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提交 067592cc 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!2590 Updated Python UTs for RandomVerticalFlipWithBBox, RandomCropWithBBox... 

!2590 Updated Python UTs for RandomVerticalFlipWithBBox, RandomCropWithBBox and RandomResizedCropWithBBox
Merge pull request !2590 from danishnxt/AugOps2
上级 b0a10c26 3d1ecaae
隐藏空白更改
内联 并排
Showing with 211 addition and 351 deletion
mindspore/ccsrc/dataset/kernels/image/random_crop_and_resize_with_bbox_op.cc
浏览文件 @ 067592cc
......@@ -30,8 +30,7 @@ Status RandomCropAndResizeWithBBoxOp::Compute(const TensorRow &input, TensorRow
BOUNDING_BOX_CHECK(input);
CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Size() >= 2, "The shape of input is abnormal");
(*output).push_back(nullptr); // init memory for return vector
(*output).push_back(nullptr);
output->resize(2);
(*output)[1] = std::move(input[1]); // move boxes over to output
size_t bboxCount = input[1]->shape()[0]; // number of rows in bbox tensor
......
mindspore/ccsrc/dataset/kernels/image/random_crop_with_bbox_op.cc
浏览文件 @ 067592cc
......@@ -36,8 +36,7 @@ Status RandomCropWithBBoxOp::Compute(const TensorRow &input, TensorRow *output)
int32_t padded_image_h;
int32_t padded_image_w;
(*output).push_back(nullptr);
(*output).push_back(nullptr);
output->resize(2);
(*output)[1] = std::move(input[1]); // since some boxes may be removed
bool crop_further = true; // Whether further cropping will be required or not, true unless required size matches
......
mindspore/ccsrc/dataset/kernels/image/random_vertical_flip_with_bbox_op.cc
浏览文件 @ 067592cc
......@@ -45,8 +45,7 @@ Status RandomVerticalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow *
RETURN_IF_NOT_OK(input[1]->SetItemAt({i, 1}, newBoxCorner_y));
}
(*output).push_back(nullptr);
(*output).push_back(nullptr);
output->resize(2);
(*output)[1] = std::move(input[1]);
return VerticalFlip(input[0], &(*output)[0]);
......
tests/ut/python/dataset/test_random_crop_and_resize_with_bbox.py
浏览文件 @ 067592cc
......@@ -13,17 +13,17 @@
# limitations under the License.
# ==============================================================================
"""
Testing RandomCropAndResizeWithBBox op
Testing RandomCropAndResizeWithBBox op in DE
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as c_vision
from mindspore import log as logger
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
GENERATE_GOLDEN = False
# updated VOC dataset with correct annotations
DATA_DIR = "../data/dataset/testVOC2012_2"
......@@ -31,8 +31,7 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
def fix_annotate(bboxes):
"""
Update Current VOC dataset format to Proposed HQ BBox format
Fix annotations to format followed by mindspore.
:param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
:return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
"""
......@@ -46,112 +45,22 @@ def fix_annotate(bboxes):
return bboxes
def add_bounding_boxes(ax, bboxes):
for bbox in bboxes:
rect = patches.Rectangle((bbox[0], bbox[1]),
bbox[2], bbox[3],
linewidth=1, edgecolor='r', facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
def vis_check(orig, aug):
if not isinstance(orig, list) or not isinstance(aug, list):
return False
if len(orig) != len(aug):
return False
return True
def visualize(orig, aug):
if not vis_check(orig, aug):
return
plotrows = 3
compset = int(len(orig)/plotrows)
orig, aug = np.array(orig), np.array(aug)
orig = np.split(orig[:compset*plotrows], compset) + [orig[compset*plotrows:]]
aug = np.split(aug[:compset*plotrows], compset) + [aug[compset*plotrows:]]
for ix, allData in enumerate(zip(orig, aug)):
base_ix = ix * plotrows # will signal what base level we're on
fig, axs = plt.subplots(len(allData[0]), 2)
fig.tight_layout(pad=1.5)
for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
cur_ix = base_ix + x
axs[x, 0].imshow(dataA["image"])
add_bounding_boxes(axs[x, 0], dataA["annotation"])
axs[x, 0].title.set_text("Original" + str(cur_ix+1))
print("Original **\n ", str(cur_ix+1), " :", dataA["annotation"])
axs[x, 1].imshow(dataB["image"])
add_bounding_boxes(axs[x, 1], dataB["annotation"])
axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
print("Augmented **\n", str(cur_ix+1), " ", dataB["annotation"], "\n")
plt.show()
# Functions to pass to Gen for creating invalid bounding boxes
def gen_bad_bbox_neg_xy(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [-50, -50, im_w - 10, im_h - 10]
return im, bbox
