p85971406
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[MD] Transform Unification - Phase 3: Update Smoke tests and remaining UTs

This commit is contained in:
Cathy Wong 2022-05-25 20:49:09 -04:00
parent a4785b9baa
commit f4fd0300c6
53 changed files with 220 additions and 208 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.cc
View File

@ -106,7 +106,7 @@ Status OneHotEncoding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tens
} else if (input->type() == DataType::DE_UINT64) {
RETURN_IF_NOT_OK(OneHotEncodingImpl<uint64_t>(input, &out, num_classes, i, smoothing_rate));
} else {
RETURN_STATUS_UNEXPECTED("OneHot: OneHot only support input of int type, but got:" + input->type().ToString());
RETURN_STATUS_UNEXPECTED("OneHot: OneHot only supports input of int type, but got:" + input->type().ToString());
}
}
out->Squeeze();

8
mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.h
View File

@ -38,15 +38,11 @@ namespace dataset {
// then it will fail.
// @param output: Tensor. The shape of the output tensor is <input_shape, numClasses>
// and the type is same as input.
// @param num_classes: Number of classes to.
// @param num_classes: Number of classes in dataset.
// @param smoothing_rate: Adjustable hyperparameter for label smoothing level.
Status OneHotEncoding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, dsize_t num_classes,
double smoothing_rate = 0);
Status OneHotEncodingUnsigned(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output,
dsize_t num_classes, int64_t index);
Status OneHotEncodingSigned(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, dsize_t num_classes,
int64_t index);
template <typename T>
Status OneHotEncodingImpl(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, dsize_t num_classes,
int64_t index, double smoothing_rate);

2
mindspore/ccsrc/minddata/dataset/kernels/ir/data/transforms_ir.cc
View File

@ -168,7 +168,7 @@ Status OneHotOperation::ValidateParams() {
LOG_AND_RETURN_STATUS_SYNTAX_ERROR(err_msg);
}
if (smoothing_rate_ < 0 || smoothing_rate_ > 1) {
std::string err_msg = "OneHot: Smoothing rate must be between 0 and 1, but got: " + std::to_string(num_classes_);
std::string err_msg = "OneHot: Smoothing rate must be between 0 and 1, but got: " + std::to_string(smoothing_rate_);
LOG_AND_RETURN_STATUS_SYNTAX_ERROR(err_msg);
}
return Status::OK();

2
mindspore/python/mindspore/dataset/transforms/transforms.py
View File

@ -554,7 +554,7 @@ class OneHot(TensorOperation):
Raises:
TypeError: `num_classes` is not of type int.
TypeError: `smoothing_rate` is not of type float.
TypeError: `smoothing_rate` is not of type float or int.
ValueError: `smoothing_rate` is not in range [0.0, 1.0].
RuntimeError: Input tensor is not of type int.
RuntimeError: Input tensor is not a 1-D tensor.

6
tests/st/data_transfer/test_tdt_data_transfer.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,8 +21,8 @@ from mindspore.common.api import _cell_graph_executor
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
import mindspore.dataset as de
from mindspore.dataset.vision import c_transforms as c_vision
from mindspore.dataset.transforms import c_transforms as c_trans
from mindspore.dataset.vision import transforms as c_vision
from mindspore.dataset.transforms import transforms as c_trans
DATA_DIR = "/home/workspace/mindspore_dataset/cifar-10-verify-bin"

4
tests/st/dataset/test_chinese_path_on_windows.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -22,7 +22,7 @@ import pytest
from PIL import Image
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.vision as vision
from mindspore.mindrecord import FileWriter, SUCCESS
def add_and_remove_cv_file(mindrecord):

