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# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from typing import Union
import torch
import torch.nn as nn
from torch.nn.modules.batchnorm import _BatchNorm
__all__ = ["init_modules", "zero_last_gamma"]
def init_modules(model: Union[nn.Module, list[nn.Module]], init_type="trunc_normal") -> None:
_DEFAULT_INIT_PARAM = {"trunc_normal": 0.02}
if isinstance(model, list):
for sub_module in model:
init_modules(sub_module, init_type)
else:
init_params = init_type.split("@")
init_params = float(init_params[1]) if len(init_params) > 1 else None
if init_type.startswith("trunc_normal"):
init_func = lambda param: nn.init.trunc_normal_(
param, std=(_DEFAULT_INIT_PARAM["trunc_normal"] if init_params is None else init_params)
)
else:
raise NotImplementedError
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.Linear, nn.ConvTranspose2d)):
init_func(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Embedding):
init_func(m.weight)
elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
m.weight.data.fill_(1)
m.bias.data.zero_()
else:
weight = getattr(m, "weight", None)
bias = getattr(m, "bias", None)
if isinstance(weight, torch.nn.Parameter):
init_func(weight)
if isinstance(bias, torch.nn.Parameter):
bias.data.zero_()
def zero_last_gamma(model: nn.Module, init_val=0) -> None:
import efficientvit.models.nn.ops as ops
for m in model.modules():
if isinstance(m, ops.ResidualBlock) and isinstance(m.shortcut, ops.IdentityLayer):
if isinstance(m.main, (ops.DSConv, ops.MBConv, ops.FusedMBConv)):
parent_module = m.main.point_conv
elif isinstance(m.main, ops.ResBlock):
parent_module = m.main.conv2
elif isinstance(m.main, ops.ConvLayer):
parent_module = m.main
elif isinstance(m.main, (ops.LiteMLA)):
parent_module = m.main.proj
else:
parent_module = None
if parent_module is not None:
norm = getattr(parent_module, "norm", None)
if norm is not None:
nn.init.constant_(norm.weight, init_val)
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