def _build_training_pipeline(config: TransformerConfig,
pre_training_dataset=None,
fine_tune_dataset=None,
test_dataset=None):
"""
Build training pipeline.
Args:
config (TransformerConfig): Config of mass model.
pre_training_dataset (Dataset): Pre-training dataset.
fine_tune_dataset (Dataset): Fine-tune dataset.
test_dataset (Dataset): Test dataset.
"""
net_with_loss = TransformerNetworkWithLoss(config, is_training=True)
net_with_loss.init_parameters_data()
if config.existed_ckpt:
if config.existed_ckpt.endswith(".npz"):
weights = np.load(config.existed_ckpt)
else:
weights = load_checkpoint(config.existed_ckpt)
for param in net_with_loss.trainable_params():
weights_name = param.name
if weights_name not in weights:
raise ValueError(
f"Param {weights_name} is not found in ckpt file.")
if isinstance(weights[weights_name], Parameter):
param.default_input = weights[weights_name].default_input
elif isinstance(weights[weights_name], Tensor):
param.default_input = Tensor(weights[weights_name].asnumpy(),
config.dtype)
elif isinstance(weights[weights_name], np.ndarray):
param.default_input = Tensor(weights[weights_name],
config.dtype)
else:
param.default_input = weights[weights_name]
else:
for param in net_with_loss.trainable_params():
name = param.name
value = param.default_input
if isinstance(value, Tensor):
if name.endswith(".gamma"):
param.default_input = one_weight(value.asnumpy().shape)
elif name.endswith(".beta") or name.endswith(".bias"):
param.default_input = zero_weight(value.asnumpy().shape)
else:
param.default_input = weight_variable(
value.asnumpy().shape)
dataset = pre_training_dataset if pre_training_dataset is not None \
else fine_tune_dataset
if dataset is None:
raise ValueError(
"pre-training dataset or fine-tuning dataset must be provided one."
)
update_steps = dataset.get_repeat_count() * dataset.get_dataset_size()
if config.lr_scheduler == "isr":
lr = Tensor(square_root_schedule(
lr=config.lr,
update_num=update_steps,
decay_start_step=config.decay_start_step,
warmup_steps=config.warmup_steps,
min_lr=config.min_lr),
dtype=mstype.float32)
elif config.lr_scheduler == "poly":
lr = Tensor(polynomial_decay_scheduler(
lr=config.lr,
min_lr=config.min_lr,
decay_steps=config.decay_steps,
total_update_num=update_steps,
warmup_steps=config.warmup_steps,
power=config.poly_lr_scheduler_power),
dtype=mstype.float32)
else:
lr = config.lr
if config.optimizer.lower() == "adam":
optimizer = Adam(net_with_loss.trainable_params(),
lr,
beta1=0.9,
beta2=0.98)
elif config.optimizer.lower() == "lamb":
lr = BertLearningRate(decay_steps=12000,
learning_rate=config.lr,
end_learning_rate=config.min_lr,
power=10.0,
warmup_steps=config.warmup_steps)
decay_params = list(
filter(
lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x
.name.lower(), net_with_loss.trainable_params()))
other_params = list(
filter(
lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.
lower(), net_with_loss.trainable_params()))
group_params = [{
'params': decay_params,
'weight_decay': 0.01
}, {
'params': other_params
}]
optimizer = Lamb(group_params, lr, eps=1e-6)
elif config.optimizer.lower() == "momentum":
optimizer = Momentum(net_with_loss.trainable_params(),
lr,
momentum=0.9)
else:
raise ValueError(f"optimizer only support `adam` and `momentum` now.")
# Dynamic loss scale.
scale_manager = DynamicLossScaleManager(
init_loss_scale=config.init_loss_scale,
scale_factor=config.loss_scale_factor,
scale_window=config.scale_window)
net_with_grads = TransformerTrainOneStepWithLossScaleCell(
network=net_with_loss,
optimizer=optimizer,
scale_update_cell=scale_manager.get_update_cell())
net_with_grads.set_train(True)
model = Model(net_with_grads)
loss_monitor = LossCallBack(config)
ckpt_config = CheckpointConfig(
save_checkpoint_steps=config.save_ckpt_steps,
keep_checkpoint_max=config.keep_ckpt_max)
rank_size = os.getenv('RANK_SIZE')
callbacks = [loss_monitor]
if rank_size is not None and int(
rank_size) > 1 and MultiAscend.get_rank() % 8 == 0:
ckpt_callback = ModelCheckpoint(
prefix=config.ckpt_prefix,
directory=os.path.join(config.ckpt_path,
'ckpt_{}'.format(os.getenv('DEVICE_ID'))),
config=ckpt_config)
callbacks.append(ckpt_callback)
if rank_size is None or int(rank_size) == 1:
ckpt_callback = ModelCheckpoint(
prefix=config.ckpt_prefix,
directory=os.path.join(config.ckpt_path,
'ckpt_{}'.format(os.getenv('DEVICE_ID'))),
config=ckpt_config)
callbacks.append(ckpt_callback)
print(f" | ALL SET, PREPARE TO TRAIN.")
_train(model=model,
config=config,
pre_training_dataset=pre_training_dataset,
fine_tune_dataset=fine_tune_dataset,
test_dataset=test_dataset,
callbacks=callbacks)
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--data_path', type=str, default="s3://hithcd/rgb/pic/",
help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="obs://hithcd/MA-hw_project_resnet18-05-30-19/output/V0408/", help='if mode is test, must provide\
path where the trained ckpt file')
parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
local_data_url = '/cache/data'
local_output_url = '/cache/ckpt'
mox.file.copy_parallel(args.data_path, local_data_url)
mox.file.copy_parallel(args.ckpt_path, local_output_url)
net = resnet18(class_num=config.class_num)
print("============== Starting Testing ==============")
param_dict = load_checkpoint(os.path.join(local_output_url, 'resnet-50_1759.ckpt'), net=net)
load_param_into_net(net, param_dict)
im = np.asarray(Image.open(os.path.join(local_data_url, '490.png')).convert('L'))
im = 255-im
im = im/255.0
input = im.reshape((1, 1, 112, 112))
input_tensor = Tensor(input, mindspore.float32)
acc = net(input_tensor)
acc = acc.asnumpy()
preds = np.argmax(acc, axis=1)
mox.file.copy_parallel(local_output_url, args.train_url)
print("Predict label:{0}, acc={1}".format(preds[0], acc[0][preds[0]]))
def yolov3_predict(instance, strategy):
network = YOLOV3DarkNet53(is_training=False)
pretrained_ckpt = '/dataset/ckpt-files/shanshui_full/yolov3.ckpt'
if not os.path.exists(pretrained_ckpt):
err_msg = "The yolov3.ckpt file does not exist!"
