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145 lines
6.3 KiB
145 lines
6.3 KiB
import time
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import copy
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import argparse
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import torch
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import torch.nn as nn
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import dataset
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import util
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import models
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from itertools import chain
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--name', required=True)
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parser.add_argument('--filename', required=True)
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parser.add_argument('--epochs', default=50)
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parser.add_argument('--seed', type=int, default=None)
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parser.add_argument('--patience', type=int, default=5)
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parser.add_argument('--dropout', type=float, default=0.5)
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parser.add_argument('--alpha', type=float, default=0.2)
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args = parser.parse_args()
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if 'atis' in args.name:
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args.dataset = 'atis'
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elif 'snips' in args.name:
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args.dataset = 'snips'
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if 'intent' in args.name:
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args.model = 'intent'
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elif 'slot' in args.name:
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args.model = 'slot'
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elif 'joint' in args.name:
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args.model = 'joint'
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print(f"seed {util.rep(args.seed)}")
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cuda = torch.cuda.is_available()
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train, valid, test, num_words, num_intent, num_slot, wordvecs = dataset.load(args.dataset, batch_size=8, seq_len=50)
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open(args.filename, 'w').close() # clear the file
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f = open(args.filename, "a")
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for filter_count in chain(range(300, 10, -5), range(10, 0, -1)):
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if args.model == 'intent':
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model = models.CNNIntent(num_words, 100, num_intent, (filter_count,), 5, args.dropout, wordvecs)
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elif args.model == 'slot':
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model = models.CNNSlot(num_words, 100, num_slot, (filter_count,), 5, args.dropout, wordvecs)
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elif args.model == 'joint':
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model = models.CNNJoint(num_words, 100, num_intent, num_slot, (filter_count,), 5, args.dropout, wordvecs)
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teacher = util.load_model(args.model, num_words, num_intent, num_slot, args.dropout, wordvecs)
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teacher.load_state_dict(torch.load(args.name))
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criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
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distill_criterion = nn.KLDivLoss(reduction='batchmean')
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optimizer = torch.optim.Adam(model.parameters())
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if cuda:
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model = model.cuda()
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teacher = teacher.cuda()
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best_valid_loss = float('inf')
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last_epoch_to_improve = 0
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best_model = model
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model_filename = f"models/{args.dataset}_{args.model}"
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if args.model == 'intent':
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for epoch in range(args.epochs):
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start_time = time.time()
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train_loss, train_acc = util.distill_intent(teacher, model, 1.0, train, distill_criterion, optimizer, cuda)
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valid_loss, valid_acc = util.valid_intent(model, valid, criterion, cuda)
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f"Epoch {epoch + 1:03} took {elapsed_time:.3f} seconds")
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print(f"\tTrain Loss: {train_loss:.5f}, Acc: {train_acc:.5f}")
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print(f"\tValid Loss: {valid_loss:.5f}, Acc: {valid_acc:.5f}")
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if valid_loss < best_valid_loss:
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last_epoch_to_improve = epoch
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best_valid_loss = valid_loss
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best_model = copy.deepcopy(model)
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print("\tNew best valid loss!")
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if last_epoch_to_improve + args.patience < epoch:
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break
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_, test_acc = util.valid_intent(best_model, test, criterion, cuda)
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print(f"Test Acc: {test_acc:.5f}")
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print(f"{sum(best_model.filter_sizes)}, {test_acc:.5f}", file=f, flush=True)
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elif args.model == 'slot':
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for epoch in range(args.epochs):
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start_time = time.time()
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train_loss, train_f1 = util.distill_slot(teacher, model, 1.0, train, distill_criterion, optimizer, cuda)
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valid_loss, valid_f1 = util.valid_slot(model, valid, criterion, cuda)
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f"Epoch {epoch + 1:03} took {elapsed_time:.3f} seconds")
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print(f"\tTrain Loss: {train_loss:.5f}, F1: {train_f1:.5f}")
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print(f"\tValid Loss: {valid_loss:.5f}, F1: {valid_f1:.5f}")
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if valid_loss < best_valid_loss:
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last_epoch_to_improve = epoch
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best_valid_loss = valid_loss
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best_model = copy.deepcopy(model)
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print("\tNew best valid loss!")
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if last_epoch_to_improve + args.patience < epoch:
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break
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_, test_f1 = util.valid_slot(best_model, test, criterion, cuda)
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print(f"Test F1: {test_f1:.5f}")
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print(f"{sum(best_model.filter_sizes)}, {test_f1:.5f}", file=f, flush=True)
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elif args.model == 'joint':
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for epoch in range(args.epochs):
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start_time = time.time()
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train_loss, (intent_train_loss, intent_train_acc), (slot_train_loss, slot_train_f1) = util.distill_joint(teacher, model, 1.0, train, distill_criterion, optimizer, cuda, args.alpha)
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valid_loss, (intent_valid_loss, intent_valid_acc), (slot_valid_loss, slot_valid_f1) = util.valid_joint(model, valid, criterion, cuda, args.alpha)
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f"Epoch {epoch + 1:03} took {elapsed_time:.3f} seconds")
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print(f"\tTrain Loss: {train_loss:.5f}, (Intent Loss: {intent_train_loss:.5f}, Acc: {intent_train_acc:.5f}), (Slot Loss: {slot_train_loss:.5f}, F1: {slot_train_f1:.5f})")
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print(f"\tValid Loss: {valid_loss:.5f}, (Intent Loss: {intent_valid_loss:.5f}, Acc: {intent_valid_acc:.5f}), (Slot Loss: {slot_valid_loss:.5f}, F1: {slot_valid_f1:.5f})")
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if valid_loss < best_valid_loss:
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last_epoch_to_improve = epoch
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best_valid_loss = valid_loss
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best_model = copy.deepcopy(model)
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print("\tNew best valid loss!")
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if last_epoch_to_improve + args.patience < epoch:
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break
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_, (_, intent_test_acc), (_, slot_test_f1) = util.valid_joint(best_model, test, criterion, cuda, args.alpha)
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print(f"Test Intent Acc: {intent_test_acc:.5f}, Slot F1: {slot_test_f1:.5f}")
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print(f"{sum(best_model.filter_sizes)}, {intent_test_acc:.5f}, {slot_test_f1:.5f}", file=f, flush=True)
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