提交 701827f5 编写于 作者: W wanghaoshuang

Add grad test and python wrapper for crop layer

上级 90ed2004
......@@ -148,7 +148,7 @@ public:
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
TensorShape outShape = outputs[0].shape();
......
......@@ -25,7 +25,7 @@ TEST(Crop, real) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {false, true}) {
FunctionCompare compare(
CpuGpuFuncCompare compare(
test_grad ? "CropGrad" : "Crop",
FuncConfig()
.set<std::vector<uint32_t>>("crop_corner", {0, 1, 1, 1})
......
......@@ -14,7 +14,6 @@ limitations under the License. */
#include "CropLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(crop, CropLayer);
......@@ -24,10 +23,9 @@ bool CropLayer::init(const LayerMap& layerMap,
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
auto& crop_conf = config_.inputs(0).crop_conf();
crop_axis_ = crop_conf.axis();
for (int i = 0; i < crop_conf.offset_size(); i++) {
crop_offsets_[i] = crop_conf.offset(i);
crop_axis_ = config_.axis();
for (int i = 0; i < config_.offset_size(); i++) {
crop_offsets_.push_back(config_.offset(i));
}
// 1. get input_0 shape
......@@ -38,7 +36,6 @@ bool CropLayer::init(const LayerMap& layerMap,
? input0_img_conf.img_size_y()
: input0_img_conf.img_size(),
input0_img_conf.img_size()});
// 2. get output shape from input_1 or crop shap conf
if (config_.inputs_size() == 2) {
auto& input1_img_conf = config_.inputs(1).image_conf();
......@@ -49,19 +46,19 @@ bool CropLayer::init(const LayerMap& layerMap,
: input1_img_conf.img_size(),
input1_img_conf.img_size()});
} else {
targetDims_ = TensorShape({crop_conf.shape(0),
crop_conf.shape(1),
crop_conf.shape(2),
crop_conf.shape(3)});
targetDims_ = TensorShape({config_.shape(0),
config_.shape(1),
config_.shape(2),
config_.shape(3)});
}
// 3. get final crop shape
int dimSize = 4;
for (int i = 0; i < dimSize; i++) {
if (i >= crop_axis_) {
crop_shape_[i] = targetDims_[i];
crop_shape_.push_back(targetDims_[i]);
} else {
crop_shape_[i] = inDims_[i];
crop_shape_.push_back(inDims_[i]);
}
}
......@@ -99,7 +96,7 @@ void CropLayer::setOutDims(const size_t batchSize) {
}
void CropLayer::setTensorDim(const size_t batchSize) {
CHECK_EQ(static_cast<int>(inputLayers_.size()), 1);
CHECK_EQ(static_cast<int>(inputLayers_.size()), 2);
inDims_.setDim(0, batchSize);
int h = inputLayers_[0]->getOutput().getFrameHeight();
if (h != 0) inDims_.setDim(2, h);
......
......@@ -56,7 +56,7 @@ add_test(NAME test_DetectionOutput
add_unittest_without_exec(test_ConvUnify
test_ConvUnify.cpp
LayerGradUtil.cpp)
add_test(NAME test_ConvUnify
COMMAND test_ConvUnify)
################# test_BatchNorm #######################
......
......@@ -1792,6 +1792,34 @@ TEST(Layer, RowConvLayer) {
}
}
TEST(Layer, CropLayer) {
TestConfig config;
// config input_0
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
ImageConfig* img = input->mutable_image_conf();
img->set_channels(4);
img->set_img_size(16);
config.layerConfig.set_axis(2);
config.layerConfig.add_offset(0);
config.layerConfig.add_offset(0);
// config input_1
config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
input = config.layerConfig.add_inputs();
img = input->mutable_image_conf();
img->set_channels(2);
img->set_img_size(8);
// config crop layer
config.layerConfig.set_type("crop");
config.layerConfig.set_name("cropLayer");
for (auto useGpu : {false, true}) {
testLayerGrad(config, "crop", 100, false, useGpu, false);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
......@@ -472,10 +472,16 @@ message LayerConfig {
// blank label used in ctc loss
optional uint32 blank = 52 [default = 0];
// stride parameter for seqlastins layer, AverageLayer, MaxLayer, which
// stride parameter for seqlastins layer, AverageLayer, MaxLayer, which
// controls the scope of pooling operation. can be set > 0.
