def teardown_module():
distributedTool.release()
def test(cloud_args=None):
"""test"""
args = parse_args(cloud_args)
context.set_context(mode=context.GRAPH_MODE,
enable_auto_mixed_precision=True,
device_target=args.platform,
save_graphs=False)
if os.getenv('DEVICE_ID', "not_set").isdigit():
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
# init distributed
if args.is_distributed:
parallel_mode = ParallelMode.DATA_PARALLEL
context.set_auto_parallel_context(parallel_mode=parallel_mode,
device_num=args.group_size,
gradients_mean=True)
args.logger.save_args(args)
# network
args.logger.important_info('start create network')
if os.path.isdir(args.pretrained):
models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
print(models)
if args.graph_ckpt:
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split(
'_')[0])
else:
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
args.models = sorted(models, key=f)
else:
args.models = [
args.pretrained,
]
for model in args.models:
de_dataset = classification_dataset(args.data_dir,
image_size=args.image_size,
per_batch_size=args.per_batch_size,
max_epoch=1,
rank=args.rank,
group_size=args.group_size,
mode='eval')
eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
network = get_network(args.backbone,
args.num_classes,
platform=args.platform)
if network is None:
raise NotImplementedError('not implement {}'.format(args.backbone))
load_pretrain_model(model, network, args)
img_tot = 0
top1_correct = 0
top5_correct = 0
if args.platform == "Ascend":
network.to_float(mstype.float16)
else:
auto_mixed_precision(network)
network.set_train(False)
t_end = time.time()
it = 0
for data, gt_classes in eval_dataloader:
output = network(Tensor(data, mstype.float32))
output = output.asnumpy()
top1_output = np.argmax(output, (-1))
top5_output = np.argsort(output)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, gt_classes)
img_tot += args.per_batch_size
if args.rank == 0 and it == 0:
t_end = time.time()
it = 1
if args.rank == 0:
time_used = time.time() - t_end
fps = (img_tot - args.per_batch_size) * args.group_size / time_used
args.logger.info(
'Inference Performance: {:.2f} img/sec'.format(fps))
results = get_result(args, model, top1_correct, top5_correct, img_tot)
top1_correct = results[0, 0]
top5_correct = results[1, 0]
img_tot = results[2, 0]
acc1 = 100.0 * top1_correct / img_tot
acc5 = 100.0 * top5_correct / img_tot
args.logger.info('after allreduce eval: top1_correct={}, tot={},'
'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot,
acc1))
args.logger.info('after allreduce eval: top5_correct={}, tot={},'
'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot,
acc5))
if args.is_distributed:
release()
def _ascend_analyse(self):
"""Collect and analyse ascend performance data"""
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(self._output_path, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
source_path = validate_and_normalize_path(source_path)
hwts_output_filename = validate_and_normalize_path(
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
hwtslog_parser.execute()
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(self._output_path,
opcompute_output_filename)
opcompute_output_filename = validate_and_normalize_path(
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict, self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
output_data_preprocess_aicpu = validate_and_normalize_path(
output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(source_path,
output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path, self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
points = None
try:
points = self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser,
source_path)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)
# analyse memory usage info
try:
self._analyse_memory_usage(points)
except (ProfilerIOException, ProfilerFileNotFoundException,
ProfilerRawFileException) as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
context.set_context(enable_profiling=False)
def test(cloud_args=None):
"""test"""
args = parse_args(cloud_args)
context.set_context(mode=context.GRAPH_MODE,
enable_auto_mixed_precision=True,
device_target=args.platform,
save_graphs=False)
if os.getenv('DEVICE_ID', "not_set").isdigit():
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
# init distributed
if args.is_distributed:
init()
args.rank = get_rank()
args.group_size = get_group_size()
parallel_mode = ParallelMode.DATA_PARALLEL
context.set_auto_parallel_context(parallel_mode=parallel_mode,
device_num=args.group_size,
parameter_broadcast=True,
mirror_mean=True)
else:
args.rank = 0
args.group_size = 1
args.outputs_dir = os.path.join(
args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
args.logger.save_args(args)
# network
args.logger.important_info('start create network')
if os.path.isdir(args.pretrained):
models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
print(models)
if args.graph_ckpt:
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split(
'_')[0])
else:
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
args.models = sorted(models, key=f)
else:
args.models = [
args.pretrained,
]
for model in args.models:
de_dataset = classification_dataset(args.data_dir,
image_size=args.image_size,
per_batch_size=args.per_batch_size,
max_epoch=1,
rank=args.rank,
group_size=args.group_size,
mode='eval')
eval_dataloader = de_dataset.create_tuple_iterator()
network = get_network(args.backbone,
args.num_classes,