return {"status": 1, "err_msg": err_msg}
param_dict = load_checkpoint(pretrained_ckpt)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('yolo_network.'):
param_dict_new[key[13:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
config = ConfigYOLOV3DarkNet53()
# init detection engine
args = edict()
args.ignore_threshold = 0.01
args.nms_thresh = 0.5
detection = DetectionEngine(args)
input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
print('Start inference....')
network.set_train(False)
ori_image = np.array(json.loads(instance['data']), dtype=instance['dtype'])
image, image_shape = data_preprocess(ori_image, config)
prediction = network(Tensor(image.reshape(1, 3, 416, 416), ms.float32),
input_shape)
output_big, output_me, output_small = prediction
output_big = output_big.asnumpy()
output_me = output_me.asnumpy()
output_small = output_small.asnumpy()
per_batch_size = 1
detection.detect([output_small, output_me, output_big], per_batch_size,
image_shape, config)
detection.do_nms_for_results()
out_img = detection.draw_boxes_in_image(ori_image)
# for i in range(len(detection.det_boxes)):
# print("x: ", detection.det_boxes[i]['bbox'][0])
# print("y: ", detection.det_boxes[i]['bbox'][1])
# print("h: ", detection.det_boxes[i]['bbox'][2])
# print("w: ", detection.det_boxes[i]['bbox'][3])
# print("score: ", round(detection.det_boxes[i]['score'], 3))
# print("category: ", detection.det_boxes[i]['category_id'])
det_boxes = detection.det_boxes
if not len(det_boxes):
err_msg = "抱歉！未检测到任何种类，无法标注。"
return {"status": 1, "err_msg": err_msg}
max_det = max(det_boxes, key=lambda k: k['score'])
max_score = max_det['score']
category = det_boxes[det_boxes.index(max_det)]['category_id']
res = {
"status": 0,
"instance": {
"boxes_num": len(det_boxes),
"max_score": round(max_score, 3),
"category": category,
"data": numpy2base64(out_img)
}
}
return res
def main():
parser = argparse.ArgumentParser(description="YOLOv3 train")
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False,
help="If set it true, only create Mindrecord, default is False.")
parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.")
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
parser.add_argument("--lr", type=float, default=0.001, help="Learning rate, default is 0.001.")
parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
parser.add_argument("--epoch_size", type=int, default=50, help="Epoch size, default is 50")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint file path")
parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size")
parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by "
"image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
"rather than image_dir and anno_path. Default is ./Mindrecord_train")
parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
"the absolute image path is joined by the image_dir "
"and the relative path in anno_path")
parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
if args_opt.distribute:
device_num = args_opt.device_num
context.reset_auto_parallel_context()
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
device_num=device_num)
init()
rank = args_opt.device_id % device_num
else:
rank = 0
device_num = 1
print("Start create dataset!")
# It will generate mindrecord file in args_opt.mindrecord_dir,
# and the file name is yolo.mindrecord0, 1, ... file_num.
if not os.path.isdir(args_opt.mindrecord_dir):
os.makedirs(args_opt.mindrecord_dir)
prefix = "yolo.mindrecord"
mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
if not os.path.exists(mindrecord_file):
if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
print("Create Mindrecord.")
data_to_mindrecord_byte_image(args_opt.image_dir,
args_opt.anno_path,
args_opt.mindrecord_dir,
prefix,
8)
print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir))
else:
raise ValueError('image_dir {} or anno_path {} does not exist'.format(\
args_opt.image_dir, args_opt.anno_path))
if not args_opt.only_create_dataset:
loss_scale = float(args_opt.loss_scale)
# When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0.
dataset = create_yolo_dataset(mindrecord_file,
batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
dataset_size = dataset.get_dataset_size()
print("Create dataset done!")
net = yolov3_resnet18(ConfigYOLOV3ResNet18())
net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
init_net_param(net, "XavierUniform")
# checkpoint
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory='./ckpt_' + str(rank) + '/', config=ckpt_config)
if args_opt.pre_trained:
if args_opt.pre_trained_epoch_size <= 0:
raise KeyError("pre_trained_epoch_size must be greater than 0.")
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
total_epoch_size = 60
if args_opt.distribute:
total_epoch_size = 160
lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=args_opt.pre_trained_epoch_size * dataset_size,
global_step=total_epoch_size * dataset_size,
decay_step=1000, decay_rate=0.95, steps=True))
opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
net = TrainingWrapper(net, opt, loss_scale)
callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
model = Model(net)
dataset_sink_mode = False
if args_opt.mode == "sink":
print("In sink mode, one epoch return a loss.")
