def __init__(self,
data_dir,
training=True,
use_third_trsfm=False,
use_auto_augment=False,
num_parallel_workers=8,
device_num=1,
device_id=0):
if not training:
trsfm = Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
if not use_third_trsfm:
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomColorAdjust(0.4, 0.4, 0.4, 0.4),
transforms.RandomGrayscale(prob=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
if use_auto_augment:
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
rand_augment = RandAugment(n=2, m=10)
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
rand_augment,
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
self.trsfm = trsfm
self.data_dir = data_dir
self.num_parallel_workers = num_parallel_workers
self.device_num = device_num
self.device_id = device_id
def __getitem__(self, index):
img_path = self.all_img_paths[index]
gt_path = self.all_gt_paths[index]
img = get_img(img_path)
bboxes, tags = get_bboxes(img, gt_path)
# multi-scale training
if self.is_transform:
img = random_scale(img, min_size=self.img_size[0])
# get gt_text and training_mask
img_h, img_w = img.shape[0: 2]
gt_text = np.zeros((img_h, img_w), dtype=np.float32)
training_mask = np.ones((img_h, img_w), dtype=np.float32)
if bboxes.shape[0] > 0:
bboxes = np.reshape(bboxes * ([img_w, img_h] * 4), (bboxes.shape[0], -1, 2)).astype('int32')
for i in range(bboxes.shape[0]):
cv2.drawContours(gt_text, [bboxes[i]], 0, i + 1, -1)
if not tags[i]:
cv2.drawContours(training_mask, [bboxes[i]], 0, 0, -1)
# get gt_kernels
gt_kernels = []
for i in range(1, self.kernel_num):
rate = 1.0 - (1.0 - self.min_scale) / (self.kernel_num - 1) * i
gt_kernel = np.zeros(img.shape[0:2], dtype=np.float32)
kernel_bboxes = shrink(bboxes, rate)
for j in range(kernel_bboxes.shape[0]):
cv2.drawContours(gt_kernel, [kernel_bboxes[j]], 0, 1, -1)
gt_kernels.append(gt_kernel)
# data augmentation
if self.is_transform:
imgs = [img, gt_text, training_mask]
imgs.extend(gt_kernels)
imgs = random_horizontal_flip(imgs)
imgs = random_rotate(imgs)
imgs = random_crop(imgs, self.img_size)
img, gt_text, training_mask, gt_kernels = imgs[0], imgs[1], imgs[2], imgs[3:]
gt_text[gt_text > 0] = 1
gt_kernels = np.array(gt_kernels)
if self.is_transform:
img = Image.fromarray(img)
img = img.convert('RGB')
img = py_transforms.RandomColorAdjust(brightness=32.0 / 255, saturation=0.5)(img)
else:
img = Image.fromarray(img)
img = img.convert('RGB')
img = py_transforms.ToTensor()(img)
img = py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)
gt_text = gt_text.astype(np.float32)
gt_kernels = gt_kernels.astype(np.float32)
training_mask = training_mask.astype(np.float32)
return img, gt_text, gt_kernels, training_mask
def util_test_normalize(mean, std, op_type):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
if op_type == "cpp":
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize(mean, std)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=decode_op, input_columns=["image"])
data = data.map(operations=normalize_op, input_columns=["image"])
elif op_type == "python":
# define map operations
transforms = [
py_vision.Decode(),
py_vision.ToTensor(),
py_vision.Normalize(mean, std)
]
transform = mindspore.dataset.transforms.py_transforms.Compose(
transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
else:
raise ValueError("Wrong parameter value")
return data
def data_generator(args):
'''Build train dataloader.'''
