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PaddleDetection

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PaddleDetection
“22b4b441e13354238b3dd67d627fe0a2abba1a5b”上不存在“...t2s/exps/git@gitcode.net:paddlepaddle/DeepSpeech.git”
未验证 提交 4eb44380 编写于 作者: J jerrywgz 提交者： GitHub
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Merge branch 'develop' into add_clip_op

上级 e402c0ec 30cc8b7a
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Showing with 2108 addition and 1348 deletion
cmake/operators.cmake
浏览文件 @ 4eb44380
......@@ -52,8 +52,8 @@ function(op_library TARGET)
endif()
if(WITH_MKLDNN)
string(REPLACE "_op" "_mkldnn_op" MKLDNN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_FILE}.cc)
list(APPEND mkldnn_cc_srcs ${MKLDNN_FILE}.cc)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/mkldnn/${MKLDNN_FILE}.cc)
list(APPEND mkldnn_cc_srcs mkldnn/${MKLDNN_FILE}.cc)
endif()
endif()
else()
......
paddle/fluid/API.spec
浏览文件 @ 4eb44380
......@@ -122,7 +122,7 @@ paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None,
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name'], varargs=None, keywords=None, defaults=(0, True, None))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
......@@ -322,10 +322,10 @@ paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_class
paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.box_clip ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
......@@ -362,6 +362,9 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.contrib.Calibrator.__init__ ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.contrib.Calibrator.sample_data ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.Calibrator.save_int8_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.reader.ctr_reader.ctr_reader ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 4eb44380
......@@ -10,8 +10,22 @@ function(pass_library TARGET DEST)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
set(targetPrefix "")
# Get optional argument
set(extraMacroArgs ${ARGN})
list(LENGTH extraMacroArgs numExtraMacroArgs)
if(numExtraMacroArgs GREATER 0)
list(GET extraMacroArgs 0 targetPrefix)
endif()
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass fuse_pass_base ${op_library_DEPS})
if(targetPrefix)
cc_library(${TARGET} SRCS ${targetPrefix}/${TARGET}.cc DEPS graph_pattern_detector pass fuse_pass_base ${op_library_DEPS})
else()
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass fuse_pass_base ${op_library_DEPS})
endif()
# add more DEST here, such as train, dist and collect USE_PASS into a file automatically.
if (${DEST} STREQUAL "base" OR ${DEST} STREQUAL "inference")
message(STATUS "add pass ${TARGET} ${DEST}")
......@@ -62,11 +76,11 @@ foreach (index RANGE 3 6)
endforeach()
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(depthwise_conv_mkldnn_pass base)
pass_library(conv_bias_mkldnn_fuse_pass inference)
pass_library(conv_relu_mkldnn_fuse_pass inference)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
pass_library(mkldnn_placement_pass base mkldnn)
pass_library(depthwise_conv_mkldnn_pass base mkldnn)
pass_library(conv_bias_mkldnn_fuse_pass inference mkldnn)
pass_library(conv_relu_mkldnn_fuse_pass inference mkldnn)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference mkldnn)
endif()
cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
......@@ -86,7 +100,7 @@ cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framewor
cc_test(test_seqpool_concat_fuse_pass SRCS seqpool_concat_fuse_pass_tester.cc DEPS seqpool_concat_fuse_pass framework_proto)
cc_test(test_is_test_pass SRCS is_test_pass_tester.cc DEPS is_test_pass)
if (WITH_MKLDNN)
cc_test(test_depthwise_conv_mkldnn_pass SRCS depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
cc_test(test_depthwise_conv_mkldnn_pass SRCS mkldnn/depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
endif ()
paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.cc → paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h"
#include <functional>
#include <string>
#include <vector>
......
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc → paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.h"
#include <functional>
#include <list>
#include <map>
......
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass_tester.cc → paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc
浏览文件 @ 4eb44380
......@@ -15,8 +15,8 @@
#include <gtest/gtest.h>
#include <string>
#include "paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.h"
namespace paddle {
namespace framework {
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.cc → paddle/fluid/framework/ir/mkldnn/conv_relu_mkldnn_fuse_pass.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_relu_mkldnn_fuse_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/platform/enforce.h"
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc → paddle/fluid/framework/ir/mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_relu_mkldnn_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
......
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.cc → paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
......
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass_tester.cc → paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass_tester.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass.h"
#include <gtest/gtest.h>
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc → paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc
浏览文件 @ 4eb44380
......@@ -12,7 +12,7 @@ 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. */
#include "paddle/fluid/framework/ir/mkldnn_placement_pass.h"
#include "paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.h"
#include <string>
namespace paddle {
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 4eb44380
......@@ -128,9 +128,9 @@ inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv
"${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz" SERIAL)
# bert, max_len=20
set(BERT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/bert20")
download_model_and_data(${BERT_INSTALL_DIR} "bert_model.tar.gz" "bert_data_len20.txt.tar.gz")
# bert, max_len=20, embedding_dim=128
set(BERT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/bert_emb128")
download_model_and_data(${BERT_INSTALL_DIR} "bert_emb128_model.tar.gz" "bert_data_len20.txt.tar.gz")
inference_analysis_api_test(test_analyzer_bert ${BERT_INSTALL_DIR} analyzer_bert_tester.cc SERIAL)
# anakin
......
paddle/fluid/inference/utils/CMakeLists.txt
浏览文件 @ 4eb44380
cc_library(benchmark SRCS benchmark.cc DEPS enforce)
cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
#cc_binary(visualizer SRCS visualizer.cc DEPS analysis
# paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)
cc_binary(visualizer SRCS visualizer.cc DEPS analysis
paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)
paddle/fluid/operators/activation_op.cc
浏览文件 @ 4eb44380
......@@ -14,7 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/activation_op.h"
#include <string>
#include "paddle/fluid/operators/mkldnn_activation_op.h"
#include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h"
#include "paddle/fluid/platform/port.h"
namespace paddle {
......
