def resnet50_predict(args_opt):
class_num = cfg.class_num
local_data_path = '/cache/data'
ckpt_file_slice = args_opt.checkpoint_path.split('/')
ckpt_file = ckpt_file_slice[len(ckpt_file_slice) - 1]
local_ckpt_path = '/cache/' + ckpt_file
# set graph mode and parallel mode
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=False)
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args_opt.data_url, dst_url=local_data_path)
mox.file.copy_parallel(src_url=args_opt.checkpoint_path,
dst_url=local_ckpt_path)
# load checkpoint into net
net = resnet50(class_num=class_num)
param_dict = load_checkpoint(local_ckpt_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# preprocess the image
images = os.listdir(local_data_path)
for image in images:
img = data_preprocess(os.path.join(local_data_path, image))
# predict model
res = net(Tensor(img.reshape((1, 3, 224, 224)),
mindspore.float32)).asnumpy()
predict_label = label_list[res[0].argmax()]
print("预测的蘑菇标签为:\n\t" + predict_label + "\n")
def create_network(name, **kwargs):
if name == 'resnet50':
return resnet50(*args, **kwargs)
if name == 'resnet101':
return resnet101(*args, **kwargs)
if name == 'se_resnet50':
return se_resnet50(*args, **kwargs)
raise NotImplementedError(f"{name} is not implemented in the repo")
def resnet50_train(args_opt):
epoch_size = args_opt.epoch_size
batch_size = cfg.batch_size
class_num = cfg.class_num
loss_scale_num = cfg.loss_scale
local_data_path = '/cache/data'
local_ckpt_path = '/cache/ckpt_file'
# set graph mode and parallel mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args_opt.data_url, dst_url=local_data_path)
# create dataset
print('Create train and evaluate dataset.')
train_dataset = create_dataset(dataset_path=local_data_path, do_train=True,
repeat_num=epoch_size, batch_size=batch_size)
train_step_size = train_dataset.get_dataset_size()
print('Create dataset success.')
# create model
net = resnet50(class_num=class_num)
# reduction='mean' means that apply reduction of mean to loss
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
lr = Tensor(get_lr(global_step=0, total_epochs=epoch_size, steps_per_epoch=train_step_size))
opt = Momentum(net.trainable_params(), lr, momentum=0.9, weight_decay=1e-4, loss_scale=loss_scale_num)
loss_scale = FixedLossScaleManager(loss_scale_num, False)
# amp_level="O2" means that the hybrid precision of O2 mode is used for training
# the whole network except that batchnorm will be cast into float16 format and dynamic loss scale will be used
# 'keep_batchnorm_fp32 = False' means that use the float16 format
model = Model(net, amp_level="O2", keep_batchnorm_fp32=False, loss_fn=loss,
optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
# define performance callback to show ips and loss callback to show loss for every epoch
time_cb = TimeMonitor(data_size=train_step_size)
performance_cb = PerformanceCallback(batch_size)
loss_cb = LossMonitor()
cb = [time_cb, performance_cb, loss_cb]
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_epochs * train_step_size,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=local_ckpt_path, config=config_ck)
cb += [ckpt_cb]
print(f'Start run training, total epoch: {epoch_size}.')
model.train(epoch_size, train_dataset, callbacks=cb)
# upload checkpoint files
print('Upload checkpoint.')
mox.file.copy_parallel(src_url=local_ckpt_path, dst_url=args_opt.train_url)
def resnet50_eval(args_opt):
class_num = cfg.class_num
local_data_path = '/cache/data'
ckpt_file_slice = args_opt.checkpoint_path.split('/')
ckpt_file = ckpt_file_slice[len(ckpt_file_slice) - 1]
local_ckpt_path = '/cache/' + ckpt_file
# set graph mode and parallel mode
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
save_graphs=False)
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args_opt.data_url, dst_url=local_data_path)
mox.file.copy_parallel(src_url=args_opt.checkpoint_path,
dst_url=local_ckpt_path)
# create dataset
dataset = create_dataset(dataset_path=local_data_path,
do_train=False,
batch_size=cfg.batch_size)
# load checkpoint into net
net = resnet50(class_num=class_num)
param_dict = load_checkpoint(local_ckpt_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# define loss and model
if not cfg.use_label_smooth:
cfg.label_smooth_factor = 0.0
loss = CrossEntropySmooth(sparse=True,
reduction='mean',
smooth_factor=cfg.label_smooth_factor,
num_classes=cfg.class_num)
model = Model(net,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
# eval model
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)