mindrecord_path = args.mindrecord_path
dst_w = args.dst_w
dst_h = args.dst_h
batch_size = args.per_batch_size
attri_num = args.attri_num
max_epoch = args.max_epoch
transform_img = F2.Compose([
F.Decode(),
F.Resize((dst_w, dst_h)),
F.RandomHorizontalFlip(prob=0.5),
F.ToTensor(),
F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
de_dataset = de.MindDataset(mindrecord_path + "0",
columns_list=["image", "label"],
num_shards=args.world_size,
shard_id=args.local_rank)
de_dataset = de_dataset.map(input_columns="image",
operations=transform_img,
num_parallel_workers=args.workers,
python_multiprocessing=True)
de_dataset = de_dataset.batch(batch_size, drop_remainder=True)
steps_per_epoch = de_dataset.get_dataset_size()
de_dataset = de_dataset.repeat(max_epoch)
de_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
num_classes = attri_num
return de_dataloader, steps_per_epoch, num_classes
def create_dataset(batch_size,
train_data_url='',
workers=8,
distributed=False,
input_size=224,
color_jitter=0.4):
"""Create ImageNet training dataset"""
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
decode_op = py_vision.Decode()
type_cast_op = c_transforms.TypeCast(mstype.int32)
random_resize_crop_bicubic = py_vision.RandomResizedCrop(
size=(input_size, input_size),
scale=SCALE,
ratio=RATIO,
interpolation=Inter.BICUBIC)
random_horizontal_flip_op = py_vision.RandomHorizontalFlip(0.5)
adjust_range = (max(0, 1 - color_jitter), 1 + color_jitter)
random_color_jitter_op = py_vision.RandomColorAdjust(
brightness=adjust_range,
contrast=adjust_range,
saturation=adjust_range)
to_tensor = py_vision.ToTensor()
normalize_op = py_vision.Normalize(IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD)
# assemble all the transforms
image_ops = py_transforms.Compose([
decode_op, random_resize_crop_bicubic, random_horizontal_flip_op,
random_color_jitter_op, to_tensor, normalize_op
])
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
dataset_train = ds.ImageFolderDataset(train_data_url,
num_parallel_workers=workers,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
dataset_train = dataset_train.map(input_columns=["image"],
operations=image_ops,
num_parallel_workers=workers)
dataset_train = dataset_train.map(input_columns=["label"],
operations=type_cast_op,
num_parallel_workers=workers)
# batch dealing
ds_train = dataset_train.batch(batch_size,
per_batch_map=split_imgs_and_labels,
input_columns=["image", "label"],
num_parallel_workers=2,
drop_remainder=True)
ds_train = ds_train.repeat(1)
return ds_train
def create_dataset_py(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
if target == "Ascend":
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
else:
init()
rank_id = get_rank()
device_num = get_group_size()
if do_train:
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=False)
image_size = 224
# define map operations
decode_op = P.Decode()
resize_crop_op = P.RandomResizedCrop(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333))
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
resize_op = P.Resize(256)
center_crop = P.CenterCrop(image_size)
to_tensor = P.ToTensor()
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# define map operations
if do_train:
trans = [decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op]
else:
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
compose = P2.Compose(trans)
ds = ds.map(operations=compose, input_columns="image", num_parallel_workers=8, python_multiprocessing=True)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def test_normalize_exception_invalid_range_py():
"""
Test Normalize in python transformation: value is not in range [0,1]
expected to raise ValueError
"""
logger.info("test_normalize_exception_invalid_range_py")
try:
_ = py_vision.Normalize([0.75, 1.25, 0.5], [0.1, 0.18, 1.32])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input mean_value is not within the required interval of [0.0, 1.0]." in str(e)
def test_normalize_exception_unequal_size_py():
"""
Test Normalize in python transformation: len(mean) != len(std)
expected to raise ValueError
"""
logger.info("test_normalize_exception_unequal_size_py")
try:
_ = py_vision.Normalize([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Length of mean and std must be equal."