def gen_bad_bbox_overflow_width(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w + 10, im_h - 10]
return im, bbox
def gen_bad_bbox_overflow_height(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w - 10, im_h + 10]
return im, bbox
def gen_bad_bbox_wrong_shape(im, bbox):
bbox = np.array([[0, 0, 0]]).astype(bbox.dtype)
return im, bbox
badGenFuncs = [gen_bad_bbox_neg_xy,
gen_bad_bbox_overflow_width,
gen_bad_bbox_overflow_height,
gen_bad_bbox_wrong_shape]
assertVal = ["min_x",
"is out of bounds of the image",
"is out of bounds of the image",
"4 features"]
# Gen Edge case BBox
def gen_bbox_edge(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w, im_h]
return im, bbox
def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
tests with MD5 check, expected to pass
"""
logger.info("test_random_resized_crop_with_bbox_op_c")
original_seed = config_get_set_seed(23415)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -164,6 +73,9 @@ def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
filename = "random_resized_crop_with_bbox_01_c_result.npz"
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
......@@ -171,20 +83,26 @@ def test_c_random_resized_crop_with_bbox_op(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
tests on dynamically generated edge case, expected to pass
"""
logger.info("test_random_resized_crop_with_bbox_op_edge_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -192,17 +110,17 @@ def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
output_columns=["annotation"],
operations=fix_annotate)
# Modify BBoxes to serve as valid edge cases
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
# maps to convert data into valid edge case data
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[gen_bbox_edge])
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
# map to apply ops
# Test Op added to list of Operations here
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
unaugSamp, augSamp = [], []
......@@ -211,21 +129,22 @@ def test_c_random_resized_crop_with_bbox_op_edge(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_c_random_resized_crop_with_bbox_op_invalid():
def test_random_resized_crop_with_bbox_op_invalid_c():
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
Tests RandomResizedCropWithBBox on invalid constructor parameters, expected to raise ValueError
"""
# Load dataset # only loading the to AugDataset as test will fail on this
logger.info("test_random_resized_crop_with_bbox_op_invalid_c")
# Load dataset, only Augmented Dataset as test will raise ValueError
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
try:
# If input range of scale is not in the order of (min, max), ValueError will be raised.
test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 0.5), (0.5, 0.5))
# maps to fix annotations to HQ standard
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -243,10 +162,11 @@ def test_c_random_resized_crop_with_bbox_op_invalid():
assert "Input range is not valid" in str(err)
def test_c_random_resized_crop_with_bbox_op_invalid2():
def test_random_resized_crop_with_bbox_op_invalid2_c():
"""
Prints images side by side with and without Aug applied + bboxes to compare and test
Tests RandomResizedCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
"""
logger.info("test_random_resized_crop_with_bbox_op_invalid2_c")
# Load dataset # only loading the to AugDataset as test will fail on this
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -254,7 +174,6 @@ def test_c_random_resized_crop_with_bbox_op_invalid2():
# If input range of ratio is not in the order of (min, max), ValueError will be raised.
test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 1), (1, 0.5))
# maps to fix annotations to HQ standard
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -272,41 +191,26 @@ def test_c_random_resized_crop_with_bbox_op_invalid2():
assert "Input range is not valid" in str(err)
def test_c_random_resized_crop_with_bbox_op_bad():
# Should Fail - Errors logged to logger
for ix, badFunc in enumerate(badGenFuncs):
try:
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
test_op = c_vision.RandomVerticalFlipWithBBox(1)
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[badFunc])
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
for _ in dataVoc2.create_dict_iterator():
break # first sample will cause exception
def test_random_resized_crop_with_bbox_op_bad_c():
"""
Test RandomCropWithBBox op with invalid bounding boxes, expected to catch multiple errors.
