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59 lines
1.7 KiB
59 lines
1.7 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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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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args.dropout = 0
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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.eval()
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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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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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count = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(count)
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if args.model == 'intent':
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_, test_acc = util.valid_intent(model, test, criterion, cuda)
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print(f"Test Acc: {test_acc:.5f}")
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elif args.model == 'slot':
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_, test_f1 = util.valid_slot(model, test, criterion, cuda)
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print(f"Test F1: {test_f1:.5f}")
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elif args.model == 'joint':
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_, (_, intent_test_acc), (_, slot_test_f1) = util.valid_joint(model, test, criterion, cuda, 0)
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print(f"Test Intent Acc: {intent_test_acc:.5f}, Slot F1: {slot_test_f1:.5f}")
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