You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/11/22 23:53:41 UTC
[GitHub] piiswrong closed pull request #8779: [Image] add random lighting
piiswrong closed pull request #8779: [Image] add random lighting
URL: https://github.com/apache/incubator-mxnet/pull/8779
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/python/mxnet/gluon/data/vision/transforms.py b/python/mxnet/gluon/data/vision/transforms.py
index e1deef631d..931d644b17 100644
--- a/python/mxnet/gluon/data/vision/transforms.py
+++ b/python/mxnet/gluon/data/vision/transforms.py
@@ -21,6 +21,7 @@
from ...block import Block, HybridBlock
from ...nn import Sequential, HybridSequential
from .... import ndarray, initializer
+from ....base import _Null
class Compose(Sequential):
@@ -151,3 +152,21 @@ def __init__(self, max_brightness=0, max_contrast=0, max_saturation=0, max_hue=0
def hybrid_forward(self, F, x):
return F.image.random_color_jitter(x, *self._args)
+
+
+class AdjustLighting(HybridBlock):
+ def __init__(self, alpha_rgb=_Null, eigval=_Null, eigvec=_Null):
+ super(AdjustLighting, self).__init__()
+ self._args = (alpha_rgb, eigval, eigvec)
+
+ def hybrid_forward(self, F, x):
+ return F.image.adjust_lighting(x, *self._args)
+
+
+class RandomLighting(HybridBlock):
+ def __init__(self, alpha_std=_Null, eigval=_Null, eigvec=_Null):
+ super(RandomLighting, self).__init__()
+ self._args = (alpha_std, eigval, eigvec)
+
+ def hybrid_forward(self, F, x):
+ return F.image.random_lighting(x, *self._args)
\ No newline at end of file
diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h
index f823c8ce06..ebbf60a0fe 100644
--- a/src/operator/image/image_random-inl.h
+++ b/src/operator/image/image_random-inl.h
@@ -26,6 +26,7 @@
#define MXNET_OPERATOR_IMAGE_IMAGE_RANDOM_INL_H_
#include <mxnet/base.h>
+#include <algorithm>
#include <vector>
#include <opencv2/opencv.hpp>
#include <opencv2/core/mat.hpp>
@@ -290,11 +291,105 @@ static void RandomColorJitter(const nnvm::NodeAttrs &attrs,
const std::vector<TBlob> &outputs) {
}
+struct AdjustLightingParam : public dmlc::Parameter<AdjustLightingParam> {
+ nnvm::Tuple<float> alpha_rgb;
+ nnvm::Tuple<float> eigval;
+ nnvm::Tuple<float> eigvec;
+ DMLC_DECLARE_PARAMETER(AdjustLightingParam) {
+ DMLC_DECLARE_FIELD(alpha_rgb)
+ .set_default({0, 0, 0})
+ .describe("The lighting alphas for the R, G, B channels.");
+ DMLC_DECLARE_FIELD(eigval)
+ .describe("Eigen value.")
+ .set_default({ 55.46, 4.794, 1.148 });
+ DMLC_DECLARE_FIELD(eigvec)
+ .describe("Eigen vector.")
+ .set_default({ -0.5675, 0.7192, 0.4009,
+ -0.5808, -0.0045, -0.8140,
+ -0.5808, -0.0045, -0.8140 });
+ }
+};
+
+struct RandomLightingParam : public dmlc::Parameter<RandomLightingParam> {
+ float alpha_std;
+ nnvm::Tuple<float> eigval;
+ nnvm::Tuple<float> eigvec;
+ DMLC_DECLARE_PARAMETER(RandomLightingParam) {
+ DMLC_DECLARE_FIELD(alpha_std)
+ .set_default(0.05)
+ .describe("Level of the lighting noise.");
+ DMLC_DECLARE_FIELD(eigval)
+ .describe("Eigen value.")
+ .set_default({ 55.46, 4.794, 1.148 });
+ DMLC_DECLARE_FIELD(eigvec)
+ .describe("Eigen vector.")
