text: # Code for "TSM: Temporal Shift Module for Efficient Video Understanding"
  # arXiv:1811.08383
  # Ji Lin*, Chuang Gan, Song Han
  # {jilin, songhan}@mit.edu, ganchuang@csail.mit.edu
  
  from torch import nn
  from ops.basic_ops import ConsensusModule
  from ops.transforms import *
  from torch.nn.init import normal_, constant_
  
  
  class TSN(nn.Module):
      def __init__(self, num_class, num_segments, modality,
                   base_model='resnet101', new_length=None,
                   consensus_type='avg', before_softmax=True,
                   dropout=0.8, img_feature_dim=256,
                   crop_num=1, partial_bn=True, print_spec=True, pretrain='imagenet',
                   is_shift=False, shift_div=8, shift_place='blockres', fc_lr5=False,
                   temporal_pool=False, non_local=False):
          super(TSN, self).__init__()
          self.modality = modality
          self.num_segments = num_segments
          self.reshape = True
          self.before_softmax = before_softmax
          self.dropout = dropout
          self.crop_num = crop_num
          self.consensus_type = consensus_type
          self.img_feature_dim = img_feature_dim  # the dimension of the CNN feature to represent each frame
          self.pretrain = pretrain
  
          self.is_shift = is_shift
          self.shift_div = shift_div
          self.shift_place = shift_place
          self.base_model_name = base_model
          self.fc_lr5 = fc_lr5
          self.temporal_pool = temporal_pool
          self.non_local = non_local
  
          if not before_softmax and consensus_type != 'avg':
              raise ValueError("Only avg consensus can be used after Softmax")
  
          if new_length is None:
              self.new_length = 1 if modality == "RGB" else 5
          else:
              self.new_length = new_length
          if print_spec:
              print(("""
      Initializing TSN with base model: {}.
      TSN Configurations:
          input_modality:     {}
          num_segments:       {}
          new_length:         {}
          consensus_module:   {}
          dropout_ratio:      {}
          img_feature_dim:    {}
              """.format(base_model, self.modality, self.num_segments, self.new_length, consensus_type, self.dropout, self.img_feature_dim)))
  
          self._prepare_base_model(base_model)
  
          feature_dim = self._prepare_tsn(num_class)
  
          if self.modality == 'Flow':
              print("Converting the ImageNet model to a flow init model")
              self.base_model = self._construct_flow_model(self.base_model)
              print("Done. Flow model ready...")
          elif self.modality == 'RGBDiff':
              print("Converting the ImageNet model to RGB+Diff init model")
              self.base_model = self._construct_diff_model(self.base_model)
              print("Done. RGBDiff model ready.")
  
          self.consensus = ConsensusModule(consensus_type)
  
          if not self.before_softmax:
              self.softmax = nn.Softmax()
  
          self._enable_pbn = partial_bn
          if partial_bn:
              self.partialBN(True)
  
      def _prepare_tsn(self, num_class):
          feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features
          if self.dropout == 0:
              setattr(self.base_model, self.base_model.last_layer_name, nn.Linear(feature_dim, num_class))
              self.new_fc = None
          else:
              setattr(self.base_model, self.base_model.last_layer_name, nn.Dropout(p=self.dropout))
              self.new_fc = nn.Linear(feature_dim, num_class)
  
          std = 0.001
          if self.new_fc is None:
              normal_(getattr(self.base_model, self.base_model.last_layer_name).weight, 0, std)
              constant_(getattr(self.base_model, self.base_model.last_layer_name).bias, 0)
          else:
              if hasattr(self.new_fc, 'weight'):
                  normal_(self.new_fc.weight, 0, std)
                  constant_(self.new_fc.bias, 0)
          return feature_dim
  
      def _prepare_base_model(self, base_model):
          print('=> base model: {}'.format(base_model))
  
          if 'resnet' in base_model:
              self.base_model = getattr(torchvision.models, base_model)(True if self.pretrain == 'imagenet' else False)
              if self.is_shift:
                  print('Adding temporal shift...')
                  from ops.temporal_shift import make_temporal_shift
                  make_temporal_shift(self.base_model, self.num_segments,
                                      n_div=self.shift_div, place=self.shift_place, temporal_pool=self.temporal_pool)
  
