def eval_alexnet():
print("============== Starting Testing ==============")
device_num = get_device_num()
if device_num > 1:
# context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
context.set_context(mode=context.GRAPH_MODE,
device_target='Davinci',
save_graphs=False)
if config.device_target == "Ascend":
context.set_context(device_id=get_device_id())
init()
elif config.device_target == "GPU":
init()
if config.dataset_name == 'cifar10':
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Momentum(network.trainable_params(), config.learning_rate,
config.momentum)
ds_eval = create_dataset_cifar10(config.data_path, config.batch_size, status="test", \
target=config.device_target)
param_dict = load_checkpoint(load_path)
print("load checkpoint from [{}].".format(load_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
elif config.dataset_name == 'imagenet':
network = AlexNet(config.num_classes, phase='test')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
ds_eval = create_dataset_imagenet(config.data_path,
config.batch_size,
training=False)
param_dict = load_checkpoint(load_path)
print("load checkpoint from [{}].".format(load_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
else:
raise ValueError("Unsupported dataset.")
if ds_eval.get_dataset_size() == 0:
raise ValueError(
"Please check dataset size > 0 and batch_size <= dataset size")
result = model.eval(ds_eval, dataset_sink_mode=config.dataset_sink_mode)
print("result : {}".format(result))
from mindspore.nn.metrics import Accuracy
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=ast.literal_eval,
default=True, help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
repeat_size = cfg.epoch_size
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
ds_eval = create_dataset_cifar10(args.data_path,
cfg.batch_size,
status="test")
acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
print("============== {} ==============".format(acc))
help='if is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode',
type=ast.literal_eval,
default=True,
help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target)
print("============== Starting Testing ==============")
if args.dataset_name == 'cifar10':
cfg = alexnet_cifar10_cfg
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = nn.Momentum(network.trainable_params(), cfg.learning_rate,
cfg.momentum)
ds_eval = create_dataset_cifar10(args.data_path,
cfg.batch_size,
status="test",
target=args.device_target)
param_dict = load_checkpoint(args.ckpt_path)
print("load checkpoint from [{}].".format(args.ckpt_path))
load_param_into_net(network, param_dict)
network.set_train(False)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
type=str,
default="./ckpt",
help='if is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode',
type=bool,
default=True,
help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target)
ds_train = create_dataset_cifar10(args.data_path, cfg.batch_size,
cfg.epoch_size)
network = AlexNet(cfg.num_classes)
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False,
sparse=True,
reduction="mean")
lr = Tensor(
get_lr(0, cfg.learning_rate, cfg.epoch_size,
ds_train.get_dataset_size()))
opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
config_ck = CheckpointConfig(
save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet",
directory=args.ckpt_path,
config=config_ck)
default="Ascend",
choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--ckpt_path',
type=str,
default="./ckpt",
help='if is test, must provide\
path where the trained ckpt file')
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.device_target)
if args_opt.dataset_name == 'cifar10':
cfg = alexnet_cifar10_cfg
elif args_opt.dataset_name == 'imagenet':
cfg = alexnet_imagenet_cfg
else:
raise ValueError("dataset is not support.")
net = AlexNet(num_classes=cfg.num_classes)
param_dict = load_checkpoint(args_opt.ckpt_path)
load_param_into_net(net, param_dict)
input_arr = Tensor(
np.random.uniform(0.0,
1.0,
size=[1, 3, cfg.image_height, cfg.image_width]),
ms.float32)
export(net, input_arr, file_name=cfg.air_name, file_format="AIR")
tr_data = ImageDataGenerator(train_file, mode='training', batch_size=batch_size,
num_classes=num_classes, shuffle=True)
val_data = ImageDataGenerator(train_file, mode='inference', batch_size=batch_size,
num_classes=num_classes, shuffle=False)
iterator = Iterator.from_structure(tr_data.data.output_types, tr_data.data.output_shapes)
next_batch = iterator.get_next()
training_init_op = iterator.make_initializer(tr_data.data)
validation_init_op = iterator.make_initializer(val_data.data)
x = tf.placeholder(tf.float32, [batch_size, 277, 277, 3])
y = tf.placeholder(tf.float32, [batch_size, num_classes])
keep_prob = tf.placeholder(tf.float32)
model = AlexNet(x, keep_prob, num_classes, train_layers)
score = model.fc8
var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers]
with tf.name_scope('cross_ent'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=score, labels=y))
with tf.name_scope('train'):
gradients = tf.gradients(loss, var_list)
gradients = list(zip(gradients, var_list))
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.apply_gradients(grads_and_vars=gradients)