26
tests/st/dataset/test_gpu_autotune.py
View File

@ -19,10 +19,8 @@ import pytest
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.py_transforms as py_transforms
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.vision.py_transforms as py_vision
import mindspore.dataset.transforms as transforms
import mindspore.dataset.vision as vision
from mindspore import context, nn
from mindspore.common import dtype as mstype, set_seed
@ -59,11 +57,11 @@ def create_dataset(data_path, batch_size=32, num_parallel_workers=1):
shift_nml = -1 * 0.1307 / 0.3081
# Define map operations
resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)
rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
rescale_op = CV.Rescale(rescale, shift)
hwc2chw_op = CV.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
resize_op = vision.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)
rescale_nml_op = vision.Rescale(rescale_nml, shift_nml)
rescale_op = vision.Rescale(rescale, shift)
hwc2chw_op = vision.HWC2CHW()
type_cast_op = transforms.TypeCast(mstype.int32)
# Apply map operations on images
mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
@ -140,20 +138,18 @@ def create_dataset_pyfunc_multiproc(data_path, batch_size=32, num_op_parallel_wo
# Define dataset with num_parallel_workers=8 for reasonable performance
data1 = ds.MnistDataset(data_path, num_parallel_workers=8)
data1 = data1.map(operations=[py_vision.ToType(np.int32)], input_columns="label",
data1 = data1.map(operations=[vision.ToType(np.int32)], input_columns="label",
num_parallel_workers=num_op_parallel_workers,
python_multiprocessing=True, max_rowsize=max_rowsize)
# Setup transforms list which include Python ops
transforms_list = [
py_vision.ToTensor(),
lambda x: x,
py_vision.HWC2CHW(),
py_vision.RandomErasing(0.9, value='random'),
py_vision.Cutout(4, 2),
vision.HWC2CHW(),
vision.RandomErasing(0.9, value='random'),
lambda y: y
]
compose_op = py_transforms.Compose(transforms_list)
compose_op = transforms.Compose(transforms_list)
data1 = data1.map(operations=compose_op, input_columns="image", num_parallel_workers=num_op_parallel_workers,
python_multiprocessing=True, max_rowsize=max_rowsize)

6
tests/st/fl/albert/src/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,7 +18,7 @@ import pickle
import numpy as np
import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as c_transforms
import mindspore.dataset.transforms as transforms
class InputFeatures:
@ -140,7 +140,7 @@ def create_ms_dataset(data_list, label_list, max_seq_length, tokenizer, batch_si
if do_shuffle:
dataset = dataset.shuffle(buffer_size=10000)
type_cast_op = c_transforms.TypeCast(ms.int32)
type_cast_op = transforms.TypeCast(ms.int32)
dataset = dataset.map(operations=[type_cast_op])
dataset = dataset.batch(batch_size=batch_size, drop_remainder=drop_remainder)
return dataset

19
tests/st/fl/cross_device_lenet/client/image_classfication_dataset_process.md
View File

@ -309,9 +309,8 @@
import numpy as np
import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as tC
import mindspore.dataset.vision.py_transforms as PV
import mindspore.dataset.transforms.py_transforms as PT
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
import mindspore
def mkdir(path):
@ -340,16 +339,16 @@
resize_height, resize_width = img_size[0], img_size[1] # 32
transform = [
PV.Decode(),
PV.Grayscale(1),
PV.Resize(size=(resize_height, resize_width)),
PV.Grayscale(3),
PV.ToTensor(),
vision.Decode(True),
vision.Grayscale(1),
vision.Resize(size=(resize_height, resize_width)),
vision.Grayscale(3),
vision.ToTensor(),
]
compose = PT.Compose(transform)
compose = transforms.Compose(transform)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=tC.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="label", operations=transforms.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="image", operations=compose)
# apply DatasetOps

19
tests/st/fl/cross_device_lenet/client/image_classfication_dataset_process_en.md
View File

@ -311,9 +311,8 @@ each user is 226.83, and the variance of the data volume of all users is 88.94.
import numpy as np
import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as tC
import mindspore.dataset.vision.py_transforms as PV
import mindspore.dataset.transforms.py_transforms as PT
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
import mindspore
def mkdir(path):
@ -342,16 +341,16 @@ each user is 226.83, and the variance of the data volume of all users is 88.94.
resize_height, resize_width = img_size[0], img_size[1] # 32
transform = [
PV.Decode(),
PV.Grayscale(1),
PV.Resize(size=(resize_height, resize_width)),
PV.Grayscale(3),
PV.ToTensor(),
vision.Decode(True),
vision.Grayscale(1),
vision.Resize(size=(resize_height, resize_width)),
vision.Grayscale(3),
vision.ToTensor(),
]
compose = PT.Compose(transform)
compose = transforms.Compose(transform)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=tC.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="label", operations=transforms.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="image", operations=compose)
# apply DatasetOps

5
tests/st/fl/cross_silo_faster_rcnn/src/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -23,9 +23,10 @@ from numpy import random
import cv2
import mmcv
import mindspore.dataset as de
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.vision as C
from mindspore.mindrecord import FileWriter
def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
"""Calculate the ious between each bbox of bboxes1 and bboxes2.