// leave empty or set to -1 to disable this stride pooling.
optional int32 seq_pool_stride = 53 [default = -1];
// for crop layer
optional int32 axis = 54 [default = 2];
repeated uint32 offset = 55;
repeated uint32 shape = 56;
}
message EvaluatorConfig {
......
......@@ -1986,6 +1986,51 @@ class PadLayer(LayerBase):
self.config.size = out_ch * out_h * out_w
@config_layer('crop')
class CropLayer(LayerBase):
def __init__(self, inputs, axis, offset, shape, name, **xargs):
super(CropLayer, self).__init__(name, 'crop', 0, inputs=inputs, **xargs)
self.conf.axis = axis
self.conf.axis = offset
self.conf.axis = shape
crop = self.inputs[0].crop
self.config.inputs[0].crop_conf.axis = crop.axis
self.config.inputs[0].crop_conf.offset.extend(crop.offset)
self.config.inputs[0].crop_conf.shape.extend(crop.shape)
# get channel, width and height from input_0 layer
input_layer = self.get_input_layer(0)
image_conf = self.config.inputs[0].image_conf
image_conf.img_size = input_layer.width
image_conf.img_size_y = input_layer.height
image_conf.channels = input_layer.size / (input_layer.width *
input_layer.height)
out_ch = image_conf.channels
out_h = image_conf.img_size
out_w = image_conf.img_size_y
if len(self.inputs) == 2:
# get channels, width and height from input_1 layer
input_layer = self.get_input_layer(1)
image_conf = self.config.inputs[1].image_conf
image_conf.img_size = input_layer.width
image_conf.img_size_y = input_layer.height
image_conf.channels = input_layer.size / (input_layer.width *
input_layer.height)
out_ch = image_conf.channels
out_h = image_conf.img_size_y
out_w = image_conf.img_size
else:
# set channels, width and heigth of current layer
if len(shape) > 2:
out_ch = shape[-3]
if len(shape) > 1:
out_h = shape[-2]
if len(shape) > 0:
out_w = shape[-1]
self.set_cnn_layer(name, out_h, out_w, out_ch)
@config_layer('batch_norm')
class BatchNormLayer(LayerBase):
layer_type = 'batch_norm'
......
......@@ -217,6 +217,7 @@ class LayerType(object):
SMOOTH_L1 = 'smooth_l1'
PRELU = 'prelu'
CROP_LAYER = 'crop'
@staticmethod
def is_layer_type(type_name):
......@@ -5853,3 +5854,56 @@ def prelu_layer(input,
layer_type=LayerType.PRELU,
parents=input,
size=l.config.size)
@wrap_name_default()
@layer_support()
def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None):
"""
The crop layer crop images by offset and shape. User can set crop shape by
args 'shape' explicitly or by reference input layer.
The example usage is:
.. code-block:: python
crop = crop_layer(input=[image_input, reference_input], axis=2, offset=[2, 3])
:param input: The input layer.If two inputs were setted,
the second input will be regarded as reference input
:type input: LayerOutput or Sequence
:param axis: start axis to be cropped. To image input layer:
- 0: batch size
- 1: channels
- 2: height
- 3: width
:type partial_sum: int
:param offset: The crop offset
:type offset: Sequence
:param shape: The shape to be cropped. Default is None.
:type shape: Sqquence | None
:param name: Name of this layer.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
input = [input]
elif isinstance(input, Projection):
input = [input]
else:
assert isinstance(input, collections.Sequence)
l = Layer(
inputs=[x.name for x in input],
axis=axis,
offset=offset,
shape=shape,
name=name,
type=LayerType.CROP_LAYER,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name=name,
layer_type=LayerType.CROP_LAYER,
parents=input,
size=l.config.size)
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