dataset_sink_mode = True
print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
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:
if args.platform == "Ascend":
init()
elif args.platform == "GPU":
init("nccl")
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 test_lenet_mnist_fuzzing():
# upload trained network
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model = Model(net)
mutate_config = [{
'method': 'Blur',
'params': {
'radius': [0.1, 0.2, 0.3],
'auto_param': [True, False]
}
}, {
'method': 'Contrast',
'params': {
'auto_param': [True]
}
}, {
'method': 'Translate',
'params': {
'auto_param': [True]
}
}, {
'method': 'Brightness',
'params': {
'auto_param': [True]
}
}, {
'method': 'Noise',
'params': {
'auto_param': [True]
}
}, {
'method': 'Scale',
'params': {
'auto_param': [True]
}
}, {
'method': 'Shear',
'params': {
'auto_param': [True]
}
}, {
'method': 'FGSM',
'params': {
'eps': [0.3, 0.2, 0.4],
'alpha': [0.1]
}
}]
# get training data
data_list = "../common/dataset/MNIST/train"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
train_images = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
train_images.append(images)
train_images = np.concatenate(train_images, axis=0)
# initialize fuzz test with training dataset
model_coverage_test = ModelCoverageMetrics(model, 10, 1000, train_images)
# fuzz test with original test data
# get test data
data_list = "../common/dataset/MNIST/test"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
test_images = []
test_labels = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
test_images = np.concatenate(test_images, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
np.array(test_images[:100]).astype(np.float32))
LOGGER.info(TAG, 'KMNC of this test is : %s',
model_coverage_test.get_kmnc())
model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
_, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
initial_seeds,
eval_metrics='auto')
if metrics:
for key in metrics:
LOGGER.info(TAG, key + ': %s', metrics[key])
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
##############export checkpoint file into air and onnx models#################
python export.py
"""
import numpy as np
import mindspore as ms
from mindspore import Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
from src.config import cifar_cfg as cfg
from src.googlenet import GoogleNet
if __name__ == '__main__':
net = GoogleNet(num_classes=cfg.num_classes)
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)
input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]),
ms.float32)
export(net, input_arr, file_name=cfg.onnx_filename, file_format="ONNX")
export(net, input_arr, file_name=cfg.air_filename, file_format="AIR")
def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoint_path="", epoch_num=1):
"""
Do train
Args:
dataset: the train dataset.
network: the network with loss
load_checkpoint_path: the file path which saved pretrained model checkpoint.
save_checkpoint_path: the file path which will save finetuned model checkpoint.
epoch_num: the number of epoch.
"""
if load_checkpoint_path == "":
raise ValueError("Pretrain model missed, finetune task must load pretrain model!")
steps_per_epoch = dataset.get_dataset_size()
# optimizer
if cfg.optimizer == 'AdamWeightDecay':
lr_schedule = GPT2LearningRate(learning_rate=cfg.AdamWeightDecay.learning_rate,
end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=cfg.AdamWeightDecay.power)
params = network.trainable_params()
decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
{'params': other_params, 'weight_decay': 0.0}]
optimizer = AdamWeightDecay(group_params, lr_schedule, eps=cfg.AdamWeightDecay.eps)
elif cfg.optimizer == 'Lamb':
lr_schedule = GPT2LearningRate(learning_rate=cfg.Lamb.learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=cfg.Lamb.power)
optimizer = Lamb(network.trainable_params(), lr_schedule)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(network.trainable_params(), cfg.Momentum.learning_rate, cfg.Momentum.momentum)
else:
raise Exception("Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]")
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)
prefix_name = "gpt2_translation_" + str(cfg.gpt2_network) + "_" + str(cfg.optimizer) + "_" \
+ str(epoch_num) + "_bs" + str(gpt2_net_cfg.batch_size)
ckpoint_cb = ModelCheckpoint(prefix=prefix_name,
directory=None if save_checkpoint_path == "" else save_checkpoint_path,
config=ckpt_config)
param_dict = load_checkpoint(load_checkpoint_path)
final_param_dict = {}
for name, _ in param_dict.items():
final_param_dict['gpt2.gpt2.' + name] = param_dict[name]
final_param_dict['gpt2.dense1.weight'] = param_dict['gpt2_embedding_lookup.embedding_table']
load_param_into_net(network, final_param_dict)
print("Load the pretrained parameter successfully! \n")
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2 ** 32, scale_factor=2, scale_window=1000)
netwithgrads = GPT2FinetuneCell(network, optimizer=optimizer, scale_update_cell=update_cell)
netwithgrads.set_train(True)
loss_cb = LossMonitor(per_print_times=1)
model = Model(netwithgrads)
callbacks = [TimeMonitor(dataset.get_dataset_size()), loss_cb, ckpoint_cb]
print("=================== Starting Training For Translation Task ====================")
model.train(epoch_num, dataset, callbacks=callbacks, dataset_sink_mode=False)
print("=================== Translation Training Success ====================")
default="./MNIST_Data",
help='path where the dataset is saved')
parser.add_argument(
'--ckpt_path',
type=str,
default="",
help='if mode is test, must provide path where the trained ckpt file')
args = parser.parse_args()
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target)
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network,
quant_delay=0,
bn_fold=False,
freeze_bn=10000,
per_channel=[True, False],
symmetric=[True, False])
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
# export network
inputs = Tensor(np.ones([1, 1, cfg.image_height, cfg.image_width]),
mindspore.float32)
quant.export(network, inputs, file_name="lenet_quant", file_format='AIR')
context.set_context(enable_task_sink=True)
print("test lenet predict start")
seed = 0
np.random.seed(seed)
batch = 1
channel = 1
input_h = 32
input_w = 32
origin_data = np.random.uniform(low=0,
high=255,
size=(batch, channel, input_h,
input_w)).astype(np.float32)
origin_data.tofile("lenet_input_data.bin")
input_data = Tensor(origin_data)
print(input_data.asnumpy())
net = LeNet()
ckpt_file_path = "./tests/ut/python/predict/checkpoint_lenet.ckpt"
predict_args = parser.parse_args()
model_path_name = predict_args.path
is_ckpt_exist = os.path.exists(ckpt_file_path)
if is_ckpt_exist:
param_dict = load_checkpoint(ckpoint_file_name=ckpt_file_path)
load_param_into_net(net, param_dict)
export(net, input_data, file_name=model_path_name, file_format='LITE')
print("test lenet predict success.")
else:
print("checkpoint file is not exist.")
def do_eval(dataset=None, network=None, metric=None, load_checkpoint_path="", eval_type=None, tokenizer_file_path="",
generate_length=1, top_k=1, top_p=1.0, temperature=1.0):
"""
Do evaluation on Translation
Args:
dataset: the eval dataset.
network: the network with loss.
metric: the evaluation method.
load_checkpoint_path: the file path which saved finetune model checkpoint.
