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152 lines
5.6 KiB
152 lines
5.6 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 models
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import dataset
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import util
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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('--zeroshot', action='store_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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parser.add_argument('--l', type=int, default=2)
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parser.add_argument('--filename')
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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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model = util.load_model(args.model, num_words, num_intent, num_slot, args.dropout, wordvecs)
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model.load_state_dict(torch.load(args.name))
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criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
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if cuda:
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model = model.cuda()
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if len(args.name.split('/')) > 1:
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nameprefix = args.name.split('/')[-1]
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else:
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nameprefix = args.name
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filename = args.filename if args.filename else f"results/{nameprefix}_{'zeroshot' if args.zeroshot else 'retrain'}_l{args.l}_alpha{args.alpha}.csv"
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if args.model == 'intent':
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open(filename, 'w').close() # clear the file
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f = open(filename, "a")
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while sum(model.filter_sizes) > 0:
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_, test_acc = util.valid_intent(model, test, criterion, cuda)
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print(f"{sum(model.filter_sizes)}, {test_acc:.5f}", file=f, flush=True)
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if sum(model.filter_sizes) > 10:
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model.prune(5, args.l)
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else:
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model.prune(1, args.l)
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if not args.zeroshot:
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optimizer = torch.optim.Adam(model.parameters())
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best_epoch = 0
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best_valid_loss, _ = util.valid_intent(model, valid, criterion, cuda)
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best_model = copy.deepcopy(model)
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epoch = 1
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while epoch <= best_epoch + args.patience:
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train_loss, train_acc = util.train_intent(model, train, criterion, optimizer, cuda)
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valid_loss, valid_acc = util.valid_intent(model, valid, criterion, cuda)
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if valid_loss < best_valid_loss:
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best_valid_loss = valid_loss
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best_epoch = epoch
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best_model = copy.deepcopy(model)
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epoch += 1
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model = best_model
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elif args.model == 'slot':
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open(filename, 'w').close() # clear the file
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f = open(filename, "a")
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while sum(model.filter_sizes) > 0:
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_, test_f1 = util.valid_slot(model, test, criterion, cuda)
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print(f"{sum(model.filter_sizes)}, {test_f1:.5f}", file=f, flush=True)
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if sum(model.filter_sizes) > 10:
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model.prune(5, args.l)
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else:
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model.prune(1, args.l)
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if not args.zeroshot:
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optimizer = torch.optim.Adam(model.parameters())
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best_epoch = 0
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best_valid_loss, _ = util.valid_slot(model, valid, criterion, cuda)
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best_model = copy.deepcopy(model)
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epoch = 1
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while epoch <= best_epoch + args.patience:
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train_loss, train_f1 = util.train_slot(model, train, criterion, optimizer, cuda)
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valid_loss, valid_f1 = util.valid_slot(model, valid, criterion, cuda)
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if valid_loss < best_valid_loss:
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best_valid_loss = valid_loss
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best_epoch = epoch
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best_model = copy.deepcopy(model)
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epoch += 1
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model = best_model
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elif args.model == 'joint':
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open(filename, 'w').close() # clear the file
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f = open(filename, "a")
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while sum(model.filter_sizes) > 0:
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_, (_, test_acc), (_, test_f1) = util.valid_joint(model, test, criterion, cuda, args.alpha)
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print(f"{sum(model.filter_sizes)}, {test_acc:.5f}, {test_f1:.5f}", file=f, flush=True)
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if sum(model.filter_sizes) > 10:
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model.prune(5, args.l)
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else:
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model.prune(1, args.l)
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if not args.zeroshot:
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optimizer = torch.optim.Adam(model.parameters())
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best_epoch = 0
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best_valid_loss, (_, _), (_, _) = util.valid_joint(model, valid, criterion, cuda, args.alpha)
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best_model = copy.deepcopy(model)
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epoch = 1
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while epoch <= best_epoch + args.patience:
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train_loss, (_, _), (_, _) = util.train_joint(model, train, criterion, optimizer, cuda, args.alpha)
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valid_loss, (_, _), (_, _) = util.valid_joint(model, valid, criterion, cuda, args.alpha)
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if valid_loss < best_valid_loss:
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best_valid_loss = valid_loss
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best_epoch = epoch
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best_model = copy.deepcopy(model)
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epoch += 1
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model = best_model
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