21
tests/st/fl/cross_silo_femnist/test_cross_silo_femnist.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -24,9 +24,8 @@ import mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
from mindspore import Tensor
import mindspore.dataset as ds
import mindspore.dataset.vision.py_transforms as PV
import mindspore.dataset.transforms.py_transforms as PT
import mindspore.dataset.transforms.c_transforms as tC
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
from mindspore.train.serialization import save_checkpoint
from mindspore.train.callback import Callback, FederatedLearningManager
from mindspore.nn.metrics import Accuracy
@ -252,16 +251,16 @@ def create_dataset_from_folder(data_path, img_size, batch_size=32, repeat_size=1
resize_height, resize_width = img_size[0], img_size[1]
transform = [
PV.Decode(),
PV.Grayscale(1),
PV.Resize(size=(resize_height, resize_width)),
PV.Grayscale(3),
PV.ToTensor()
vision.Decode(True),
vision.Grayscale(1),
vision.Resize(size=(resize_height, resize_width)),
vision.Grayscale(3),
vision.ToTensor()
]
compose = PT.Compose(transform)
compose = transforms.Compose(transform)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=tC.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="label", operations=transforms.TypeCast(mindspore.int32))
mnist_ds = mnist_ds.map(input_columns="image", operations=compose)
# apply DatasetOps

4
tests/st/frontend_compile_cache/run_lenet_ps.py
View File

@ -18,8 +18,8 @@ import sys
import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore.common import dtype as mstype
from mindspore.dataset.vision import Inter

6
tests/st/mem_reuse/resnet_cifar_memreuse.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,8 +21,8 @@ from resnet import resnet50
import mindspore.common.dtype as mstype
import mindspore.context as context
import mindspore.dataset as de
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.communication.management import init

6
tests/st/mem_reuse/resnet_cifar_normal.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,8 +21,8 @@ from resnet import resnet50
import mindspore.common.dtype as mstype
import mindspore.context as context
import mindspore.dataset as de
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.communication.management import init

4
tests/st/model_zoo_tests/transformer/test_transformer.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -25,7 +25,7 @@ from mindspore.train.model import Model
from mindspore.train.loss_scale_manager import DynamicLossScaleManager
from mindspore.train.callback import Callback
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as deC
import mindspore.dataset.transforms as deC
from mindspore import context
from easydict import EasyDict as edict
from tests.models.official.nlp.transformer.src.transformer_model import TransformerConfig

7
tests/st/model_zoo_tests/yolov3/src/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -22,14 +22,14 @@ from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
from PIL import Image
import mindspore.dataset as de
from mindspore.mindrecord import FileWriter
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.vision as C
from src.config import ConfigYOLOV3ResNet18
iter_cnt = 0
_NUM_BOXES = 50
np.random.seed(1)
de.config.set_seed(1)
def preprocess_fn(image, box, is_training):
"""Preprocess function for dataset."""
config_anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 163, 326]
@ -46,7 +46,6 @@ def preprocess_fn(image, box, is_training):
num_layers = anchors.shape[0] // 3
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
true_boxes = np.array(true_boxes, dtype='float32')
# input_shape = np.array([in_shape, in_shape], dtype='int32')
input_shape = np.array(in_shape, dtype='int32')
boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]

