def load_weights(self, pretrained_path):
model = CRNN10(self.class_num, self.pool_type, self.pool_size, self.interp_ratio)
checkpoint = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['model_state_dict'])
self.conv_block1 = model.conv_block1
self.conv_block2 = model.conv_block2
self.conv_block3 = model.conv_block3
self.conv_block4 = model.conv_block4
init_gru(self.gru)
init_layer(self.event_fc)
init_layer(self.azimuth_fc)
init_layer(self.elevation_fc)
def __init__(self,
class_num,
pool_type='avg',
pool_size=(2, 2),
pretrained_path=None):
super().__init__(class_num,
pool_type,
pool_size,
pretrained_path=pretrained_path)
if pretrained_path:
self.load_weights(pretrained_path)
self.gru = nn.GRU(input_size=512,
hidden_size=256,
num_layers=1,
batch_first=True,
bidirectional=True)
init_gru(self.gru)
init_layer(self.event_fc)
init_layer(self.azimuth_fc)
init_layer(self.elevation_fc)
def init_weights(self):
init_gru(self.gru)
init_layer(self.event_fc)
init_layer(self.azimuth_fc)
init_layer(self.elevation_fc)
def init_weights(self):
init_gru(self.gru_1)
init_gru(self.gru_2)
init_layer(self.event_fc)