Example #1
0
  def _finish_init(self):
    self.final_num_epoch = self.num_epoch
    self.curr_model = []
    self.divide_layers_to_stack()
    self.conv_stack = net.FastNet.split_conv_to_stack(self.conv_params)
    self.fc_stack = net.FastNet.split_fc_to_stack(self.fc_params)


    self.fc_tmp = [self.fc_stack['fc8'][0], self.softmax_param]
    del self.fc_stack['fc8']
    self.stack = self.fc_stack

    self.initialize_model()
    pprint.pprint(self.stack)

    self.num_epoch = self.frag_epoch
    net = parser.load_from_checkpoint(param_file, 
                                      checkpoint_dumper.get_checkpoint(), 
                                      image_shape)
train_range = range(101, 1301) #1,2,3,....,40
test_range = range(1, 101) #41, 42, ..., 48
data_provider = 'imagenet'


train_dp = data.get_by_name(data_provider)(data_dir,train_range)
test_dp = data.get_by_name(data_provider)(data_dir, test_range)


checkpoint_dumper = trainer.CheckpointDumper(checkpoint_dir, test_id)

save_freq = 100
test_freq = 100
adjust_freq = 100
factor = 1
num_epoch = 5
learning_rate = 0.1
batch_size = 128
image_color = 3
image_size = 224
image_shape = (image_color, image_size, image_size, batch_size)

net = parser.load_from_checkpoint(param_file, 
                                  checkpoint_dumper.get_checkpoint(),
                                  image_shape)

param_dict = globals()
print type(param_dict)
t = trainer.Trainer(**param_dict)
t.train()
Example #3
0
output_method = 'disk'

train_range = range(101, 1301)  #1,2,3,....,40
test_range = range(1, 101)  #41, 42, ..., 48
data_provider = 'imagenet'

train_dp = data.get_by_name(data_provider)(data_dir, train_range)
test_dp = data.get_by_name(data_provider)(data_dir, test_range)

checkpoint_dumper = trainer.CheckpointDumper(checkpoint_dir, test_id)

save_freq = 100
test_freq = 100
adjust_freq = 100
factor = 1
num_epoch = 5
learning_rate = 0.1
batch_size = 128
image_color = 3
image_size = 224
image_shape = (image_color, image_size, image_size, batch_size)

net = parser.load_from_checkpoint(param_file,
                                  checkpoint_dumper.get_checkpoint(),
                                  image_shape)

param_dict = globals()
print type(param_dict)
t = trainer.Trainer(**param_dict)
t.train()