"""
if load_checkpoint_path == "":
raise ValueError("Finetune model missed, evaluation task must load finetune model!")
if metric.lower() == "bleu":
print("Prepare to calculate the BLEU score ...")
gpt2_translation = network(config=gpt2_net_cfg,
is_training=False,
use_one_hot_embeddings=False)
gpt2_translation.set_train(False)
param_dict = load_checkpoint(load_checkpoint_path)
if eval_type == "zero-shot":
final_param_dict = {}
for name, _ in param_dict.items():
final_param_dict['gpt2.' + name] = param_dict[name]
final_param_dict['dense1.weight'] = param_dict['gpt2_embedding_lookup.embedding_table']
load_param_into_net(gpt2_translation, final_param_dict)
print("load pretrained parameter successfully!\n")
elif eval_type == "finetuned":
load_param_into_net(gpt2_translation, param_dict)
print("load finetuned parameter successfully!\n")
else:
raise ValueError("Evaluation type missed, eval_type should be [zero-shot, finetuned]")
model = Model(gpt2_translation)
tokenizer = Tokenizer(vocab_file=tokenizer_file_path + 'gpt2-vocab.json',
merge_file=tokenizer_file_path + 'gpt2-merges.txt')
callback = BLEU(tokenizer)
translation_generator = GenerateForTranslation(decoder=model,
config=gpt2_net_cfg,
tokenizer=tokenizer,
generate_length=1,
use_hint=True,
select_first_sentence=True,
topk_num=top_k,
topp_prob=float(top_p),
temperature=float(temperature)
)
columns_list = ["input_ids", "input_mask", "label_ids"]
print("==================== [BLEU] Testing ====================")
num_data = 1
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(data[i])
input_ids, input_mask, label_ids = input_data
print("| Data count: {}".format(num_data * gpt2_net_cfg.batch_size))
print("input_ids shape: {}".format(input_ids.shape))
print("input_mask shape: {}".format(input_mask.shape))
print("label_ids shape: {}".format(label_ids.shape))
ts_predict_list, ref_list = translation_generator.generate_for_translation(input_ids)
print("| Batch Reference translation:\n{}\n".format(ref_list))
if ref_list == '' or ref_list is None:
print("Sorry ref_list is None, skip it!")
continue
else:
print(" | Batch Predict translation:\n{}\n".format(ts_predict_list))
callback.update(ref_list, ts_predict_list)
num_data += 1
print("\n\n")
print("**************************************************************")
eval_result_print(metric, callback)
print("********************** Testing Finished **********************")
else:
raise ValueError("metric method not supported in translation, support: [BLEU]")
def load_backbone(net, ckpt_path, args):
"""Load darknet53 backbone checkpoint."""
param_dict = load_checkpoint(ckpt_path)
yolo_backbone_prefix = 'feature_map.backbone'
darknet_backbone_prefix = 'network.backbone'
find_param = []
not_found_param = []
net.init_parameters_data()
for name, cell in net.cells_and_names():
if name.startswith(yolo_backbone_prefix):
name = name.replace(yolo_backbone_prefix, darknet_backbone_prefix)
if isinstance(cell, (nn.Conv2d, nn.Dense)):
darknet_weight = '{}.weight'.format(name)
darknet_bias = '{}.bias'.format(name)
if darknet_weight in param_dict:
cell.weight.set_data(param_dict[darknet_weight].data)
find_param.append(darknet_weight)
else:
not_found_param.append(darknet_weight)
if darknet_bias in param_dict:
cell.bias.set_data(param_dict[darknet_bias].data)
find_param.append(darknet_bias)
else:
not_found_param.append(darknet_bias)
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
darknet_moving_mean = '{}.moving_mean'.format(name)
darknet_moving_variance = '{}.moving_variance'.format(name)
darknet_gamma = '{}.gamma'.format(name)
darknet_beta = '{}.beta'.format(name)
if darknet_moving_mean in param_dict:
cell.moving_mean.set_data(
param_dict[darknet_moving_mean].data)
find_param.append(darknet_moving_mean)
else:
not_found_param.append(darknet_moving_mean)
if darknet_moving_variance in param_dict:
cell.moving_variance.set_data(
param_dict[darknet_moving_variance].data)
find_param.append(darknet_moving_variance)
else:
not_found_param.append(darknet_moving_variance)
if darknet_gamma in param_dict:
cell.gamma.set_data(param_dict[darknet_gamma].data)
find_param.append(darknet_gamma)
else:
not_found_param.append(darknet_gamma)
if darknet_beta in param_dict:
cell.beta.set_data(param_dict[darknet_beta].data)
find_param.append(darknet_beta)
else:
not_found_param.append(darknet_beta)
args.logger.info('================found_param {}========='.format(
len(find_param)))
args.logger.info(find_param)
args.logger.info('================not_found_param {}========='.format(
len(not_found_param)))
args.logger.info(not_found_param)
args.logger.info('=====load {} successfully ====='.format(ckpt_path))
return net
def validation(net, model_path, data_dir, filename, num_consumer, batch):
param_dict = load_checkpoint(model_path)
load_param_into_net(net, param_dict)
auc = val(net, data_dir, filename, num_consumer, batch)
return auc
def test_trains(args):
'''test trains'''
print('----eval----begin----')
model_path = args.pretrained
result_file = model_path.replace('.ckpt', '.txt')
