def test_lenet_MNIST():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "lenet"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
train_log = os.path.join(cur_model_path, "train_ascend.log")
ckpt_file = os.path.join(cur_model_path, "ckpt/checkpoint_lenet-10_1875.ckpt")
infer_log = os.path.join(cur_model_path, "infer_ascend.log")
dataset_path = os.path.join(utils.data_root, "mnist")
exec_network_shell = "cd {0}; python train.py --data_path={1} > {2} 2>&1"\
.format(model_name, dataset_path, train_log)
ret = os.system(exec_network_shell)
assert ret == 0
exec_network_shell = "cd {0}; python eval.py --data_path={1} --ckpt_path={2} > {3} 2>&1"\
.format(model_name, dataset_path, ckpt_file, infer_log)
ret = os.system(exec_network_shell)
assert ret == 0
per_step_time = utils.get_perf_data(train_log)
print("per_step_time is", per_step_time)
assert per_step_time < 1.3
pattern = r"'Accuracy': ([\d\.]+)}"
acc = utils.parse_log_file(pattern, infer_log)
print("acc is", acc)
assert acc[0] > 0.98
def test_resnet50_cifar10_gpu():
cur_path = os.getcwd()
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "resnet"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, "resnet")
old_list = ["total_epochs=config.epoch_size", "config.epoch_size - config.pretrain_epoch_size"]
new_list = ["total_epochs=10", "10"]
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
dataset_path = os.path.join(utils.data_root, "cifar-10-batches-bin")
exec_network_shell = "cd resnet/scripts; sh run_distribute_train_gpu.sh resnet50 cifar10 {}".format(dataset_path)
logger.warning("cmd [{}] is running...".format(exec_network_shell))
os.system(exec_network_shell)
cmd = "ps -ef | grep python | grep train.py | grep -v grep"
ret = utils.process_check(100, cmd)
assert ret
log_file = os.path.join(cur_model_path, "scripts/train_parallel/log")
pattern = r"per step time: ([\d\.]+) ms"
step_time_list = utils.parse_log_file(pattern, log_file)[8:]
per_step_time = sum(step_time_list) / len(step_time_list)
print("step time list is", step_time_list)
assert per_step_time < 115
loss_list = utils.get_loss_data_list(log_file)[-8:]
print("loss_list is", loss_list)
assert sum(loss_list) / len(loss_list) < 0.70
def test_resnet50_cifar10_ascend():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "resnet"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, "resnet")
old_list = ["total_epochs=config.epoch_size", "config.epoch_size - config.pretrain_epoch_size"]
new_list = ["total_epochs=10", "10"]
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
dataset_path = os.path.join(utils.data_root, "cifar-10-batches-bin")
exec_network_shell = "cd resnet/scripts; bash run_distribute_train.sh resnet50 cifar10 {} {}"\
.format(utils.rank_table_path, dataset_path)
os.system(exec_network_shell)
cmd = "ps -ef | grep python | grep train.py | grep -v grep"
ret = utils.process_check(100, cmd)
assert ret
log_file = os.path.join(cur_model_path, "scripts/train_parallel{}/log")
for i in range(8):
per_step_time = utils.get_perf_data(log_file.format(i))
assert per_step_time < 20.0
loss_list = []
for i in range(8):
loss = utils.get_loss_data_list(log_file.format(i))
loss_list.append(loss[-1])
assert sum(loss_list) / len(loss_list) < 0.70
def test_bgcf_export_mindir():
cur_path = os.getcwd()
model_path = "{}/../../../../model_zoo/official/gnn".format(cur_path)
model_name = "bgcf"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
ckpt_path = os.path.join(utils.ckpt_root, "bgcf/bgcf_trained.ckpt")
exec_export_shell = "cd {}; python export.py --ckpt_file={} --file_format=MINDIR".format(
model_name, ckpt_path)
os.system(exec_export_shell)
assert os.path.exists(
