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support flownet2 with arbitary input size

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revise default proto to match the filename in documentations

fix a bug

beautify python codes

fix bug

beautify codes

add test samples with larger/smaller size

remove unless code

using bytearray without creating tmp file

remove useless codes
This commit is contained in:
zhaoyue-zephyrus
2020-07-22 23:07:17 +08:00
parent fe9ff64d64
commit e231be86b7
3 changed files with 41 additions and 15 deletions
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modules/dnn/src/layers/scale_layer.cpp
+15 -9
View File
@@ -15,6 +15,7 @@ Implementation of Scale layer.
#include "../op_inf_engine.hpp"
#include "../ie_ngraph.hpp"
#include <opencv2/imgproc.hpp>
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
@@ -324,7 +325,7 @@ public:
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert_N(inputs.size() == 1, blobs.size() == 3);
CV_Assert_N(blobs[0].total() == 1, blobs[1].total() == total(inputs[0], 1),
CV_Assert_N(blobs[0].total() == 1,
blobs[2].total() == inputs[0][1]);
outputs.assign(1, inputs[0]);
@@ -347,15 +348,20 @@ public:
float* outData = outputs[0].ptr<float>();
Mat data_mean_cpu = blobs[1].clone();
Mat mean_resize = Mat(inputs[0].size[3], inputs[0].size[2], CV_32FC3);
Mat mean_3d = Mat(data_mean_cpu.size[3], data_mean_cpu.size[2], CV_32FC3, data_mean_cpu.ptr<float>(0));
resize(mean_3d, mean_resize, Size(inputs[0].size[3], inputs[0].size[2]));
int new_size[] = {1, mean_resize.channels(), mean_resize.cols, mean_resize.rows};
Mat data_mean_cpu_resize = mean_resize.reshape(1, *new_size);
Mat data_mean_per_channel_cpu = blobs[2].clone();
const int numWeights = data_mean_cpu.total();
const int numWeights = data_mean_cpu_resize.total();
CV_Assert(numWeights != 0);
++num_iter;
if (num_iter <= recompute_mean)
{
data_mean_cpu *= (num_iter - 1);
data_mean_cpu_resize *= (num_iter - 1);
const int batch = inputs[0].size[0];
float alpha = 1.0 / batch;
@@ -364,15 +370,15 @@ public:
Mat inpSlice(1, numWeights, CV_32F, inpData);
inpSlice = alpha * inpSlice;
add(data_mean_cpu.reshape(1, 1), inpSlice, data_mean_cpu.reshape(1, 1));
add(data_mean_cpu_resize.reshape(1, 1), inpSlice, data_mean_cpu_resize.reshape(1, 1));
inpData += numWeights;
}
data_mean_cpu *= (1.0 / num_iter);
data_mean_cpu_resize *= (1.0 / num_iter);
int newsize[] = {blobs[1].size[1], (int)blobs[1].total(2)};
reduce(data_mean_cpu.reshape(1, 2, &newsize[0]), data_mean_per_channel_cpu, 1, REDUCE_SUM, CV_32F);
int newsize[] = {inputs[0].size[1], (int)inputs[0].total(2)};
reduce(data_mean_cpu_resize.reshape(1, 2, &newsize[0]), data_mean_per_channel_cpu, 1, REDUCE_SUM, CV_32F);
int area = blobs[1].total(2);
int area = inputs[0].total(2);
data_mean_per_channel_cpu *= (1.0 / area);
}
@@ -387,7 +393,7 @@ public:
Mat inpSlice(1, numWeights, CV_32F, inpData);
Mat outSlice(1, numWeights, CV_32F, outData);
add(inpSlice, (-1) * data_mean_cpu, outSlice);
add(inpSlice, (-1) * data_mean_cpu_resize, outSlice);
inpData += numWeights;
outData += numWeights;
}
modules/dnn/test/test_layers.cpp
+2
View File
@@ -646,6 +646,8 @@ TEST_P(Test_Caffe_layers, DataAugmentation)
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("data_augmentation", true, false);
testLayerUsingCaffeModels("data_augmentation_2x1", true, false);
testLayerUsingCaffeModels("data_augmentation_8x6", true, false);
}
TEST_P(Test_Caffe_layers, Resample)