if os.path.exists(result_file):
os.remove(result_file)
epoch_result = open(result_file, 'a')
epoch_result.write(model_path + '\n')
network = FaceQABackbone()
ckpt_path = model_path
if os.path.isfile(ckpt_path):
param_dict = load_checkpoint(ckpt_path)
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)
else:
print('wrong model path')
return 1
path = args.eval_dir
kp_error_all = [[], [], [], [], []]
eulers_error_all = [[], [], []]
kp_ipn = []
file_list = os.listdir(path)
for file_name in tqdm(file_list):
if file_name.endswith('jpg'):
img_path = os.path.join(path, file_name)
img, img_ori = read_img(img_path)
txt_path = img_path.replace('jpg', 'txt')
if os.path.exists(txt_path):
euler_kps_do = True
x_length = img_ori.shape[1]
y_length = img_ori.shape[0]
eulers_gt, kp_list = read_gt(txt_path, x_length, y_length)
else:
euler_kps_do = False
continue
out = network(img)
_, _, kp_coord_ori, eulers_ori, _ = get_md_output(out)
if euler_kps_do:
eulgt = list(eulers_gt)
for euler_id, _ in enumerate(eulers_ori):
eulori = eulers_ori[euler_id]
eulers_error_all[euler_id].append(abs(eulori-float(eulgt[euler_id])))
eye01 = kp_list[0]
eye02 = kp_list[1]
eye_dis = 1
cur_flag = True
if eye01[0] < 0 or eye01[1] < 0 or eye02[0] < 0 or eye02[1] < 0:
cur_flag = False
else:
eye_dis = np.sqrt(np.square(abs(eye01[0]-eye02[0]))+np.square(abs(eye01[1]-eye02[1])))
cur_error_list = []
for i in range(5):
kp_coord_gt = kp_list[i]
kp_coord_model = kp_coord_ori[i]
if kp_coord_gt[0] != -1:
dis = np.sqrt(np.square(
kp_coord_gt[0] - kp_coord_model[0]) + np.square(kp_coord_gt[1] - kp_coord_model[1]))
kp_error_all[i].append(dis)
cur_error_list.append(dis)
if cur_flag:
kp_ipn.append(sum(cur_error_list)/len(cur_error_list)/eye_dis)
kp_ave_error = []
for kps, _ in enumerate(kp_error_all):
kp_ave_error.append("%.3f" % (sum(kp_error_all[kps])/len(kp_error_all[kps])))
euler_ave_error = []
elur_mae = []
for eulers, _ in enumerate(eulers_error_all):
euler_ave_error.append("%.3f" % (sum(eulers_error_all[eulers])/len(eulers_error_all[eulers])))
elur_mae.append((sum(eulers_error_all[eulers])/len(eulers_error_all[eulers])))
print(r'5 keypoints average err:'+str(kp_ave_error))
print(r'3 eulers average err:'+str(euler_ave_error))
print('IPN of 5 keypoints:'+str(sum(kp_ipn)/len(kp_ipn)*100))
print('MAE of elur:'+str(sum(elur_mae)/len(elur_mae)))
epoch_result.write(str(sum(kp_ipn)/len(kp_ipn)*100)+'\t'+str(sum(elur_mae)/len(elur_mae))+'\t'
+ str(kp_ave_error)+'\t'+str(euler_ave_error)+'\n')
print('----eval----end----')
return 0
def test():
"""The function of eval."""
start_time = time.time()
args = parse_args()
devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target,
save_graphs=True,
device_id=devid)
# logger
args.outputs_dir = os.path.join(
args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
rank_id = int(
os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0
args.logger = get_logger(args.outputs_dir, rank_id)
context.reset_auto_parallel_context()
parallel_mode = ParallelMode.STAND_ALONE
context.set_auto_parallel_context(parallel_mode=parallel_mode,
gradients_mean=True,
device_num=1)
args.logger.info('Creating Network....')
network = YOLOV3DarkNet53(is_training=False)
args.logger.info(args.pretrained)
if os.path.isfile(args.pretrained):
param_dict = load_checkpoint(args.pretrained)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('yolo_network.'):
param_dict_new[key[13:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load_model {} success'.format(args.pretrained))
else:
args.logger.info('{} not exists or not a pre-trained file'.format(
args.pretrained))
assert FileNotFoundError(
'{} not exists or not a pre-trained file'.format(args.pretrained))
exit(1)
data_root = args.data_root
ann_file = args.annFile
config = ConfigYOLOV3DarkNet53()
if args.testing_shape:
config.test_img_shape = conver_testing_shape(args)
ds, data_size = create_yolo_dataset(data_root,
ann_file,
is_training=False,
batch_size=args.per_batch_size,
max_epoch=1,
device_num=1,
rank=rank_id,
shuffle=False,
config=config)
args.logger.info('testing shape : {}'.format(config.test_img_shape))
args.logger.info('totol {} images to eval'.format(data_size))
network.set_train(False)
# init detection engine
detection = DetectionEngine(args)
input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
args.logger.info('Start inference....')
for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):
image = data["image"]
image_shape = data["image_shape"]
image_id = data["img_id"]
prediction = network(image, input_shape)
output_big, output_me, output_small = prediction
output_big = output_big.asnumpy()
output_me = output_me.asnumpy()
output_small = output_small.asnumpy()
image_id = image_id.asnumpy()
image_shape = image_shape.asnumpy()
detection.detect([output_small, output_me, output_big],
args.per_batch_size, image_shape, image_id)
if i % 1000 == 0:
args.logger.info('Processing... {:.2f}% '.format(
i * args.per_batch_size / data_size * 100))
args.logger.info('Calculating mAP...')