6
tests/st/model_zoo_tests/yolov3_darknet53/src/yolo_dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -20,7 +20,7 @@ import cv2
from PIL import Image
from pycocotools.coco import COCO
import mindspore.dataset as de
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.vision as CV
from src.distributed_sampler import DistributedSampler
from src.transforms import reshape_fn, MultiScaleTrans
@ -45,7 +45,7 @@ def has_valid_annotation(anno):
# if all boxes have close to zero area, there is no annotation
if _has_only_empty_bbox(anno):
return False
# keypoints task have a slight different critera for considering
# keypoints task have a slight different criteria for considering
# if an annotation is valid
if "keypoints" not in anno[0]:
return True

4
tests/st/networks/models/bert/bert_performance/test_bert_tdt_lossscale.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,7 +21,7 @@ import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms as C
from mindspore import context
from mindspore import log as logger
from mindspore.ops import operations as P

4
tests/st/networks/models/bert/bert_performance/test_bert_thor.py
View File

@ -1,4 +1,4 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -31,7 +31,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.nn.optim import thor
from mindspore.train.model import Model
from mindspore.train.train_thor import ConvertModelUtils
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms as C
from tests.models.official.nlp.bert.src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepCell
from tests.models.official.nlp.bert.src.utils import get_bert_thor_lr, get_bert_thor_damping

4
tests/st/networks/models/bert/bert_precision/test_bert_tdt_lossscale.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,7 +21,7 @@ import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms as C
from mindspore import context
from mindspore import log as logger
from mindspore.ops import operations as P

4
tests/st/networks/models/bert/src/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,7 +18,7 @@ Data operations, will be used in run_pretrain.py
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms as C
from mindspore import log as logger
from .config import bert_net_cfg

4
tests/st/networks/models/deeplabv3/src/md_dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
@ -16,7 +16,7 @@
import numpy as np
from PIL import Image
import mindspore.dataset as de
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.vision as C
from .ei_dataset import HwVocRawDataset
from .utils import custom_transforms as tr

6
tests/st/networks/models/resnet50/src/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,8 +18,8 @@
import os
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
import mindspore.dataset.vision as C
import mindspore.dataset.transforms as C2
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):

30
tests/st/networks/models/resnet50/src_thor/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -50,18 +50,18 @@ def create_dataset1(dataset_path, do_train, batch_size=32, train_image_size=224,
trans = []
if do_train:
trans += [
ds.vision.c_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
ds.vision.RandomCrop((32, 32), (4, 4, 4, 4)),
ds.vision.RandomHorizontalFlip(prob=0.5)
]
trans += [
ds.vision.c_transforms.Resize((train_image_size, train_image_size)),
ds.vision.c_transforms.Rescale(1.0 / 255.0, 0.0),
ds.vision.c_transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
ds.vision.c_transforms.HWC2CHW()
ds.vision.Resize((train_image_size, train_image_size)),
ds.vision.Rescale(1.0 / 255.0, 0.0),
ds.vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
ds.vision.HWC2CHW()
]
type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
type_cast_op = ds.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="label",
num_parallel_workers=get_num_parallel_workers(8))
@ -117,18 +117,18 @@ def create_dataset2(dataset_path, do_train, batch_size=32, train_image_size=224,
# define map operations
if do_train:
trans = [
ds.vision.c_transforms.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
ds.vision.c_transforms.RandomHorizontalFlip(prob=0.5)
ds.vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
ds.vision.RandomHorizontalFlip(prob=0.5)
]
else:
trans = [
ds.vision.c_transforms.Decode(),
ds.vision.c_transforms.Resize(256),
ds.vision.c_transforms.CenterCrop(eval_image_size)
ds.vision.Decode(),
ds.vision.Resize(256),
ds.vision.CenterCrop(eval_image_size)
]
trans_norm = [ds.vision.c_transforms.Normalize(mean=mean, std=std), ds.vision.c_transforms.HWC2CHW()]
trans_norm = [ds.vision.Normalize(mean=mean, std=std), ds.vision.HWC2CHW()]
type_cast_op = ds.transforms.c_transforms.TypeCast(ms.int32)
type_cast_op = ds.transforms.TypeCast(ms.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=get_num_parallel_workers(12))
data_set = data_set.map(operations=trans_norm, input_columns="image",

