def __init__(self):
super(TSN_BIT, self).__init__()
self.tsn = TSN(num_class, num_segments=num_segments, modality=modality,
base_model=arch,
consensus_type=crop_fusion_type,
dropout=0.7)
self.activation = nn.LeakyReLU()
self.fc1 = nn.Linear(101, 32)
self.fc2 = nn.Linear(32, 8)
self.model_name = 'TSN_TemResGen_Kalman_2019-03-11_15-28-57.pth'
self._load_pretrained_model(self.model_name)
def __init__(self):
super(TSN_BIT, self).__init__()
self.tsn = TSN(num_class, num_segments=num_segments, modality=modality,
base_model=arch,
consensus_type=crop_fusion_type,
dropout=0.7)
self.activation = nn.LeakyReLU()
self.fc1 = nn.Linear(51, 32)
self.fc2 = nn.Linear(32, 21)
self.model_name = 'TSN_RGB_2019-01-24_12-26-11.pth'
self._load_pretrained_model(self.model_name)
def __init__(self):
super(TSN_BIT, self).__init__()
self.tsn = TSN(num_class, num_segments=num_segments, modality=modality,
base_model=arch,
consensus_type=crop_fusion_type,
dropout=0.7)
self.activation = nn.LeakyReLU()
self.fc1 = nn.Linear(101, 32)
self.fc2 = nn.Linear(32, 8)
self.model_name = 'TSN_Flow_2019-01-23_17-06-15.pth'
# self._load_tsn_rgb_weight()
self._load_pretrained_model(self.model_name)