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Merge pull request #20429 from rogday:tf_importer_asym_pads

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This commit is contained in:
Alexander Alekhin 2021-07-22 16:44:47 +00:00
commit b42152ffeb
2 changed files with 112 additions and 10 deletions
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112
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -404,12 +404,53 @@ void setKSize(LayerParams &layerParams, const tensorflow::NodeDef &layer)
}
}
void setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer)
void setPadMode(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "padding"))
layerParams.set("pad_mode", getLayerAttr(layer, "padding").s());
}
bool getExplicitPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer, int64_t (&pads)[8])
{
if (!layerParams.has("pad_mode") ||
layerParams.get("pad_mode").getStringValue() != "EXPLICIT")
{
return false;
}
CV_Assert(hasLayerAttr(layer, "explicit_paddings"));
const tensorflow::AttrValue& protoPads = getLayerAttr(layer, "explicit_paddings");
if (protoPads.list().i_size() != 8)
{
CV_Error(Error::StsNotImplemented, "Unsupported asymmetric padding configuration.");
}
int n = sizeof(pads) / sizeof(pads[0]);
for (int i = 0; i < n; ++i)
{
pads[i] = protoPads.list().i(i);
}
if (getDataLayout(layer) != DATA_LAYOUT_NCHW)
{
CV_LOG_DEBUG(NULL, "DNN/TF: Data format " << getLayerAttr(layer, "data_format").s() << ", assuming NHWC.");
// Perhaps, we have NHWC padding dimensions order.
// N H W C
// 0 1 2 3 4 5 6 7
std::swap(pads[2], pads[6]);
std::swap(pads[3], pads[7]);
// N C W H
// 0 1 2 3 4 5 6 7
std::swap(pads[4], pads[6]);
std::swap(pads[5], pads[7]);
// N C H W
// 0 1 2 3 4 5 6 7
}
return true;
}
Pin parsePin(const std::string &name)
{
Pin pin(name);
@ -510,6 +551,7 @@ protected:
private:
void addPermuteLayer(const int* order, const std::string& permName, Pin& inpId);
void setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer, std::string& inputName, float value = 0.);
typedef void (TFImporter::*TFImporterNodeParser)(tensorflow::GraphDef&, const tensorflow::NodeDef&, LayerParams&);
typedef std::map<std::string, TFImporterNodeParser> DispatchMap;
@ -551,6 +593,31 @@ private:
void parseCustomLayer (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams);
};
void TFImporter::setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer, std::string& inputName, float value)
{
setPadMode(layerParams, layer);
int64_t pads[8];
if (!getExplicitPadding(layerParams, layer, pads))
{
return;
}
LayerParams padLp;
padLp.name = layer.name() + "/pad";
padLp.type = "Padding";
padLp.set("paddings", DictValue::arrayInt(pads, sizeof(pads) / sizeof(pads[0])));
padLp.set("value", value);
int id = dstNet.addLayer(padLp.name, padLp.type, padLp);
layer_id[padLp.name] = id;
connect(layer_id, dstNet, parsePin(inputName), id, 0);
inputName = padLp.name;
layerParams.set("pad_mode", "VALID");
}
const TFImporter::DispatchMap TFImporter::buildDispatchMap()
{
static DispatchMap dispatch;
@ -787,7 +854,7 @@ void TFImporter::parseConvolution(tensorflow::GraphDef& net, const tensorflow::N
setStrides(layerParams, layer);
if (!layerParams.has("pad_w") && !layerParams.has("pad_h"))
setPadding(layerParams, layer);
setPadding(layerParams, layer, input);
// The final node of dilated convolution subgraph.
next_layers = getNextLayers(net, name, "BatchToSpaceND");
@ -1232,20 +1299,21 @@ void TFImporter::parseMaxPool(tensorflow::GraphDef& net, const tensorflow::NodeD
{
const std::string& name = layer.name();
const int num_inputs = layer.input_size();
std::string inputName = layer.input(0);
CV_CheckGT(num_inputs, 0, "");
layerParams.set("pool", "max");
setKSize(layerParams, layer);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