def create_imagenet_dataset(imagenet_dir):
ds = de.ImageFolderDataset(imagenet_dir)
transform = Compose([
F.Decode(),
F.RandomHorizontalFlip(0.5),
F.ToTensor(),
F.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
F.RandomErasing()
])
ds = ds.map(operations=transform, input_columns="image")
ds = ds.shuffle(buffer_size=5)
ds = ds.repeat(3)
return ds
def create_dataset_val(batch_size=128,
val_data_url='',
workers=8,
distributed=False,
input_size=224):
"""Create ImageNet validation dataset"""
if not os.path.exists(val_data_url):
raise ValueError('Path not exists')
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
dataset = ds.ImageFolderDataset(val_data_url,
num_parallel_workers=workers,
num_shards=rank_size,
shard_id=rank_id)
scale_size = None
if isinstance(input_size, tuple):
assert len(input_size) == 2
if input_size[-1] == input_size[-2]:
scale_size = int(math.floor(input_size[0] / DEFAULT_CROP_PCT))
else:
scale_size = tuple([int(x / DEFAULT_CROP_PCT) for x in input_size])
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
type_cast_op = c_transforms.TypeCast(mstype.int32)
decode_op = py_vision.Decode()
resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
center_crop = py_vision.CenterCrop(size=input_size)
to_tensor = py_vision.ToTensor()
normalize_op = py_vision.Normalize(IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD)
image_ops = py_transforms.Compose(
[decode_op, resize_op, center_crop, to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"],
operations=type_cast_op,
num_parallel_workers=workers)
dataset = dataset.map(input_columns=["image"],
operations=image_ops,
num_parallel_workers=workers)
dataset = dataset.batch(batch_size,
per_batch_map=split_imgs_and_labels,
input_columns=["image", "label"],
num_parallel_workers=2,
drop_remainder=True)
dataset = dataset.repeat(1)
return dataset
def util_test_normalize_grayscale(num_output_channels, mean, std):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
transforms = [
py_vision.Decode(),
py_vision.Grayscale(num_output_channels),
py_vision.ToTensor(),
py_vision.Normalize(mean, std)
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
return data
def __call__(self, imgs, labels, batchInfo):
# assert the imgs object are pil_images
ret_imgs = []
ret_labels = []
py_to_pil_op = P.ToPIL()
to_tensor = P.ToTensor()
normalize_op = P.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
rand_augment_ops = transform_utils.rand_augment_transform(
self.config_str, self.hparams)
for i, image in enumerate(imgs):
img_pil = py_to_pil_op(image)
img_pil = rand_augment_ops(img_pil)
img_array = to_tensor(img_pil)
img_array = normalize_op(img_array)
ret_imgs.append(img_array)
ret_labels.append(labels[i])
return np.array(ret_imgs), np.array(ret_labels)
def test_normalize_op_py(plot=False):
"""
Test Normalize in python transformations
"""
logger.info("Test Normalize in python")
mean = [0.475, 0.45, 0.392]
std = [0.275, 0.267, 0.278]
# define map operations
transforms = [py_vision.Decode(), py_vision.ToTensor()]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
normalize_op = py_vision.Normalize(mean, std)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(operations=transform, input_columns=["image"])
data1 = data1.map(operations=normalize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=transform, input_columns=["image"])
num_iter = 0
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(
np.uint8)
image_np_normalized = (
normalize_np(item2["image"].transpose(1, 2, 0), mean, std) *
255).astype(np.uint8)
image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(
np.uint8)
mse = diff_mse(image_de_normalized, image_np_normalized)
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
assert mse < 0.01
if plot:
visualize_image(image_original, image_de_normalized, mse,
image_np_normalized)
num_iter += 1
def load_images(paths, batch_size=128):
'''Load images.'''
