def init_subnet_data_provider(self):
if self.output_method == 'disk':
dp = DataProvider.get_by_name('intermediate')
count = self.train_dumper.get_count()
self.train_dp = dp(self.train_output_filename, range(0, count), 'fc')
count = self.test_dumper.get_count()
self.test_dp = dp(self.test_output_filename, range(0, count), 'fc')
elif self.output_method == 'memory':
dp = DataProvider.get_by_name('memory')
self.train_dp = dp(self.train_dumper)
self.test_dp = dp(self.test_dumper)
def init_subnet_data_provider(self):
if self.output_method == 'disk':
dp = DataProvider.get_by_name('intermediate')
count = self.train_dumper.get_count()
self.train_dp = dp(self.train_output_filename, range(0, count),
'fc')
count = self.test_dumper.get_count()
self.test_dp = dp(self.test_output_filename, range(0, count), 'fc')
elif self.output_method == 'memory':
dp = DataProvider.get_by_name('memory')
self.train_dp = dp(self.train_dumper)
self.test_dp = dp(self.test_dumper)
def set_category_range(self, r):
dp = DataProvider.get_by_name(self.data_provider)
self.train_dp = dp(self.data_dir,
self.train_range,
category_range=range(r))
self.test_dp = dp(self.data_dir,
self.test_range,
category_range=range(r))
def set_num_group(self, n):
dp = DataProvider.get_by_name(self.data_provider)
self.train_dp = dp(self.data_dir, self.train_range, n)
self.test_dp = dp(self.data_dir, self.test_range, n)
def init_subnet_data_provider(self):
dp = DataProvider.get_by_name('intermediate')
count = self.train_dumper.get_count()
self.train_dp = dp(self.train_output_filename, range(0, count), 'fc')
count = self.test_dumper.get_count()
self.test_dp = dp(self.test_output_filename, range(0, count), 'fc')
#create a checkpoint dumper
image_shape = (param_dict['image_color'], param_dict['image_size'],
param_dict['image_size'], param_dict['batch_size'])
param_dict['image_shape'] = image_shape
cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'],
param_dict['test_id'])
param_dict['checkpoint_dumper'] = cp_dumper
#create the init_model
init_model = cp_dumper.get_checkpoint()
if init_model is None:
init_model = parse_config_file(args.param_file)
param_dict['init_model'] = init_model
#create train dataprovider and test dataprovider
dp_class = DataProvider.get_by_name(param_dict['data_provider'])
train_dp = dp_class(param_dict['data_dir'], param_dict['train_range'])
test_dp = dp_class(param_dict['data_dir'], param_dict['test_range'])
param_dict['train_dp'] = train_dp
param_dict['test_dp'] = test_dp
#get all extra information
param_dict['num_epoch'] = args.num_epoch
num_batch = util.string_to_int_list(args.num_batch)
if len(num_batch) == 1:
param_dict['num_batch'] = num_batch[0]
else:
param_dict['num_batch'] = num_batch
param_dict['num_group_list'] = util.string_to_int_list(args.num_group_list)
param_dict['num_caterange_list'] = util.string_to_int_list(
def set_num_group(self, n): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range, n) self.test_dp = dp(self.data_dir, self.test_range, n)
def set_category_range(self, r): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range, category_range = range(r)) self.test_dp = dp(self.data_dir, self.test_range, category_range = range(r))
def init_subnet_data_provider(self):
dp = DataProvider.get_by_name('intermediate')
count = self.train_dumper.get_count()
self.train_dp = dp(self.train_output_filename, range(0, count), 'fc')
count = self.test_dumper.get_count()
self.test_dp = dp(self.test_output_filename, range(0, count), 'fc')
#create a checkpoint dumper
image_shape = (param_dict['image_color'], param_dict['image_size'], param_dict['image_size'], param_dict['batch_size'])
param_dict['image_shape'] = image_shape
cp_dumper = CheckpointDumper(param_dict['checkpoint_dir'], param_dict['test_id'])
param_dict['checkpoint_dumper'] = cp_dumper
#create the init_model
init_model = cp_dumper.get_checkpoint()
if init_model is None:
init_model = parse_config_file(args.param_file)
param_dict['init_model'] = init_model
#create train dataprovider and test dataprovider
dp_class = DataProvider.get_by_name(param_dict['data_provider'])
train_dp = dp_class(param_dict['data_dir'], param_dict['train_range'])
test_dp = dp_class(param_dict['data_dir'], param_dict['test_range'])
param_dict['train_dp'] = train_dp
param_dict['test_dp'] = test_dp
#get all extra information
param_dict['num_epoch'] = args.num_epoch
num_batch = util.string_to_int_list(args.num_batch)
if len(num_batch) == 1:
param_dict['num_batch'] = num_batch[0]
else:
param_dict['num_batch'] = num_batch
param_dict['num_group_list'] = util.string_to_int_list(args.num_group_list)
def init_data_provider(self): dp = DataProvider.get_by_name(self.data_provider) self.train_dp = dp(self.data_dir, self.train_range) self.test_dp = dp(self.data_dir, self.test_range)