paddle/fluid/operators/beam_search_op.cc
浏览文件 @ 4eb44380
......@@ -51,6 +51,9 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("selected_scores",
"A LoDTensor containing the accumulated scores corresponding to "
"Output(selected_ids).");
AddOutput(
"parent_idx",
"A Tensor preserving the selected_ids' parent indice in pre_ids.");
// Attributes stored in AttributeMap
AddAttr<int>("level", "the level of LoDTensor");
......
paddle/fluid/operators/beam_search_op.h
浏览文件 @ 4eb44380
......@@ -41,13 +41,15 @@ class BeamSearchOpKernel : public framework::OpKernel<T> {
auto selected_ids = context.Output<framework::LoDTensor>("selected_ids");
auto selected_scores =
context.Output<framework::LoDTensor>("selected_scores");
auto* parent_idx = context.Output<framework::Tensor>("parent_idx");
PADDLE_ENFORCE_NOT_NULL(selected_ids);
PADDLE_ENFORCE_NOT_NULL(selected_scores);
PADDLE_ENFORCE_NOT_NULL(parent_idx);
math::BeamSearchFunctor<DeviceContext, T> alg;
alg(context.template device_context<DeviceContext>(), pre_ids, pre_scores,
ids, scores, selected_ids, selected_scores, level, beam_size, end_id,
is_accumulated);
ids, scores, selected_ids, selected_scores, parent_idx, level,
beam_size, end_id, is_accumulated);
}
};
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 4eb44380
......@@ -32,6 +32,7 @@ polygon_box_transform_op.cu)
detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
detection_library(box_clip_op SRCS box_clip_op.cc box_clip_op.cu)
detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc)
if(WITH_GPU)
detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub)
......
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @ 4eb44380
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/detection/box_coder_op.h"
#include <vector>
namespace paddle {
namespace operators {
......@@ -32,32 +33,57 @@ class BoxCoderOp : public framework::OperatorWithKernel {
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
"The rank of Input of PriorBoxVar must be 2");
"The rank of Input PriorBox must be 2");
PADDLE_ENFORCE_EQ(prior_box_dims[1], 4,
"The shape of PriorBox is [N, 4]");
if (ctx->HasInput("PriorBoxVar")) {
auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
PADDLE_ENFORCE(
prior_box_var_dims.size() == 1 || prior_box_var_dims.size() == 2,
"Input(PriorBoxVar) of BoxCoderOp should be 1 or 2.");
if (prior_box_var_dims.size() == 1) {
PADDLE_ENFORCE_EQ(
prior_box_var_dims[0], 4,
"The 1st dimension of Input(PriorBoxVar) should be 4"
"when the rank is 1.");
} else {
PADDLE_ENFORCE_EQ(
prior_box_dims, prior_box_var_dims,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox when the rank is 2.)");
}
}
}
auto code_type =
GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
if (code_type == BoxCodeType::kEncodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
"The rank of Input of TargetBox must be 2");
PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
"The shape of TargetBox is [M, 4]");
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
"The rank of Input of TargetBox must be 3");
auto code_type = GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
int axis = ctx->Attrs().Get<int>("axis");
if (code_type == BoxCodeType::kEncodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
"The rank of Input TargetBox must be 2");
PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
"The shape of TargetBox is [M, 4]");
ctx->SetOutputDim(
"OutputBox",
framework::make_ddim({target_box_dims[0], prior_box_dims[0], 4}));
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
"The rank of Input TargetBox must be 3");
if (axis == 0) {
PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
} else if (axis == 1) {
PADDLE_ENFORCE_EQ(target_box_dims[0], prior_box_dims[0]);
} else {
PADDLE_THROW("axis must be 0 or 1.");
}
PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
ctx->ShareDim("TargetBox", /*->*/ "OutputBox");
}
if (code_type == BoxCodeType::kDecodeCenterSize && axis == 1) {
ctx->ShareLoD("PriorBox", /*->*/ "OutputBox");
} else {
ctx->ShareLoD("TargetBox", /*->*/ "OutputBox");
}
ctx->SetOutputDim(
"OutputBox",
framework::make_ddim({target_box_dims[0], prior_box_dims[0], 4}));
ctx->ShareLoD("TargetBox", /*->*/ "OutputBox");
}
};
......@@ -100,6 +126,21 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default true) "
"whether treat the priorbox as a noramlized box")
.SetDefault(true);
AddAttr<int>("axis",
"(int, default 0)"
"which axis in PriorBox to broadcast for box decode,"
"for example, if axis is 0 and TargetBox has shape"
"[N, M, 4] and PriorBox has shape [M, 4], then PriorBox "
"will broadcast to [N, M, 4] for decoding. It is only valid"
"when code type is decode_center_size")
.SetDefault(0)
.InEnum({0, 1});
AddAttr<std::vector<float>>(
"variance",
"(vector<float>, default {}),"
"variance of prior box with shape [4]. PriorBoxVar and variance can"
"not be provided at the same time.")
.SetDefault(std::vector<float>{});
AddOutput("OutputBox",
"(LoDTensor or Tensor) "
"When code_type is 'encode_center_size', the output tensor of "
......@@ -138,7 +179,11 @@ where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
encoded/decoded coordinates, width and height.
encoded/decoded coordinates, width and height.
During Box Decoding, two modes for broadcast are supported. Say target box has
shape [N, M, 4], and the shape of prior box can be [N, 4] or [M, 4]. Then prior
box will broadcast to target box along the assigned axis.