detection.do_nms_for_results()
result_file_path = detection.write_result()
args.logger.info('result file path: {}'.format(result_file_path))
eval_result = detection.get_eval_result()
cost_time = time.time() - start_time
args.logger.info('\n=============coco eval reulst=========\n' +
eval_result)
args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
type=bool,
default=True,
help='dataset_sink_mode is False or True')
args = parser.parse_args()
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE,
device_target=args.device_target)
ds_train = create_dataset(os.path.join(args.data_path, "train"),
cfg.batch_size, cfg.epoch_size)
step_size = ds_train.get_dataset_size()
# define fusion network
network = LeNet5Fusion(cfg.num_classes)
# load quantization aware network checkpoint
param_dict = load_checkpoint(args.ckpt_path, network.type)
load_param_into_net(network, param_dict)
# convert fusion network to quantization aware network
network = quant.convert_quant_network(network,
quant_delay=0,
bn_fold=False,
freeze_bn=10000)
# define network loss
net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False,
sparse=True,
reduction="mean")
# define network optimization
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
# call back and monitor
def test_fast_gradient_sign_method():
"""
FGSM-Attack test
"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# upload trained network
ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_name)
load_param_into_net(net, load_dict)
# get test data
data_list = "./MNIST_unzip/test"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
# prediction accuracy before attack
model = Model(net)
batch_num = 3 # the number of batches of attacking samples
test_images = []
test_labels = []
predict_labels = []
i = 0
for data in ds.create_tuple_iterator():
i += 1
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),
axis=1)
predict_labels.append(pred_labels)
if i >= batch_num:
break
predict_labels = np.concatenate(predict_labels)
true_labels = np.argmax(np.concatenate(test_labels), axis=1)
accuracy = np.mean(np.equal(predict_labels, true_labels))
LOGGER.info(TAG, "prediction accuracy before attacking is : %s", accuracy)
# attacking
attack = FastGradientSignMethod(net, eps=0.3)
start_time = time.clock()
adv_data = attack.batch_generate(np.concatenate(test_images),
np.concatenate(test_labels), batch_size=32)
stop_time = time.clock()
np.save('./adv_data', adv_data)
pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
# rescale predict confidences into (0, 1).
pred_logits_adv = softmax(pred_logits_adv, axis=1)
pred_labels_adv = np.argmax(pred_logits_adv, axis=1)
accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))
LOGGER.info(TAG, "prediction accuracy after attacking is : %s", accuracy_adv)
attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),
np.concatenate(test_labels),
adv_data.transpose(0, 2, 3, 1),
pred_logits_adv)
LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
attack_evaluate.mis_classification_rate())
LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
attack_evaluate.avg_conf_adv_class())
LOGGER.info(TAG, 'The average confidence of true class is : %s',
attack_evaluate.avg_conf_true_class())
LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
'samples and adversarial samples are: %s',
attack_evaluate.avg_lp_distance())
LOGGER.info(TAG, 'The average structural similarity between original '
'samples and adversarial samples are: %s',
attack_evaluate.avg_ssim())
LOGGER.info(TAG, 'The average costing time is %s',
(stop_time - start_time)/(batch_num*batch_size))
def run_gru_eval():
"""
Transformer evaluation.
"""
parser = argparse.ArgumentParser(description='GRU eval')
parser.add_argument(
"--device_target",
type=str,
default="Ascend",
help="device where the code will be implemented, default is Ascend")
parser.add_argument('--device_id',
type=int,
default=0,
help='device id of GPU or Ascend, default is 0')
parser.add_argument('--device_num',
type=int,
default=1,
help='Use device nums, default is 1')
parser.add_argument('--ckpt_file',
type=str,
default="",
help='ckpt file path')
parser.add_argument("--dataset_path",
type=str,
default="",
help="Dataset path, default: f`sns.")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, reserve_class_name_in_scope=False, \
device_id=args.device_id, save_graphs=False)
dataset = create_gru_dataset(epoch_count=config.num_epochs, batch_size=config.eval_batch_size, \
dataset_path=args.dataset_path, rank_size=args.device_num, rank_id=0, do_shuffle=False, is_training=False)
dataset_size = dataset.get_dataset_size()
print("dataset size is {}".format(dataset_size))
network = Seq2Seq(config, is_training=False)
network = GRUInferCell(network)
network.set_train(False)
if args.ckpt_file != "":
parameter_dict = load_checkpoint(args.ckpt_file)
load_param_into_net(network, parameter_dict)
model = Model(network)
predictions = []
source_sents = []
target_sents = []
eval_text_len = 0
for batch in dataset.create_dict_iterator(output_numpy=True, num_epochs=1):
source_sents.append(batch["source_ids"])
target_sents.append(batch["target_ids"])
source_ids = Tensor(batch["source_ids"], mstype.int32)
target_ids = Tensor(batch["target_ids"], mstype.int32)
predicted_ids = model.predict(source_ids, target_ids)
print("predicts is ", predicted_ids.asnumpy())
print("target_ids is ", target_ids)
predictions.append(predicted_ids.asnumpy())
eval_text_len = eval_text_len + 1
f_output = open(config.output_file, 'w')
f_target = open(config.target_file, "w")
for batch_out, true_sentence in zip(predictions, target_sents):
for i in range(config.eval_batch_size):
target_ids = [str(x) for x in true_sentence[i].tolist()]
f_target.write(" ".join(target_ids) + "\n")
token_ids = [str(x) for x in batch_out[i].tolist()]
f_output.write(" ".join(token_ids) + "\n")
f_output.close()
f_target.close()
parser.add_argument('--train_url', type=str, default=None, help='Train output path')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
context.set_context(device_id=device_id)
context.set_context(enable_mem_reuse=True)
local_data_url = '/cache/data'
local_ckpt_url = '/cache/ckpt'
if args_opt.checkpoint_path:
checkpoint_file=os.path.join(local_ckpt_url,os.path.split(args_opt.checkpoint_path)[1])
mox.file.copy_parallel(args_opt.data_url,local_data_url)
mox.file.copy_parallel(args_opt.checkpoint_path,checkpoint_file)
net = vgg16(num_classes=cfg.num_classes)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
param_dict = load_checkpoint(checkpoint_file)
load_param_into_net(net, param_dict)
net.set_train(False)
dataset = dataset.create_dataset(local_data_url, 1, False)
res = model.eval(dataset)
print("result: ", res)
def test_load_checkpoint_error_filename():
ckpoint_file_name = 1
with pytest.raises(ValueError):
load_checkpoint(ckpoint_file_name)
loss_scale = float(config.loss_scale)
# When create MindDataset, using the fitst mindrecord file, such as MaskRcnn.mindrecord0.
dataset = create_maskrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
device_num=device_num, rank_id=rank)
dataset_size = dataset.get_dataset_size()
print("total images num: ", dataset_size)
print("Create dataset done!")
