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maml_ssl_main_rfs.py
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import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4,5'
import time
import json
import logging
import math
import pickle
import torch
from collections import defaultdict
from configuration import arg_parser
import common_tools as ct
from datasets_meta.dataloader_meta import BatchMetaDataLoader
from maml.datasets_benchmark_rfs import get_benchmark_by_name_rfs
from maml.metalearners import RFS
args = arg_parser.parse_args()
ct.set_random_seeds(args.seed)
INTERVAL = 50
INTERVAL_VAL = args.interval_val
def cat_data(result_dict, new_dict):
for key, values in new_dict.items():
result_dict[key] += values
return result_dict
def append_data(result_dict, new_dict):
for key, values in new_dict.items():
result_dict[key].append(values)
return result_dict
def maml_ssl_main(args, device):
benchmark = get_benchmark_by_name_rfs(args.dataset,
args.data_folder,
args.scenario,
args.num_ways,
args.num_shots, # shots in support set
args.num_shots_test_meta_train, # shots in query set for meta-train
args.num_shots_test_meta_test, # shots in query set for meta-test
args.num_shots_unlabeled, # num of unlabeled images for meta-train tasks
args.num_shots_unlabeled_evaluate, # num of unlabeled images per class
args.num_classes_distractor, # with distractor
args.num_shots_distractor, # with distractor
args.num_shots_distractor_eval, # with distractor
args.num_unlabel_total, # for "random"
args.num_unlabel_total_evaluate, # for "random"
hidden_size=args.hidden_size)
meta_train_dataloader = BatchMetaDataLoader(benchmark.meta_train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=False) # possible to avoid the leaking caffe2 warning
meta_valid_dataloader = BatchMetaDataLoader(benchmark.meta_val_dataset,
batch_size=args.batch_size_val,
shuffle=True, # make it to be false to debug
num_workers=args.num_workers,
pin_memory=False)
meta_test_dataloader = BatchMetaDataLoader(benchmark.meta_test_dataset,
batch_size=args.batch_size_test,
shuffle=True, # make it to be false to debug
num_workers=args.num_workers,
pin_memory=False)
meta_optimizer = torch.optim.Adam(benchmark.model.parameters(), lr=args.meta_lr)
# debugging
metalearner = RFS(benchmark.model,
meta_optimizer,
step_size=args.step_size,
first_order=args.first_order,
num_adaptation_steps=args.num_steps,
num_adaptation_steps_test=args.num_steps_evaluate,
loss_function=benchmark.loss_function,
coef_inner=args.coef_inner,
coef_outer=args.coef_outer,
device=device)
# if args.ssl_algo == "SMI":
# metalearner = ModelAgnosticMetaLearning(benchmark.model,
# meta_optimizer,
# step_size=args.step_size,
# first_order=args.first_order,
# num_adaptation_steps=args.num_steps,
# num_adaptation_steps_test=args.num_steps_evaluate,
# loss_function=benchmark.loss_function,
# coef_inner=args.coef_inner,
# coef_outer=args.coef_outer,
# device=device)
# else:
# metalearner = ModelAgnosticMetaLearningBaseline(benchmark.model,
# meta_optimizer,
# step_size=args.step_size,
# first_order=args.first_order,
# num_adaptation_steps=args.num_steps,
# num_adaptation_steps_test=args.num_steps_evaluate,
# loss_function=benchmark.loss_function,
# coef_inner=args.coef_inner,
# coef_outer=args.coef_outer,
# device=device)
best_value = None
real_datasets = ["miniimagenet", "omniglot", "tieredimagenet", "cifarfs"]
results_train = defaultdict(list) # store all results from meta-training
results_valid = defaultdict(list) # store all results from meta-validation
results_test = defaultdict(list)
results_mean_val_tst_epochs = {"mean_loss_val": [],
"mean_accu_val": [],
"mean_loss_tst": [],
"mean_accu_tst": [],
"accus_tst_ci": []} # loss and accu of query set from meta-validation