              if self.non_local:
                  print('Adding non-local module...')
                  from ops.non_local import make_non_local
                  make_non_local(self.base_model, self.num_segments)
  
              self.base_model.last_layer_name = 'fc'
              self.input_size = 224
              self.input_mean = [0.485, 0.456, 0.406]
              self.input_std = [0.229, 0.224, 0.225]
  
              self.base_model.avgpool = nn.AdaptiveAvgPool2d(1)
  
              if self.modality == 'Flow':
                  self.input_mean = [0.5]
                  self.input_std = [np.mean(self.input_std)]
              elif self.modality == 'RGBDiff':
                  self.input_mean = [0.485, 0.456, 0.406] + [0] * 3 * self.new_length
                  self.input_std = self.input_std + [np.mean(self.input_std) * 2] * 3 * self.new_length
  
          elif base_model == 'mobilenetv2':
              from archs.mobilenet_v2 import mobilenet_v2, InvertedResidual
              self.base_model = mobilenet_v2(True if self.pretrain == 'imagenet' else False)
  
              self.base_model.last_layer_name = 'classifier'
              self.input_size = 224
              self.input_mean = [0.485, 0.456, 0.406]
              self.input_std = [0.229, 0.224, 0.225]
  
              self.base_model.avgpool = nn.AdaptiveAvgPool2d(1)
              if self.is_shift:
                  from ops.temporal_shift import TemporalShift
                  for m in self.base_model.modules():
                      if isinstance(m, InvertedResidual) and len(m.conv) == 8 and m.use_res_connect:
                          if self.print_spec:
                              print('Adding temporal shift... {}'.format(m.use_res_connect))
                          m.conv[0] = TemporalShift(m.conv[0], n_segment=self.num_segments, n_div=self.shift_div)
              if self.modality == 'Flow':
                  self.input_mean = [0.5]
                  self.input_std = [np.mean(self.input_std)]
              elif self.modality == 'RGBDiff':
                  self.input_mean = [0.485, 0.456, 0.406] + [0] * 3 * self.new_length
                  self.input_std = self.input_std + [np.mean(self.input_std) * 2] * 3 * self.new_length
  
          elif base_model == 'BNInception':
              from archs.bn_inception import bninception
              self.base_model = bninception(pretrained=self.pretrain)
              self.input_size = self.base_model.input_size
              self.input_mean = self.base_model.mean
              self.input_std = self.base_model.std
              self.base_model.last_layer_name = 'fc'
              if self.modality == 'Flow':
                  self.input_mean = [128]
              elif self.modality == 'RGBDiff':
                  self.input_mean = self.input_mean * (1 + self.new_length)
              if self.is_shift:
                  print('Adding temporal shift...')
                  self.base_model.build_temporal_ops(
                      self.num_segments, is_temporal_shift=self.shift_place, shift_div=self.shift_div)
          else:
              raise ValueError('Unknown base model: {}'.format(base_model))
  
      def train(self, mode=True):
          """
          Override the default train() to freeze the BN parameters
          :return:
          """
          super(TSN, self).train(mode)
          count = 0
          if self._enable_pbn and mode:
              print("Freezing BatchNorm2D except the first one.")
              for m in self.base_model.modules():
                  if isinstance(m, nn.BatchNorm2d):
                      count += 1
                      if count >= (2 if self._enable_pbn else 1):
                          m.eval()
                          # shutdown update in frozen mode
                          m.weight.requires_grad = False
                          m.bias.requires_grad = False
  
      def partialBN(self, enable):
          self._enable_pbn = enable
  
      def get_optim_policies(self):
          first_conv_weight = []
          first_conv_bias = []
          normal_weight = []
          normal_bias = []
          lr5_weight = []
          lr10_bias = []
          bn = []
          custom_ops = []
  
