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- import time
- import argparse
-
- import torch
- import sklearn
- import numpy as np
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- import dataset
- import util
-
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('--model', choices=['intent', 'slot', 'joint'])
- parser.add_argument('--dataset', choices=['atis', 'snips'])
- parser.add_argument('--filters', type=int)
- parser.add_argument('--runs', type=int, default=15)
- parser.add_argument('--gpu', action="store_true") # pass for CUDA, otherwise will run on CPU
-
- args = parser.parse_args()
- args.dropout = 0
-
- train, valid, test, num_words, num_intent, num_slot, wordvecs = dataset.load(args.dataset, batch_size=8, seq_len=50)
-
- # change the model below
-
-
-
-
- for sparsity in [0, 20, 40, 60, 80, 90, 95, 99]:
- filters = 300 - 300 * (sparsity / 100)
- model = util.load_model(args.model, num_words, num_intent, num_slot, args.dropout, wordvecs, 100, int(filters))
- if args.gpu:
- model = model.cuda()
- print(f"sparsity {sparsity}, params {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
- times = []
- criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
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- for i in range(args.runs):
- start_time = time.time()
- if args.model == 'intent':
- util.valid_intent(model, test, criterion, args.gpu)
- elif args.model == 'slot':
- util.valid_slot(model, test, criterion, args.gpu)
- elif args.model == 'joint':
- util.valid_joint(model, test, criterion, args.gpu, 0.2)
- 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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