def _get_model_desc(self):
model_desc = self.trainer.model_desc
if not model_desc:
if ModelConfig.model_desc_file is not None:
desc_file = ModelConfig.model_desc_file
desc_file = desc_file.replace("{local_base_path}",
self.trainer.local_base_path)
if ":" not in desc_file:
desc_file = os.path.abspath(desc_file)
if ":" in desc_file:
local_desc_file = FileOps.join_path(
self.trainer.local_output_path,
os.path.basename(desc_file))
FileOps.copy_file(desc_file, local_desc_file)
desc_file = local_desc_file
model_desc = Config(desc_file)
logger.info("net_desc:{}".format(model_desc))
elif ModelConfig.model_desc is not None:
model_desc = ModelConfig.model_desc
elif ModelConfig.models_folder is not None:
folder = ModelConfig.models_folder.replace(
"{local_base_path}", self.trainer.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
model_desc = Config(desc_file)
return model_desc
def dataset_init(self):
"""Costruct method, which will load some dataset information."""
self.args.root_HR = FileOps.download_dataset(self.args.root_HR)
self.args.root_LR = FileOps.download_dataset(self.args.root_LR)
if self.args.subfile is not None:
with open(self.args.subfile
) as f: # lmdb format has no self.args.subfile
file_names = sorted([line.rstrip('\n') for line in f])
self.datatype = util.get_files_datatype(file_names)
self.paths_HR = [
os.path.join(self.args.root_HR, file_name)
for file_name in file_names
]
self.paths_LR = [
os.path.join(self.args.root_LR, file_name)
for file_name in file_names
]
else:
self.datatype = util.get_datatype(self.args.root_LR)
self.paths_LR = util.get_paths_from_dir(self.args.root_LR)
self.paths_HR = util.get_paths_from_dir(self.args.root_HR)
if self.args.save_in_memory:
self.imgs_LR = [self._read_img(path) for path in self.paths_LR]
self.imgs_HR = [self._read_img(path) for path in self.paths_HR]
def before_train(self, logs=None):
"""Call before_train of the managed callbacks."""
super().before_train(logs)
"""Be called before the training process."""
hpo_result = FileOps.load_pickle(
FileOps.join_path(self.trainer.local_output_path,
'best_config.pickle'))
logging.info("loading stage1_hpo_result \n{}".format(hpo_result))
feature_interaction_score = hpo_result['feature_interaction_score']
print('feature_interaction_score:', feature_interaction_score)
sorted_pairs = sorted(feature_interaction_score.items(),
key=lambda x: abs(x[1]),
reverse=True)
if ModelConfig.model_desc:
fis_ratio = ModelConfig.model_desc["custom"]["fis_ratio"]
else:
fis_ratio = 1.0
top_k = int(len(feature_interaction_score) * min(1.0, fis_ratio))
self.selected_pairs = list(map(lambda x: x[0], sorted_pairs[:top_k]))
# add selected_pairs
setattr(ModelConfig.model_desc['custom'], 'selected_pairs',
self.selected_pairs)
def dataset_init(self):
"""Construct method.
If both data_dir and label_dir are provided, then use data_dir and label_dir
Otherwise use data_path and list_file.
"""
if "data_dir" in self.args and "label_dir" in self.args:
self.args.data_dir = FileOps.download_dataset(self.args.data_dir)
self.args.label_dir = FileOps.download_dataset(self.args.label_dir)
self.data_files = sorted(glob.glob(osp.join(self.args.data_dir, "*")))
self.label_files = sorted(glob.glob(osp.join(self.args.label_dir, "*")))
else:
if "data_path" not in self.args or "list_file" not in self.args:
raise Exception("You must provide a data_path and a list_file!")
self.args.data_path = FileOps.download_dataset(self.args.data_path)
with open(osp.join(self.args.data_path, self.args.list_file)) as f:
lines = f.readlines()
self.data_files = [None] * len(lines)
self.label_files = [None] * len(lines)
for i, line in enumerate(lines):
data_file_name, label_file_name = line.strip().split()
self.data_files[i] = osp.join(self.args.data_path, data_file_name)
self.label_files[i] = osp.join(self.args.data_path, label_file_name)
datatype = self._get_datatype()
if datatype == "image":
self.read_fn = self._read_item_image
else:
self.read_fn = self._read_item_pickle
def _save_checkpoint(self, epoch, best=False):
"""Save model weights.
