wehub-resource-sync
103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Optional
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import numpy as np
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import torch
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import torch.nn as nn
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from ...apps.trainer.run_config import Scheduler
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from ...models.nn.ops import IdentityLayer, ResidualBlock
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from ...models.utils import build_kwargs_from_config
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__all__ = ["apply_drop_func"]
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def apply_drop_func(network: nn.Module, drop_config: Optional[dict[str, Any]]) -> None:
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if drop_config is None:
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return
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drop_lookup_table = {
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"droppath": apply_droppath,
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}
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drop_func = drop_lookup_table[drop_config["name"]]
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drop_kwargs = build_kwargs_from_config(drop_config, drop_func)
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drop_func(network, **drop_kwargs)
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def apply_droppath(
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network: nn.Module,
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drop_prob: float,
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linear_decay=True,
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scheduled=True,
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skip=0,
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) -> None:
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all_valid_blocks = []
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for m in network.modules():
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for name, sub_module in m.named_children():
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if isinstance(sub_module, ResidualBlock) and isinstance(sub_module.shortcut, IdentityLayer):
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all_valid_blocks.append((m, name, sub_module))
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all_valid_blocks = all_valid_blocks[skip:]
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for i, (m, name, sub_module) in enumerate(all_valid_blocks):
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prob = drop_prob * (i + 1) / len(all_valid_blocks) if linear_decay else drop_prob
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new_module = DropPathResidualBlock(
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sub_module.main,
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sub_module.shortcut,
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sub_module.post_act,
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sub_module.pre_norm,
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prob,
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scheduled,
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)
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m._modules[name] = new_module
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class DropPathResidualBlock(ResidualBlock):
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def __init__(
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self,
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main: nn.Module,
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shortcut: Optional[nn.Module],
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post_act=None,
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pre_norm: Optional[nn.Module] = None,
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######################################
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drop_prob: float = 0,
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scheduled=True,
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):
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super().__init__(main, shortcut, post_act, pre_norm)
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self.drop_prob = drop_prob
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self.scheduled = scheduled
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if not self.training or self.drop_prob == 0 or not isinstance(self.shortcut, IdentityLayer):
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return ResidualBlock.forward(self, x)
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else:
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drop_prob = self.drop_prob
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if self.scheduled:
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drop_prob *= np.clip(Scheduler.PROGRESS, 0, 1)
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1)
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random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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res = self.forward_main(x) / keep_prob * random_tensor + self.shortcut(x)
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if self.post_act:
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res = self.post_act(res)
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return res
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