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import torch.nn as nn
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from mmengine.logging import MMLogger
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from mmengine.runner.checkpoint import (_load_checkpoint,
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_load_checkpoint_with_prefix)
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from mmpretrain.models import MobileOne
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from mmaction.registry import MODELS
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from .resnet_tsm import TemporalShift
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@MODELS.register_module()
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class MobileOneTSM(MobileOne):
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"""MobileOne backbone for TSM.
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Args:
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arch (str | dict): MobileOne architecture. If use string, choose
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from 's0', 's1', 's2', 's3' and 's4'. If use dict, it should
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have below keys:
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- num_blocks (Sequence[int]): Number of blocks in each stage.
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- width_factor (Sequence[float]): Width factor in each stage.
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- num_conv_branches (Sequence[int]): Number of conv branches
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in each stage.
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- num_se_blocks (Sequence[int]): Number of SE layers in each
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stage, all the SE layers are placed in the subsequent order
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in each stage.
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Defaults to 's0'.
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num_segments (int): Number of frame segments. Defaults to 8.
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is_shift (bool): Whether to make temporal shift in reset layers.
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Defaults to True.
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shift_div (int): Number of div for shift. Defaults to 8.
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pretraind2d (bool): Whether to load pretrained 2D model.
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Defaults to True.
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**kwargs (keyword arguments, optional): Arguments for MobileOne.
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"""
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def __init__(self,
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arch: str,
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num_segments: int = 8,
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is_shift: bool = True,
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shift_div: int = 8,
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pretrained2d: bool = True,
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**kwargs):
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super().__init__(arch, **kwargs)
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self.num_segments = num_segments
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self.is_shift = is_shift
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self.shift_div = shift_div
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self.pretrained2d = pretrained2d
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self.init_structure()
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def make_temporal_shift(self):
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"""Make temporal shift for some layers.
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To make reparameterization work, we can only build the shift layer
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before the 'block', instead of the 'blockres'
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"""
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def make_block_temporal(stage, num_segments):
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"""Make temporal shift on some blocks.
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Args:
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stage (nn.Module): Model layers to be shifted.
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num_segments (int): Number of frame segments.
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Returns:
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nn.Module: The shifted blocks.
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"""
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blocks = list(stage.children())
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for i, b in enumerate(blocks):
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blocks[i] = TemporalShift(
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b, num_segments=num_segments, shift_div=self.shift_div)
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return nn.Sequential(*blocks)
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self.stage0 = make_block_temporal(
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nn.Sequential(self.stage0), self.num_segments)[0]
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for i in range(1, 5):
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temporal_stage = make_block_temporal(
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getattr(self, f'stage{i}'), self.num_segments)
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setattr(self, f'stage{i}', temporal_stage)
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def init_structure(self):
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"""Initiate the parameters either from existing checkpoint or from
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scratch."""
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if self.is_shift:
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self.make_temporal_shift()
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def load_original_weights(self, logger):
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assert self.init_cfg.get('type') == 'Pretrained', (
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'Please specify '
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'init_cfg to use pretrained 2d checkpoint')
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self.pretrained = self.init_cfg.get('checkpoint')
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prefix = self.init_cfg.get('prefix')
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if prefix is not None:
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original_state_dict = _load_checkpoint_with_prefix(
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prefix, self.pretrained, map_location='cpu')
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else:
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original_state_dict = _load_checkpoint(
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self.pretrained, map_location='cpu')
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if 'state_dict' in original_state_dict:
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original_state_dict = original_state_dict['state_dict']
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wrapped_layers_map = dict()
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for name, module in self.named_modules():
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ori_name = name
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for wrap_prefix in ['.net']:
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if wrap_prefix in ori_name:
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ori_name = ori_name.replace(wrap_prefix, '')
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wrapped_layers_map[ori_name] = name
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for param_name in list(original_state_dict.keys()):
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layer_name = '.'.join(param_name.split('.')[:-1])
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if layer_name in wrapped_layers_map:
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wrapped_name = param_name.replace(
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layer_name, wrapped_layers_map[layer_name])
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original_state_dict[wrapped_name] = original_state_dict.pop(
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param_name)
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msg = self.load_state_dict(original_state_dict, strict=True)
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logger.info(msg)
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def init_weights(self):
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"""Initiate the parameters either from existing checkpoint or from
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scratch."""
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if self.pretrained2d:
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logger = MMLogger.get_current_instance()
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self.load_original_weights(logger)
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else:
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super().init_weights()
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def forward(self, x):
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"""unpack tuple result."""
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x = super().forward(x)
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if isinstance(x, tuple):
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assert len(x) == 1
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x = x[0]
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return x
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