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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) | |