)DOC");
}
};
......
paddle/fluid/operators/detection/box_coder_op.cu
浏览文件 @ 4eb44380
......@@ -9,6 +9,9 @@ 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. */
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/detection/box_coder_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -16,11 +19,11 @@ namespace paddle {
namespace operators {
template <typename T>
__global__ void EncodeCenterSizeKernel(const T* prior_box_data,
const T* prior_box_var_data,
const T* target_box_data, const int row,
const int col, const int len,
const bool normalized, T* output) {
__global__ void EncodeCenterSizeKernel(
const T* prior_box_data, const T* prior_box_var_data,
const T* target_box_data, const int row, const int col, const int len,
const bool normalized, const T prior_box_var_size, const float* variance,
const int var_size, T* output) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < row * col) {
const int row_idx = idx / col;
......@@ -30,11 +33,9 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
T prior_box_height = prior_box_data[col_idx * len + 3] -
prior_box_data[col_idx * len + 1] +
(normalized == false);
T prior_box_center_x =
(prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
prior_box_data[col_idx * len + 1]) /
2;
T prior_box_center_x = prior_box_data[col_idx * len] + prior_box_width / 2;
T prior_box_center_y =
prior_box_data[col_idx * len + 1] + prior_box_height / 2;
T target_box_center_x =
(target_box_data[row_idx * len + 2] + target_box_data[row_idx * len]) /
......@@ -55,58 +56,73 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
if (prior_box_var_data) {
output[idx * len] /= prior_box_var_data[col_idx * len];
output[idx * len + 1] /= prior_box_var_data[col_idx * len + 1];
output[idx * len + 2] /= prior_box_var_data[col_idx * len + 2];
output[idx * len + 3] /= prior_box_var_data[col_idx * len + 3];
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = col_idx * len;
}
output[idx * len] /= prior_box_var_data[prior_var_offset];
output[idx * len + 1] /= prior_box_var_data[prior_var_offset + 1];
output[idx * len + 2] /= prior_box_var_data[prior_var_offset + 2];
output[idx * len + 3] /= prior_box_var_data[prior_var_offset + 3];
} else if (var_size == 4) {
for (int k = 0; k < 4; ++k) {
output[idx * len + k] /= static_cast<T>(variance[k]);
}
}
}
}
template <typename T>
__global__ void DecodeCenterSizeKernel(const T* prior_box_data,
const T* prior_box_var_data,
const T* target_box_data, const int row,
const int col, const int len,
const bool normalized, T* output) {
__global__ void DecodeCenterSizeKernel(
const T* prior_box_data, const T* prior_box_var_data,
const T* target_box_data, const int row, const int col, const int len,
const bool normalized, const T prior_box_var_size, const float* variance,
const int var_size, const int axis, T* output) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
int prior_box_offset = 0;
if (idx < row * col) {
const int col_idx = idx % col;
T prior_box_width = prior_box_data[col_idx * len + 2] -
prior_box_data[col_idx * len] + (normalized == false);
T prior_box_height = prior_box_data[col_idx * len + 3] -
prior_box_data[col_idx * len + 1] +
const int row_idx = idx / col;
prior_box_offset = axis == 0 ? col_idx * len : row_idx * len;
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
T prior_box_height = prior_box_data[prior_box_offset + 3] -
prior_box_data[prior_box_offset + 1] +
(normalized == false);
T prior_box_center_x =
(prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
prior_box_data[col_idx * len + 1]) /
2;
prior_box_data[prior_box_offset] + prior_box_width / 2;
T prior_box_center_y =
prior_box_data[prior_box_offset + 1] + prior_box_height / 2;
T target_box_width, target_box_height;
T target_box_center_x, target_box_center_y;
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var_data) {
target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
target_box_data[idx * len + 2]) *
prior_box_width;
target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
target_box_data[idx * len + 3]) *
prior_box_height;
target_box_center_x = prior_box_var_data[col_idx * len] *
target_box_data[idx * len] * prior_box_width +
prior_box_center_x;
target_box_center_y = prior_box_var_data[col_idx * len + 1] *
target_box_data[idx * len + 1] *
prior_box_height +
prior_box_center_y;
} else {
target_box_width = exp(target_box_data[idx * len + 2]) * prior_box_width;
target_box_height =
exp(target_box_data[idx * len + 3]) * prior_box_height;
target_box_center_x =
target_box_data[idx * len] * prior_box_width + prior_box_center_x;
target_box_center_y = target_box_data[idx * len + 1] * prior_box_height +
prior_box_center_y;
int prior_var_offset = 0;
if (prior_box_var_size == 2) {
prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
}
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
box_var_h = prior_box_var_data[prior_var_offset + 3];
} else if (var_size == 4) {
box_var_x = static_cast<T>(variance[0]);
box_var_y = static_cast<T>(variance[1]);
box_var_w = static_cast<T>(variance[2]);
box_var_h = static_cast<T>(variance[3]);
}
target_box_width =
exp(box_var_w * target_box_data[idx * len + 2]) * prior_box_width;
target_box_height =
exp(box_var_h * target_box_data[idx * len + 3]) * prior_box_height;
target_box_center_x =
box_var_x * target_box_data[idx * len] * prior_box_width +
prior_box_center_x;
target_box_center_y =
box_var_y * target_box_data[idx * len + 1] * prior_box_height +
prior_box_center_y;
output[idx * len] = target_box_center_x - target_box_width / 2;
output[idx * len + 1] = target_box_center_y - target_box_height / 2;
......@@ -127,36 +143,64 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
auto* output_box = context.Output<framework::Tensor>("OutputBox");
std::vector<float> variance = context.Attr<std::vector<float>>("variance");
const T* prior_box_data = prior_box->data<T>();
const T* target_box_data = target_box->data<T>();
const T* prior_box_var_data = nullptr;
if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
auto prior_box_var_size = 0;
if (prior_box_var) {
