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55 lines
1.7 KiB
55 lines
1.7 KiB
import time
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import argparse
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import torch
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import sklearn
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import numpy as np
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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('--model', choices=['intent', 'slot', 'joint'])
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parser.add_argument('--dataset', choices=['atis', 'snips'])
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parser.add_argument('--filters', type=int)
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parser.add_argument('--runs', type=int, default=15)
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parser.add_argument('--gpu', action="store_true") # pass for CUDA, otherwise will run on CPU
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args = parser.parse_args()
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args.dropout = 0
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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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# change the model below
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for sparsity in [0, 20, 40, 60, 80, 90, 95, 99]:
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filters = 300 - 300 * (sparsity / 100)
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model = util.load_model(args.model, num_words, num_intent, num_slot, args.dropout, wordvecs, 100, int(filters))
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if args.gpu:
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model = model.cuda()
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print(f"sparsity {sparsity}, params {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
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times = []
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criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
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for i in range(args.runs):
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start_time = time.time()
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if args.model == 'intent':
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util.valid_intent(model, test, criterion, args.gpu)
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elif args.model == 'slot':
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util.valid_slot(model, test, criterion, args.gpu)
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elif args.model == 'joint':
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util.valid_joint(model, test, criterion, args.gpu, 0.2)
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end_time = time.time()
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elapsed_time = end_time - start_time
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times.append(elapsed_time)
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print(f"mean: {sum(times) / float(len(times)):.5f} sec, std deviation: {np.std(times):.5f}")
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