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Update generate_model_from_checkpoint.py (#901)
This commit is contained in:
Yifan Yang
GitHub
parent
c102e7fbf0
commit
c34ee67691
@ -1,203 +0,0 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: 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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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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(1) use the checkpoint exp_dir/epoch-xxx.pt
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./pruned_transducer_stateless7/generate_averaged_model.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`.
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You can later load it by `torch.load("epoch-28-avg-15.pt")`.
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(2) use the checkpoint exp_dir/checkpoint-iter.pt
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./pruned_transducer_stateless7/generate_averaged_model.py \
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--iter 22000 \
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--avg 5 \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`.
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You can later load it by `torch.load("iter-22000-avg-5.pt")`.
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"""
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import argparse
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from pathlib import Path
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from typing import Dict, List
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import sentencepiece as spm
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import torch
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from asr_datamodule import LibriSpeechAsrDataModule
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from train import add_model_arguments, get_params, get_transducer_model
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from icefall.checkpoint import (
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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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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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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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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"--exp-dir",
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type=str,
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default="pruned_transducer_stateless7/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500/bpe.model",
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help="Path to the BPE model",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
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)
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add_model_arguments(parser)
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return parser
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@torch.no_grad()
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def main():
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parser = get_parser()
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LibriSpeechAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(args))
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if params.iter > 0:
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params.suffix = f"iter-{params.iter}-avg-{params.avg}"
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else:
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params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
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print("Script started")
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device = torch.device("cpu")
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print(f"Device: {device}")
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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> is defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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print("About to create model")
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model = get_transducer_model(params)
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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 --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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print(
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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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filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt"
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torch.save({"model": model.state_dict()}, filename)
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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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print(
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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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filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt"
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torch.save({"model": model.state_dict()}, filename)
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num_param = sum([p.numel() for p in model.parameters()])
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print(f"Number of model parameters: {num_param}")
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print("Done!")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,282 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: 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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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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(1) use the averaged model with checkpoint exp_dir/epoch-xxx.pt
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./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
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--epoch 28 \
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--avg 15 \
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--use-averaged-model True \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `epoch-28-avg-15-use-averaged-model.pt` in the given `exp_dir`.
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You can later load it by `torch.load("epoch-28-avg-15-use-averaged-model.pt")`.
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(2) use the averaged model with checkpoint exp_dir/checkpoint-iter.pt
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./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
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--iter 22000 \
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--avg 5 \
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--use-averaged-model True \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `iter-22000-avg-5-use-averaged-model.pt` in the given `exp_dir`.
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You can later load it by `torch.load("iter-22000-avg-5-use-averaged-model.pt")`.
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(3) use the original model with checkpoint exp_dir/epoch-xxx.pt
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./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
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--epoch 28 \
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--avg 15 \
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--use-averaged-model False \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`.
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You can later load it by `torch.load("epoch-28-avg-15.pt")`.
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(4) use the original model with checkpoint exp_dir/checkpoint-iter.pt
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./pruned_transducer_stateless7/generate_model_from_checkpoint.py \
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--iter 22000 \
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--avg 5 \
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--use-averaged-model False \
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--exp-dir ./pruned_transducer_stateless7/exp
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It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`.
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You can later load it by `torch.load("iter-22000-avg-5.pt")`.
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"""
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import argparse
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from pathlib import Path
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from typing import Dict, List
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import sentencepiece as spm
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import torch
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from train import add_model_arguments, get_params, get_transducer_model
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from icefall.utils import str2bool
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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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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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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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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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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."
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"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="pruned_transducer_stateless7/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500/bpe.model",
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help="Path to the BPE model",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
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)
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add_model_arguments(parser)
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return parser
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@torch.no_grad()
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def main():
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parser = get_parser()
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(args))
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if params.iter > 0:
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params.suffix = f"iter-{params.iter}-avg-{params.avg}"
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else:
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params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
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if params.use_averaged_model:
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params.suffix += "-use-averaged-model"
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print("Script started")
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device = torch.device("cpu")
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print(f"Device: {device}")
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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> is defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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print("About to create model")
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model = get_transducer_model(params)
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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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print(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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filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt"
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torch.save({"model": model.state_dict()}, filename)
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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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filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt"
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torch.save({"model": model.state_dict()}, filename)
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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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print(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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filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt"
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torch.save({"model": model.state_dict()}, filename)
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else:
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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 --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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print(
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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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filename = (
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params.exp_dir
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/ f"iter-{params.iter}-avg-{params.avg}-use-averaged-model.pt"
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)
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torch.save({"model": model.state_dict()}, filename)
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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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print(
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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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filename = (
|
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params.exp_dir
|
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/ f"epoch-{params.epoch}-avg-{params.avg}-use-averaged-model.pt"
|
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)
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torch.save({"model": model.state_dict()}, filename)
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|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
print(f"Number of model parameters: {num_param}")
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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Reference in New Issue
Block a user