6
tests/st/networks/test_gpu_lenet.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -20,8 +20,8 @@ import pytest
import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import Tensor, ParameterTuple
from mindspore.common import dtype as mstype

4
tests/st/networks/test_gradient_accumulation.py
View File

@ -3,8 +3,8 @@ import os
import pytest
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as CT
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as CT
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import ParameterTuple
from mindspore import context

4
tests/st/ops/ascend/test_tdt_data_ms.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,7 +17,7 @@ import numpy as np
import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.vision as vision
import mindspore.nn as nn
from mindspore.common.api import _cell_graph_executor
from mindspore.common.tensor import Tensor

6
tests/st/probability/bnn_layers/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,8 +17,8 @@ Produce the dataset
"""
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision as CV
import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore import dtype as mstype

4
tests/st/probability/dpn/test_gpu_svi_cvae.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -16,7 +16,7 @@ import os
from mindspore import dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import context, Tensor
import mindspore.ops as ops

4
tests/st/probability/dpn/test_gpu_svi_vae.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -16,7 +16,7 @@ import os
from mindspore import dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import context, Tensor
import mindspore.ops as ops

4
tests/st/probability/dpn/test_gpu_vae_gan.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,7 +18,7 @@ The VAE interface can be called to construct VAE-GAN network.
import os
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import context
import mindspore.ops as ops

6
tests/st/probability/toolbox/test_uncertainty.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -14,8 +14,8 @@
# ============================================================================
""" test uncertainty toolbox """
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore import context, Tensor
from mindspore import dtype as mstype

6
tests/st/probability/transforms/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,8 +17,8 @@ Produce the dataset
"""
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision as CV
import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore import dtype as mstype

6
tests/st/probability/zhusuan/vae/utils.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -19,8 +19,8 @@ import numpy as np
from mindspore.common import dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
from mindspore.dataset.transforms.vision import Inter

6
tests/st/profiler/test_profiler.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -24,8 +24,8 @@ from mindspore import dataset as ds
from mindspore import nn, Tensor, context
from mindspore.nn.metrics import Accuracy
from mindspore.nn.optim import Momentum
from mindspore.dataset.transforms import c_transforms as C
from mindspore.dataset.vision import c_transforms as CV
from mindspore.dataset.transforms import transforms as C
from mindspore.dataset.vision import transforms as CV
from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype
from mindspore.common.initializer import TruncatedNormal

6
tests/st/ps/full_ps/test_full_ps_lenet.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,8 +18,8 @@ import argparse
import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
import mindspore.nn as nn
from mindspore.common import dtype as mstype
from mindspore.dataset.vision import Inter

6
tests/st/ps/part_ps/test_ps_embedding_heterogeneous_conv2d_adam.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,8 +18,8 @@ import argparse
import numpy as np
import mindspore.context as context
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as CV
from mindspore.common import dtype as mstype
from mindspore.dataset.vision import Inter
from mindspore.common.tensor import Tensor

4
tests/st/pynative/test_pynative_resnet50_ascend.py
View File

@ -22,8 +22,8 @@ import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
import mindspore.ops.functional as F

6
tests/st/pynative/test_pynative_resnet50_gpu.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -19,8 +19,8 @@ import numpy as np
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
import mindspore.ops.functional as F

6
tests/st/quantization/lenet_quant/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,8 +17,8 @@ Produce the dataset
"""
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision as CV
import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore.common import dtype as mstype

6
tests/st/quantization/mobilenetv2_quant/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,8 +17,8 @@
from functools import partial
import mindspore.dataset as ds
import mindspore.common.dtype as mstype
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
import mindspore.dataset.vision as C
import mindspore.dataset.transforms as C2
def create_dataset(dataset_path, config, repeat_num=1, batch_size=32):

6
tests/st/quantization/resnet50_quant/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -18,8 +18,8 @@ from functools import partial
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C2
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms as C2
import mindspore.dataset.vision as C
def create_dataset(dataset_path, config, repeat_num=1, batch_size=32):

