Source code for mmaction.models.backbones.mobilenet_v2_tsm

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.logging import MMLogger
from mmengine.runner.checkpoint import _load_checkpoint

from mmaction.registry import MODELS
from .mobilenet_v2 import InvertedResidual, MobileNetV2
from .resnet_tsm import TemporalShift

[docs]@MODELS.register_module() class MobileNetV2TSM(MobileNetV2): """MobileNetV2 backbone for TSM. Args: num_segments (int): Number of frame segments. Defaults to 8. is_shift (bool): Whether to make temporal shift in reset layers. Defaults to True. shift_div (int): Number of div for shift. Defaults to 8. pretraind2d (bool): Whether to load pretrained 2D model. Defaults to True. **kwargs (keyword arguments, optional): Arguments for MobilNetV2. """ def __init__(self, num_segments=8, is_shift=True, shift_div=8, pretrained2d=True, **kwargs): super().__init__(**kwargs) self.num_segments = num_segments self.is_shift = is_shift self.shift_div = shift_div self.pretrained2d = pretrained2d self.init_structure()
[docs] def make_temporal_shift(self): """Make temporal shift for some layers.""" for m in self.modules(): if isinstance(m, InvertedResidual) and \ len(m.conv) == 3 and m.use_res_connect: m.conv[0] = TemporalShift( m.conv[0], num_segments=self.num_segments, shift_div=self.shift_div, )
[docs] def init_structure(self): """Initiate the parameters either from existing checkpoint or from scratch.""" if self.is_shift: self.make_temporal_shift()
def load_original_weights(self, logger): original_state_dict = _load_checkpoint( self.pretrained, map_location='cpu') if 'state_dict' in original_state_dict: original_state_dict = original_state_dict['state_dict'] wrapped_layers_map = dict() for name, module in self.named_modules(): ori_name = name for wrap_prefix in ['.net']: if wrap_prefix in ori_name: ori_name = ori_name.replace(wrap_prefix, '') wrapped_layers_map[ori_name] = name # convert wrapped keys for param_name in list(original_state_dict.keys()): layer_name = '.'.join(param_name.split('.')[:-1]) if layer_name in wrapped_layers_map: wrapped_name = param_name.replace( layer_name, wrapped_layers_map[layer_name]) original_state_dict[wrapped_name] = original_state_dict.pop( param_name) msg = self.load_state_dict(original_state_dict, strict=True)
[docs] def init_weights(self): """Initiate the parameters either from existing checkpoint or from scratch.""" if self.pretrained2d: logger = MMLogger.get_current_instance() self.load_original_weights(logger) else: if self.pretrained: self.init_cfg = dict( type='Pretrained', checkpoint=self.pretrained) super().init_weights()
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