          conv_cnt = 0
          bn_cnt = 0
          for m in self.modules():
              if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv3d):
                  ps = list(m.parameters())
                  conv_cnt += 1
                  if conv_cnt == 1:
                      first_conv_weight.append(ps[0])
                      if len(ps) == 2:
                          first_conv_bias.append(ps[1])
                  else:
                      normal_weight.append(ps[0])
                      if len(ps) == 2:
                          normal_bias.append(ps[1])
              elif isinstance(m, torch.nn.Linear):
                  ps = list(m.parameters())
                  if self.fc_lr5:
                      lr5_weight.append(ps[0])
                  else:
                      normal_weight.append(ps[0])
                  if len(ps) == 2:
                      if self.fc_lr5:
                          lr10_bias.append(ps[1])
                      else:
                          normal_bias.append(ps[1])
  
              elif isinstance(m, torch.nn.BatchNorm2d):
                  bn_cnt += 1
                  # later BN's are frozen
                  if not self._enable_pbn or bn_cnt == 1:
                      bn.extend(list(m.parameters()))
              elif isinstance(m, torch.nn.BatchNorm3d):
                  bn_cnt += 1
                  # later BN's are frozen
                  if not self._enable_pbn or bn_cnt == 1:
                      bn.extend(list(m.parameters()))
              elif len(m._modules) == 0:
                  if len(list(m.parameters())) > 0:
                      raise ValueError("New atomic module type: {}. Need to give it a learning policy".format(type(m)))
  
          return [
              {'params': first_conv_weight, 'lr_mult': 5 if self.modality == 'Flow' else 1, 'decay_mult': 1,
               'name': "first_conv_weight"},
              {'params': first_conv_bias, 'lr_mult': 10 if self.modality == 'Flow' else 2, 'decay_mult': 0,
               'name': "first_conv_bias"},
              {'params': normal_weight, 'lr_mult': 1, 'decay_mult': 1,
               'name': "normal_weight"},
              {'params': normal_bias, 'lr_mult': 2, 'decay_mult': 0,
               'name': "normal_bias"},
              {'params': bn, 'lr_mult': 1, 'decay_mult': 0,
               'name': "BN scale/shift"},
              {'params': custom_ops, 'lr_mult': 1, 'decay_mult': 1,
               'name': "custom_ops"},
              # for fc
              {'params': lr5_weight, 'lr_mult': 5, 'decay_mult': 1,
               'name': "lr5_weight"},
              {'params': lr10_bias, 'lr_mult': 10, 'decay_mult': 0,
               'name': "lr10_bias"},
          ]
  
      def forward(self, input, no_reshape=False):
          if not no_reshape:
              sample_len = (3 if self.modality == "RGB" else 2) * self.new_length
  
              if self.modality == 'RGBDiff':
                  sample_len = 3 * self.new_length
                  input = self._get_diff(input)
  
              base_out = self.base_model(input.view((-1, sample_len) + input.size()[-2:]))
          else:
              base_out = self.base_model(input)
  
          if self.dropout > 0:
              base_out = self.new_fc(base_out)
  
          if not self.before_softmax:
              base_out = self.softmax(base_out)
  
          if self.reshape:
              if self.is_shift and self.temporal_pool:
                  base_out = base_out.view((-1, self.num_segments // 2) + base_out.size()[1:])
              else:
                  base_out = base_out.view((-1, self.num_segments) + base_out.size()[1:])
              output = self.consensus(base_out)
              return output.squeeze(1)
  
      def _get_diff(self, input, keep_rgb=False):
          input_c = 3 if self.modality in ["RGB", "RGBDiff"] else 2
          input_view = input.view((-1, self.num_segments, self.new_length + 1, input_c,) + input.size()[2:])
          if keep_rgb:
              new_data = input_view.clone()
          else:
              new_data = input_view[:, :, 1:, :, :, :].clone()
  
          for x in reversed(list(range(1, self.new_length + 1))):
              if keep_rgb:
                  new_data[:, :, x, :, :, :] = input_view[:, :, x, :, :, :] - input_view[:, :, x - 1, :, :, :]
              else:
                  new_data[:, :, x - 1, :, :, :] = input_view[:, :, x, :, :, :] - input_view[:, :, x - 1, :, :, :]
  
          return new_data
  
      def _construct_flow_model(self, base_model):
          # modify the convolution layers
          # Torch models are usually defined in a hierarchical way.
          # nn.modules.children() return all sub modules in a DFS manner
          modules = list(self.base_model.modules())
          first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv2d), list(range(len(modules)))))[0]
          conv_layer = modules[first_conv_idx]
          container = modules[first_conv_idx - 1]
  