PADDLE_ENFORCE(variance.empty(),
"Input 'PriorBoxVar' and attribute 'variance' should not"
"be used at the same time.");
prior_box_var_data = prior_box_var->data<T>();
prior_box_var_size = prior_box_var->dims().size();
}
if (!(variance.empty())) {
PADDLE_ENFORCE(static_cast<int>(variance.size()) == 4,
"Size of attribute 'variance' should be 4");
}
if (target_box->lod().size()) {
PADDLE_ENFORCE_EQ(target_box->lod().size(), 1,
"Only support 1 level of LoD.");
}
const int var_size = static_cast<int>(variance.size());
auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
bool normalized = context.Attr<bool>("box_normalized");
int axis = context.Attr<int>("axis");
auto row = target_box->dims()[0];
auto col = prior_box->dims()[0];
if (code_type == BoxCodeType::kDecodeCenterSize) {
col = target_box->dims()[1];
}
auto len = prior_box->dims()[1];
int block = 512;
int grid = (row * col + block - 1) / block;
auto& device_ctx = context.cuda_device_context();
auto& allocator =
platform::DeviceTemporaryAllocator::Instance().Get(device_ctx);
int bytes = var_size * sizeof(float);
auto dev_var = allocator.Allocate(bytes);
float* dev_var_data = reinterpret_cast<float*>(dev_var->ptr());
auto cplace = platform::CPUPlace();
const auto gplace = boost::get<platform::CUDAPlace>(context.GetPlace());
memory::Copy(gplace, dev_var_data, cplace, &variance[0], bytes,
device_ctx.stream());
output_box->mutable_data<T>({row, col, len}, context.GetPlace());
T* output = output_box->data<T>();
auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
bool normalized = context.Attr<bool>("box_normalized");
if (code_type == BoxCodeType::kEncodeCenterSize) {
EncodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
prior_box_data, prior_box_var_data, target_box_data, row, col, len,
normalized, output);
normalized, prior_box_var_size, dev_var_data, var_size, output);
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
DecodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
prior_box_data, prior_box_var_data, target_box_data, row, col, len,
normalized, output);
normalized, prior_box_var_size, dev_var_data, var_size, axis, output);
}
}
};
......
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @ 4eb44380
......@@ -11,6 +11,7 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -34,7 +35,8 @@ class BoxCoderKernel : public framework::OpKernel<T> {
void EncodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
const bool normalized, T* output) const {
const bool normalized,
const std::vector<float> variance, T* output) const {
int64_t row = target_box->dims()[0];
int64_t col = prior_box->dims()[0];
int64_t len = prior_box->dims()[1];
......@@ -53,10 +55,9 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T prior_box_height = prior_box_data[j * len + 3] -
prior_box_data[j * len + 1] +
(normalized == false);
T prior_box_center_x =
(prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
T prior_box_center_x = prior_box_data[j * len] + prior_box_width / 2;
T prior_box_center_y =
(prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
prior_box_data[j * len + 1] + prior_box_height / 2;
T target_box_center_x =
(target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
......@@ -78,10 +79,18 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height));
if (prior_box_var) {
output[offset] /= prior_box_var_data[j * len];
output[offset + 1] /= prior_box_var_data[j * len + 1];
output[offset + 2] /= prior_box_var_data[j * len + 2];
output[offset + 3] /= prior_box_var_data[j * len + 3];
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
prior_var_offset = j * len;
}
output[offset] /= prior_box_var_data[prior_var_offset];
output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
output[offset + 3] /= prior_box_var_data[prior_var_offset + 3];
} else if (!(variance.empty())) {
for (int k = 0; k < 4; ++k) {
output[offset + k] /= static_cast<T>(variance[k]);
}
}
}
}
......@@ -89,58 +98,71 @@ class BoxCoderKernel : public framework::OpKernel<T> {
void DecodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
const bool normalized, T* output) const {
const bool normalized, const int axis,
const std::vector<float> variance, T* output) const {
int64_t row = target_box->dims()[0];
int64_t col = prior_box->dims()[0];
int64_t len = prior_box->dims()[1];
int64_t col = target_box->dims()[1];
int64_t len = target_box->dims()[2];
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
int prior_box_offset = 0;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
size_t offset = i * col * len + j * len;
T prior_box_width = prior_box_data[j * len + 2] -
prior_box_data[j * len] + (normalized == false);
T prior_box_height = prior_box_data[j * len + 3] -
prior_box_data[j * len + 1] +
if (axis == 0) {
prior_box_offset = j * len;
} else if (axis == 1) {
prior_box_offset = i * len;
}
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
T prior_box_height = prior_box_data[prior_box_offset + 3] -
prior_box_data[prior_box_offset + 1] +
(normalized == false);
T prior_box_center_x =
(prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
prior_box_data[prior_box_offset] + prior_box_width / 2;
T prior_box_center_y =
(prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
prior_box_data[prior_box_offset + 1] + prior_box_height / 2;
T target_box_center_x = 0, target_box_center_y = 0;
T target_box_width = 0, target_box_height = 0;
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var) {
target_box_center_x = prior_box_var_data[j * len] *
target_box_data[offset] * prior_box_width +
prior_box_center_x;
target_box_center_y = prior_box_var_data[j * len + 1] *
target_box_data[offset + 1] *
prior_box_height +
prior_box_center_y;
target_box_width = std::exp(prior_box_var_data[j * len + 2] *
target_box_data[offset + 2]) *
prior_box_width;
target_box_height = std::exp(prior_box_var_data[j * len + 3] *
target_box_data[offset + 3]) *
prior_box_height;
} else {
target_box_center_x =
target_box_data[offset] * prior_box_width + prior_box_center_x;
target_box_center_y = target_box_data[offset + 1] * prior_box_height +