platform=args.platform)
if network is None:
raise NotImplementedError('not implement {}'.format(args.backbone))
param_dict = load_checkpoint(model)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('network.'):
param_dict_new[key[8:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load model {} success'.format(model))
img_tot = 0
top1_correct = 0
top5_correct = 0
if args.platform == "Ascend":
network.to_float(mstype.float16)
else:
auto_mixed_precision(network)
network.set_train(False)
t_end = time.time()
it = 0
for data, gt_classes in eval_dataloader:
output = network(Tensor(data, mstype.float32))
output = output.asnumpy()
top1_output = np.argmax(output, (-1))
top5_output = np.argsort(output)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, gt_classes)
img_tot += args.per_batch_size
if args.rank == 0 and it == 0:
t_end = time.time()
it = 1
if args.rank == 0:
time_used = time.time() - t_end
fps = (img_tot - args.per_batch_size) * args.group_size / time_used
args.logger.info(
'Inference Performance: {:.2f} img/sec'.format(fps))
results = [[top1_correct], [top5_correct], [img_tot]]
args.logger.info('before results={}'.format(results))
if args.is_distributed:
model_md5 = model.replace('/', '')
tmp_dir = '/cache'
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(
args.rank, model_md5)
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(
args.rank, model_md5)
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(
args.rank, model_md5)
np.save(top1_correct_npy, top1_correct)
np.save(top5_correct_npy, top5_correct)
np.save(img_tot_npy, img_tot)
while True:
rank_ok = True
for other_rank in range(args.group_size):
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(
other_rank, model_md5)
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(
other_rank, model_md5)
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(
other_rank, model_md5)
if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
not os.path.exists(img_tot_npy):
rank_ok = False
if rank_ok:
break
top1_correct_all = 0
top5_correct_all = 0
img_tot_all = 0
for other_rank in range(args.group_size):
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(
other_rank, model_md5)
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(
other_rank, model_md5)
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(
other_rank, model_md5)
top1_correct_all += np.load(top1_correct_npy)
top5_correct_all += np.load(top5_correct_npy)
img_tot_all += np.load(img_tot_npy)
results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
results = np.array(results)
else:
results = np.array(results)
args.logger.info('after results={}'.format(results))
top1_correct = results[0, 0]
top5_correct = results[1, 0]
img_tot = results[2, 0]
acc1 = 100.0 * top1_correct / img_tot
acc5 = 100.0 * top5_correct / img_tot
args.logger.info('after allreduce eval: top1_correct={}, tot={},'
'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot,
acc1))
args.logger.info('after allreduce eval: top5_correct={}, tot={},'
'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot,
acc5))
if args.is_distributed:
release()
def _release_parallel(self):
if self._init_parallel_flag:
release()
def analyse(self):
"""
Collect and analyse performance data, called after training or during training.
Examples:
>>> from mindspore.profiler import Profiler
>>> import mindspore.context
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
>>> model = Model()
>>> model.train()
>>> profiler.analyse()
"""
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
result = hwtslog_parser.execute()
if not result:
logger.error("Profiling: fail to parse hwts log file.")
return
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(self._output_path,
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict, self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(source_path,
output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path, self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)
def analyse(self):
"""
Collect and analyse performance data, called after training or during training.
Examples:
>>> from mindspore.profiler import Profiler
>>> import mindspore.context
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler()
>>> model = Model()
>>> model.train()
>>> profiler.analyse()
"""
if self._device_target and self._device_target == "GPU":
if context.get_auto_parallel_context(
'device_num') > 1 and self._dev_id != get_rank():
self._dev_id = get_rank()
logger.error(
'Please check the Profiler object initialized after set_auto_parallel_context() '
'and init(). Profiler should be initialized after these code. '
)
self._gpu_profiler.stop()
timeline_generator = self._generate_timeline()
# parse minddata pipeline operator and queue for GPU
try:
pipeline_parser = MinddataPipelineParser(
self._output_path, self._dev_id, self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(
is_training_mode_flag=timeline_generator.check_op_name(
'Gradients'))
except ProfilerException as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
elif self._device_target and self._device_target == "Ascend":
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(self._output_path, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
source_path = validate_and_normalize_path(source_path)
hwts_output_filename = validate_and_normalize_path(
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
_ = hwtslog_parser.execute()
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(
self._output_path, opcompute_output_filename)