          # modify parameters, assume the first blob contains the convolution kernels
          params = [x.clone() for x in conv_layer.parameters()]
          kernel_size = params[0].size()
          new_kernel_size = kernel_size[:1] + (2 * self.new_length, ) + kernel_size[2:]
          new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
  
          new_conv = nn.Conv2d(2 * self.new_length, conv_layer.out_channels,
                               conv_layer.kernel_size, conv_layer.stride, conv_layer.padding,
                               bias=True if len(params) == 2 else False)
          new_conv.weight.data = new_kernels
          if len(params) == 2:
              new_conv.bias.data = params[1].data # add bias if neccessary
          layer_name = list(container.state_dict().keys())[0][:-7] # remove .weight suffix to get the layer name
  
          # replace the first convlution layer
          setattr(container, layer_name, new_conv)
  
          if self.base_model_name == 'BNInception':
              import torch.utils.model_zoo as model_zoo
              sd = model_zoo.load_url('https://www.dropbox.com/s/35ftw2t4mxxgjae/BNInceptionFlow-ef652051.pth.tar?dl=1')
              base_model.load_state_dict(sd)
              print('=> Loading pretrained Flow weight done...')
          else:
              print('#' * 30, 'Warning! No Flow pretrained model is found')
          return base_model
  
      def _construct_diff_model(self, base_model, keep_rgb=False):
          # modify the convolution layers
          # Torch models are usually defined in a hierarchical way.
          # nn.modules.children() return all sub modules in a DFS manner
          modules = list(self.base_model.modules())
          first_conv_idx = filter(lambda x: isinstance(modules[x], nn.Conv2d), list(range(len(modules))))[0]
          conv_layer = modules[first_conv_idx]
          container = modules[first_conv_idx - 1]
  
          # modify parameters, assume the first blob contains the convolution kernels
          params = [x.clone() for x in conv_layer.parameters()]
          kernel_size = params[0].size()
          if not keep_rgb:
              new_kernel_size = kernel_size[:1] + (3 * self.new_length,) + kernel_size[2:]
              new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()
          else:
              new_kernel_size = kernel_size[:1] + (3 * self.new_length,) + kernel_size[2:]
              new_kernels = torch.cat((params[0].data, params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()),
                                      1)
              new_kernel_size = kernel_size[:1] + (3 + 3 * self.new_length,) + kernel_size[2:]
  
          new_conv = nn.Conv2d(new_kernel_size[1], conv_layer.out_channels,
                               conv_layer.kernel_size, conv_layer.stride, conv_layer.padding,
                               bias=True if len(params) == 2 else False)
          new_conv.weight.data = new_kernels
          if len(params) == 2:
              new_conv.bias.data = params[1].data  # add bias if neccessary
          layer_name = list(container.state_dict().keys())[0][:-7]  # remove .weight suffix to get the layer name
  
          # replace the first convolution layer
          setattr(container, layer_name, new_conv)
          return base_model
  
      @property
      def crop_size(self):
          return self.input_size
  
      @property
      def scale_size(self):
          return self.input_size * 256 // 224
  
      def get_augmentation(self, flip=True):
          if self.modality == 'RGB':
              if flip:
                  return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75, .66]),
                                                         GroupRandomHorizontalFlip(is_flow=False)])
              else:
                  print('#' * 20, 'NO FLIP!!!')
                  return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75, .66])])
          elif self.modality == 'Flow':
              return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75]),
                                                     GroupRandomHorizontalFlip(is_flow=True)])
          elif self.modality == 'RGBDiff':
              return torchvision.transforms.Compose([GroupMultiScaleCrop(self.input_size, [1, .875, .75]),
                                                     GroupRandomHorizontalFlip(is_flow=False)])
  