"""
logger.info("test_random_resized_crop_with_bbox_op_bad_c")
test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
except RuntimeError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert assertVal[ix] in str(err)
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
if __name__ == "__main__":
test_c_random_resized_crop_with_bbox_op(plot_vis=True)
test_c_random_resized_crop_with_bbox_op_edge(plot_vis=True)
test_c_random_resized_crop_with_bbox_op_invalid()
test_c_random_resized_crop_with_bbox_op_invalid2()
test_c_random_resized_crop_with_bbox_op_bad()
test_random_resized_crop_with_bbox_op_c(plot_vis=True)
test_random_resized_crop_with_bbox_op_edge_c(plot_vis=True)
test_random_resized_crop_with_bbox_op_invalid_c()
test_random_resized_crop_with_bbox_op_invalid2_c()
test_random_resized_crop_with_bbox_op_bad_c()
tests/ut/python/dataset/test_random_crop_with_bbox.py
浏览文件 @ 067592cc
......@@ -13,18 +13,18 @@
# limitations under the License.
# ==============================================================================
"""
Testing RandomCropWithBBox op
Testing RandomCropWithBBox op in DE
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as c_vision
import mindspore.dataset.transforms.vision.utils as mode
from mindspore import log as logger
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
GENERATE_GOLDEN = False
# updated VOC dataset with correct annotations
DATA_DIR = "../data/dataset/testVOC2012_2"
......@@ -32,8 +32,7 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
def fix_annotate(bboxes):
"""
Update Current VOC dataset format to Proposed HQ BBox format
Fix annotations to format followed by mindspore.
:param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
:return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
"""
......@@ -47,113 +46,19 @@ def fix_annotate(bboxes):
return bboxes
def add_bounding_boxes(ax, bboxes):
for bbox in bboxes:
rect = patches.Rectangle((bbox[0], bbox[1]),
bbox[2], bbox[3],
linewidth=1, edgecolor='r', facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
def vis_check(orig, aug):
if not isinstance(orig, list) or not isinstance(aug, list):
return False
if len(orig) != len(aug):
return False
return True
def visualize(orig, aug):
if not vis_check(orig, aug):
return
plotrows = 3
compset = int(len(orig)/plotrows)
orig, aug = np.array(orig), np.array(aug)
orig = np.split(orig[:compset*plotrows], compset) + [orig[compset*plotrows:]]
aug = np.split(aug[:compset*plotrows], compset) + [aug[compset*plotrows:]]
for ix, allData in enumerate(zip(orig, aug)):
base_ix = ix * plotrows # will signal what base level we're on
fig, axs = plt.subplots(len(allData[0]), 2)
fig.tight_layout(pad=1.5)
for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
cur_ix = base_ix + x
axs[x, 0].imshow(dataA["image"])
add_bounding_boxes(axs[x, 0], dataA["annotation"])
axs[x, 0].title.set_text("Original" + str(cur_ix+1))
print("Original **\n ", str(cur_ix+1), " :", dataA["annotation"])
axs[x, 1].imshow(dataB["image"])
add_bounding_boxes(axs[x, 1], dataB["annotation"])
axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
print("Augmented **\n", str(cur_ix+1), " ", dataB["annotation"], "\n")
plt.show()
# Functions to pass to Gen for creating invalid bounding boxes
def gen_bad_bbox_neg_xy(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [-50, -50, im_w - 10, im_h - 10]
return im, bbox
def gen_bad_bbox_overflow_width(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w + 10, im_h - 10]
return im, bbox
def gen_bad_bbox_overflow_height(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w - 10, im_h + 10]
return im, bbox
def gen_bad_bbox_wrong_shape(im, bbox):
bbox = np.array([[0, 0, 0]]).astype(bbox.dtype)
return im, bbox
badGenFuncs = [gen_bad_bbox_neg_xy,
gen_bad_bbox_overflow_width,
gen_bad_bbox_overflow_height,
gen_bad_bbox_wrong_shape]
assertVal = ["min_x",
"is out of bounds of the image",
"is out of bounds of the image",
"4 features"]
# Gen Edge case BBox
def gen_bbox_edge(im, bbox):
im_h, im_w = im.shape[0], im.shape[1]
bbox[0][:4] = [0, 0, im_w, im_h]
return im, bbox
def test_random_crop_with_bbox_op_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes
Prints images and bboxes side by side with and without RandomCropWithBBox Op applied
"""
logger.info("test_random_crop_with_bbox_op_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
# define test OP with values to match existing Op unit - test
# define test OP with values to match existing Op UT
test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -173,14 +78,17 @@ def test_random_crop_with_bbox_op_c(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_crop_with_bbox_op2_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes
With Fill Value
Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
