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# 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
@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()
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,
)
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)
logger.info(msg)
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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