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from mmengine.logging import MMLogger
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from mmengine.runner.checkpoint import _load_checkpoint
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from mmaction.registry import MODELS
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from .mobilenet_v2 import InvertedResidual, MobileNetV2
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from .resnet_tsm import TemporalShift
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@MODELS.register_module()
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class MobileNetV2TSM(MobileNetV2):
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"""MobileNetV2 backbone for TSM.
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Args:
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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 MobilNetV2.
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"""
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def __init__(self,
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num_segments=8,
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is_shift=True,
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shift_div=8,
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pretrained2d=True,
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**kwargs):
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super().__init__(**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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for m in self.modules():
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if isinstance(m, InvertedResidual) and \
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len(m.conv) == 3 and m.use_res_connect:
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m.conv[0] = TemporalShift(
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m.conv[0],
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num_segments=self.num_segments,
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shift_div=self.shift_div,
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)
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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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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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if self.pretrained:
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self.init_cfg = dict(
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type='Pretrained', checkpoint=self.pretrained)
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super().init_weights()
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