:param epoch: current epoch
:type epoch: int
"""
save_dir = os.path.join(self.worker_path, str(epoch))
FileOps.make_dir(save_dir)
for name in self.model.model_names:
if isinstance(name, str):
save_filename = '%s_net_%s.pth' % (epoch, name)
save_path = FileOps.join_path(save_dir, save_filename)
net = getattr(self.model, 'net' + name)
best_file = FileOps.join_path(self.worker_path,
"model_{}.pth".format(name))
if vega.is_gpu_device() and torch.cuda.is_available():
# torch.save(net.module.cpu().state_dict(), save_path)
torch.save(net.module.state_dict(), save_path)
# net.cuda()
if best:
torch.save(net.module.state_dict(), best_file)
elif vega.is_npu_device():
torch.save(net.state_dict(), save_path)
if best:
torch.save(net.state_dict(), best_file)
else:
torch.save(net.cpu().state_dict(), save_path)
if best:
torch.save(net.cpu().state_dict(), best_file)
def save_report(self, records):
"""Save report to `reports.json`."""
try:
_file = FileOps.join_path(TaskOps().local_output_path,
"reports.json")
FileOps.make_base_dir(_file)
data = {"_steps_": []}
for step in self.step_names:
if step in self.steps:
data["_steps_"].append(self.steps[step])
else:
data["_steps_"].append({
"step_name": step,
"status": Status.unstarted
})
for record in records:
if record.step_name in data:
data[record.step_name].append(record.to_dict())
else:
data[record.step_name] = [record.to_dict()]
with open(_file, "w") as f:
json.dump(data, f, indent=4, cls=JsonEncoder)
except Exception:
logging.warning(traceback.format_exc())
def after_valid(self, logs=None):
"""Call after_valid of the managed callbacks."""
self.model = self.trainer.model
feature_interaction_score = self.model.get_feature_interaction_score()
print('get feature_interaction_score', feature_interaction_score)
feature_interaction = []
for feature in feature_interaction_score:
if abs(feature_interaction_score[feature]) > 0:
feature_interaction.append(feature)
print('get feature_interaction', feature_interaction)
curr_auc = float(self.trainer.valid_metrics.results['auc'])
if curr_auc > self.best_score:
best_config = {
'score': curr_auc,
'feature_interaction': feature_interaction
}
logging.info("BEST CONFIG IS\n{}".format(best_config))
pickle_result_file = FileOps.join_path(
self.trainer.local_output_path, 'best_config.pickle')
logging.info("Saved to {}".format(pickle_result_file))
FileOps.dump_pickle(best_config, pickle_result_file)
self.best_score = curr_auc
def _backup(self):
"""Backup result worker folder."""
if self.need_backup is True and self.backup_base_path is not None:
backup_worker_path = FileOps.join_path(self.backup_base_path,
self.get_worker_subpath())
FileOps.copy_folder(
self.get_local_worker_path(self.step_name, self.worker_id),
backup_worker_path)
def __init__(self, **kwargs):
"""Construct the Imagenet class."""
Dataset.__init__(self, **kwargs)
self.args.data_path = FileOps.download_dataset(self.args.data_path)
split = 'train' if self.mode == 'train' else 'val'
local_data_path = FileOps.join_path(self.args.data_path, split)
delattr(self, 'loader')
ImageFolder.__init__(self,
root=local_data_path,
transform=Compose(self.transforms.__transform__))
def before_train(self, logs=None):
"""Be called before the whole train process."""
self.trainer.config.call_metrics_on_train = False
self.cfg = self.trainer.config
self.worker_id = self.trainer.worker_id
self.local_base_path = self.trainer.local_base_path
self.local_output_path = self.trainer.local_output_path
self.result_path = FileOps.join_path(self.trainer.local_base_path,
"result")
FileOps.make_dir(self.result_path)
self.logger_patch()
def before_train(self, logs=None):
"""Call before_train of the managed callbacks."""
super().before_train(logs)
"""Be called before the training process."""
hpo_result = FileOps.load_pickle(FileOps.join_path(
self.trainer.local_output_path, 'best_config.pickle'))
logging.info("loading stage1_hpo_result \n{}".format(hpo_result))
self.selected_pairs = hpo_result['feature_interaction']
logging.info('feature_interaction:', self.selected_pairs)
# add selected_pairs
setattr(ModelConfig.model_desc['custom'], 'selected_pairs', self.selected_pairs)
def save_results(self):
"""Save the results of evolution contains the information of pupulation and elitism."""
