mirror of
https://github.com/wassname/Castor.git
synced 2026-09-06 16:30:49 +08:00
Ralph Tang
e4149ff400
* Use PackedSequence in ConvRNNs instead - Zero-padding is incompatible with the current model * Update test script * Clean up comments and logic * Add TQDM to requirements * Add results using default hyperparameters
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import argparse
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import os
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.utils as utils
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import data
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import model
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--no_cuda", action="store_true", default=False)
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parser.add_argument("--input_file", default="saves/model.pt", type=str)
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parser.add_argument("--data_dir", default="data", type=str)
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parser.add_argument("--gpu_number", default=0, type=int)
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args = parser.parse_args()
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conv_rnn = torch.load(args.input_file)
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if not args.no_cuda:
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torch.cuda.set_device(args.gpu_number)
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conv_rnn.cuda()
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_, _, test_set = data.SSTDataset.load_sst_sets("data")
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test_loader = utils.data.DataLoader(test_set, batch_size=len(test_set), collate_fn=conv_rnn.convert_dataset)
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conv_rnn.eval()
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for test_in, test_out in test_loader:
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scores = conv_rnn(*test_in)
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n_correct = (torch.max(scores, 1)[1].view(-1).data == test_out.data).float().sum()
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accuracy = n_correct / len(test_set)
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print("Test set accuracy: {}".format(accuracy))
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if __name__ == "__main__":
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main()
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