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* sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * bugfix * update with main (#1631) * update seaco finetune * v1.0.24 --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * update with main (#1638) * update seaco finetune * v1.0.24 * update rwkv template --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * whisper * whisper * update style * update style --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
#!/usr/bin/env python3
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"""Initialize modules for espnet2 neural networks."""
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import math
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import torch
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def initialize(model: torch.nn.Module, init: str):
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"""Initialize weights of a neural network module.
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Parameters are initialized using the given method or distribution.
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Custom initialization routines can be implemented into submodules
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as function `espnet_initialization_fn` within the custom module.
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Args:
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model: Target.
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init: Method of initialization.
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"""
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# weight init
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for p in model.parameters():
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if p.dim() > 1:
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if init == "xavier_uniform":
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torch.nn.init.xavier_uniform_(p.data)
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elif init == "xavier_normal":
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torch.nn.init.xavier_normal_(p.data)
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elif init == "kaiming_uniform":
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torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
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elif init == "kaiming_normal":
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torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
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else:
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raise ValueError("Unknown initialization: " + init)
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# bias init
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for p in model.parameters():
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if p.dim() == 1:
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p.data.zero_()
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# reset some modules with default init
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for m in model.modules():
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if isinstance(m, (torch.nn.Embedding, torch.nn.LayerNorm, torch.nn.GroupNorm)):
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m.reset_parameters()
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if hasattr(m, "espnet_initialization_fn"):
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m.espnet_initialization_fn()
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# TODO(xkc): Hacking s3prl_frontend and wav2vec2encoder initialization
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if getattr(model, "encoder", None) and getattr(
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model.encoder, "reload_pretrained_parameters", None
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):
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model.encoder.reload_pretrained_parameters()
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if getattr(model, "frontend", None) and getattr(
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model.frontend, "reload_pretrained_parameters", None
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):
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model.frontend.reload_pretrained_parameters()
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