mirror of
https://github.com/storytold/torchscript-models.git
synced 2026-10-19 07:42:59 +00:00
Brandon Thomas
101 lines
3.5 KiB
Python
Executable File
101 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python3
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# From: https://github.com/pytorch/pytorch/issues/20356#issuecomment-545572400
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import torch
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# Import glow-tts code
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import sys
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sys.path.append("./glow-tts")
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import utils
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import models
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import commons
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from text.symbols import symbols
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# Nvidia Apex
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#from apex import amp
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#from apex.parallel import DistributedDataParallel as DDP
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# Data
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import numpy as np
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from text import text_to_sequence, cmudict
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trace_model = False
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if sys.argv[1] == 'true':
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trace_model = True
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elif sys.argv[1] == 'false':
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trace_model = False
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else:
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raise Exception('Scripting or tracing?')
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torch.set_grad_enabled(False)
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def load(model_checkpoint_path):
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model_dir = "./logs/base/"
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hps = utils.get_hparams_from_dir(model_dir)
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print('Checkpoint path: {}'.format(model_checkpoint_path))
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with torch.no_grad():
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# NB(bt): This should fix the following:
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# RuntimeError: Cannot insert a Tensor that requires grad as a constant.
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model = models.FlowGenerator(
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len(symbols),
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out_channels=hps.data.n_mel_channels,
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**hps.model) #.to("cuda")
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utils.load_checkpoint(model_checkpoint_path, model)
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model.decoder.store_inverse() # do not calcuate jacobians for fast decoding
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_ = model.eval()
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return (hps, model)
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#model_checkpoint_path = '/home/bt/models/glow-tts-jaywalnut/pretrained.pth'
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model_checkpoint_path = '/home/bt/models/glow-tts-jaywalnut/glow_tts_ljs_txlearn_arpabet_2020-06-01_G_1689.pth'
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model_checkpoint_path = '/home/bt/models/glow-tts-jaywalnut/glow_tts_ljs_txlearn_arpabet_2020-06-01_G_2590.pth'
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model_checkpoint_path = '/home/bt/models/glow-tts-jaywalnut/glow_tts_ljs_txlearn_arpabet_2020-06-01_G_3614.pth'
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hps, glow_model = load(model_checkpoint_path)
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output_filename = 'glow_tts_ljs_txlearn_arpabet_2020-06-01_G_2590.torchjit'
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output_filename = 'glow_tts_ljs_txlearn_arpabet_2020-06-01_G_3614.torchjit'
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cmu_dict = cmudict.CMUDict(hps.data.cmudict_path)
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tst_stn = "This is a test of the machine learning model. How does this sound?"
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seq = text_to_sequence(tst_stn, ['english_cleaners'], cmu_dict)
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sequence = np.array(seq)[None, :] # NB: None adds a new axis.
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print("".join([symbols[c] for c in sequence[0]]))
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torched = torch.from_numpy(sequence)
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x_tst = torch.autograd.Variable(torched).long()
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x_tst_lengths = torch.tensor([x_tst.shape[1]])
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print('\n tst_stn', len(tst_stn), tst_stn)
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print('\n seq', len(seq), seq)
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print('\n sequence', len(sequence), sequence.size, sequence)
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print('\n torched', len(torched), torched.shape, torched)
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print('\n x_tst', len(x_tst), x_tst)
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print('\n x_tst_lengths', len(x_tst_lengths), x_tst_lengths)
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# NB: Tracing evaluates the model on input and unrolls and hardcodes branching
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# and loops. Scripting allows these to remain by converting the entire program
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# to TorchScript. Scripting seems MUCH faster, is also harder get working.
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#trace_model = True # TODO: Try True.
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if trace_model:
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print('Tracing model and saving as: {}'.format(output_filename))
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#mel_file = '/home/bt/dev/voder/data/mels/LJ002-0320.mel'
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#mel = torch.load(mel_file, map_location=torch.device('cpu'))
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# NB: Getting `Tracing failed sanity checks! Graphs differed across invocations!`
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inputs = (x_tst, x_tst_lengths)
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#traced_script_module = torch.jit.trace(glow_model, inputs, check_trace=True)
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traced_script_module = torch.jit.trace(glow_model, inputs, check_trace=False)
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traced_script_module.save(output_filename)
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else:
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print('Saving script model as: {}'.format(output_filename))
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container = torch.jit.script(glow_model)
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container.save(output_filename)
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