opcompute_output_filename = validate_and_normalize_path(
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict,
self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
output_data_preprocess_aicpu = validate_and_normalize_path(
output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(
source_path, output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path,
self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(
self._output_path, self._dev_id, self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)
os.environ['PROFILING_MODE'] = str("false")
context.set_context(enable_profiling=False)
def test_train():
'''
finetune function
pytest -s finetune.py::test_train
'''
devid = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=devid,
enable_mem_reuse=True,
enable_task_sink=True)
#BertCLSTrain for classification
#BertNERTrain for sequence labeling
if cfg.task == 'NER':
if cfg.use_crf:
netwithloss = BertNER(bert_net_cfg,
True,
num_labels=len(tag_to_index),
use_crf=True,
tag_to_index=tag_to_index,
dropout_prob=0.1)
else:
netwithloss = BertNER(bert_net_cfg,
True,
num_labels=cfg.num_labels,
dropout_prob=0.1)
else:
netwithloss = BertCLS(bert_net_cfg,
True,
num_labels=cfg.num_labels,
dropout_prob=0.1)
dataset = get_dataset(bert_net_cfg.batch_size, cfg.epoch_num)
# optimizer
steps_per_epoch = dataset.get_dataset_size()
if cfg.optimizer == 'AdamWeightDecayDynamicLR':
optimizer = AdamWeightDecayDynamicLR(
netwithloss.trainable_params(),
decay_steps=steps_per_epoch * cfg.epoch_num,
learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
power=cfg.AdamWeightDecayDynamicLR.power,
warmup_steps=steps_per_epoch,
weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
eps=cfg.AdamWeightDecayDynamicLR.eps)
elif cfg.optimizer == 'Lamb':
optimizer = Lamb(netwithloss.trainable_params(),
decay_steps=steps_per_epoch * cfg.epoch_num,
start_learning_rate=cfg.Lamb.start_learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
power=cfg.Lamb.power,
warmup_steps=steps_per_epoch,
decay_filter=cfg.Lamb.decay_filter)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(netwithloss.trainable_params(),
learning_rate=cfg.Momentum.learning_rate,
momentum=cfg.Momentum.momentum)
else:
raise Exception("Optimizer not supported.")
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix=cfg.ckpt_prefix,
directory=cfg.ckpt_dir,
config=ckpt_config)
param_dict = load_checkpoint(cfg.pre_training_ckpt)
load_param_into_net(netwithloss, param_dict)
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2**32,
scale_factor=2,
scale_window=1000)
netwithgrads = BertFinetuneCell(netwithloss,
optimizer=optimizer,
scale_update_cell=update_cell)
model = Model(netwithgrads)
model.train(cfg.epoch_num, dataset, callbacks=[LossCallBack(), ckpoint_cb])
D.release()
def test(cloud_args=None):
"""
network eval function. Get top1 and top5 ACC from classification.
The result will be save at [./outputs] by default.
"""
args = parse_args(cloud_args)
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target,
save_graphs=True)
if args.device_target == 'Ascend':
devid = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=devid)
# init distributed
if args.is_distributed:
init()
args.rank = get_rank()
args.group_size = get_group_size()
args.outputs_dir = os.path.join(
args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
args.logger.save_args(args)
# network
args.logger.important_info('start create network')
if os.path.isdir(args.pretrained):
models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')
[0])
args.models = sorted(models, key=f)
else:
args.models = [
args.pretrained,
]
for model in args.models:
de_dataset = classification_dataset(args.data_dir,
image_size=args.image_size,
per_batch_size=args.per_batch_size,
max_epoch=1,
rank=args.rank,
group_size=args.group_size,
mode='eval')
eval_dataloader = de_dataset.create_tuple_iterator()
network = DenseNet121(args.num_classes)
param_dict = load_checkpoint(model)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('network.'):
param_dict_new[key[8:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load model {} success'.format(model))
if args.device_target == 'Ascend':
network.add_flags_recursive(fp16=True)
img_tot = 0
top1_correct = 0
top5_correct = 0
network.set_train(False)
for data, gt_classes in eval_dataloader:
output = network(Tensor(data, mstype.float32))
output = output.asnumpy()
gt_classes = gt_classes.asnumpy()
top1_output = np.argmax(output, (-1))
top5_output = np.argsort(output)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, gt_classes)
img_tot += args.per_batch_size
results = [[top1_correct], [top5_correct], [img_tot]]
args.logger.info('before results={}'.format(results))
if args.is_distributed:
results = generate_results(model, args.rank, args.group_size,
top1_correct, top5_correct, img_tot)
results = np.array(results)
else:
results = np.array(results)
args.logger.info('after results={}'.format(results))
top1_correct = results[0, 0]
top5_correct = results[1, 0]
img_tot = results[2, 0]
acc1 = 100.0 * top1_correct / img_tot
acc5 = 100.0 * top5_correct / img_tot
args.logger.info(
'after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.
format(top1_correct, img_tot, acc1))
args.logger.info(
'after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.
format(top5_correct, img_tot, acc5))
if args.is_distributed:
release()
def _release_parallel(self):
release()
def test(cloud_args=None):
"""
network eval function. Get top1 and top5 ACC from classification.