prior_box_center_y;
target_box_width =
std::exp(target_box_data[offset + 2]) * prior_box_width;
target_box_height =
std::exp(target_box_data[offset + 3]) * prior_box_height;
int prior_var_offset = 0;
if (prior_box_var->dims().size() == 2) {
if (axis == 0)
prior_var_offset = j * len;
else if (axis == 1)
prior_var_offset = i * len;
}
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
box_var_h = prior_box_var_data[prior_var_offset + 3];
} else if (!(variance.empty())) {
box_var_x = static_cast<T>(variance[0]);
box_var_y = static_cast<T>(variance[1]);
box_var_w = static_cast<T>(variance[2]);
box_var_h = static_cast<T>(variance[3]);
}
target_box_center_x =
box_var_x * target_box_data[offset] * prior_box_width +
prior_box_center_x;
target_box_center_y =
box_var_y * target_box_data[offset + 1] * prior_box_height +
prior_box_center_y;
target_box_width =
std::exp(box_var_w * target_box_data[offset + 2]) * prior_box_width;
target_box_height = std::exp(box_var_h * target_box_data[offset + 3]) *
prior_box_height;
output[offset] = target_box_center_x - target_box_width / 2;
output[offset + 1] = target_box_center_y - target_box_height / 2;
......@@ -157,26 +179,40 @@ class BoxCoderKernel : public framework::OpKernel<T> {
auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
auto* output_box = context.Output<framework::Tensor>("OutputBox");
std::vector<float> variance = context.Attr<std::vector<float>>("variance");
const int axis = context.Attr<int>("axis");
if (target_box->lod().size()) {
PADDLE_ENFORCE_EQ(target_box->lod().size(), 1UL,
"Only support 1 level of LoD.");
}
if (prior_box_var) {
PADDLE_ENFORCE(variance.empty(),
"Input 'PriorBoxVar' and attribute 'variance' should not"
"be used at the same time.");
}
if (!(variance.empty())) {
PADDLE_ENFORCE(static_cast<int>(variance.size()) == 4,
"Size of attribute 'variance' should be 4");
}
auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
bool normalized = context.Attr<bool>("box_normalized");
auto row = target_box->dims()[0];
auto col = prior_box->dims()[0];
if (code_type == BoxCodeType::kDecodeCenterSize) {
col = target_box->dims()[1];
}
auto len = prior_box->dims()[1];
output_box->mutable_data<T>({row, col, len}, context.GetPlace());
auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
bool normalized = context.Attr<bool>("box_normalized");
T* output = output_box->data<T>();
if (code_type == BoxCodeType::kEncodeCenterSize) {
EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
output);
variance, output);
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
DecodeCenterSize(target_box, prior_box, prior_box_var, normalized,
output);
DecodeCenterSize(target_box, prior_box, prior_box_var, normalized, axis,
variance, output);
}
}
};
......
paddle/fluid/operators/yolov3_loss_op.cc → paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ 4eb44380
......@@ -9,7 +9,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/operators/detection/yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
......@@ -29,23 +29,33 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Input(GTLabel) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Loss"),
"Output(Loss) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("ObjectnessMask"),
"Output(ObjectnessMask) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("GTMatchMask"),
"Output(GTMatchMask) of Yolov3LossOp should not be null.");
auto dim_x = ctx->GetInputDim("X");
auto dim_gtbox = ctx->GetInputDim("GTBox");
auto dim_gtlabel = ctx->GetInputDim("GTLabel");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
int anchor_num = anchors.size() / 2;
auto anchor_mask = ctx->Attrs().Get<std::vector<int>>("anchor_mask");
int mask_num = anchor_mask.size();
auto class_num = ctx->Attrs().Get<int>("class_num");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
"Input(X) dim[3] and dim[4] should be euqal.");
PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(
dim_x[1], mask_num * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
"Input(GTBox) should be a 3-D tensor");
PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
"Input(GTBox) should be a 2-D tensor");
"Input(GTLabel) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
"Input(GTBox) and Input(GTLabel) dim[0] should be same");
PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
......@@ -54,11 +64,22 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Attr(anchors) length should be greater then 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
"Attr(anchors) length should be even integer.");
for (size_t i = 0; i < anchor_mask.size(); i++) {
PADDLE_ENFORCE_LT(
anchor_mask[i], anchor_num,
"Attr(anchor_mask) should not crossover Attr(anchors).");
}
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
std::vector<int64_t> dim_out({1});
std::vector<int64_t> dim_out({dim_x[0]});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
std::vector<int64_t> dim_obj_mask({dim_x[0], mask_num, dim_x[2], dim_x[3]});
ctx->SetOutputDim("ObjectnessMask", framework::make_ddim(dim_obj_mask));
std::vector<int64_t> dim_gt_match_mask({dim_gtbox[0], dim_gtbox[1]});
ctx->SetOutputDim("GTMatchMask", framework::make_ddim(dim_gt_match_mask));
}
protected:
......@@ -73,11 +94,11 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YOLO v3 loss operator, "
"The input tensor of YOLOv3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box");
"keys of each anchor box");
AddInput("GTBox",
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
......@@ -89,32 +110,39 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("GTLabel",
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shoudl be an integer to indicate the "
"and each element should be an integer to indicate the "
"box class id.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]");
"This is a 1-D tensor with shape of [N]");
AddOutput("ObjectnessMask",
"This is an intermediate tensor with shape of [N, M, H, W], "
"M is the number of anchor masks. This parameter caches the "
"mask for calculate objectness loss in gradient kernel.")