6
tests/st/summary/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,9 +17,9 @@ import os
from mindspore import dataset as ds
from mindspore.common import dtype as mstype
from mindspore.dataset.transforms import c_transforms as C
from mindspore.dataset.transforms import transforms as C
from mindspore.dataset.vision import Inter
from mindspore.dataset.vision import c_transforms as CV
from mindspore.dataset.vision import transforms as CV
def create_mnist_dataset(mode='train', num_samples=2, batch_size=2):

6
tests/st/tbe_networks/resnet_cifar.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -23,8 +23,8 @@ import mindspore.ops.functional as F
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context

6
tests/st/tbe_networks/test_resnet_cifar_1p.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -20,8 +20,8 @@ from resnet import resnet50
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
import mindspore.ops.functional as F
from mindspore import Tensor

6
tests/st/tbe_networks/test_resnet_cifar_8p.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -21,8 +21,8 @@ import numpy as np
from resnet import resnet50
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
import mindspore.dataset.transforms as C
import mindspore.dataset.vision as vision
import mindspore.nn as nn
import mindspore.ops.functional as F
from mindspore import Tensor

16
tests/ut/python/dataset/test_get_image_num_channels.py
View File

@ -12,13 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Test MindData vision utility get_image_num_channels
"""
import numpy as np
import pytest
from PIL import Image
import mindspore.dataset.vision.utils as vision
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.vision.utils as vision_utils
import mindspore.dataset.vision as vision
from mindspore import log as logger
@ -30,8 +32,8 @@ def test_get_image_num_channels_output_array():
"""
expect_output = 3
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
input_array = C.Decode()(img)
output = vision.get_image_num_channels(input_array)
input_array = vision.Decode()(img)
output = vision_utils.get_image_num_channels(input_array)
assert expect_output == output
@ -44,7 +46,7 @@ def test_get_image_num_channels_output_img():
testdata = "../data/dataset/apple.jpg"
img = Image.open(testdata)
expect_channel = 3
output_channel = vision.get_image_num_channels(img)
output_channel = vision_utils.get_image_num_channels(img)
assert expect_channel == output_channel
@ -58,7 +60,7 @@ def test_get_image_num_channels_invalid_input():
def test_invalid_input(test_name, image, error, error_msg):
logger.info("Test get_image_num_channels with wrong params: {0}".format(test_name))
with pytest.raises(error) as error_info:
vision.get_image_num_channels(image)
vision_utils.get_image_num_channels(image)
assert error_msg in str(error_info.value)
invalid_input = 1

38
tests/ut/python/dataset/test_get_image_size.py
View File

@ -12,52 +12,54 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Test MindData vision utility get_image_size
"""
import numpy as np
import pytest
from PIL import Image
import mindspore.dataset.vision.utils as vision
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.vision.utils as vision_utils
import mindspore.dataset.vision as vision
from mindspore import log as logger
def test_get_image_size_output_array():
"""
Feature: get_image_num_channels array(after Decode array.shape is HWC)
Description: Test get_image_num_channels
Feature: get_image_size
Description: Test get_image_size array
Expectation: The returned result is as expected
"""
expect = [2268, 4032]
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
input_array = C.Decode()(img)
output = vision.get_image_size(input_array)
input_array = vision.Decode()(img)
output = vision_utils.get_image_size(input_array)
assert expect == output
def test_get_image_num_size_output_img():
def test_get_image_size_output_img():
"""
Feature: get_image_num_channels img(Image.size is [H, W])
Description: Test get_image_num_channels
Feature: get_image_size
Description: Test get_image_size image (Image.size is [H, W])
Expectation: The returned result is as expected
"""
expect = [2268, 4032]
img = Image.open("../data/dataset/apple.jpg")
output_size = vision.get_image_size(img)
output_size = vision_utils.get_image_size(img)
assert expect == output_size
def test_get_image_num_channels_invalid_input():
def test_get_image_size_invalid_input():
"""
Feature: get_image_num_channels
Description: Test get_image_num_channels invalid input
Feature: get_image_size
Description: Test get_image_size invalid input
Expectation: Correct error is raised as expected
"""
def test_invalid_input(test_name, image, error, error_msg):
logger.info("Test GetImageNumChannels with wrong params: {0}".format(test_name))
logger.info("Test GetImageSize with wrong params: {0}".format(test_name))
with pytest.raises(error) as error_info:
vision.get_image_size(image)
vision_utils.get_image_size(image)
assert error_msg in str(error_info.value)
invalid_input = 1
@ -71,5 +73,5 @@ def test_get_image_num_channels_invalid_input():
if __name__ == "__main__":
test_get_image_size_output_array()
test_get_image_num_size_output_img()
test_get_image_num_channels_invalid_input()
test_get_image_size_output_img()
test_get_image_size_invalid_input()