The result will be save at [./outputs] by default.
"""
args = parse_args(cloud_args)
# init distributed
if args.is_distributed:
init()
args.rank = get_rank()
args.group_size = get_group_size()
args.outputs_dir = os.path.join(
args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
args.logger.save_args(args)
# network
args.logger.important_info('start create network')
if os.path.isdir(args.pretrained):
models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
f = lambda x: -1 * int(
os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')
[0])
args.models = sorted(models, key=f)
else:
args.models = [
args.pretrained,
]
for model in args.models:
de_dataset = classification_dataset(args.data_dir,
image_size=args.image_size,
per_batch_size=args.per_batch_size,
max_epoch=1,
rank=args.rank,
group_size=args.group_size,
mode='eval')
eval_dataloader = de_dataset.create_tuple_iterator()
network = DenseNet121(args.num_classes)
param_dict = load_checkpoint(model)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('network.'):
param_dict_new[key[8:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load model {} success'.format(model))
network.add_flags_recursive(fp16=True)
img_tot = 0
top1_correct = 0
top5_correct = 0
network.set_train(False)
for data, gt_classes in eval_dataloader:
output = network(Tensor(data, mstype.float32))
output = output.asnumpy()
gt_classes = gt_classes.asnumpy()
top1_output = np.argmax(output, (-1))
top5_output = np.argsort(output)[:, -5:]
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
top5_correct += get_top5_acc(top5_output, gt_classes)
img_tot += args.per_batch_size
results = [[top1_correct], [top5_correct], [img_tot]]
args.logger.info('before results={}'.format(results))
if args.is_distributed:
model_md5 = model.replace('/', '')
tmp_dir = '../cache'
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
top1_correct_npy = '{}/top1_rank_{}_{}.npy'.format(
tmp_dir, args.rank, model_md5)
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(
tmp_dir, args.rank, model_md5)
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(
tmp_dir, args.rank, model_md5)
np.save(top1_correct_npy, top1_correct)
np.save(top5_correct_npy, top5_correct)
np.save(img_tot_npy, img_tot)
while True:
rank_ok = True
for other_rank in range(args.group_size):
top1_correct_npy = '{}/top1_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) \
or not os.path.exists(img_tot_npy):
rank_ok = False
if rank_ok:
break
top1_correct_all = 0
top5_correct_all = 0
img_tot_all = 0
for other_rank in range(args.group_size):
top1_correct_npy = '{}/top1_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
top5_correct_npy = '{}/top5_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
img_tot_npy = '{}/img_tot_rank_{}_{}.npy'.format(
tmp_dir, other_rank, model_md5)
top1_correct_all += np.load(top1_correct_npy)
top5_correct_all += np.load(top5_correct_npy)
img_tot_all += np.load(img_tot_npy)
results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
results = np.array(results)
else:
results = np.array(results)
args.logger.info('after results={}'.format(results))
top1_correct = results[0, 0]
top5_correct = results[1, 0]
img_tot = results[2, 0]
acc1 = 100.0 * top1_correct / img_tot
acc5 = 100.0 * top5_correct / img_tot
args.logger.info(
'after allreduce eval: top1_correct={}, tot={}, acc={:.2f}%'.
format(top1_correct, img_tot, acc1))
args.logger.info(
'after allreduce eval: top5_correct={}, tot={}, acc={:.2f}%'.
format(top5_correct, img_tot, acc5))
if args.is_distributed:
release()
def analyse(self):
"""
Collect and analyse performance data, called after training or during training.
Examples:
>>> from mindinsight.profiler import Profiler
>>> context.set_context(mode=context.GRAPH_MODE, device_target=“Ascend”,
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
>>> model = Model(train_network)
>>> dataset = get_dataset()
>>> model.train(2, dataset)
>>> profiler.analyse()
"""
try:
from mindspore.communication.management import release
release()
except ImportError:
logger.error("Profiling: fail to import release from mindspore.")
logger.info("begin profiler analyse")
job_id = self._get_profiling_job_id()
if not job_id:
msg = ("Fail to get profiling job, please check whether job dir was generated under path %s" \
% PROFILING_LOG_BASE_PATH)
raise RuntimeError(msg)
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path, hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
result = hwtslog_parser.execute()
if not result:
logger.error("Profiling: fail to parse hwts log file.")
return
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id, self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(self._output_path, opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename, opcompute_output_filename, op_task_dict)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(self._output_path, output_data_preprocess_aicpu)
try:
aicpu_data_parser = DataPreProcessParser(source_path, output_data_preprocess_aicpu)
aicpu_data_parser.execute()
except FileNotFoundError as err:
logger.exception(err)
# analyse op compute time info
try:
self._analyser_op_info()
except MindInsightException as err:
logger.error(err.message)
