| |
| |
| |
|
|
| import torch |
| import torch.nn as nn |
| from torch.nn.modules.batchnorm import _BatchNorm |
|
|
| __all__ = ["init_modules", "zero_last_gamma"] |
|
|
|
|
| def init_modules(model: nn.Module or 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=(init_params or _DEFAULT_INIT_PARAM["trunc_normal"]) |
| ) |
| 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) |
|
|