"""
import os
# import sys
# sys.path.append(os.path.join(os.getcwd(), 'utils'))
from utils.config import config
import numpy as np
import mindspore as ms
from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
from src.alexnet import AlexNet
if os.path.exists(config.data_path_local):
ckpt_path = config.ckpt_path_local
else:
ckpt_path = os.path.join(config.data_path, 'checkpoint_alexnet-30_1562.ckpt')
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
if config.device_target == "Ascend":
context.set_context(device_id=config.device_id)
if __name__ == '__main__':
net = AlexNet(num_classes=config.num_classes)
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict)
input_arr = Tensor(np.zeros([config.batch_size, 3, config.image_height, config.image_width]), ms.float32)
export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
save_model_scalar(MODEL_SCALAR_TYPE.TrainLoss, loss)
return loss
class MyCallback(Callback):
def __init__(self):
super(MyCallback, self).__init__()
def step_end(self, run_context):
cb_params = run_context.original_args()
set_iteration(cb_params.cur_step_num)
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
network = AlexNet(num_classes=10)
ds_train = create_dataset_mnist("./dataset/10-batches-bin", cfg.batch_size,
cfg.epoch_size)
loss = MyLoss(is_grad=False, sparse=True, reduction="mean")
lr = Tensor(
get_lr(0, cfg.learning_rate, cfg.epoch_size, ds_train.get_dataset_size()))
opt = MyOptimizer(network.trainable_params(), lr, cfg.momentum)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet",
directory="./ckpt",
config=config_ck)
def train_alexnet():
print(config)
print('device id:', get_device_id())
print('device num:', get_device_num())
print('rank id:', get_rank_id())
print('job id:', get_job_id())
device_target = config.device_target
context.set_context(mode=context.GRAPH_MODE,
device_target=config.device_target)
context.set_context(save_graphs=False)
device_num = get_device_num()
if config.dataset_name == "cifar10":
if device_num > 1:
config.learning_rate = config.learning_rate * device_num
config.epoch_size = config.epoch_size * 2
elif config.dataset_name == "imagenet":
pass
else:
raise ValueError("Unsupported dataset.")
if device_num > 1:
context.reset_auto_parallel_context()
context.set_auto_parallel_context(device_num=device_num, \
parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
if device_target == "Ascend":
context.set_context(device_id=get_device_id())
init()
elif device_target == "GPU":
init()
else:
context.set_context(device_id=get_device_id())
if config.dataset_name == "cifar10":
ds_train = create_dataset_cifar10(config.data_path,
config.batch_size,
target=config.device_target)
elif config.dataset_name == "imagenet":
ds_train = create_dataset_imagenet(config.data_path, config.batch_size)
else:
raise ValueError("Unsupported dataset.")
if ds_train.get_dataset_size() == 0:
raise ValueError(
"Please check dataset size > 0 and batch_size <= dataset size")
network = AlexNet(config.num_classes, phase='train')
loss_scale_manager = None
metrics = None
step_per_epoch = ds_train.get_dataset_size(
) if config.sink_size == -1 else config.sink_size
if config.dataset_name == 'cifar10':
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
lr = Tensor(
get_lr_cifar10(0, config.learning_rate, config.epoch_size,
step_per_epoch))
opt = nn.Momentum(network.trainable_params(), lr, config.momentum)
metrics = {"Accuracy": Accuracy()}
elif config.dataset_name == 'imagenet':
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
lr = Tensor(
get_lr_imagenet(config.learning_rate, config.epoch_size,
step_per_epoch))
opt = nn.Momentum(params=get_param_groups(network),
learning_rate=lr,
momentum=config.momentum,
weight_decay=config.weight_decay,
loss_scale=config.loss_scale)
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
if config.is_dynamic_loss_scale == 1:
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536,
scale_factor=2,
scale_window=2000)
else:
loss_scale_manager = FixedLossScaleManager(
config.loss_scale, drop_overflow_update=False)
else:
raise ValueError("Unsupported dataset.")
if device_target == "Ascend":
model = Model(network,
loss_fn=loss,
optimizer=opt,
metrics=metrics,
amp_level="O2",
keep_batchnorm_fp32=False,
loss_scale_manager=loss_scale_manager)
elif device_target == "GPU":
model = Model(network,
loss_fn=loss,
optimizer=opt,
metrics=metrics,
loss_scale_manager=loss_scale_manager)
else:
raise ValueError("Unsupported platform.")
if device_num > 1:
ckpt_save_dir = os.path.join(config.checkpoint_path + "_" +
str(get_rank()))
else:
ckpt_save_dir = config.checkpoint_path
time_cb = TimeMonitor(data_size=step_per_epoch)
config_ck = CheckpointConfig(
save_checkpoint_steps=config.save_checkpoint_steps,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet",
directory=ckpt_save_dir,
config=config_ck)
print("============== Starting Training ==============")
model.train(config.epoch_size,
ds_train,
callbacks=[time_cb, ckpoint_cb,
LossMonitor()],
dataset_sink_mode=config.dataset_sink_mode,
sink_size=config.sink_size)
def alexnet(*args, **kwargs):
return AlexNet(*args, **kwargs)