with md5 check, expected to pass
"""
logger.info("test_random_crop_with_bbox_op2_c")
original_seed = config_get_set_seed(593447)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -189,7 +97,6 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
# define test OP with values to match existing Op unit - test
test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], fill_value=(255, 255, 255))
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -202,6 +109,9 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
filename = "random_crop_with_bbox_01_c_result.npz"
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
......@@ -209,14 +119,20 @@ def test_random_crop_with_bbox_op2_c(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_with_bbox_op3_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes
With Padding Mode passed
Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
with Padding Mode explicitly passed
"""
logger.info("test_random_crop_with_bbox_op3_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -224,7 +140,6 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
# define test OP with values to match existing Op unit - test
test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -244,14 +159,16 @@ def test_random_crop_with_bbox_op3_c(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes
Testing for an Edge case
Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
applied on dynamically generated edge case, expected to pass
"""
logger.info("test_random_crop_with_bbox_op_edge_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -259,7 +176,6 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
# define test OP with values to match existing Op unit - test
test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -267,17 +183,17 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
output_columns=["annotation"],
operations=fix_annotate)
# Modify BBoxes to serve as valid edge cases
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
# maps to convert data into valid edge case data
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[gen_bbox_edge])
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
# map to apply ops
# Test Op added to list of Operations here
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
unaugSamp, augSamp = [], []
......@@ -286,13 +202,15 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
augSamp.append(Aug)
if plot_vis:
visualize(unaugSamp, augSamp)
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_crop_with_bbox_op_invalid_c():
"""
Checking for invalid params passed to Aug Constructor
Test RandomCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
"""
logger.info("test_random_crop_with_bbox_op_invalid_c")
# Load dataset
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -300,8 +218,6 @@ def test_random_crop_with_bbox_op_invalid_c():
# define test OP with values to match existing Op unit - test
test_op = c_vision.RandomCropWithBBox([512, 512, 375])
# maps to fix annotations to HQ standard
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -320,35 +236,20 @@ def test_random_crop_with_bbox_op_invalid_c():
def test_random_crop_with_bbox_op_bad_c():
# Should Fail - Errors logged to logger
for ix, badFunc in enumerate(badGenFuncs):
try:
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[badFunc])
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
for _ in dataVoc2.create_dict_iterator():
break # first sample will cause exception
except RuntimeError as err:
logger.info("Got an exception in DE: {}".format(str(err)))
assert assertVal[ix] in str(err)
"""
Tests RandomCropWithBBox Op with invalid bounding boxes, expected to catch multiple errors.
"""
logger.info("test_random_crop_with_bbox_op_bad_c")
test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
if __name__ == "__main__":
......
tests/ut/python/dataset/test_random_vertical_flip_with_bbox.py
浏览文件 @ 067592cc
......@@ -13,14 +13,17 @@
# limitations under the License.
# ==============================================================================
"""
Testing RandomVerticalFlipWithBBox op
Testing RandomVerticalFlipWithBBox op in DE
"""
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as c_vision
from mindspore import log as logger
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
GENERATE_GOLDEN = False
# updated VOC dataset with correct annotations
DATA_DIR = "../data/dataset/testVOC2012_2"
......@@ -28,10 +31,9 @@ DATA_DIR = "../data/dataset/testVOC2012_2"
def fix_annotate(bboxes):
"""
Update Current VOC dataset format to Proposed HQ BBox format
:param bboxes: as [label, x_min, y_min, w, h, truncate, difficult]
:return: annotation as [x_min, y_min, w, h, label, truncate, difficult]
Fix annotations to format followed by mindspore.
