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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/12 21:28:45 UTC
[GitHub] piiswrong closed pull request #8919: [Vision] add test cases for flip, normalize, to_tensor
piiswrong closed pull request #8919: [Vision] add test cases for flip, normalize, to_tensor
URL: https://github.com/apache/incubator-mxnet/pull/8919
This is a PR merged from a forked repository.
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diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py
index 8daf88e6f4..38eb6902dc 100644
--- a/python/mxnet/gluon/data/vision/transforms.py
+++ b/python/mxnet/gluon/data/vision/transforms.py
@@ -184,26 +184,26 @@ def forward(self, x):
return image.imresize(x, *self._args)
-class RandomHorizontalFlip(HybridBlock):
- """Randomly flip the input image horizontally with a probability
+class RandomFlipLeftRight(HybridBlock):
+ """Randomly flip the input image left to right with a probability
of 0.5.
"""
def __init__(self):
- super(RandomHorizontalFlip, self).__init__()
+ super(RandomFlipLeftRight, self).__init__()
def hybrid_forward(self, F, x):
- return F.image.random_horizontal_flip(x)
+ return F.image.random_flip_left_right(x)
-class RandomVerticalFlip(HybridBlock):
- """Randomly flip the input image vertically with a probability
+class RandomFlipTopBottom(HybridBlock):
+ """Randomly flip the input image top to bottom with a probability
of 0.5.
"""
def __init__(self):
- super(RandomVerticalFlip, self).__init__()
+ super(RandomFlipTopBottom, self).__init__()
def hybrid_forward(self, F, x):
- return F.image.random_vertical_flip(x)
+ return F.image.random_flip_top_bottom(x)
class RandomBrightness(HybridBlock):
diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h
index cbc7f4012b..ec961492f9 100644
--- a/src/operator/image/image_random-inl.h
+++ b/src/operator/image/image_random-inl.h
@@ -85,14 +85,6 @@ void ToTensor(const nnvm::NodeAttrs &attrs,
});
}
-inline bool TensorShape(const nnvm::NodeAttrs& attrs,
- std::vector<TShape> *in_attrs,
- std::vector<TShape> *out_attrs) {
- TShape& dshape = (*in_attrs)[0];
- SHAPE_ASSIGN_CHECK(*out_attrs, 0, dshape);
- return true;
-}
-
struct NormalizeParam : public dmlc::Parameter<NormalizeParam> {
nnvm::Tuple<float> mean;
nnvm::Tuple<float> std;
@@ -179,16 +171,16 @@ inline bool ImageShape(const nnvm::NodeAttrs& attrs,
return true;
}
-template<typename DType>
-void FlipImpl(const TShape &shape, DType *src, DType *dst, int axis) {
+template<typename DType, int axis>
+void FlipImpl(const TShape &shape, DType *src, DType *dst) {
int head = 1, mid = shape[axis], tail = 1;
for (int i = 0; i < axis; ++i) head *= shape[i];
for (int i = axis+1; i < shape.ndim(); ++i) tail *= shape[i];
for (int i = 0; i < head; ++i) {
- for (int j = 0; j < (mid >>2); ++j) {
- int idx1 = (i*mid + j)*tail;
- int idx2 = idx1 + (mid - (j<<2))*tail;
+ for (int j = 0; j < (mid >> 1); ++j) {
+ int idx1 = (i*mid + j) * tail;
+ int idx2 = idx1 + (mid-(j << 1)-1) * tail;
for (int k = 0; k < tail; ++k, ++idx1, ++idx2) {
DType tmp = src[idx1];
dst[idx1] = src[idx2];
@@ -198,7 +190,31 @@ void FlipImpl(const TShape &shape, DType *src, DType *dst, int axis) {
}
}
-void RandomHorizontalFlip(
+void FlipLeftRight(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector<TBlob> &inputs,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &outputs) {
+ using namespace mshadow;
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ FlipImpl<DType, 1>(inputs[0].shape_, inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>());
+ });
+}
+
+void FlipTopBottom(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector<TBlob> &inputs,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &outputs) {
+ using namespace mshadow;
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ FlipImpl<DType, 0>(inputs[0].shape_, inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>());
+ });
+}
+
+void RandomFlipLeftRight(
const nnvm::NodeAttrs &attrs,
const OpContext &ctx,
const std::vector<TBlob> &inputs,
@@ -207,14 +223,19 @@ void RandomHorizontalFlip(
using namespace mshadow;
Stream<cpu> *s = ctx.get_stream<cpu>();
Random<cpu> *prnd = ctx.requested[0].get_random<cpu, float>(s);
- if (std::bernoulli_distribution()(prnd->GetRndEngine())) return;
MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
- FlipImpl(inputs[0].shape_, inputs[0].dptr<DType>(),
- outputs[0].dptr<DType>(), 1);
+ if (std::bernoulli_distribution()(prnd->GetRndEngine())) {
+ if (outputs[0].dptr_ != inputs[0].dptr_) {
+ std::memcpy(outputs[0].dptr_, inputs[0].dptr_, inputs[0].Size() * sizeof(DType));
+ }
+ } else {
+ FlipImpl<DType, 1>(inputs[0].shape_, inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>());
+ }
});
}
-void RandomVerticalFlip(
+void RandomFlipTopBottom(
const nnvm::NodeAttrs &attrs,
const OpContext &ctx,
const std::vector<TBlob> &inputs,
@@ -223,10 +244,15 @@ void RandomVerticalFlip(
using namespace mshadow;
Stream<cpu> *s = ctx.get_stream<cpu>();
Random<cpu> *prnd = ctx.requested[0].get_random<cpu, float>(s);
- if (std::bernoulli_distribution()(prnd->GetRndEngine())) return;
MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
- FlipImpl(inputs[0].shape_, inputs[0].dptr<DType>(),
- outputs[0].dptr<DType>(), 0);
+ if (std::bernoulli_distribution()(prnd->GetRndEngine())) {
+ if (outputs[0].dptr_ != inputs[0].dptr_) {
+ std::memcpy(outputs[0].dptr_, inputs[0].dptr_, inputs[0].Size() * sizeof(DType));
+ }
+ } else {
+ FlipImpl<DType, 0>(inputs[0].shape_, inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>());
+ }
});
}
diff --git a/src/operator/image/image_random.cc b/src/operator/image/image_random.cc
index 481dfce010..26f520bb8c 100644
--- a/src/operator/image/image_random.cc
+++ b/src/operator/image/image_random.cc
@@ -48,7 +48,6 @@ NNVM_REGISTER_OP(_image_to_tensor)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_copy" })
.add_argument("data", "NDArray-or-Symbol", "The input.");
-
NNVM_REGISTER_OP(_image_normalize)
.describe(R"code()code" ADD_FILELINE)
.set_num_inputs(1)
@@ -65,11 +64,21 @@ NNVM_REGISTER_OP(_image_normalize)
.add_argument("data", "NDArray-or-Symbol", "The input.")