os.path.join(cur_model_path, "{}.mindir".format(model_name)))
def test_DeeplabV3_voc2007():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "deeplabv3"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
old_list = [
'/PATH/TO/EXPERIMENTS_DIR', '/PATH/TO/MODEL_ZOO_CODE',
'/PATH/TO/MINDRECORD_NAME', '/PATH/TO/PRETRAIN_MODEL',
"\\${train_code_path}/src/tools/rank_table_8p.json"
]
new_list = [
cur_model_path + '/train', cur_model_path,
os.path.join(utils.data_root,
"voc/voc2012/mindrecord_train/vocaug_mindrecord0"),
os.path.join(utils.ckpt_root, "deeplabv3/resnet101_ascend.ckpt"),
utils.rank_table_path
]
utils.exec_sed_command(
old_list, new_list,
os.path.join(cur_model_path, "scripts/run_distribute_train_s16_r1.sh"))
old_list = ['model.train(args.train_epochs', 'callbacks=cbs']
new_list = ['model.train(70', 'callbacks=cbs, sink_size=2']
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "train.py"))
exec_network_shell = "cd {}; sh scripts/run_distribute_train_s16_r1.sh".format(
model_name)
ret = os.system(exec_network_shell)
assert ret == 0
cmd = "ps -ef | grep python | grep train.py | grep -v grep"
ret = utils.process_check(100, cmd)
assert ret
log_file = os.path.join(cur_model_path, "train/device{}/log")
for i in range(8):
per_step_time = utils.get_perf_data(log_file.format(i))
print("per_step_time is", per_step_time)
assert per_step_time < 530.0
loss_list = []
for i in range(8):
loss = utils.get_loss_data_list(log_file.format(i))
print("loss is", loss[-1])
loss_list.append(loss[-1])
assert sum(loss_list) / len(loss_list) < 2.5
def test_SSD_mobilenet_v1_fpn_coco2017():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "ssd"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
old_list = [
"/data/MindRecord_COCO", "/ckpt/mobilenet_v1.ckpt", "/data/coco2017"
]
new_list = [
os.path.join(utils.data_root,
"coco/coco2017/mindrecord_train/ssd_mindrecord"),
os.path.join(utils.ckpt_root, "ssd_mobilenet_v1/mobilenet-v1.ckpt"),
os.path.join(utils.data_root, "coco/coco2017")
]
utils.exec_sed_command(
old_list, new_list,
os.path.join(cur_model_path, "src/config_ssd_mobilenet_v1_fpn.py"))
old_list = ["ssd300"]
new_list = ["ssd_mobilenet_v1_fpn"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "src/config.py"))
old_list = ["args_opt.epoch_size", "dataset_sink_mode=dataset_sink_mode"]
new_list = ["5", "dataset_sink_mode=dataset_sink_mode, sink_size=100"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "train.py"))
exec_network_shell = "cd {0}; sh -x scripts/run_distribute_train.sh 8 {1} 0.2 coco {2}"\
.format(model_name, 60, utils.rank_table_path)
os.system(exec_network_shell)
cmd = "ps -ef | grep train.py | grep coco | grep device_num | grep device_id | grep -v grep"
ret = utils.process_check(120, cmd)
assert ret
log_file = os.path.join(cur_model_path, "LOG{}/log.txt")
for i in range(8):
per_step_time = utils.get_perf_data(log_file.format(i))
print("per_step_time is", per_step_time)
assert per_step_time < 545
loss_list = []
for i in range(8):
loss = utils.get_loss_data_list(log_file.format(i))
print("loss is", loss[-1])
loss_list.append(loss[-1])
assert sum(loss_list) / len(loss_list) < 2.72
def test_BGCF_amazon_beauty():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/gnn".format(cur_path)
model_name = "bgcf"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
old_list = ["--datapath=../data_mr"]
new_list = [
"--datapath={}".format(
os.path.join(utils.data_root, "amazon_beauty/mindrecord_train"))
]
utils.exec_sed_command(
old_list, new_list,
os.path.join(cur_model_path, "scripts/run_train_ascend.sh"))
old_list = ["default=600,"]
