From 30c40c643c19f6e2ac8679fa76d09d0f9ceccc65 Mon Sep 17 00:00:00 2001
From: chenmengzheAAA <123789350+chenmengzheAAA@users.noreply.github.com>
Date: 星期四, 14 九月 2023 18:00:43 +0800
Subject: [PATCH] Update modelscope_models.md
---
funasr/export/export_model.py | 89 +++++++++++++++++++++++++-------------------
1 files changed, 50 insertions(+), 39 deletions(-)
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index b69eeee..6ab9408 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -1,16 +1,12 @@
-import json
-from typing import Union, Dict
-from pathlib import Path
-from typeguard import check_argument_types
-
import os
-import logging
import torch
-
-from funasr.export.models import get_model
-import numpy as np
import random
-from funasr.utils.types import str2bool
+import logging
+import numpy as np
+from pathlib import Path
+from typing import Union, Dict, List
+from funasr.export.models import get_model
+from funasr.utils.types import str2bool, str2triple_str
# torch_version = float(".".join(torch.__version__.split(".")[:2]))
# assert torch_version > 1.9
@@ -24,18 +20,16 @@
fallback_num: int = 0,
audio_in: str = None,
calib_num: int = 200,
+ model_revision: str = None,
):
- assert check_argument_types()
self.set_all_random_seed(0)
- if cache_dir is None:
- cache_dir = Path.home() / ".cache" / "export"
- self.cache_dir = Path(cache_dir)
+ self.cache_dir = cache_dir
self.export_config = dict(
feats_dim=560,
onnx=False,
)
- print("output dir: {}".format(self.cache_dir))
+
self.onnx = onnx
self.device = device
self.quant = quant
@@ -43,6 +37,7 @@
self.frontend = None
self.audio_in = audio_in
self.calib_num = calib_num
+ self.model_revision = model_revision
def _export(
@@ -52,7 +47,7 @@
verbose: bool = False,
):
- export_dir = self.cache_dir / tag_name.replace(' ', '-')
+ export_dir = self.cache_dir
os.makedirs(export_dir, exist_ok=True)
# export encoder1
@@ -61,14 +56,22 @@
model,
self.export_config,
)
- model.eval()
- # self._export_onnx(model, verbose, export_dir)
- if self.onnx:
- self._export_onnx(model, verbose, export_dir)
+ if isinstance(model, List):
+ for m in model:
+ m.eval()
+ if self.onnx:
+ self._export_onnx(m, verbose, export_dir)
+ else:
+ self._export_torchscripts(m, verbose, export_dir)
+ print("output dir: {}".format(export_dir))
else:
- self._export_torchscripts(model, verbose, export_dir)
-
- print("output dir: {}".format(export_dir))
+ model.eval()
+ # self._export_onnx(model, verbose, export_dir)
+ if self.onnx:
+ self._export_onnx(model, verbose, export_dir)
+ else:
+ self._export_torchscripts(model, verbose, export_dir)
+ print("output dir: {}".format(export_dir))
def _torch_quantize(self, model):
@@ -173,7 +176,8 @@
model_dir = tag_name
if model_dir.startswith('damo'):
from modelscope.hub.snapshot_download import snapshot_download
- model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
+ model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir, revision=self.model_revision)
+ self.cache_dir = model_dir
if mode is None:
import json
@@ -193,6 +197,7 @@
config, model_file, cmvn_file, 'cpu'
)
self.frontend = model.frontend
+ self.export_config["feats_dim"] = 560
elif mode.startswith('offline'):
from funasr.tasks.vad import VADTask
config = os.path.join(model_dir, 'vad.yaml')
@@ -230,7 +235,7 @@
# model_script = torch.jit.script(model)
model_script = model #torch.jit.trace(model)
model_path = os.path.join(path, f'{model.model_name}.onnx')
-
+ # if not os.path.exists(model_path):
torch.onnx.export(
model_script,
dummy_input,
@@ -246,24 +251,26 @@
from onnxruntime.quantization import QuantType, quantize_dynamic
import onnx
quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
- onnx_model = onnx.load(model_path)
- nodes = [n.name for n in onnx_model.graph.node]
- nodes_to_exclude = [m for m in nodes if 'output' in m]
- quantize_dynamic(
- model_input=model_path,
- model_output=quant_model_path,
- op_types_to_quantize=['MatMul'],
- per_channel=True,
- reduce_range=False,
- weight_type=QuantType.QUInt8,
- nodes_to_exclude=nodes_to_exclude,
- )
+ if not os.path.exists(quant_model_path):
+ onnx_model = onnx.load(model_path)
+ nodes = [n.name for n in onnx_model.graph.node]
+ nodes_to_exclude = [m for m in nodes if 'output' in m or 'bias_encoder' in m or 'bias_decoder' in m]
+ quantize_dynamic(
+ model_input=model_path,
+ model_output=quant_model_path,
+ op_types_to_quantize=['MatMul'],
+ per_channel=True,
+ reduce_range=False,
+ weight_type=QuantType.QUInt8,
+ nodes_to_exclude=nodes_to_exclude,
+ )
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
- parser.add_argument('--model-name', type=str, required=True)
+ # parser.add_argument('--model-name', type=str, required=True)
+ parser.add_argument('--model-name', type=str, action="append", required=True, default=[])
parser.add_argument('--export-dir', type=str, required=True)
parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
parser.add_argument('--device', type=str, default='cpu', help='["cpu", "cuda"]')
@@ -271,6 +278,7 @@
parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
+ parser.add_argument('--model_revision', type=str, default=None, help='model_revision')
args = parser.parse_args()
export_model = ModelExport(
@@ -281,5 +289,8 @@
fallback_num=args.fallback_num,
audio_in=args.audio_in,
calib_num=args.calib_num,
+ model_revision=args.model_revision,
)
- export_model.export(args.model_name)
+ for model_name in args.model_name:
+ print("export model: {}".format(model_name))
+ export_model.export(model_name)
--
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