# Training loop
epoch_desc_train = 'Epoch {{0: <{0}d}} (meta-train)'.format(1 + int(math.log10(args.num_epochs)))
epoch_desc_val = 'Epoch {{0: <{0}d}} (meta-valid)'.format(1 + int(math.log10(args.num_epochs)))
epoch_desc_tst = 'Epoch {{0: <{0}d}} (meta-test)'.format(1 + int(math.log10(args.num_epochs)))
# # load the saved variables to resume the training
# if args.resume:
# # saved results
# with open(path_resume+ "results_train.pkl", "rb") as f:
# results_train = pickle.load(f)
# with open(path_resume+ "results_valid.pkl", "rb") as f:
# results_valid = pickle.load(f)
# with open(path_resume+ "results_test.pkl", "rb") as f:
# results_test = pickle.load(f)
# with open(path_resume+ "results_mean_valid_test.json", "rb") as f:
# results_mean_val_tst_epochs = json.load(f)
start_epoch = 0
for epoch in range(start_epoch+1, args.num_epochs+1):
# meta training
result_train_per_epoch = metalearner.train(meta_train_dataloader,
max_batches=args.num_batches,
batch_size=args.batch_size,
verbose=args.verbose,
progress=epoch,
desc=epoch_desc_train.format(epoch),
)
results_train = cat_data(results_train, result_train_per_epoch)
if epoch % INTERVAL_VAL == 0 and epoch >= 40:
# if epoch > 2:
# meta validation
if args.ssl_algo == "VAT":
results_mean_val, results_all_tasks_val, _ = metalearner.evaluate(meta_valid_dataloader,
max_batches=args.num_batches,
batch_size=args.batch_size_val,
verbose=args.verbose,
progress=epoch,
desc=epoch_desc_val.format(epoch))
results_valid = append_data(results_valid, results_all_tasks_val)
results_mean_val_tst_epochs["mean_loss_val"].append(results_mean_val['mean_outer_loss'])
results_mean_val_tst_epochs["mean_accu_val"].append(results_mean_val["accuracies_after"])
else:
results_mean_val, results_all_tasks_val = {}, {}
results_mean_val['accuracies_after'] = 0
results_mean_val['mean_outer_loss'] = 0
# meta test
# results_mean_tst, results_all_tasks_tst = {}, {}
if args.dataset in real_datasets:
results_mean_tst, results_all_tasks_tst, ci95 = metalearner.evaluate(meta_test_dataloader,
max_batches=args.num_batches_eval,
batch_size=args.batch_size_test,
verbose=args.verbose,
progress=epoch,
desc=epoch_desc_tst.format(epoch))
results_test = append_data(results_test, results_all_tasks_tst)
results_mean_val_tst_epochs["mean_loss_tst"].append(results_mean_tst['mean_outer_loss'])
results_mean_val_tst_epochs["mean_accu_tst"].append(results_mean_tst["accuracies_after"])
results_mean_val_tst_epochs["accus_tst_ci"].append(ci95)
else:
results_mean_tst, results_all_tasks_tst = {}, {}
# save the validation acc and loss during each epoch
rst_path_valid_test = os.path.abspath(os.path.join(args.output_subfolder, "results_mean_valid_test.json"))
with open(rst_path_valid_test, "w") as f:
json.dump(results_mean_val_tst_epochs, f, indent=2)
# ### Save the best model based on validation set
save_model = False
if 'accuracies_after' in results_mean_val:
if (best_value is None) or (best_value < results_mean_val['accuracies_after']):
best_value = results_mean_val['accuracies_after']
save_model = True
elif (best_value is None) or (best_value > results_mean_val['mean_outer_loss']):
best_value = results_mean_val['mean_outer_loss']
save_model = True
else:
save_model = False
if save_model and (args.output_folder is not None):
print(f"^^^^^ Best model at Epoch: {epoch}")
best_epoch={"epoch": epoch,
"valid": results_mean_val,
"test": results_mean_tst,
}
with open(f"{args.model_path}.th", 'wb') as f:
# with open(f"{args.model_path}_epoch_{epoch}.th", 'wb') as f:
torch.save(benchmark.model.state_dict(), f)
with open(args.result_path, 'wb') as handle:
pickle.dump(best_epoch, handle, protocol=pickle.HIGHEST_PROTOCOL)
# ###
# save model every interval_val epochs
with open(f"{args.model_path}_epoch_{epoch}.th", 'wb') as f:
torch.save(benchmark.model.state_dict(), f)
# save some intemediate results, overwrite them epoch by epoch
result_train_path = os.path.abspath(os.path.join(args.output_subfolder, f'results_train.pkl'))
with open(result_train_path, "wb") as f:
pickle.dump(results_train, f, protocol=pickle.HIGHEST_PROTOCOL)
result_valid_path = os.path.abspath(os.path.join(args.output_subfolder, f"results_valid.pkl"))
with open(result_valid_path, "wb") as f:
pickle.dump(results_valid, f, protocol=pickle.HIGHEST_PROTOCOL)
result_test_path = os.path.abspath(os.path.join(args.output_subfolder, f"results_test.pkl"))
with open(result_test_path, "wb") as f:
pickle.dump(results_test, f, protocol=pickle.HIGHEST_PROTOCOL)
if hasattr(benchmark.meta_train_dataset, 'close'):
benchmark.meta_train_dataset.close()
benchmark.meta_val_dataset.close()
def main():
start = time.time() # float
ct.create_path(args.output_folder)
specific_file_name, tag = "", ""
# base_path/ssl_path/N-way K-shot/specific_model
# base model folder, storing all results of all experiments
base_path = os.path.join(args.output_folder, f"{args.dataset}_{args.scenario}")
# specific model folder, storing the specific model (specific combination in the configure file)
if args.ssl_algo == "SMI":
ssl_path = os.path.join(base_path, f"{args.ssl_algo}_{args.selection_option}_firstOrder_{args.first_order}")
else:
ssl_path = os.path.join(base_path, f"{args.ssl_algo}_firstOrder_{args.first_order}")
# specific stopping policy model results
few_shot_path = os.path.join(ssl_path, f"#way_{args.num_ways}_#shot_{args.num_shots}")
if args.ssl_algo in ["SMI", "SMIcomb"]:
tag = '_'.join(['BudgetS', str(args.budget_s), 'BudgetQ', str(args.budget_q), "TrueLabel", str(args.select_true_label)])
elif args.ssl_algo == "PL":
tag = '_'.join(['TH', str(args.pl_threshold), "TrueLabel", str(args.select_true_label)])
elif args.ssl_algo in ["PLtopZ", "PLtopZperClass", "PLtopZperClassPLtopZ"]:
tag = '_'.join(['TopZs', str(args.pl_num_topz), 'TopZq', str(args.pl_num_topz_outer), "TrueLabel", str(args.select_true_label)])
if not args.resume:
specific_file_name = time.strftime('%Y-%m-%d-%H%M%S') + "_" + tag + "_" + '_'.join(
['LabelRatio', str(args.ratio), '#ShotU', str(args.num_shots_unlabeled), '#InnerLR', str(args.step_size), 'Seed', str(args.seed), ])
specific_model_path = os.path.join(few_shot_path, specific_file_name)
ct.create_path(specific_model_path)
args.output_subfolder = os.path.abspath(specific_model_path) # absolute path
else:
args.output_subfolder = os.path.abspath(args.checkpoint_path) # absolute path
ct.set_logger('{}/log_file_outerLossAcc_seed_{}'.format(args.output_subfolder, args.seed), 'err') # log file
# ct.set_logger('{}/log_file_seed_{}'.format(args.output_subfolder, args.seed), 'out') # log file
print('Random Seed: {}'.format(args.seed))
ct.set_random_seeds(args.seed)
device = ct.set_device(args.gpu_id)
args.model_path = os.path.abspath(os.path.join(args.output_subfolder, 'best_model'))
args.result_path = os.path.abspath(os.path.join(args.output_subfolder, 'best_model_valid_test_result.pkl'))
# save the config, json is better here because it is easily to open directly
with open(os.path.join(args.output_subfolder, 'config.json'), 'w') as f:
json.dump(vars(args), f, indent=2)
print("****** Model parameters: \n{")
for key, value in vars(args).items():
print(f"{key} : {value}")
print("} ****** \n")
maml_ssl_main(args, device)
time_used = "{:.2f}".format(time.time() - start)
print(f"Total time used: {time_used}")
with open(os.path.join(args.output_subfolder, 'others.json'), 'w') as fi:
json.dump({"TimeUsed (s)": float(time_used)}, fi, indent=2)
if __name__ == "__main__":
# torch.cuda.set_per_process_memory_fraction(0.9, 0)
main()