_path = FileOps.join_path(self.local_output_path, General.step_name)
FileOps.make_dir(_path)
arch_file = FileOps.join_path(_path, 'arch.txt')
arch_child = FileOps.join_path(_path, 'arch_child.txt')
sel_arch_file = FileOps.join_path(_path, 'selected_arch.npy')
sel_arch = []
with open(arch_file, 'a') as fw_a, open(arch_child, 'a') as fw_ac:
writer_a = csv.writer(fw_a, lineterminator='\n')
writer_ac = csv.writer(fw_ac, lineterminator='\n')
writer_ac.writerow(
['Population Iteration: ' + str(self.evolution_count + 1)])
for c in range(self.individual_num):
writer_ac.writerow(
self._log_data(net_info_type='active_only',
pop=self.pop[c],
value=self.pop[c].fitness))
writer_a.writerow(
['Population Iteration: ' + str(self.evolution_count + 1)])
for c in range(self.elitism_num):
writer_a.writerow(
self._log_data(net_info_type='active_only',
pop=self.elitism[c],
value=self.elit_fitness[c]))
sel_arch.append(self.elitism[c].gene)
sel_arch = np.stack(sel_arch)
np.save(sel_arch_file, sel_arch)
if self.backup_base_path is not None:
FileOps.copy_folder(self.local_output_path, self.backup_base_path)
def _init_dataloader(self):
"""Init dataloader from timm."""
if self.distributed and hvd.local_rank(
) == 0 and 'remote_data_dir' in self.config.dataset:
FileOps.copy_folder(self.config.dataset.remote_data_dir,
self.config.dataset.data_dir)
if self.distributed:
hvd.join()
args = self.config.dataset
train_dir = os.path.join(self.config.dataset.data_dir, 'train')
dataset_train = Dataset(train_dir)
world_size, rank = None, None
if self.distributed:
world_size, rank = hvd.size(), hvd.rank()
self.trainer.train_loader = create_loader(
dataset_train,
input_size=tuple(args.input_size),
batch_size=args.batch_size,
is_training=True,
use_prefetcher=self.config.prefetcher,
rand_erase_prob=args.reprob,
rand_erase_mode=args.remode,
rand_erase_count=args.recount,
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation='random',
mean=tuple(args.mean),
std=tuple(args.std),
num_workers=args.workers,
distributed=self.distributed,
world_size=world_size,
rank=rank)
valid_dir = os.path.join(self.config.dataset.data_dir, 'val')
dataset_eval = Dataset(valid_dir)
self.trainer.valid_loader = create_loader(
dataset_eval,
input_size=tuple(args.input_size),
batch_size=4 * args.batch_size,
is_training=False,
use_prefetcher=self.config.prefetcher,
interpolation=args.interpolation,
mean=tuple(args.mean),
std=tuple(args.std),
num_workers=args.workers,
distributed=self.distributed,
world_size=world_size,
rank=rank)
self.trainer.batch_num_train = len(self.trainer.train_loader)
self.trainer.batch_num_valid = len(self.trainer.valid_loader)
def _get_current_step_records(self):
step_name = General.step_name
models_folder = PipeStepConfig.pipe_step.get("models_folder")
cur_index = PipelineConfig.steps.index(step_name)
if cur_index >= 1 or models_folder:
if not models_folder:
models_folder = FileOps.join_path(
TaskOps().local_output_path,
PipelineConfig.steps[cur_index - 1])
models_folder = models_folder.replace("{local_base_path}",
TaskOps().local_base_path)
records = ReportServer().load_records_from_model_folder(
models_folder)
else:
records = self._load_single_model_records()
final_records = []
for record in records:
if not record.weights_file:
logger.error("Model file is not existed, id={}".format(
record.worker_id))
else:
record.step_name = General.step_name
final_records.append(record)
logging.debug("Records: {}".format(final_records))
return final_records
def load_master_ip():
"""Get the ip and port that write in a system path.
here will not download anything from S3.
