import os import torch import pickle from options import config from data_generation import generate from embedding_module import EmbeddingModule import learn2learn as l2l import random from learnToLearnTest import test from fast_adapt import fast_adapt import gc from learn2learn.optim.transforms import KroneckerTransform import argparse from clustering import ClustringModule, Trainer import numpy as np from torch.nn import functional as F def parse_args(): print("==============") parser = argparse.ArgumentParser([], description='Fast Context Adaptation via Meta-Learning (CAVIA),' 'Clasification experiments.') print("==============\n") parser.add_argument('--seed', type=int, default=53) parser.add_argument('--task', type=str, default='multi', help='problem setting: sine or celeba') parser.add_argument('--tasks_per_metaupdate', type=int, default=32, help='number of tasks in each batch per meta-update') parser.add_argument('--lr_inner', type=float, default=5e-6, help='inner-loop learning rate (per task)') parser.add_argument('--lr_meta', type=float, default=5e-5, help='outer-loop learning rate (used with Adam optimiser)') # parser.add_argument('--lr_meta_decay', type=float, default=0.9, help='decay factor for meta learning rate') parser.add_argument('--inner', type=int, default=2, help='number of gradient steps in inner loop (during training)') parser.add_argument('--inner_eval', type=int, default=2, help='number of gradient updates at test time (for evaluation)') parser.add_argument('--first_order', action='store_true', default=False, help='run first order approximation of CAVIA') parser.add_argument('--adapt_transform', action='store_true', default=False, help='run adaptation transform') parser.add_argument('--transformer', type=str, default="kronoker", help='transformer type') parser.add_argument('--meta_algo', type=str, default="gbml", help='MAML/MetaSGD/GBML') parser.add_argument('--gpu', type=int, default=0, help='number of gpu to run the code') parser.add_argument('--epochs', type=int, default=config['num_epoch'], help='number of gpu to run the code') # parser.add_argument('--data_root', type=str, default="./movielens/ml-1m", help='path to data root') # parser.add_argument('--num_workers', type=int, default=4, help='num of workers to use') # parser.add_argument('--test', action='store_true', default=False, help='num of workers to use') # parser.add_argument('--embedding_dim', type=int, default=32, help='num of workers to use') # parser.add_argument('--first_fc_hidden_dim', type=int, default=64, help='num of workers to use') # parser.add_argument('--second_fc_hidden_dim', type=int, default=64, help='num of workers to use') # parser.add_argument('--num_epoch', type=int, default=30, help='num of workers to use') # parser.add_argument('--num_genre', type=int, default=25, help='num of workers to use') # parser.add_argument('--num_director', type=int, default=2186, help='num of workers to use') # parser.add_argument('--num_actor', type=int, default=8030, help='num of workers to use') # parser.add_argument('--num_rate', type=int, default=6, help='num of workers to use') # parser.add_argument('--num_gender', type=int, default=2, help='num of workers to use') # parser.add_argument('--num_age', type=int, default=7, help='num of workers to use') # parser.add_argument('--num_occupation', type=int, default=21, help='num of workers to use') # parser.add_argument('--num_zipcode', type=int, default=3402, help='num of workers to use') # parser.add_argument('--rerun', action='store_true', default=False, # help='Re-run experiment (will override previously saved results)') args = parser.parse_args() # use the GPU if available # args.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # print('Running on device: {}'.format(args.device)) return args if __name__ == '__main__': args = parse_args() print(args) if config['use_cuda']: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) master_path = "/media/external_10TB/10TB/maheri/define_task_melu_data" config['master_path'] = master_path # DATA GENERATION print("DATA GENERATION PHASE") if not os.path.exists("{}/".format(master_path)): os.mkdir("{}/".format(master_path)) # preparing dataset. It needs about 22GB of your hard disk space. generate(master_path) # TRAINING print("TRAINING PHASE") embedding_dim = config['embedding_dim'] fc1_in_dim = config['embedding_dim'] * 8 fc2_in_dim = config['first_fc_hidden_dim'] fc2_out_dim = config['second_fc_hidden_dim'] use_cuda = config['use_cuda'] fc1 = torch.nn.Linear(fc1_in_dim, fc2_in_dim) fc2 = torch.nn.Linear(fc2_in_dim, fc2_out_dim) linear_out = torch.nn.Linear(fc2_out_dim, 1) head = torch.nn.Sequential(fc1, fc2, linear_out) if use_cuda: emb = EmbeddingModule(config).cuda() else: emb = EmbeddingModule(config) # META LEARNING print("META LEARNING PHASE") # define transformer transform = None if args.transformer == "kronoker": transform = KroneckerTransform(l2l.nn.KroneckerLinear) elif args.transformer == "linear": transform = l2l.optim.ModuleTransform(torch.nn.Linear) trainer = Trainer(config) # define meta