain_model/model_latest_express_code_7_13.pth')
# load_weights(model, optimizer, config['distributed'], checkpoint_path=checkpoint_path)
epochs = config['train']['epochs']
warmup_iters = config['lr_scheduler']['args']['warmup_epoch']*len(train_loader)
# scheduler = WarmupPolyLR(optimizer, max_iters=epochs * len(train_loader),
# warmup_iters=warmup_iters, **config['lr_scheduler']['args'])
train(model, optimizer, epochs, criterion, train_loader, config, post_process, eval_loader,output_path)
from matplotlib import pyplot as plt
plt.plot(lr_list)
plt.savefig('./show_lr_word_industry.png')
if done:
print()
counter = 0
reward_sum = 0
save_info("submit_logs/second/log_{}.json".format(episodes),
env.get_episode_info())
episodes += 1
(observation, _) = env.reset(False)
if observation is None:
break
if __name__ == '__main__':
directory = '/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_30_reward_9841.04/'
directories = [[
#'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_48_reward_9840.57/',
#'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_30_reward_9841.04/',
#'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_66_reward_9851.11//',
'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_100_reward_9854.58/',
'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_1/episode_73_reward_9867.67/',
'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_0/episode_38_reward_9879.41//',
'/data/svidchenko/afterlearning/moar_features_1/sgdr_1/saved_models/exploiting_virtual_thread_3/episode_9_reward_9881.80//'
]]
config = parse_config(directory)
config['environment']['wrapper']['repeat_actions'] = 3
submit(config, directories, repeats=1)
from training.managers import TrainManager
from utils.util import parse_config
CONFIG_FILENAME = 'config.json'
AFTERLEARN_CONFIG_FILENAME = 'afterlearn_config.json'
if __name__ == '__main__':
config = parse_config(config_name=CONFIG_FILENAME)
manager = TrainManager(config)
manager.manage()
#
# directories = [
# # '/home/ivan/data/prosthetics/exps/6/saved_models/exploiting_common_thread_0/episode_676_reward_2406.04/',
# # '/home/ivan/data/prosthetics/exps/6/saved_models/exploiting_common_thread_0/episode_684_reward_2407.29/',
# # '/home/ivan/data/prosthetics/exps/6/saved_models/exploiting_common_thread_0/episode_696_reward_2414.30/'
# ]
directory = './../../logdir/saved_models/moar_features_1/'
# directory = './saved_models/exploiting_thread_1/episode_516_reward_9465.81/'
directories = [
# './../../logdir/saved_models/coors_rotation/second',
# './../../logdir/saved_models/coors_rotation/third'
]
config = parse_config()
config["environment"]["wrapper"]["target_transformer_config"]["noise"] = 0.
config["environment"]["wrapper"]["target_transformer_type"] = "normal"
config["environment"]["wrapper"]["repeat_actions"] = 3
config["environment"]["core"]["max_steps"] = 1000
config["environment"]["wrapper"]["reward_aggregations"] = [{
"class": "TransformAndBound",
"config": {
"move": -19.0,
"scale": 2.0,
"bound": 1.0
}
}]
rewards = []
for seed_plus in range(40):
def multitest(model_sets, log, averaging_models=(2, 3, 4), seeds_per_thread=4, repeat_set=(1, 2), model_workers=10,
average_weights=False):
total_results = []
model_sets = all_combinations(model_sets)
# model_sets = list(reversed([[s] for s in model_sets]))
random.shuffle(model_sets)
random.shuffle(model_sets)
random.shuffle(model_sets)
try:
for directories in model_sets:
for repeats in repeat_set:
directory = directories[0]
dirs = list(reversed(directories))
config = parse_config(directory)
rewards, modified_rewards, step_counts, infos, rws = evaluate(config, dirs,
seeds_per_thread=seeds_per_thread,
repeats=repeats,
model_workers=model_workers,
average_weights=average_weights)
logging_list = []
for r, mr, sc, inf in zip(rewards, modified_rewards, step_counts, infos):
logging_list.append({
"reward": r,
"modified_reward": mr,
"step_count": sc,
"info": inf
})
total_results.append({
"models": dirs,
"repeats": repeats,
"result": logging_list,
"rewards": [mean_confidence_interval(rewards, confidence=0.95),
mean_confidence_interval(rewards, confidence=0.99),
np.quantile(rewards, [0.05, 0.1, 0.25, 0.5])],
"rewards_without_falling": [mean_confidence_interval(rws, confidence=0.95),
mean_confidence_interval(rws, confidence=0.99),
np.quantile(rws, [0.05, 0.1, 0.25, 0.5])],
"falling_rate": {
"300": len([st for st in step_counts if st < 300]) / len(step_counts),
"600": len([st for st in step_counts if st < 600]) / len(step_counts),
"900": len([st for st in step_counts if st < 900]) / len(step_counts),
"1000": len([st for st in step_counts if st < 1000]) / len(step_counts)
}
})
for i in range(10):
print()
print_model(total_results[-1])
print('='*20)
print()
print()
for res in sorted(total_results, key=lambda res: res["rewards_without_falling"][0][0], reverse=True)[
:min(len(total_results), 5)]:
print_model(res)
for i in range(10):
print()
except Exception as e:
print("Exception", e)
pass
finally:
for i in range(10):
print()
print("Results:")
for res in total_results:
print_model(res)
save_info(log, total_results)
global maxAcc
maxAcc = 0
if (args.cuda == True):
print("GPU in use", torch.cuda.is_available(), torch.cuda.current_device(),
torch.cuda.device_count(),
torch.cuda.get_device_name(torch.cuda.current_device()))
kwargs = {'num_workers': 4, 'pin_memory': False} if args.cuda else {}
if (args.name == None):
name = 'FPGA4HEPmodel'
else:
name = args.name
yamlConfig = parse_config('./yaml_IP_OP_config.yml')
print("Reading dataset...")
X_train_val, X_test, y_train_val, y_test, labels, train_loader, test_loader, input_shape, output_shape = get_features(
yamlConfig, args.batch_size, args.test_batch_size)
dtype = torch.cuda.FloatTensor if args.cuda else torch.FloatTensor
model = LFC(num_classes=5, weight_bit_width=8, act_bit_width=8, in_bit_width=8)
if args.cuda:
model.cuda()
print(model)
## Optimizers
optimizer = optim.SGD(filter(lambda x: x.requires_grad, model.parameters()),
lr=args.lr,
momentum=args.momentum,