net = Mask_Rcnn_Resnet50(config=config)
net = net.set_train()
load_path = args_opt.pre_trained
if load_path != "":
param_dict = load_checkpoint(load_path)
if config.pretrain_epoch_size == 0:
for item in list(param_dict.keys()):
if not (item.startswith('backbone') or item.startswith('rcnn_mask')):
param_dict.pop(item)
load_param_into_net(net, param_dict)
loss = LossNet()
lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
mstype.float32)
opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
weight_decay=config.weight_decay, loss_scale=config.loss_scale)
net_with_loss = WithLossCell(net, loss)
if args_opt.run_distribute:
net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
def test_load_checkpoint_empty_file():
os.mknod("empty.ckpt")
with pytest.raises(ValueError):
load_checkpoint("empty.ckpt")
single_scale_trans = SingleScaleTrans(resize=args.input_shape)
ds = ds.batch(
args.batch_size,
per_batch_map=single_scale_trans,
input_columns=["image", "annotation", "image_name", "image_size"],
num_parallel_workers=8)
args.steps_per_epoch = ds.get_dataset_size()
# backbone
network = backbone_HwYolov3(num_classes, num_anchors_list, args)
# load pretrain model
if os.path.isfile(args.pretrained):
param_dict = load_checkpoint(args.pretrained)
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)
print('load model {} success'.format(args.pretrained))
else:
print(
'load model {} failed, please check the path of model, evaluating end'
.format(args.pretrained))
exit(0)
def _load_checkpoint(self):
from mindspore.train.serialization import load_checkpoint
param_dict = load_checkpoint(self.checkpoint_path)
return {k: v.asnumpy() for k, v in param_dict.items()}
def do_train(dataset=None,
network=None,
load_checkpoint_path="",
save_checkpoint_path="",
epoch_num=1):
""" do train """
if load_checkpoint_path == "":
raise ValueError(
"Pretrain model missed, finetune task must load pretrain model!")
steps_per_epoch = dataset.get_dataset_size()
# optimizer
if optimizer_cfg.optimizer == 'AdamWeightDecay':
lr_schedule = BertLearningRate(
learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate,
end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=optimizer_cfg.AdamWeightDecay.power)
params = network.trainable_params()
decay_params = list(
filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
other_params = list(
filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x),
params))
group_params = [{
'params':
decay_params,
'weight_decay':
optimizer_cfg.AdamWeightDecay.weight_decay
}, {
'params': other_params,
'weight_decay': 0.0
}]
optimizer = AdamWeightDecay(group_params,
lr_schedule,
eps=optimizer_cfg.AdamWeightDecay.eps)
elif optimizer_cfg.optimizer == 'Lamb':
lr_schedule = BertLearningRate(
learning_rate=optimizer_cfg.Lamb.learning_rate,
end_learning_rate=optimizer_cfg.Lamb.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=optimizer_cfg.Lamb.power)
optimizer = Lamb(network.trainable_params(), learning_rate=lr_schedule)
elif optimizer_cfg.optimizer == 'Momentum':
optimizer = Momentum(
network.trainable_params(),
learning_rate=optimizer_cfg.Momentum.learning_rate,
momentum=optimizer_cfg.Momentum.momentum)
else:
raise Exception(
"Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]"
)
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(
prefix="squad",
directory=None if save_checkpoint_path == "" else save_checkpoint_path,
config=ckpt_config)
param_dict = load_checkpoint(load_checkpoint_path)
load_param_into_net(network, param_dict)
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2**32,
scale_factor=2,
scale_window=1000)
netwithgrads = BertSquadCell(network,
optimizer=optimizer,
scale_update_cell=update_cell)
model = Model(netwithgrads)
callbacks = [
TimeMonitor(dataset.get_dataset_size()),
LossCallBack(), ckpoint_cb
]
model.train(epoch_num, dataset, callbacks=callbacks)
def do_eval(dataset=None,
network=None,
metric=None,
load_checkpoint_path="",
translate_direction="en-fr"):
"""
Do evaluation on summarization
Args:
dataset: the eval dataset.
network: the network with loss.
metric: the evaluation method.
load_checkpoint_path: the file path which saved finetune model checkpoint.
"""
if load_checkpoint_path == "":
raise ValueError(
"Finetune model missed, evaluation task must load finetune model!")
if metric.lower() == "bleu":
print("Prepare to calculate the BLEU score ...")
gpt2_loss = network(config=gpt2_net_cfg,
is_training=False,
use_one_hot_embeddings=False)
gpt2_loss.set_train(False)
param_dict = load_checkpoint(load_checkpoint_path)
reorganized_param_dict = dict()
for netName in param_dict:
reorganized_param_dict['gpt2.' + netName] = param_dict[netName]
reorganized_param_dict['lm_head.weight'] = param_dict[
'gpt2_embedding_lookup.embedding_table']
load_param_into_net(gpt2_loss, reorganized_param_dict)
# for item in gpt2_loss.get_parameters():
# print('name: ',item.data.name)
model = Model(gpt2_loss)
tokenizer = Tokenizer(
vocab_file='./src/utils/pretrain-data/gpt2-vocab.json',
merge_file='./src/utils/pretrain-data/gpt2-merges.txt')
callback = BLEU(tokenizer)
sample = Sample(model,
tokenizer=tokenizer,
model_config=gpt2_net_cfg,
topk_num=0,
topp_prob=0.92,
min_tokens_to_keep=1,
demo_mode=False,
early_stop=True)
columns_list = ["input_ids", "input_mask", "label_ids"]
for data in dataset.create_dict_iterator():
input_data = []
for i in columns_list:
input_data.append(data[i])
input_ids, input_mask, label_ids = input_data
print("input_ids shape: {}".format(input_ids.shape))
print("label_ids shape: {}".format(label_ids.shape))
print("============= Translation Testing =============")
#input_str,ref_str = sample.extract_string_from_tensor(input_ids,mode="pair")
hypo, ref = sample.generate_for_Translation(
input_ids, max_generate_length=150)
print("REF str:\n ", ref, "\nHYPO str:\n", hypo, "\n")
#print("LENGTH: ",len(ref[1])," and ",len(hypo[1]),"\n")
callback.update(ref, hypo)
print("==============================================")
eval_result_print(metric, callback)
print("==============================================")
print("************** Translation Testing Finished **************")
else:
raise ValueError(
"metric method not supported in summarization, support: [Rouge]")
init()
epoch_size = args_opt.epoch_size
net = resnet50(args_opt.batch_size, args_opt.num_classes)
ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
0.01, 0.9)
model = Model(net, loss_fn=ls, optimizer=opt, metrics={'acc'})
# as for train, users could use model.train
if args_opt.do_train:
dataset = create_dataset()
batch_num = dataset.get_dataset_size()
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num,
keep_checkpoint_max=35)
ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10",
directory="./",
config=config_ck)
loss_cb = LossMonitor()
model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
# as for evaluation, users could use model.eval
if args_opt.do_eval:
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
eval_dataset = create_dataset(training=False)
res = model.eval(eval_dataset)
print("result: ", res)
def do_train(dataset=None,
network=None,
load_checkpoint_path="",
save_checkpoint_path="",
epoch_num=1):
"""
Do train
Args:
dataset: the train dataset.