ll = []
resize = V.Resize((96, 64))
transform = T.Compose(
[V.ToTensor(),
V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
for i, _ in enumerate(paths):
im = Image.open(paths[i])
im = resize(im)
img = np.array(im)
ts = transform(img)
ll.append(ts[0])
if len(ll) == batch_size:
yield np.stack(ll, axis=0)
ll.clear()
if ll:
yield np.stack(ll, axis=0)
def IC15_TEST_Generator():
ic15_test_data_dir = config.TEST_ROOT_DIR + 'ch4_test_images/'
img_size = config.INFER_LONG_SIZE
img_size = img_size if (img_size is None
or isinstance(img_size, tuple)) else (img_size,
img_size)
data_dirs = [ic15_test_data_dir]
all_img_paths = []
for data_dir in data_dirs:
img_names = [
i for i in os.listdir(data_dir)
if os.path.splitext(i)[-1].lower() in ['.jpg', '.jpeg', '.png']
]
img_paths = []
for _, img_name in enumerate(img_names):
img_path = data_dir + img_name
img_paths.append(img_path)
all_img_paths.extend(img_paths)
dataset_length = len(all_img_paths)
for index in range(dataset_length):
img_path = all_img_paths[index]
img_name = np.array(os.path.split(img_path)[-1])
img = get_img(img_path)
long_size = max(img.shape[:2])
img_resized = np.zeros((long_size, long_size, 3), np.uint8)
img_resized[:img.shape[0], :img.shape[1], :] = img
img_resized = cv2.resize(img_resized, dsize=img_size)
img_resized = Image.fromarray(img_resized)
img_resized = img_resized.convert('RGB')
img_resized = py_transforms.ToTensor()(img_resized)
img_resized = py_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224,
0.225])(img_resized)
yield img, img_resized, img_name
def get_de_dataset(args):
'''get_de_dataset'''
lbl_transforms = [F.ToType(np.int32)]
transform_label = F2.Compose(lbl_transforms)
drop_remainder = False
transforms = [
F.ToPIL(),
F.RandomHorizontalFlip(),
F.ToTensor(),
F.Normalize(mean=[0.5], std=[0.5])
]
transform = F2.Compose(transforms)
cache_path = os.path.join('cache', os.path.basename(args.data_dir),
'data_cache.pkl')
print(cache_path)
if not os.path.exists(os.path.dirname(cache_path)):
os.makedirs(os.path.dirname(cache_path))
dataset = CustomDataset(args.data_dir, cache_path, args.is_distributed)
args.logger.info("dataset len:{}".format(dataset.__len__()))
sampler = DistributedCustomSampler(dataset,
num_replicas=args.world_size,
rank=args.local_rank,
is_distributed=args.is_distributed)
de_dataset = de.GeneratorDataset(dataset, ["image", "label"],
sampler=sampler)
args.logger.info("after sampler de_dataset datasize :{}".format(
de_dataset.get_dataset_size()))
de_dataset = de_dataset.map(input_columns="image", operations=transform)
de_dataset = de_dataset.map(input_columns="label",
operations=transform_label)
de_dataset = de_dataset.project(columns=["image", "label"])
de_dataset = de_dataset.batch(args.per_batch_size,
drop_remainder=drop_remainder)
num_iter_per_npu = math.ceil(
len(dataset) * 1.0 / args.world_size / args.per_batch_size)
num_classes = len(dataset.classes)
return de_dataset, num_iter_per_npu, num_classes
def data_generator_eval(args):
'''Build eval dataloader.'''
mindrecord_path = args.mindrecord_path
dst_w = args.dst_w
dst_h = args.dst_h
batch_size = 1
attri_num = args.attri_num
transform_img = F2.Compose([F.Decode(),
F.Resize((dst_w, dst_h)),
F.ToTensor(),
F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"])
de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=args.workers,
python_multiprocessing=True)
de_dataset = de_dataset.batch(batch_size)
de_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
steps_per_epoch = de_dataset.get_dataset_size()
print("image number:{0}".format(steps_per_epoch))
num_classes = attri_num
return de_dataloader, steps_per_epoch, num_classes
best_acc = 0 # best test accuracy
start_epoch = 0
feature_dim = args.low_dim
wG = 0
start_time = time.time()
print("==> Loading data")
# Data Loading code
transform_train = Compose([
decode,
py_trans.Pad(10),
py_trans.RandomCrop((args.img_h, args.img_w)),
py_trans.RandomHorizontalFlip(),
py_trans.ToTensor(),
py_trans.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
transform_test = Compose([
decode,
py_trans.Resize((args.img_h, args.img_w)),
py_trans.ToTensor(),
py_trans.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
ifDebug_dic = {"yes": True, "no": False}
if dataset_type == "SYSU":
# train_set
ifDebug = {}
trainset_generator = SYSUDatasetGenerator(data_dir=data_path,
def main(args):
if not os.path.exists(args.test_dir):
args.logger.info('ERROR, test_dir is not exists, please set test_dir in config.py.')
return 0
all_start_time = time.time()
net = get_model(args)
compile_time_used = time.time() - all_start_time
args.logger.info('INFO, graph compile finished, time used:{:.2f}s, start calculate img embedding'.