.AsIntermediate();
AddOutput("GTMatchMask",
"This is an intermediate tensor with shape of [N, B], "
"B is the max box number of GT boxes. This parameter caches "
"matched mask index of each GT boxes for gradient calculate.")
.AsIntermediate();
AddAttr<int>("class_num", "The number of classes to predict.");
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.");
"it will be parsed pair by pair.")
.SetDefault(std::vector<int>{});
AddAttr<std::vector<int>>("anchor_mask",
"The mask index of anchors used in "
"current YOLOv3 loss calculation.")
.SetDefault(std::vector<int>{});
AddAttr<int>("downsample_ratio",
"The downsample ratio from network input to YOLOv3 loss "
"input, so 32, 16, 8 should be set for the first, second, "
"and thrid YOLOv3 loss operators.")
.SetDefault(32);
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
.SetDefault(1.0);
AddAttr<float>(
"loss_weight_conf_target",
"The weight of confidence score loss in locations with target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_conf_notarget",
"The weight of confidence score loss in locations without "
"target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_class", "The weight of classification loss.")
.SetDefault(1.0);
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
......@@ -147,17 +175,28 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
confidence score loss, and classification loss. The L2 loss is used for
box coordinates (w, h), and sigmoid cross entropy loss is used for box
coordinates (x, y), confidence score loss and classification loss.
Each groud truth box find a best matching anchor box in all anchors,
prediction of this anchor box will incur all three parts of losses, and
prediction of anchor boxes with no GT box matched will only incur objectness
loss.
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follow.
$$
weight_{box} = 2.0 - t_w * t_h
$$
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
$$
)DOC");
}
......@@ -196,6 +235,8 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetInput("ObjectnessMask", Output("ObjectnessMask"));
op->SetInput("GTMatchMask", Output("GTMatchMask"));
op->SetAttrMap(Attrs());
......
paddle/fluid/operators/detection/yolov3_loss_op.h 0 → 100644
浏览文件 @ 4eb44380
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T>
static inline bool LessEqualZero(T x) {
return x < 1e-6;
}
template <typename T>
static T SigmoidCrossEntropy(T x, T label) {
return (x > 0 ? x : 0.0) - x * label + std::log(1.0 + std::exp(-std::abs(x)));
}
template <typename T>
static T L2Loss(T x, T y) {
return 0.5 * (y - x) * (y - x);
}
template <typename T>
static T SigmoidCrossEntropyGrad(T x, T label) {
return 1.0 / (1.0 + std::exp(-x)) - label;
}
template <typename T>
static T L2LossGrad(T x, T y) {
return x - y;
}
static int GetMaskIndex(std::vector<int> mask, int val) {
for (size_t i = 0; i < mask.size(); i++) {
if (mask[i] == val) {
return i;
}
}
return -1;
}
template <typename T>
struct Box {
T x, y, w, h;
};
template <typename T>
static inline T sigmoid(T x) {
return 1.0 / (1.0 + std::exp(-x));
}
template <typename T>
static inline Box<T> GetYoloBox(const T* x, std::vector<int> anchors, int i,
int j, int an_idx, int grid_size,
int input_size, int index, int stride) {
Box<T> b;
b.x = (i + sigmoid<T>(x[index])) / grid_size;
b.y = (j + sigmoid<T>(x[index + stride])) / grid_size;
b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
return b;
}
template <typename T>
static inline Box<T> GetGtBox(const T* gt, int batch, int max_boxes, int idx) {
Box<T> b;
b.x = gt[(batch * max_boxes + idx) * 4];
b.y = gt[(batch * max_boxes + idx) * 4 + 1];
b.w = gt[(batch * max_boxes + idx) * 4 + 2];
b.h = gt[(batch * max_boxes + idx) * 4 + 3];
return b;
}
template <typename T>
static inline T BoxOverlap(T c1, T w1, T c2, T w2) {
T l1 = c1 - w1 / 2.0;
T l2 = c2 - w2 / 2.0;
T left = l1 > l2 ? l1 : l2;
T r1 = c1 + w1 / 2.0;
T r2 = c2 + w2 / 2.0;
T right = r1 < r2 ? r1 : r2;
return right - left;
}
template <typename T>
static inline T CalcBoxIoU(Box<T> b1, Box<T> b2) {
T w = BoxOverlap(b1.x, b1.w, b2.x, b2.w);
T h = BoxOverlap(b1.y, b1.h, b2.y, b2.h);
T inter_area = (w < 0 || h < 0) ? 0.0 : w * h;
T union_area = b1.w * b1.h + b2.w * b2.h - inter_area;
return inter_area / union_area;
}
static inline int GetEntryIndex(int batch, int an_idx, int hw_idx, int an_num,
int an_stride, int stride, int entry) {
return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
}
template <typename T>
static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
std::vector<int> anchors, int an_idx,
int box_idx, int gi, int gj, int grid_size,
int input_size, int stride) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
loss[0] += L2Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L2Loss<T>(input[box_idx + 3 * stride], th) * scale;
}
template <typename T>
static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
Box<T> gt, std::vector<int> anchors,
int an_idx, int box_idx, int gi, int gj,
int grid_size, int input_size, int stride) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
input_grad[box_idx] =
SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
input_grad[box_idx + stride] =
SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
input_grad[box_idx + 2 * stride] =
L2LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
input_grad[box_idx + 3 * stride] =
L2LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
}
template <typename T>
static inline void CalcLabelLoss(T* loss, const T* input, const int index,