network: the network with loss
load_checkpoint_path: the file path which saved pretrain model checkpoint.
save_checkpoint_path: the file path which will save finetune model checkpoint.
epoch_num: the number of epoch
"""
if load_checkpoint_path == "":
raise ValueError(
"Pretrain model missed, finetune task must load pretrain model!")
steps_per_epoch = dataset.get_dataset_size(
) # samples / batch_size doing####
#Select Optimizer
if cfg.optimizer == 'AdamWeightDecay':
lr_schedule = GPT2LearningRate(
learning_rate=cfg.AdamWeightDecay.learning_rate,
end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=cfg.AdamWeightDecay.power)
params = network.trainable_params(
) # return a list of all trainable parmeters of the network
# Use parameter groups and set different values
decay_params = list(filter(cfg.AdamWeightDecay.decay_filter,
params)) # without layernorm and bias
other_params = list(
filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x),
params)) # with layernorm and bias
group_params = [{
'params': decay_params,
'weight_decay': cfg.AdamWeightDecay.weight_decay
}, {
'params': other_params,
'weight_decay': 0.0
}]
optimizer = AdamWeightDecay(group_params,
lr_schedule,
eps=cfg.AdamWeightDecay.eps)
elif cfg.optimizer == 'Lamb':
lr_schedule = GPT2LearningRate(
learning_rate=cfg.Lamb.learning_rate,
end_learning_rate=cfg.Lamb.end_learning_rate,
warmup_steps=int(steps_per_epoch * epoch_num * 0.1),
decay_steps=steps_per_epoch * epoch_num,
power=cfg.Lamb.power)
optimizer = Lamb(network.trainable_params(), lr_schedule)
elif cfg.optimizer == 'Momentum':
optimizer = Momentum(network.trainable_params(),
cfg.Momentum.learning_rate, cfg.Momentum.momentum)
else:
raise Exception(
"Optimizer not supported. support: [AdamWeightDecay, Lamb, Momentum]"
)
# load checkpoint into network
ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(
prefix="gpt2_translation_en_fr_",
directory=None if save_checkpoint_path == "" else save_checkpoint_path,
config=ckpt_config)
param_dict = load_checkpoint(load_checkpoint_path)
final_param_dict = {}
for k, v in param_dict.items():
final_param_dict['gpt2_loss.gpt2.gpt2.' + k] = param_dict[k]
# set the weights of final linear weights to weights of gpt2 token embedding
final_param_dict['gpt2_loss.gpt2.dense1.weight'] = param_dict[
'gpt2_embedding_lookup.embedding_table']
load_param_into_net(network, final_param_dict)
print("| loading the pretrained weights | \n")
update_cell = DynamicLossScaleUpdateCell(loss_scale_value=2**32,
scale_factor=2,
scale_window=1000)
netwithgrads = GPT2FinetuneCell(network,
optimizer=optimizer,
scale_update_cell=update_cell)
netwithgrads.set_train(True)
loss_cb = LossMonitor()
model = Model(netwithgrads, amp_level='O2')
callbacks = [TimeMonitor(dataset.get_dataset_size()), loss_cb, ckpoint_cb]
print(
"============== Starting Training For Translation Task ==============")
model.train(epoch_num, dataset, callbacks=callbacks)
print(
"============== Translation Training Success ==============")
auto_parallel_context().set_all_reduce_fusion_split_indices([85, 160])
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
# create dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
batch_size=config.batch_size, target=target)
step_size = dataset.get_dataset_size()
# define net
net = resnet(class_num=config.class_num)
if args_opt.parameter_server:
net.set_param_ps()
# init weight
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
else:
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
cell.weight.shape,
cell.weight.dtype)
if isinstance(cell, nn.Dense):
cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
cell.weight.shape,
cell.weight.dtype)
# init lr
if args_opt.net == "resnet50":
if args_opt.dataset == "cifar10":
if args_opt.platform != 'GPU':
raise ValueError("Only supported GPU training.")
context.set_context(mode=context.GRAPH_MODE,
device_target=args_opt.platform)
net = efficientnet_b0(
num_classes=cfg.num_classes,
drop_rate=cfg.drop,
drop_connect_rate=cfg.drop_connect,
global_pool=cfg.gp,
bn_tf=cfg.bn_tf,
)
ckpt = load_checkpoint(args_opt.checkpoint)
load_param_into_net(net, ckpt)
net.set_train(False)
val_data_url = args_opt.data_path
dataset = create_dataset_val(cfg.batch_size,
val_data_url,
workers=cfg.workers,
distributed=False)
loss = LabelSmoothingCrossEntropy(smooth_factor=cfg.smoothing)
eval_metrics = {
'Loss': nn.Loss(),
'Top1-Acc': nn.Top1CategoricalAccuracy(),
'Top5-Acc': nn.Top5CategoricalAccuracy()
}
model = Model(net, loss, optimizer=None, metrics=eval_metrics)