new_list = ["default=50,"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "src/config.py"))
old_list = ["context.set_context(device_id=int(parser.device))"]
new_list = ["context.set_context()"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "train.py"))
exec_network_shell = "cd {}/scripts; bash run_train_ascend.sh".format(
model_name)
ret = os.system(exec_network_shell)
assert ret == 0
cmd = "ps -ef|grep python |grep train.py|grep amazon_beauty|grep -v grep"
ret = utils.process_check(300, cmd)
assert ret
log_file = os.path.join(cur_model_path, "scripts/train/log")
pattern1 = r"loss ([\d\.\+]+)\,"
loss_list = utils.parse_log_file(pattern1, log_file)
loss_list = loss_list[-5:]
print("last 5 epoch average loss is", sum(loss_list) / len(loss_list))
assert sum(loss_list) / len(loss_list) < 6400
pattern1 = r"cost:([\d\.\+]+)"
epoch_time_list = utils.parse_log_file(pattern1, log_file)[1:]
print("per epoch time:", sum(epoch_time_list) / len(epoch_time_list))
assert sum(epoch_time_list) / len(epoch_time_list) < 1.9
def test_transformer_export_mindir():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/nlp".format(cur_path)
model_name = "transformer"
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
export_file = "transformer80_bs_0"
ckpt_path = os.path.join(utils.ckpt_root,
"transformer/transformer_trained.ckpt")
print("ckpt_path:", ckpt_path)
old_list = ["'model_file': '/your/path/checkpoint_file'"]
new_list = ["'model_file': '{}'".format(ckpt_path)]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "src/eval_config.py"))
old_list = ["context.set_context(device_id=args.device_id)"]
new_list = ["context.set_context()"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "export.py"))
exec_export_shell = "cd transformer; python -u export.py --file_name={}" \
" --file_format=MINDIR".format(export_file)
os.system(exec_export_shell)
assert os.path.exists(
os.path.join(cur_model_path, "{}.mindir".format(export_file)))
def test_yolov3_darknet_8p():
cur_path = os.path.dirname(os.path.abspath(__file__))
model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
model_name = "yolov3_darknet53"
dataset_path = os.path.join(utils.data_root, "coco/coco2014/")
ckpt_path = os.path.join(utils.ckpt_root,
"yolov3_darknet/yolov3_darknet53_pretrain.ckpt")
utils.copy_files(model_path, cur_path, model_name)
cur_model_path = os.path.join(cur_path, model_name)
train_file = os.path.join(cur_model_path, "train.py")
old_list = ["--lr_scheduler=cosine_annealing"]
new_list = ["--lr_scheduler=cosine_annealing --training_shape=416"]
utils.exec_sed_command(
old_list, new_list,
os.path.join(cur_model_path, "scripts/run_distribute_train.sh"))
old_list = ["default=100", "max_epoch=args.max_epoch"]
new_list = ["default=10", "max_epoch=1"]
utils.exec_sed_command(old_list, new_list, train_file)
old_list = ["sampler=distributed_sampler"]
new_list = ["sampler=distributed_sampler, num_samples=100*batch_size"]
utils.exec_sed_command(old_list, new_list,
os.path.join(cur_model_path, "src/yolo_dataset.py"))
exec_network_shell = "cd yolov3_darknet53/scripts; bash run_distribute_train.sh {0} {1} {2}"\
.format(dataset_path, ckpt_path, utils.rank_table_path)
os.system(exec_network_shell)
cmd = "ps -ef | grep python | grep train.py | grep -v grep"
ret = utils.process_check(120, cmd)
assert ret
train_log_file = os.path.join(
cur_path, "yolov3_darknet53/scripts/train_parallel0/log.txt")
pattern1 = r", *([\d\.]+) imgs/sec"
pattern2 = r"loss:*([\d\.]+),"
fps_list = utils.parse_log_file(pattern1, train_log_file)[1:]
assert sum(fps_list) / len(fps_list) > 480
loss_list = utils.parse_log_file(pattern2, train_log_file)
assert loss_list[-1] < 280