"""
temp_folder = TaskOps().temp_path
FileOps.make_dir(temp_folder)
file_path = os.path.join(temp_folder, 'ip_address.txt')
if os.path.isfile(file_path):
with open(file_path, 'r') as f:
ip = f.readline().strip()
port = f.readline().strip()
logging.info("get write ip, ip={}, port={}".format(ip, port))
return ip, port
else:
return None, None
def save_master_ip(ip_address, port, args):
"""Write the ip and port in a system path.
:param str ip_address: The `ip_address` need to write.
:param str port: The `port` need to write.
:param argparse.ArgumentParser args: `args` is a argparse that should
contain `init_method`, `rank` and `world_size`.
"""
temp_folder = TaskOps().temp_path
FileOps.make_dir(temp_folder)
file_path = os.path.join(temp_folder, 'ip_address.txt')
logging.info("write ip, file path={}".format(file_path))
with open(file_path, 'w') as f:
f.write(ip_address + "\n")
f.write(port + "\n")
def _save_pb_model(self, weight_file, model_id):
from tensorflow.python.framework import graph_util
valid_data = self.trainer.valid_loader.input_fn()
iterator = valid_data.make_one_shot_iterator()
one_element = iterator.get_next()
with tf.Session() as sess:
batch = sess.run(one_element)
input_shape = batch[0].shape
with tf.Graph().as_default():
input_holder_shape = (None, ) + tuple(input_shape[1:])
input_holder = tf.placeholder(dtype=tf.float32,
shape=input_holder_shape)
self.trainer.model.training = False
output = self.trainer.model(input_holder)
if isinstance(output, tuple):
output_name = [output[0].name.split(":")[0]]
else:
output_name = [output.name.split(":")[0]]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if weight_file is not None:
saver = tf.train.Saver()
last_weight_file = tf.train.latest_checkpoint(weight_file)
if last_weight_file:
saver.restore(sess, last_weight_file)
constant_graph = graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_name)
output_graph = FileOps.join_path(weight_file,
'{}.pb'.format(model_id))
with tf.gfile.FastGFile(output_graph, mode='wb') as f:
f.write(constant_graph.SerializeToString())
def __init__(self, **kwargs):
"""Construct the dataset."""
super().__init__(**kwargs)
self.args.data_path = FileOps.download_dataset(self.args.data_path)
dataset_pairs = dict(train=create_train_subset(self.args.data_path),
test=create_test_subset(self.args.data_path),
val=create_test_subset(self.args.data_path))
if self.mode not in dataset_pairs.keys():
raise NotImplementedError(
f'mode should be one of {dataset_pairs.keys()}')
self.image_annot_path_pairs = dataset_pairs.get(self.mode)
self.codec_obj = PointLaneCodec(input_width=512,
input_height=288,
anchor_stride=16,
points_per_line=72,
class_num=2)
self.encode_lane = self.codec_obj.encode_lane
read_funcs = dict(
CULane=_read_culane_type_annot,
CurveLane=_read_curvelane_type_annot,
)
if self.args.dataset_format not in read_funcs:
raise NotImplementedError(
f'dataset_format should be one of {read_funcs.keys()}')
self.read_annot = read_funcs.get(self.args.dataset_format)
self.with_aug = self.args.get('with_aug', False)
def __init__(self, **kwargs):
"""Construct the Cifar10 class."""
Dataset.__init__(self, **kwargs)
self.args.data_path = FileOps.download_dataset(self.args.data_path)
is_train = self.mode == 'train' or self.mode == 'val' and self.args.train_portion < 1
self.base_folder = 'cifar-100-python'
if is_train:
files_list = ["train"]
else:
files_list = ['test']
self.data = []
self.targets = []
# now load the picked numpy arrays
for file_name in files_list:
file_path = os.path.join(self.args.data_path, self.base_folder,
file_name)
with open(file_path, 'rb') as f:
entry = pickle.load(f, encoding='latin1')
self.data.append(entry['data'])
if 'labels' in entry:
self.targets.extend(entry['labels'])
else:
self.targets.extend(entry['fine_labels'])
self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
self.data = self.data.transpose((0, 2, 3, 1)) # convert to HWC
def update(self, record):
"""Update current performance into hpo score board.