:param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
:return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
"""
for bbox in bboxes:
tmp = bbox[0]
......@@ -45,9 +47,9 @@ def fix_annotate(bboxes):
def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes to
compare and test
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied
"""
logger.info("test_random_vertical_flip_with_bbox_op_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
......@@ -57,7 +59,6 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
test_op = c_vision.RandomVerticalFlipWithBBox(1)
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -82,9 +83,12 @@ def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
"""
Prints images side by side with and without Aug applied + bboxes to
compare and test
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
tests with MD5 check, expected to pass
"""
logger.info("test_random_vertical_flip_with_bbox_op_rand_c")
original_seed = config_get_set_seed(29847)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
......@@ -93,9 +97,8 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
test_op = c_vision.RandomVerticalFlipWithBBox(0.6)
test_op = c_vision.RandomVerticalFlipWithBBox(0.8)
# maps to fix annotations to HQ standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -108,6 +111,56 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
columns_order=["image", "annotation"],
operations=[test_op])
filename = "random_vertical_flip_with_bbox_01_c_result.npz"
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=False):
"""
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
applied on dynamically generated edge case, expected to pass
"""
logger.info("test_random_vertical_flip_with_bbox_op_edge_c")
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
test_op = c_vision.RandomVerticalFlipWithBBox(1)
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# maps to convert data into valid edge case data
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
# Test Op added to list of Operations here
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
......@@ -119,16 +172,15 @@ def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
def test_random_vertical_flip_with_bbox_op_invalid_c():
# Should Fail
# Load dataset
"""
Test RandomVerticalFlipWithBBox Op on invalid constructor parameters, expected to raise ValueError
"""
logger.info("test_random_vertical_flip_with_bbox_op_invalid_c")
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
decode=True, shuffle=False)
try:
test_op = c_vision.RandomVerticalFlipWithBBox(2)
# maps to fix annotations to HQ standard
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
......@@ -148,9 +200,9 @@ def test_random_vertical_flip_with_bbox_op_invalid_c():
def test_random_vertical_flip_with_bbox_op_bad_c():
"""
Test RandomHorizontalFlipWithBBox op with invalid bounding boxes
Tests RandomVerticalFlipWithBBox Op with invalid bounding boxes, expected to catch multiple errors
"""
logger.info("test_random_horizontal_bbox_invalid_bounds_c")
logger.info("test_random_vertical_flip_with_bbox_op_bad_c")
test_op = c_vision.RandomVerticalFlipWithBBox(1)
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
......@@ -166,5 +218,6 @@ def test_random_vertical_flip_with_bbox_op_bad_c():
if __name__ == "__main__":
test_random_vertical_flip_with_bbox_op_c(plot_vis=True)
test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=True)
test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=True)
test_random_vertical_flip_with_bbox_op_invalid_c()
test_random_vertical_flip_with_bbox_op_bad_c()
tests/ut/python/dataset/util.py
浏览文件 @ 067592cc
......@@ -312,34 +312,39 @@ def visualize_with_bounding_boxes(orig, aug, plot_rows=3):
if len(orig) != len(aug) or not orig:
return
comp_set = int(len(orig)/plot_rows)
batch_size = int(len(orig)/plot_rows) # creates batches of images to plot together
split_point = batch_size * plot_rows
orig, aug = np.array(orig), np.array(aug)
if len(orig) > plot_rows:
orig = np.split(orig[:comp_set*plot_rows], comp_set) + [orig[comp_set*plot_rows:]]
aug = np.split(aug[:comp_set*plot_rows], comp_set) + [aug[comp_set*plot_rows:]]
# Create batches of required size and add remainder to last batch
orig = np.split(orig[:split_point], batch_size) + ([orig[split_point:]] if (split_point < orig.shape[0]) else []) # check to avoid empty arrays being added
aug = np.split(aug[:split_point], batch_size) + ([aug[split_point:]] if (split_point < aug.shape[0]) else [])
else:
orig = [orig]
aug = [aug]
for ix, allData in enumerate(zip(orig, aug)):
base_ix = ix * plot_rows # will signal what base level we're on
base_ix = ix * plot_rows # current batch starting index
curPlot = len(allData[0])
sub_plot_count = 2 if (len(allData[0]) < 2) else len(allData[0]) # if 1 image remains, create subplot for 2 to simplify axis selection
fig, axs = plt.subplots(sub_plot_count, 2)
fig, axs = plt.subplots(curPlot, 2)
fig.tight_layout(pad=1.5)
for x, (dataA, dataB) in enumerate(zip(allData[0], allData[1])):
cur_ix = base_ix + x
(axA, axB) = (axs[x, 0], axs[x, 1]) if (curPlot > 1) else (axs[0], axs[1]) # select plotting axes based on number of image rows on plot - else case when 1 row
axs[x, 0].imshow(dataA["image"])
add_bounding_boxes(axs[x, 0], dataA["annotation"])
axs[x, 0].title.set_text("Original" + str(cur_ix+1))
logger.info("Original **\n{} : {}".format(str(cur_ix+1), dataA["annotation"]))
axA.imshow(dataA["image"])
add_bounding_boxes(axA, dataA["annotation"])
axA.title.set_text("Original" + str(cur_ix+1))
axs[x, 1].imshow(dataB["image"])
add_bounding_boxes(axs[x, 1], dataB["annotation"])
axs[x, 1].title.set_text("Augmented" + str(cur_ix+1))
axB.imshow(dataB["image"])
add_bounding_boxes(axB, dataB["annotation"])
axB.title.set_text("Augmented" + str(cur_ix+1))
logger.info("Original **\n{} : {}".format(str(cur_ix+1), dataA["annotation"]))
logger.info("Augmented **\n{} : {}\n".format(str(cur_ix+1), dataB["annotation"]))
plt.show()
......
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