setPadding(layerParams, layer, inputName, -std::numeric_limits<float>::infinity());
// Test_TensorFlow_nets.EAST_text_detection/1, NGRAPH/CPU
layerParams.set("ceil_mode", false);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
connectToAllBlobs(layer_id, dstNet, parsePin(inputName), id, num_inputs);
}
void TFImporter::parseAvgPool(tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams)
@ -1258,7 +1326,7 @@ void TFImporter::parseAvgPool(tensorflow::GraphDef& net, const tensorflow::NodeD
layerParams.set("ave_pool_padded_area", false);
setKSize(layerParams, layer);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
setPadMode(layerParams, layer);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
@ -1673,7 +1741,7 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
// input: "weights"
// input: "input"
const std::string& name = layer.name();
std::string name = layer.name();
const int num_inputs = layer.input_size();
CV_CheckEQ(num_inputs, 3, "Expected output shape, weights and input nodes");
@ -1704,7 +1772,21 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
layerParams.set("num_output", kshape[1]);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
setPadMode(layerParams, layer);
int64_t pads[8];
bool explicit_pads = getExplicitPadding(layerParams, layer, pads);
int64_t begs[4] = {};
int64_t ends[4] = {-1, -1, -1, -1};
if (explicit_pads)
{
name += "/deconv";
layerParams.set("pad_mode", "VALID");
for (int i = 2; i < 4; ++i) // begins=[0, 0, a, b], ends=[-1, -1, c, d]
{
begs[i] = pads[2*i];
ends[i] = -1 - pads[2*i + 1];
}
}
// For convolution layer, output shape computes as
// o = 1 + (i - k + 2*p) / s
@ -1721,8 +1803,9 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
const int strideY = layerParams.get<int>("stride_h");
const int strideX = layerParams.get<int>("stride_w");
Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0));
const int outH = outShape.at<int>(1);
const int outW = outShape.at<int>(2);
int shift = (getDataLayout(layer) == DATA_LAYOUT_NCHW);
const int outH = outShape.at<int>(1 + shift) + begs[2] - 1 - ends[2];
const int outW = outShape.at<int>(2 + shift) + begs[3] - 1 - ends[3];
if (layerParams.get<String>("pad_mode") == "SAME")
{
layerParams.set("adj_w", (outW - 1) % strideX);
@ -1738,6 +1821,16 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
// one input only
connect(layer_id, dstNet, parsePin(layer.input(2)), id, 0);
if (explicit_pads) // If we have explicit paddings, remove extra data
{
layerParams.set("begin", DictValue::arrayInt(begs, sizeof(begs) / sizeof(begs[0])));
layerParams.set("end", DictValue::arrayInt(ends, sizeof(ends) / sizeof(ends[0])));
int id = dstNet.addLayer(layer.name(), "Slice", layerParams);
layer_id[layer.name()] = id;
connect(layer_id, dstNet, parsePin(name), id, 0);
}
}
void TFImporter::parseBlockLSTM(tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams)
@ -2717,7 +2810,6 @@ void TFImporter::populateNet()
addConstNodes(netBin, value_id, layers_to_ignore);
addConstNodes(netTxt, value_id, layers_to_ignore);
for (int li = 0; li < layersSize; li++)
{
const tensorflow::NodeDef& layer = net.node(li);

10
modules/dnn/test/test_tf_importer.cpp
View File

@ -203,6 +203,16 @@ TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("keras_pad_concat");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric)
{
runTensorFlowNet("conv2d_asymmetric_pads_nchw");
runTensorFlowNet("conv2d_asymmetric_pads_nhwc");
runTensorFlowNet("max_pool2d_asymmetric_pads_nchw");
runTensorFlowNet("max_pool2d_asymmetric_pads_nhwc");
runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nchw");
runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nhwc");
}
TEST_P(Test_TensorFlow_layers, padding_same)
{
// Reference output values are in range [0.0006, 2.798]