1
tests/ut/python/dataset/test_pyfunc_multiprocess.py
View File

@ -80,7 +80,6 @@ def create_dataset_pyop_multiproc(num_parallel_workers=None, max_rowsize=16, bat
lambda x: x,
vision.HWC2CHW(),
vision.RandomErasing(0.9, value='random'),
vision.CutOut(4, 2, is_hwc=False),
lambda y: y
]
compose_op = transforms.Compose(transforms_list)

18
tests/ut/python/profiler/parser/test_minddata_analyzer.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -22,7 +22,7 @@ import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms as transforms
import mindspore._c_dataengine as cde
from mindspore.profiler.parser.minddata_analyzer import MinddataProfilingAnalyzer
@ -143,14 +143,16 @@ class TestMinddataProfilingAnalyzer:
def test_analyze_basic(self, tmp_path):
"""
Test MindData profiling analyze summary files exist with basic pipeline.
Also test basic content (subset of keys and values) from the returned summary result.
Feature: MindData Profiling Analyzer
Description: Test MindData profiling analyze summary files exist with basic pipeline.
Also test basic content (subset of keys and values) from the returned summary result.
Expectation: MindData Profiling Analyzer output is as expected
"""
# Create this basic and common linear pipeline
# Generator -> Map -> Batch -> Repeat -> EpochCtrl
data1 = ds.GeneratorDataset(self.mysource, ["col1"])
type_cast_op = C.TypeCast(mstype.int32)
type_cast_op = transforms.TypeCast(mstype.int32)
data1 = data1.map(operations=type_cast_op, input_columns="col1")
data1 = data1.batch(16)
data1 = data1.repeat(2)
@ -206,13 +208,15 @@ class TestMinddataProfilingAnalyzer:
def test_analyze_sequential_pipelines_invalid(self, tmp_path):
"""
Test invalid scenario in which MinddataProfilingAnalyzer is called for two sequential pipelines.
Feature: MindData Profiling Analyzer
Description: Test invalid scenario in which MinddataProfilingAnalyzer is called for two sequential pipelines.
Expectation: MindData Profiling Analyzer output in each pipeline is as expected
"""
# Create the pipeline
# Generator -> Map -> Batch -> EpochCtrl
data1 = ds.GeneratorDataset(self.mysource, ["col1"])
type_cast_op = C.TypeCast(mstype.int32)
type_cast_op = transforms.TypeCast(mstype.int32)
data1 = data1.map(operations=type_cast_op, input_columns="col1")
data1 = data1.batch(64)

7
tests/ut/python/train/dataset.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -17,13 +17,14 @@ import os
from mindspore import dataset as ds
from mindspore.common import dtype as mstype
from mindspore.dataset.transforms import c_transforms as C
import mindspore.dataset.transforms as C
from mindspore.dataset.vision import Inter
from mindspore.dataset.vision import c_transforms as CV
import mindspore.dataset.vision as CV
from mindspore import nn, Tensor
from mindspore.common.initializer import Normal
from mindspore.ops import operations as P
def create_mnist_dataset(mode='train', num_samples=2, batch_size=2):
"""create dataset for train or test"""
mnist_path = '/home/workspace/mindspore_dataset/mnist'

19
tests/ut/python/utils/test_export.py
View File

@ -1,10 +1,25 @@
# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
# ============================================================================
"""Test export"""
import os
import numpy as np
import mindspore.nn as nn
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as CT
import mindspore.dataset.vision as CV
import mindspore.dataset.transforms as CT
from mindspore.dataset.vision import Inter
from mindspore import context
from mindspore.common import dtype as mstype