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https://github.com/k2-fsa/icefall.git
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add export.py
This commit is contained in:
Yifan Yang
parent
afc9f9c413
commit
90e758b908
@ -112,7 +112,6 @@ def main():
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for torch_v, onnx_v in zip(
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(torch_log_prob, torch_h0, torch_c0), (onnx_log_prob, onnx_h0, onnx_c0)
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):
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assert torch.allclose(torch_v, onnx_v, atol=1e-5), (
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torch_v.shape,
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onnx_v.shape,
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@ -5,16 +5,16 @@
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import argparse
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import logging
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from pathlib import Path
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from typing import Dict
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import onnx
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import torch
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from model import RnnLmModel
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from onnxruntime.quantization import QuantType, quantize_dynamic
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from train import get_params
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from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
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from icefall.utils import AttributeDict, str2bool
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from typing import Dict
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from train import get_params
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def add_meta_data(filename: str, meta_data: Dict[str, str]):
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@ -1,5 +1,7 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
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# Yifan Yang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -25,7 +27,12 @@ from pathlib import Path
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import torch
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from model import RnnLmModel
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from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.utils import AttributeDict, str2bool
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@ -37,18 +44,10 @@ def get_parser():
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parser.add_argument(
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"--epoch",
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type=int,
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default=29,
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help="It specifies the checkpoint to use for decoding."
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"Note: Epoch counts from 0.",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=5,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch'. ",
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default=30,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 1.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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@ -61,6 +60,35 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=9,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch' and '--iter'",
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)
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parser.add_argument(
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"--use-averaged-model",
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type=str2bool,
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default=True,
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help="Whether to load averaged model. Currently it only supports "
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"using --epoch. If True, it would decode with the averaged model "
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"over the epoch range from `epoch-avg` (excluded) to `epoch`."
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"Actually only the models with epoch number of `epoch-avg` and "
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"`epoch` are loaded for averaging. ",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="rnnlm/exp",
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help="""It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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)
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parser.add_argument(
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"--vocab-size",
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type=int,
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@ -98,20 +126,14 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="rnn_lm/exp",
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help="""It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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""",
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)
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parser.add_argument(
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"--jit",
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type=str2bool,
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default=True,
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default=False,
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help="""True to save a model after applying torch.jit.script.
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It will generate a file named cpu_jit.pt
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Check ./jit_pretrained.py for how to use it.
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""",
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)
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@ -144,37 +166,82 @@ def main():
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model.to(device)
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if params.iter > 0:
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filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
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: params.avg
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]
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if len(filenames) == 0:
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raise ValueError(
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f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
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)
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elif len(filenames) < params.avg:
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raise ValueError(
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f"Not enough checkpoints ({len(filenames)}) found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(
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average_checkpoints(filenames, device=device), strict=False
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)
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elif params.avg == 1:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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if not params.use_averaged_model:
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if params.iter > 0:
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filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
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: params.avg
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]
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if len(filenames) == 0:
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raise ValueError(
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f"No checkpoints found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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elif len(filenames) < params.avg:
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raise ValueError(
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f"Not enough checkpoints ({len(filenames)}) found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(average_checkpoints(filenames, device=device))
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elif params.avg == 1:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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else:
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start = params.epoch - params.avg + 1
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filenames = []
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for i in range(start, params.epoch + 1):
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if i >= 1:
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filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(average_checkpoints(filenames, device=device))
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else:
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start = params.epoch - params.avg + 1
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filenames = []
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for i in range(start, params.epoch + 1):
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if i >= 0:
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filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(
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average_checkpoints(filenames, device=device), strict=False
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)
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if params.iter > 0:
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filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
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: params.avg + 1
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]
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if len(filenames) == 0:
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raise ValueError(
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f"No checkpoints found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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elif len(filenames) < params.avg + 1:
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raise ValueError(
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f"Not enough checkpoints ({len(filenames)}) found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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filename_start = filenames[-1]
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filename_end = filenames[0]
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logging.info(
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"Calculating the averaged model over iteration checkpoints"
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f" from {filename_start} (excluded) to {filename_end}"
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)
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model.to(device)
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model.load_state_dict(
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average_checkpoints_with_averaged_model(
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filename_start=filename_start,
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filename_end=filename_end,
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device=device,
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)
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)
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else:
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assert params.avg > 0, params.avg
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start = params.epoch - params.avg
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assert start >= 1, start
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filename_start = f"{params.exp_dir}/epoch-{start}.pt"
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filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
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logging.info(
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f"Calculating the averaged model over epoch range from "
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f"{start} (excluded) to {params.epoch}"
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)
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model.to(device)
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model.load_state_dict(
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average_checkpoints_with_averaged_model(
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filename_start=filename_start,
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filename_end=filename_end,
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device=device,
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)
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)
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model.to("cpu")
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model.eval()
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