format(compile_time_used))
img_transforms = transforms.Compose([vision.ToTensor(), vision.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
#for test images
args.logger.info('INFO, start step1, calculate test img embedding, weight file = {}'.format(args.weight))
step1_start_time = time.time()
ds, img_tot, all_labels = get_dataloader(args.test_img_predix, args.test_img_list,
args.test_batch_size, img_transforms)
args.logger.info('INFO, dataset total test img:{}, total test batch:{}'.format(img_tot, ds.get_dataset_size()))
test_embedding_tot_np = np.zeros((img_tot, args.emb_size))
test_img_labels = all_labels
data_loader = ds.create_dict_iterator(output_numpy=True, num_epochs=1)
for i, data in enumerate(data_loader):
img, idxs = data["image"], data["index"]
out = net(Tensor(img)).asnumpy().astype(np.float32)
embeddings = l2normalize(out)
for batch in range(embeddings.shape[0]):
test_embedding_tot_np[idxs[batch]] = embeddings[batch]
try:
check_minmax(args, np.linalg.norm(test_embedding_tot_np, ord=2, axis=1))
except ValueError:
return 0
test_embedding_tot = {}
for idx, label in enumerate(test_img_labels):
test_embedding_tot[label] = test_embedding_tot_np[idx]
step2_start_time = time.time()
step1_time_used = step2_start_time - step1_start_time
args.logger.info('INFO, step1 finished, time used:{:.2f}s, start step2, calculate dis img embedding'.
format(step1_time_used))
# for dis images
ds_dis, img_tot, _ = get_dataloader(args.dis_img_predix, args.dis_img_list, args.dis_batch_size, img_transforms)
dis_embedding_tot_np = np.zeros((img_tot, args.emb_size))
total_batch = ds_dis.get_dataset_size()
args.logger.info('INFO, dataloader total dis img:{}, total dis batch:{}'.format(img_tot, total_batch))
start_time = time.time()
img_per_gpu = int(math.ceil(1.0 * img_tot / args.world_size))
delta_num = img_per_gpu * args.world_size - img_tot
start_idx = img_per_gpu * args.local_rank - max(0, args.local_rank - (args.world_size - delta_num))
data_loader = ds_dis.create_dict_iterator(output_numpy=True, num_epochs=1)
for idx, data in enumerate(data_loader):
img = data["image"]
out = net(Tensor(img)).asnumpy().astype(np.float32)
embeddings = l2normalize(out)
dis_embedding_tot_np[start_idx:(start_idx + embeddings.shape[0])] = embeddings
start_idx += embeddings.shape[0]
if args.local_rank % 8 == 0 and idx % args.log_interval == 0 and idx > 0:
speed = 1.0 * (args.dis_batch_size * args.log_interval * args.world_size) / (time.time() - start_time)
time_left = (total_batch - idx - 1) * args.dis_batch_size *args.world_size / speed
args.logger.info('INFO, processed [{}/{}], speed: {:.2f} img/s, left:{:.2f}s'.
