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!10033 GPU update benchmark 

From: @VectorSL
Reviewed-by: @cristoval,@limingqi107
Signed-off-by: @limingqi107
This commit is contained in:
mindspore-ci-bot 2020-12-16 16:55:45 +08:00 committed by Gitee
parent 41bd077e09 c6b5a7db03
commit 0d035c4008
1 changed files with 26 additions and 7 deletions
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31
model_zoo/official/cv/resnet/gpu_resnet_benchmark.py
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@ -22,7 +22,7 @@ from mindspore import Tensor
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model
from mindspore.context import ParallelMode
from mindspore.train.callback import Callback, LossMonitor, ModelCheckpoint, CheckpointConfig
from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.train.serialization import load_checkpoint, load_param_into_net
@ -59,13 +59,33 @@ class MyTimeMonitor(Callback):
def step_begin(self, run_context):
self.step_time = time.time()
def step_end(self, run_context):
cb_params = run_context.original_args()
loss = cb_params.net_outputs
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
cb_params.cur_epoch_num, cur_step_in_epoch))
step_mseconds = (time.time() - self.step_time) * 1000
fps = self.batch_size / step_mseconds *1000 * self.size
print("Epoch time: {:5.3f} ms, fps: {:d} img/sec.".format(step_mseconds, int(fps)), flush=True, end=" ")
print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss),
"Epoch time: {:5.3f} ms, fps: {:d} img/sec.".format(step_mseconds, int(fps)), flush=True)
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU", dtype="fp16"):
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU", dtype="fp16",
device_num=1):
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=4, shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=4, shuffle=True,
num_shards=device_num, shard_id=get_rank())
image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
@ -185,8 +205,7 @@ def train():
if mode == context.PYNATIVE_MODE:
print_per_steps = 1
time_cb = MyTimeMonitor(total_batch, print_per_steps)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
cb = [time_cb]
if save_ckpt:
config_ck = CheckpointConfig(save_checkpoint_steps=5 * step_size, keep_checkpoint_max=5)
ckpt_cb = ModelCheckpoint(prefix="resnet_benchmark", directory=ckpt_save_dir, config=config_ck)