+ .set_default({ -0.5675, 0.7192, 0.4009,
+ -0.5808, -0.0045, -0.8140,
+ -0.5808, -0.0045, -0.8140 });
+ }
+};
+
+void AdjustLightingImpl(uint8_t* dst, const uint8_t* src,
+ float alpha_r, float alpha_g, float alpha_b,
+ const nnvm::Tuple<float> eigval, const nnvm::Tuple<float> eigvec,
+ int H, int W) {
+ alpha_r *= eigval[0];
+ alpha_g *= eigval[1];
+ alpha_b *= eigval[2];
+ float pca_r = alpha_r * eigvec[0] + alpha_g * eigvec[1] + alpha_b * eigvec[2];
+ float pca_g = alpha_r * eigvec[3] + alpha_g * eigvec[4] + alpha_b * eigvec[5];
+ float pca_b = alpha_r * eigvec[6] + alpha_g * eigvec[7] + alpha_b * eigvec[8];
+ for (int i = 0; i < H * W; i++) {
+ int base_ind = 3 * i;
+ float in_r = static_cast<float>(src[base_ind]);
+ float in_g = static_cast<float>(src[base_ind + 1]);
+ float in_b = static_cast<float>(src[base_ind + 2]);
+ dst[base_ind] = std::min(255, std::max(0, static_cast<int>(in_r + pca_r)));
+ dst[base_ind + 1] = std::min(255, std::max(0, static_cast<int>(in_g + pca_g)));
+ dst[base_ind + 2] = std::min(255, std::max(0, static_cast<int>(in_b + pca_b)));
+ }
+}
+
+static void AdjustLighting(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;
+ const AdjustLightingParam ¶m = nnvm::get<AdjustLightingParam>(attrs.parsed);
+ CHECK_EQ(param.eigval.ndim(), 3) << "There should be 3 numbers in the eigval.";
+ CHECK_EQ(param.eigvec.ndim(), 9) << "There should be 9 numbers in the eigvec.";
+ CHECK_EQ(inputs[0].ndim(), 3);
+ CHECK_EQ(inputs[0].size(2), 3);
+ int H = inputs[0].size(0);
+ int W = inputs[0].size(1);
+ AdjustLightingImpl(outputs[0].dptr<uint8_t>(), inputs[0].dptr<uint8_t>(),
+ param.alpha_rgb[0], param.alpha_rgb[1], param.alpha_rgb[2],
+ param.eigval, param.eigvec, H, W);
+}
+
static void RandomLighting(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;
+ const RandomLightingParam ¶m = nnvm::get<RandomLightingParam>(attrs.parsed);
+ CHECK_EQ(param.eigval.ndim(), 3) << "There should be 3 numbers in the eigval.";
+ CHECK_EQ(param.eigvec.ndim(), 9) << "There should be 9 numbers in the eigvec.";
+ CHECK_EQ(inputs[0].ndim(), 3);
+ CHECK_EQ(inputs[0].size(2), 3);
+ int H = inputs[0].size(0);
+ int W = inputs[0].size(1);
+ Stream<cpu> *s = ctx.get_stream<cpu>();
+ Random<cpu> *prnd = ctx.requested[0].get_random<cpu, real_t>(s);
+ std::normal_distribution<float> dist(0, param.alpha_std);
+ float alpha_r = dist(prnd->GetRndEngine());
+ float alpha_g = dist(prnd->GetRndEngine());
+ float alpha_b = dist(prnd->GetRndEngine());
+ AdjustLightingImpl(outputs[0].dptr<uint8_t>(), inputs[0].dptr<uint8_t>(),
+ alpha_r, alpha_g, alpha_b,
+ param.eigval, param.eigvec, H, W);
}
diff --git a/src/operator/image/image_random.cc b/src/operator/image/image_random.cc
index 7ff73284a0..5b47f50313 100644
--- a/src/operator/image/image_random.cc
+++ b/src/operator/image/image_random.cc
@@ -35,9 +35,6 @@ NNVM_REGISTER_OP(_image_to_tensor)
.describe(R"code()code" ADD_FILELINE)
.set_num_inputs(1)
.set_num_outputs(1)
-.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
- return std::vector<ResourceRequest>{ResourceRequest::kRandom};
-})
.set_attr<nnvm::FInferShape>("FInferShape", ToTensorShape)
.set_attr<nnvm::FInferType>("FInferType", ToTensorType)
.set_attr<FCompute>("FCompute<cpu>", ToTensor)
@@ -51,9 +48,6 @@ NNVM_REGISTER_OP(_image_normalize)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser(ParamParser<NormalizeParam>)
-.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
- return std::vector<ResourceRequest>{ResourceRequest::kRandom};
-})
.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
@@ -126,5 +120,42 @@ NNVM_REGISTER_OP(_image_random_saturation)
.add_argument("data", "NDArray-or-Symbol", "The input.")
.add_arguments(RandomSaturationParam::__FIELDS__());
+DMLC_REGISTER_PARAMETER(AdjustLightingParam);
+NNVM_REGISTER_OP(_image_adjust_lighting)
+.describe(R"code(Adjust the lighting level of the input. Follow the AlexNet style.)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<AdjustLightingParam>)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+ [](const NodeAttrs& attrs){
+ return std::vector<std::pair<int, int> >{{0, 0}};
+ })
+.set_attr<FCompute>("FCompute<cpu>", AdjustLighting)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_copy" })
+.add_argument("data", "NDArray-or-Symbol", "The input.")
+.add_arguments(AdjustLightingParam::__FIELDS__());
+
+DMLC_REGISTER_PARAMETER(RandomLightingParam);
+NNVM_REGISTER_OP(_image_random_lighting)
+.describe(R"code(Randomly add PCA noise. Follow the AlexNet style.)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<RandomLightingParam>)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
+ return std::vector<ResourceRequest>{ResourceRequest::kRandom};
+})
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+ [](const NodeAttrs& attrs){
+ return std::vector<std::pair<int, int> >{{0, 0}};
+ })
+.set_attr<FCompute>("FCompute<cpu>", RandomLighting)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_copy" })
+.add_argument("data", "NDArray-or-Symbol", "The input.")
+.add_arguments(RandomLightingParam::__FIELDS__());
+
} // namespace op
} // namespace mxnet
diff --git a/tests/python/unittest/test_gluon_data_vision.py b/tests/python/unittest/test_gluon_data_vision.py
new file mode 100644
index 0000000000..0c9e5c1c11
--- /dev/null
+++ b/tests/python/unittest/test_gluon_data_vision.py
@@ -0,0 +1,40 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+from __future__ import print_function
+import mxnet as mx
+import mxnet.ndarray as nd
+import numpy as np
+from mxnet import gluon
+from mxnet.gluon.data.vision.transforms import AdjustLighting
+from mxnet.test_utils import assert_almost_equal
+
+def test_adjust_lighting():
+ 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)
+
+if __name__ == '__main__':
+ import nose
+ nose.runmodule()
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services