.add_arguments(NormalizeParam::__FIELDS__());
+MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_left_right)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", FlipLeftRight);
+
+MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_left_right)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", RandomFlipLeftRight);
-MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_horizontal_flip)
+MXNET_REGISTER_IMAGE_AUG_OP(_image_flip_top_bottom)
.describe(R"code()code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", RandomHorizontalFlip);
+.set_attr<FCompute>("FCompute<cpu>", FlipTopBottom);
+MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_flip_top_bottom)
+.describe(R"code()code" ADD_FILELINE)
+.set_attr<FCompute>("FCompute<cpu>", RandomFlipTopBottom);
MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_brightness)
.describe(R"code()code" ADD_FILELINE)
@@ -77,7 +86,6 @@ MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_brightness)
.set_attr<FCompute>("FCompute<cpu>", RandomBrightness)
.add_arguments(RandomEnhanceParam::__FIELDS__());
-
MXNET_REGISTER_IMAGE_RND_AUG_OP(_image_random_contrast)
.describe(R"code()code" ADD_FILELINE)
.set_attr_parser(ParamParser<RandomEnhanceParam>)
diff --git a/tests/python/unittest/test_gluon_data_vision.py b/tests/python/unittest/test_gluon_data_vision.py
index 0c9e5c1c11..5e9ff879fb 100644
--- a/tests/python/unittest/test_gluon_data_vision.py
+++ b/tests/python/unittest/test_gluon_data_vision.py
@@ -18,22 +18,39 @@
import mxnet as mx
import mxnet.ndarray as nd
import numpy as np
+from PIL import Image
from mxnet import gluon
-from mxnet.gluon.data.vision.transforms import AdjustLighting
+from mxnet.gluon.data.vision import transforms
from mxnet.test_utils import assert_almost_equal
+from mxnet.test_utils import almost_equal
-def test_adjust_lighting():
+def test_to_tensor():
data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
- alpha_rgb = [0.05, 0.06, 0.07]
- eigval = np.array([55.46, 4.794, 1.148])
- eigvec = np.array([[-0.5675, 0.7192, 0.4009],
- [-0.5808, -0.0045, -0.8140],
- [-0.5808, -0.0045, -0.8140]])
- f = AdjustLighting(alpha_rgb=alpha_rgb, eigval=eigval.ravel().tolist(), eigvec=eigvec.ravel().tolist())
- out_nd = f(nd.array(data_in, dtype=np.uint8))
- out_gt = np.clip(data_in.astype(np.float32)
- + np.dot(eigvec * alpha_rgb, eigval.reshape((3, 1))).reshape((1, 1, 3)), 0, 255).astype(np.uint8)
- assert_almost_equal(out_nd.asnumpy(), out_gt)
+ out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
+ assert_almost_equal(out_nd.asnumpy(), np.transpose(
+ data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
+
+def test_normalize():
+ data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
+ data_in = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
+ out_nd = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in)
+ data_expected = data_in.asnumpy()
+ data_expected[:][:][0] = data_expected[:][:][0] / 3.0
+ data_expected[:][:][1] = (data_expected[:][:][1] - 1.0) / 2.0
+ data_expected[:][:][2] = data_expected[:][:][2] - 2.0
+ assert_almost_equal(data_expected, out_nd.asnumpy())
+
+def test_flip_left_right():
+ data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
+ pil_img = Image.fromarray(data_in).transpose(Image.FLIP_LEFT_RIGHT)
+ data_trans = nd.image.flip_left_right(nd.array(data_in, dtype='uint8'))
+ assert_almost_equal(np.array(pil_img), data_trans.asnumpy())
+
+def test_flip_top_bottom():
+ data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
+ pil_img = Image.fromarray(data_in).transpose(Image.FLIP_TOP_BOTTOM)
+ data_trans = nd.image.flip_top_bottom(nd.array(data_in, dtype='uint8'))
+ assert_almost_equal(np.array(pil_img), data_trans.asnumpy())
if __name__ == '__main__':
import nose
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