algorithm if args.meta_algo == "maml": trainer = l2l.algorithms.MAML(trainer, lr=args.lr_inner, first_order=args.first_order) elif args.meta_algo == 'metasgd': trainer = l2l.algorithms.MetaSGD(trainer, lr=args.lr_inner, first_order=args.first_order) elif args.meta_algo == 'gbml': trainer = l2l.algorithms.GBML(trainer, transform=transform, lr=args.lr_inner, adapt_transform=args.adapt_transform, first_order=args.first_order) if use_cuda: trainer.cuda() # Setup optimization print("SETUP OPTIMIZATION PHASE") all_parameters = list(emb.parameters()) + list(trainer.parameters()) optimizer = torch.optim.Adam(all_parameters, lr=args.lr_meta) # loss = torch.nn.MSELoss(reduction='mean') # Load training dataset. print("LOAD DATASET PHASE") training_set_size = int(len(os.listdir("{}/warm_state".format(master_path))) / 4) supp_xs_s = [] supp_ys_s = [] query_xs_s = [] query_ys_s = [] batch_size = config['batch_size'] # torch.cuda.empty_cache() print("\n\n\n") for iteration in range(args.epochs): num_batch = int(training_set_size / batch_size) indexes = list(np.arange(training_set_size)) random.shuffle(indexes) for i in range(num_batch): meta_train_error = 0.0 optimizer.zero_grad() print("EPOCH: ", iteration, " BATCH: ", i) supp_xs, supp_ys, query_xs, query_ys = [], [], [], [] for idx in range(batch_size * i, batch_size * (i + 1)): supp_xs.append(pickle.load(open("{}/warm_state/supp_x_{}.pkl".format(master_path, indexes[idx]), "rb"))) supp_ys.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(master_path, indexes[idx]), "rb"))) query_xs.append( pickle.load(open("{}/warm_state/query_x_{}.pkl".format(master_path, indexes[idx]), "rb"))) query_ys.append( pickle.load(open("{}/warm_state/query_y_{}.pkl".format(master_path, indexes[idx]), "rb"))) batch_sz = len(supp_xs) if use_cuda: for j in range(batch_size): supp_xs[j] = supp_xs[j].cuda() supp_ys[j] = supp_ys[j].cuda() query_xs[j] = query_xs[j].cuda() query_ys[j] = query_ys[j].cuda() for task in range(batch_sz): # Compute meta-training loss # sxs = supp_xs[task].cuda() # qxs = query_xs[task].cuda() # sys = supp_ys[task].cuda() # qys = query_ys[task].cuda() learner = trainer.clone() temp_sxs = emb(supp_xs[task]) temp_qxs = emb(query_xs[task]) evaluation_error = fast_adapt(learner, temp_sxs, temp_qxs, supp_ys[task], query_ys[task], args.inner) evaluation_error.backward() meta_train_error += evaluation_error.item() # supp_xs[task].cpu() # query_xs[task].cpu() # supp_ys[task].cpu() # query_ys[task].cpu() # Print some metrics print('Iteration', iteration) print('Meta Train Error', meta_train_error / batch_sz) # Average the accumulated gradients and optimize for p in all_parameters: p.grad.data.mul_(1.0 / batch_sz) optimizer.step() # torch.cuda.empty_cache() del (supp_xs, supp_ys, query_xs, query_ys, learner, temp_sxs, temp_qxs) gc.collect() print("===============================================\n") if iteration % 2 == 0: # testing print("start of test phase") trainer.eval() with open("results2.txt", "a") as f: f.write("epoch:{}\n".format(iteration)) for test_state in ['user_cold_state', 'item_cold_state', 'user_and_item_cold_state']: test_dataset = None test_set_size = int(len(os.listdir("{}/{}".format(master_path, test_state))) / 4) supp_xs_s = [] supp_ys_s = [] query_xs_s = [] query_ys_s = [] gc.collect() print("===================== " + test_state + " =====================") mse, ndc1, ndc3 = test(emb, trainer, test_dataset, batch_size=config['batch_size'],num_epoch=config['num_epoch'],test_state=test_state,args=args) with open("results2.txt", "a") as f: f.write("{}\t{}\t{}\n".format(mse, ndc1, ndc3)) print("===================================================") del (test_dataset) gc.collect() trainer.train() with open("results2.txt", "a") as f: f.write("\n") print("\n\n\n") # save model # final_model = torch.nn.Sequential(emb, head) # torch.save(final_model.state_dict(), master_path + "/models_gbml.pkl") # testing # print("start of test phase") # for test_state in ['warm_state', 'user_cold_state', 'item_cold_state', 'user_and_item_cold_state']: # test_dataset = None # test_set_size = int(len(os.listdir("{}/{}".format(master_path, test_state))) / 4) # supp_xs_s = [] # supp_ys_s = [] # query_xs_s = [] # query_ys_s = [] # for idx in range(test_set_size): # supp_xs_s.append(pickle.load(open("{}/{}/supp_x_{}.pkl".format(master_path, test_state, idx), "rb"))) # supp_ys_s.append(pickle.load(open("{}/{}/supp_y_{}.pkl".format(master_path, test_state, idx), "rb"))) # query_xs_s.append(pickle.load(open("{}/{}/query_x_{}.pkl".format(master_path, test_state, idx), "rb"))) # query_ys_s.append(pickle.load(open("{}/{}/query_y_{}.pkl".format(master_path, test_state, idx), "rb"))) # test_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)) # del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s) # # print("===================== " + test_state + " =====================") # test(emb, head, test_dataset, batch_size=config['batch_size'], num_epoch=args.epochs, # adaptation_step=args.inner_eval) # print("===================================================\n\n\n") # print(args)