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fix lint

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This commit is contained in:
lyuxiang.lx
2025-02-06 16:07:13 +08:00
parent 24f796a2b1
commit 2a3e033ee1
17 changed files with 187 additions and 135 deletions
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36
cosyvoice/bin/export_onnx.py
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@@ -99,7 +99,7 @@ def main():
option.intra_op_num_threads = 1
providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
sess_options=option, providers=providers)
sess_options=option, providers=providers)
for _ in tqdm(range(10)):
x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
@@ -131,31 +131,33 @@ def main():
torch.onnx.export(
estimator,
(x, mask, mu, t, spks, cond,
cache['down_blocks_conv_cache'],
cache['down_blocks_kv_cache'],
cache['mid_blocks_conv_cache'],
cache['mid_blocks_kv_cache'],
cache['up_blocks_conv_cache'],
cache['up_blocks_kv_cache'],
cache['final_blocks_conv_cache']),
cache['down_blocks_conv_cache'],
cache['down_blocks_kv_cache'],
cache['mid_blocks_conv_cache'],
cache['mid_blocks_kv_cache'],
cache['up_blocks_conv_cache'],
cache['up_blocks_kv_cache'],
cache['final_blocks_conv_cache']),
'{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
export_params=True,
opset_version=18,
do_constant_folding=True,
input_names=['x', 'mask', 'mu', 't', 'spks', 'cond', 'down_blocks_conv_cache', 'down_blocks_kv_cache', 'mid_blocks_conv_cache', 'mid_blocks_kv_cache', 'up_blocks_conv_cache', 'up_blocks_kv_cache', 'final_blocks_conv_cache'],
output_names=['estimator_out', 'down_blocks_conv_cache_out', 'down_blocks_kv_cache_out', 'mid_blocks_conv_cache_out', 'mid_blocks_kv_cache_out', 'up_blocks_conv_cache_out', 'up_blocks_kv_cache_out', 'final_blocks_conv_cache_out'],
input_names=['x', 'mask', 'mu', 't', 'spks', 'cond', 'down_blocks_conv_cache', 'down_blocks_kv_cache', 'mid_blocks_conv_cache', 'mid_blocks_kv_cache',
'up_blocks_conv_cache', 'up_blocks_kv_cache', 'final_blocks_conv_cache'],
output_names=['estimator_out', 'down_blocks_conv_cache_out', 'down_blocks_kv_cache_out', 'mid_blocks_conv_cache_out', 'mid_blocks_kv_cache_out',
'up_blocks_conv_cache_out', 'up_blocks_kv_cache_out', 'final_blocks_conv_cache_out'],
dynamic_axes={
'x': {2: 'seq_len'},
'mask': {2: 'seq_len'},
'mu': {2: 'seq_len'},
'cond': {2: 'seq_len'},
'down_blocks_kv_cache': {3: 'seq_len'},
'mid_blocks_kv_cache': {3: 'seq_len'},
'up_blocks_kv_cache': {3: 'seq_len'},
'down_blocks_kv_cache': {3: 'cache_in_len'},
'mid_blocks_kv_cache': {3: 'cache_in_len'},
'up_blocks_kv_cache': {3: 'cache_in_len'},
'estimator_out': {2: 'seq_len'},
'down_blocks_kv_cache_out': {3: 'seq_len'},
'mid_blocks_kv_cache_out': {3: 'seq_len'},
'up_blocks_kv_cache_out': {3: 'seq_len'},
'down_blocks_kv_cache_out': {3: 'cache_out_len'},
'mid_blocks_kv_cache_out': {3: 'cache_out_len'},
'up_blocks_kv_cache_out': {3: 'cache_out_len'},
}
)
@@ -165,7 +167,7 @@ def main():
option.intra_op_num_threads = 1
providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
sess_options=option, providers=providers)
sess_options=option, providers=providers)
for _ in tqdm(range(10)):
x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)