:param hps: hyper parameters need to update
:param performance: trainer performance
"""
super().update(record)
config_id = str(record.get('worker_id'))
step_name = record.get('step_name')
worker_result_path = self.get_local_worker_path(step_name, config_id)
new_worker_result_path = FileOps.join_path(self.local_base_path, 'cache', config_id, 'checkpoint')
FileOps.make_dir(worker_result_path)
FileOps.make_dir(new_worker_result_path)
if os.path.exists(new_worker_result_path):
shutil.rmtree(new_worker_result_path)
shutil.copytree(worker_result_path, new_worker_result_path)
def load_records_from_model_folder(cls, model_folder):
"""Transfer json_file to records."""
if not model_folder or not os.path.exists(model_folder):
logging.error("Failed to load records from model folder, folder={}".format(model_folder))
return []
records = []
pattern = FileOps.join_path(model_folder, "desc_*.json")
files = glob.glob(pattern)
for _file in files:
try:
with open(_file) as f:
worker_id = _file.split(".")[-2].split("_")[-1]
weights_file = os.path.join(os.path.dirname(_file), "model_{}".format(worker_id))
if vega.is_torch_backend():
weights_file = '{}.pth'.format(weights_file)
elif vega.is_ms_backend():
weights_file = '{}.ckpt'.format(weights_file)
if not os.path.exists(weights_file):
weights_file = None
sample = dict(worker_id=worker_id, desc=json.load(f), weights_file=weights_file)
record = ReportRecord().load_dict(sample)
records.append(record)
except Exception as ex:
logging.info('Can not read records from json because {}'.format(ex))
return records
def _new_model_init(self):
"""Init new model.
:return: initial model after loading pretrained model
:rtype: torch.nn.Module
"""
init_model_file = self.config.init_model_file
if ":" in init_model_file:
local_path = FileOps.join_path(
self.trainer.get_local_worker_path(),
os.path.basename(init_model_file))
FileOps.copy_file(init_model_file, local_path)
self.config.init_model_file = local_path
network_desc = copy.deepcopy(self.base_net_desc)
network_desc.backbone.cfgs = network_desc.backbone.base_cfgs
model_init = NetworkDesc(network_desc).to_model()
return model_init
def get_pareto_list_size(self):
"""Get the number of pareto list."""
pareto_list_size = 0
pareto_file_locate = FileOps.join_path(self.local_base_path, "result",
"pareto_front.csv")
if os.path.exists(pareto_file_locate):
pareto_front_df = pd.read_csv(pareto_file_locate)
pareto_list_size = pareto_front_df.size
return pareto_list_size
def _saved_multi_checkpoint(self, epoch):
"""Save multi tasks checkpoint."""
FileOps.make_dir(self.trainer.get_local_worker_path(),
self.trainer.multi_task)
checkpoint_file = FileOps.join_path(
self.trainer.get_local_worker_path(), self.trainer.multi_task,
self.trainer.checkpoint_file_name)
logging.debug("Start Save Multi Task Model, model_file=%s",
self.trainer.model_pickle_file_name)
if vega.is_torch_backend():
ckpt = {
'epoch': epoch,
'weight': self.trainer.model.state_dict(),
'optimizer': self.trainer.optimizer.state_dict(),
'lr_scheduler': self.trainer.lr_scheduler.state_dict(),
}
torch.save(ckpt, checkpoint_file)
self.trainer.checkpoint_file = checkpoint_file
def _save_descript(self):
"""Save result descript."""
template_file = self.config.darts_template_file
genotypes = self.search_alg.codec.calc_genotype(
self._get_arch_weights())
if template_file == "{default_darts_cifar10_template}":
template = DartsNetworkTemplateConfig.cifar10
elif template_file == "{default_darts_cifar100_template}":
template = DartsNetworkTemplateConfig.cifar100
elif template_file == "{default_darts_imagenet_template}":
template = DartsNetworkTemplateConfig.imagenet
else:
dst = FileOps.join_path(self.trainer.get_local_worker_path(),
os.path.basename(template_file))
FileOps.copy_file(template_file, dst)
template = Config(dst)
model_desc = self._gen_model_desc(genotypes, template)
self.trainer.config.codec = model_desc
def __init__(self, **kwargs):
"""Construct the Mnist class."""