format(idx, total_batch, speed, time_left))
start_time = time.time()
try:
check_minmax(args, np.linalg.norm(dis_embedding_tot_np, ord=2, axis=1))
except ValueError:
return 0
step3_start_time = time.time()
step2_time_used = step3_start_time - step2_start_time
args.logger.info('INFO, step2 finished, time used:{:.2f}s, start step3, calculate top1 acc'.format(step2_time_used))
# clear npu memory
img = None
net = None
dis_embedding_tot_np = np.transpose(dis_embedding_tot_np, (1, 0))
args.logger.info('INFO, calculate top1 acc dis_embedding_tot_np shape:{}'.format(dis_embedding_tot_np.shape))
# find best match
assert len(args.test_img_list) % 2 == 0
task_num = int(len(args.test_img_list) / 2)
correct = np.array([0] * (2 * task_num))
tot = np.array([0] * task_num)
for i in range(int(len(args.test_img_list) / 2)):
jk_list = args.test_img_list[2 * i]
zj_list = args.test_img_list[2 * i + 1]
zj2jk_pairs = sorted(generate_test_pair(jk_list, zj_list))
sampler = DistributedSampler(zj2jk_pairs)
args.logger.info('INFO, calculate top1 acc sampler len:{}'.format(len(sampler)))
for idx in sampler:
out1, out2 = cal_topk(args, idx, zj2jk_pairs, test_embedding_tot, dis_embedding_tot_np)
correct[2 * i] += out1[0]
correct[2 * i + 1] += out1[1]
tot[i] += out2[0]
args.logger.info('local_rank={},tot={},correct={}'.format(args.local_rank, tot, correct))
step3_time_used = time.time() - step3_start_time
args.logger.info('INFO, step3 finished, time used:{:.2f}s'.format(step3_time_used))
args.logger.info('weight:{}'.format(args.weight))
for i in range(int(len(args.test_img_list) / 2)):
test_set_name = 'test_dataset'
zj2jk_acc = correct[2 * i] / tot[i]
jk2zj_acc = correct[2 * i + 1] / tot[i]
avg_acc = (zj2jk_acc + jk2zj_acc) / 2
results = '[{}]: zj2jk={:.4f}, jk2zj={:.4f}, avg={:.4f}'.format(test_set_name, zj2jk_acc, jk2zj_acc, avg_acc)
args.logger.info(results)
args.logger.info('INFO, tot time used: {:.2f}s'.format(time.time() - all_start_time))
return 0
def create_dataset_py(dataset_path,
do_train,
config,
device_target,
repeat_num=1,
batch_size=32):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1.
batch_size(int): the batch size of dataset. Default: 32.
Returns:
dataset
"""
if device_target == "Ascend":
rank_size = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if do_train:
if rank_size == 1:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
else:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=False)
else:
raise ValueError("Unsupported device target.")
resize_height = 224
if do_train:
buffer_size = 20480
# apply shuffle operations
data_set = data_set.shuffle(buffer_size=buffer_size)
# define map operations
decode_op = P.Decode()
resize_crop_op = P.RandomResizedCrop(resize_height,
scale=(0.08, 1.0),
ratio=(0.75, 1.333))
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
resize_op = P.Resize(256)
center_crop = P.CenterCrop(resize_height)
to_tensor = P.ToTensor()
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if do_train:
trans = [
decode_op, resize_crop_op, horizontal_flip_op, to_tensor,
normalize_op
]
else:
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
compose = P2.Compose(trans)
data_set = data_set.map(operations=compose,
input_columns="image",
num_parallel_workers=8,
python_multiprocessing=True)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def create_dataset(dataset_path,
do_train,
config,
platform,
repeat_num=1,
batch_size=100):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
Returns:
dataset
"""
if platform == "Ascend":
rank_size = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if rank_size == 1:
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
elif platform == "GPU":
if do_train:
from mindspore.communication.management import get_rank, get_group_size
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=get_group_size(),
shard_id=get_rank())
else:
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=False)
else:
raise ValueError("Unsupported platform.")
resize_height = config.image_height
buffer_size = 1000
# define map operations
resize_crop_op = C.RandomCropDecodeResize(resize_height,
scale=(0.08, 1.0),
ratio=(0.75, 1.333))
horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
color_op = C.RandomColorAdjust(brightness=0.4,
contrast=0.4,
saturation=0.4)
rescale_op = C.Rescale(1 / 255.0, 0)
normalize_op = C.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
change_swap_op = C.HWC2CHW()
# define python operations
decode_p = P.Decode()
resize_p = P.Resize(256, interpolation=Inter.BILINEAR)
center_crop_p = P.CenterCrop(224)
totensor = P.ToTensor()
normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
composeop = P2.Compose(
[decode_p, resize_p, center_crop_p, totensor, normalize_p])
if do_train:
trans = [
resize_crop_op, horizontal_flip_op, color_op, rescale_op,
normalize_op, change_swap_op
]
else:
trans = composeop
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(input_columns="image",
operations=trans,
num_parallel_workers=8)
data_set = data_set.map(input_columns="label_list",
operations=type_cast_op,
num_parallel_workers=8)
# apply shuffle operations
data_set = data_set.shuffle(buffer_size=buffer_size)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