const int label, const int class_num,
const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? 1.0 : 0.0);
}
}
template <typename T>
static inline void CalcLabelLossGrad(T* input_grad, const T loss,
const T* input, const int index,
const int label, const int class_num,
const int stride) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
input_grad[index + i * stride] =
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
}
}
template <typename T>
static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
const int n, const int an_num, const int h,
const int w, const int stride,
const int an_stride) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
// positive sample: obj = 1
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0);
} else if (obj > -0.5) {
// negetive sample: obj = 0
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
}
}
}
objness += stride;
input += an_stride;
}
}
}
template <typename T>
static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
const T* input, const T* objness,
const int n, const int an_num,
const int h, const int w,
const int stride, const int an_stride) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * loss[i];
} else if (obj > -0.5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
}
}
}
objness += stride;
input += an_stride;
input_grad += an_stride;
}
}
}
template <typename T>
static void inline GtValid(bool* valid, const T* gtbox, const int n,
const int b) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < b; j++) {
if (LessEqualZero(gtbox[j * 4 + 2]) || LessEqualZero(gtbox[j * 4 + 3])) {
valid[j] = false;
} else {
valid[j] = true;
}
}
valid += b;
gtbox += b * 4;
}
}
template <typename T>
class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* loss = ctx.Output<Tensor>("Loss");
auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
const int mask_num = anchor_mask.size();
const int b = gt_box->dims()[1];
int input_size = downsample_ratio * h;
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
T* loss_data = loss->mutable_data<T>({n}, ctx.GetPlace());
memset(loss_data, 0, loss->numel() * sizeof(T));
T* obj_mask_data =
objness_mask->mutable_data<T>({n, mask_num, h, w}, ctx.GetPlace());
memset(obj_mask_data, 0, objness_mask->numel() * sizeof(T));
int* gt_match_mask_data =
gt_match_mask->mutable_data<int>({n, b}, ctx.GetPlace());
// calc valid gt box mask, avoid calc duplicately in following code
Tensor gt_valid_mask;
bool* gt_valid_mask_data =
gt_valid_mask.mutable_data<bool>({n, b}, ctx.GetPlace());
GtValid<T>(gt_valid_mask_data, gt_box_data, n, b);
for (int i = 0; i < n; i++) {
for (int j = 0; j < mask_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
// each predict box find a best match gt box, if overlap is bigger
// then ignore_thresh, ignore the objectness loss.
int box_idx =
GetEntryIndex(i, j, k * w + l, mask_num, an_stride, stride, 0);
Box<T> pred = GetYoloBox(input_data, anchors, l, k, anchor_mask[j],
h, input_size, box_idx, stride);
T best_iou = 0;
for (int t = 0; t < b; t++) {
if (!gt_valid_mask_data[i * b + t]) {
continue;
}
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
T iou = CalcBoxIoU(pred, gt);
if (iou > best_iou) {
best_iou = iou;
}
}
// If best IoU is bigger then ignore_thresh,
// ignore the objectness loss.
if (best_iou > ignore_thresh) {
int obj_idx = (i * mask_num + j) * stride + k * w + l;
obj_mask_data[obj_idx] = static_cast<T>(-1);
}
// all losses should be calculated if best IoU
// is bigger then truth thresh, but currently,
// truth thresh is an unreachable value as 1.0.
}
}
}
for (int t = 0; t < b; t++) {
if (!gt_valid_mask_data[i * b + t]) {
gt_match_mask_data[i * b + t] = -1;
continue;
}
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
Box<T> gt_shift = gt;
gt_shift.x = 0.0;
gt_shift.y = 0.0;
T best_iou = 0.0;
int best_n = 0;
// each gt box find a best match anchor box as positive sample,
// for positive sample, all losses should be calculated, and for
// other samples, only objectness loss is required.
for (int an_idx = 0; an_idx < an_num; an_idx++) {
Box<T> an_box;
an_box.x = 0.0;
an_box.y = 0.0;
an_box.w = anchors[2 * an_idx] / static_cast<T>(input_size);
an_box.h = anchors[2 * an_idx + 1] / static_cast<T>(input_size);
float iou = CalcBoxIoU<T>(an_box, gt_shift);
if (iou > best_iou) {
best_iou = iou;
best_n = an_idx;
}
}
int mask_idx = GetMaskIndex(anchor_mask, best_n);
gt_match_mask_data[i * b + t] = mask_idx;
if (mask_idx >= 0) {
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLoss<T>(loss_data + i, input_data, gt, anchors, best_n,
box_idx, gi, gj, h, input_size, stride);
int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
obj_mask_data[obj_idx] = 1.0;
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label,
class_num, stride);
}
}
}
CalcObjnessLoss<T>(loss_data, input_data + 4 * stride, obj_mask_data, n,
mask_num, h, w, stride, an_stride);
}
};
template <typename T>
class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
auto* objness_mask = ctx.Input<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Input<Tensor>("GTMatchMask");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input_grad->dims()[0];
const int c = input_grad->dims()[1];
const int h = input_grad->dims()[2];
const int w = input_grad->dims()[3];
const int mask_num = anchor_mask.size();
const int b = gt_match_mask->dims()[1];
int input_size = downsample_ratio * h;
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