Dataset.__init__(self, **kwargs)
self.args.data_path = FileOps.download_dataset(self.args.data_path)
MNIST.__init__(self,
root=self.args.data_path,
train=self.train,
transform=self.transforms,
download=self.args.download)
def dataset_init(self):
"""Initialize dataset."""
self.args.HR_dir = FileOps.download_dataset(self.args.HR_dir)
self.args.LR_dir = FileOps.download_dataset(self.args.LR_dir)
self.Y_paths = sorted(self.make_dataset(
self.args.LR_dir,
float("inf"))) if self.args.LR_dir is not None else None
self.HR_paths = sorted(
self.make_dataset(
self.args.HR_dir,
float("inf"))) if self.args.HR_dir is not None else None
self.trans_norm = transforms.Compose(
[transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
for i in range(len(self.HR_paths)):
file_name = os.path.basename(self.HR_paths[i])
if (file_name.find("0401") >= 0):
logging.info(
"We find the possion of NO. 401 in the HR patch NO. {}".
format(i))
self.HR_paths = self.HR_paths[:i]
break
for i in range(len(self.Y_paths)):
file_name = os.path.basename(self.Y_paths[i])
if (file_name.find("0401") >= 0):
logging.info(
"We find the possion of NO. 401 in the LR patch NO. {}".
format(i))
self.Y_paths = self.Y_paths[i:]
break
self.Y_size = len(self.Y_paths)
if self.train:
self.load_size = self.args.load_size
self.crop_size = self.args.crop_size
self.upscale = self.args.upscale
self.augment_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip()
])
self.HR_transform = transforms.RandomCrop(
int(self.crop_size * self.upscale))
self.LR_transform = transforms.RandomCrop(self.crop_size)
def _load_checkpoint(self):
"""Load checkpoint."""
if vega.is_torch_backend():
if hasattr(self.trainer.config, "checkpoint_path"):
checkpoint_path = self.trainer.config.checkpoint_path
else:
checkpoint_path = self.trainer.get_local_worker_path()
checkpoint_file = FileOps.join_path(
checkpoint_path, self.trainer.checkpoint_file_name)
if os.path.exists(checkpoint_file):
try:
logging.info("Load checkpoint file, file={}".format(
checkpoint_file))
checkpoint = torch.load(checkpoint_file)
if self.trainer.multi_task:
self.trainer.model.load_state_dict(
checkpoint["weight"], strict=False)
multi_task_checkpoint = torch.load(
FileOps.join_path(
checkpoint_path, self.trainer.multi_task,
self.trainer.checkpoint_file_name))
self.trainer.optimizer.load_state_dict(
multi_task_checkpoint["optimizer"])
self.trainer.lr_scheduler.load_state_dict(
multi_task_checkpoint["lr_scheduler"])
else:
self.trainer.model.load_state_dict(
checkpoint["weight"])
self.trainer.optimizer.load_state_dict(
checkpoint["optimizer"])
self.trainer.lr_scheduler.load_state_dict(
checkpoint["lr_scheduler"])
if self.trainer._resume_training:
# epoch = checkpoint["epoch"]
self.trainer._start_epoch = checkpoint["epoch"]
logging.info(
"Resume fully train, change start epoch to {}".
format(self.trainer._start_epoch))
except Exception as e:
logging.info("Load checkpoint failed {}".format(e))
else:
logging.info(
"skip loading checkpoint file that do not exist, {}".
format(checkpoint_file))
def set_trainer(self, trainer):
"""Set trainer object for current callback."""
self.trainer = trainer
self.trainer._train_loop = self._train_loop
self.cfg = self.trainer.config
self._worker_id = self.trainer._worker_id
self.worker_path = self.trainer.get_local_worker_path()
self.output_path = self.trainer.local_output_path
self.best_model_name = "model_best"
self.best_model_file = FileOps.join_path(
self.worker_path, "model_{}.pth".format(self.trainer.worker_id))
def __init__(self, **kwargs):
"""Init Cifar10."""
super(Imagenet, self).__init__(**kwargs)
self.data_path = FileOps.download_dataset(self.args.data_path)
self.fp16 = self.args.fp16
self.num_parallel_batches = self.args.num_parallel_batches
self.image_size = self.args.image_size
self.drop_remainder = self.args.drop_last
if self.data_path == 'null' or not self.data_path:
self.data_path = None
self.num_parallel_calls = self.args.num_parallel_calls
