| | |
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| | | pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
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|---|
| | | list_ls.append(torch.cat([l, pad_l], 0))
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|---|
| | | return torch.stack(list_ls, 0), fires
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|---|
| | |
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| | |
|
|---|
| | | def CifPredictorV2_test():
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|---|
| | | x = torch.rand([2, 21, 2])
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|---|
| | | x_len = torch.IntTensor([6, 21])
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|---|
| | | |
|---|
| | | mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
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|---|
| | | x = x * mask[:, :, None]
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| | | |
|---|
| | | predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
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| | | # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
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| | | predictor_scripts.save('test.pt')
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|---|
| | | loaded = torch.jit.load('test.pt')
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|---|
| | | cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
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|---|
| | | # print(cif_output)
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|---|
| | | print(predictor_scripts.code)
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|---|
| | | # predictor = CifPredictorV2(2, 1, 1)
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|---|
| | | # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
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|---|
| | | print(cif_output)
|
|---|
| | |
|
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| | |
|
|---|
| | | def CifPredictorV2_export_test():
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|---|
| | | x = torch.rand([2, 21, 2])
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|---|
| | | x_len = torch.IntTensor([6, 21])
|
|---|
| | | |
|---|
| | | mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
|
|---|
| | | x = x * mask[:, :, None]
|
|---|
| | | |
|---|
| | | # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
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|---|
| | | # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
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|---|
| | | predictor = CifPredictorV2(2, 1, 1)
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|---|
| | | predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :]))
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|---|
| | | predictor_trace.save('test_trace.pt')
|
|---|
| | | loaded = torch.jit.load('test_trace.pt')
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|---|
| | | |
|---|
| | | x = torch.rand([3, 30, 2])
|
|---|
| | | x_len = torch.IntTensor([6, 20, 30])
|
|---|
| | | mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
|
|---|
| | | x = x * mask[:, :, None]
|
|---|
| | | cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
|
|---|
| | | print(cif_output)
|
|---|
| | | # print(predictor_trace.code)
|
|---|
| | | # predictor = CifPredictorV2(2, 1, 1)
|
|---|
| | | # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
|
|---|
| | | # print(cif_output)
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | if __name__ == '__main__':
|
|---|
| | | # CifPredictorV2_test()
|
|---|
| | | CifPredictorV2_export_test() |
|---|