const T* loss_grad_data = loss_grad->data<T>();
const T* obj_mask_data = objness_mask->data<T>();
const int* gt_match_mask_data = gt_match_mask->data<int>();
T* input_grad_data =
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
for (int i = 0; i < n; i++) {
for (int t = 0; t < b; t++) {
int mask_idx = gt_match_mask_data[i * b + t];
if (mask_idx >= 0) {
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLossGrad<T>(
input_grad_data, loss_grad_data[i], input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h, input_size, stride);
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLossGrad<T>(input_grad_data, loss_grad_data[i], input_data,
label_idx, label, class_num, stride);
}
}
}
CalcObjnessLossGrad<T>(input_grad_data + 4 * stride, loss_grad_data,
input_data + 4 * stride, obj_mask_data, n, mask_num,
h, w, stride, an_stride);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/dropout_op.cu
浏览文件 @ 4eb44380
......@@ -114,4 +114,5 @@ REGISTER_OP_CUDA_KERNEL(
ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
ops::DropoutGradKernel<plat::CUDADeviceContext, plat::float16>,
ops::DropoutGradKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/gather_op.cu
浏览文件 @ 4eb44380
......@@ -31,7 +31,7 @@ class GatherOpCUDAKernel : public framework::OpKernel<T> {
auto *output = ctx.Output<Tensor>("Out");
output->mutable_data<T>(ctx.GetPlace());
if (x->numel() == 0) return;
GPUGather<T>(ctx.device_context(), *x, *index, output);
}
};
......@@ -45,14 +45,13 @@ class GatherGradOpCUDAKernel : public framework::OpKernel<T> {
auto *Index = ctx.Input<Tensor>("Index");
auto *dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *dO = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto *x = ctx.Input<Tensor>("X");
dX->mutable_data<T>(ctx.GetPlace());
auto dxt = framework::EigenVector<T>::Flatten(*dX);
auto &place = *ctx.template device_context<platform::CUDADeviceContext>()
.eigen_device();
dxt.device(place) = dxt.constant(static_cast<T>(0));
if (dO->numel() == 0) return;
GPUScatterAssign<T>(ctx.device_context(), *dO, *Index, dX);
}
};
......@@ -61,11 +60,14 @@ class GatherGradOpCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(gather, ops::GatherOpCUDAKernel<float>,
ops::GatherOpCUDAKernel<double>,
ops::GatherOpCUDAKernel<int64_t>,
ops::GatherOpCUDAKernel<int>);
ops::GatherOpCUDAKernel<int>,
ops::GatherOpCUDAKernel<plat::float16>);
REGISTER_OP_CUDA_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel<float>,
ops::GatherGradOpCUDAKernel<double>,
ops::GatherGradOpCUDAKernel<int64_t>,
ops::GatherGradOpCUDAKernel<int>);
ops::GatherGradOpCUDAKernel<int>,
ops::GatherGradOpCUDAKernel<plat::float16>);
paddle/fluid/operators/gather_op.h
浏览文件 @ 4eb44380
......@@ -35,7 +35,7 @@ class GatherOpKernel : public framework::OpKernel<T> {
auto *output = ctx.Output<Tensor>("Out");
output->mutable_data<T>(ctx.GetPlace());
if (x->numel() == 0) return;
CPUGather<T>(ctx.device_context(), *x, *index, output);
}
};
......@@ -56,7 +56,7 @@ class GatherGradientOpKernel : public framework::OpKernel<T> {
auto &place = *ctx.template device_context<platform::CPUDeviceContext>()
.eigen_device();
dxt.device(place) = dxt.constant(static_cast<T>(0));
if (dO->numel() == 0) return;
ScatterAssign<T>(ctx.device_context(), *dO, *Index, dX);
}
};
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 4eb44380
......@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/fluid/operators/lookup_table_op.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
......@@ -193,8 +194,11 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(lookup_table, ops::LookupTableCUDAKernel<float>,
ops::LookupTableCUDAKernel<double>);
ops::LookupTableCUDAKernel<double>,
ops::LookupTableCUDAKernel<plat::float16>);
REGISTER_OP_CUDA_KERNEL(lookup_table_grad,
ops::LookupTableGradCUDAKernel<float>,
ops::LookupTableGradCUDAKernel<double>);
ops::LookupTableGradCUDAKernel<double>,
ops::LookupTableGradCUDAKernel<plat::float16>);
paddle/fluid/operators/math/beam_search.cc
浏览文件 @ 4eb44380
......@@ -29,8 +29,9 @@ class BeamSearchFunctor<platform::CPUDeviceContext, T> {
const framework::LoDTensor *ids,
const framework::LoDTensor *scores,
framework::LoDTensor *selected_ids,
framework::LoDTensor *selected_scores, size_t level,
size_t beam_size, int end_id, bool is_accumulated) {
framework::LoDTensor *selected_scores,
framework::Tensor *parent_idx, size_t level, size_t beam_size,
int end_id, bool is_accumulated) {
auto abs_lod = framework::ToAbsOffset(scores->lod());
auto &high_level = abs_lod[level];
......@@ -57,11 +58,13 @@ class BeamSearchFunctor<platform::CPUDeviceContext, T> {
std::vector<int64_t>({static_cast<int>(num_instances), 1}));
selected_ids->Resize(dims);
selected_scores->Resize(dims);
parent_idx->Resize({static_cast<int64_t>(num_instances)});
auto *selected_ids_data =
selected_ids->mutable_data<int64_t>(platform::CPUPlace());
auto *selected_scores_data =
selected_scores->mutable_data<float>(platform::CPUPlace());
auto *parent_idx_data = parent_idx->mutable_data<int>(platform::CPUPlace());
// fill in data
std::vector<size_t> low_level;
......@@ -69,6 +72,7 @@ class BeamSearchFunctor<platform::CPUDeviceContext, T> {
for (auto &items : selected_items) {
low_level.push_back(low_offset);
for (auto &item : items) {
parent_idx_data[low_offset] = static_cast<int>(low_level.size() - 1);
selected_ids_data[low_offset] = item.id;
selected_scores_data[low_offset] = item.score;
low_offset++;
......
paddle/fluid/operators/math/beam_search.cu
浏览文件 @ 4eb44380
......@@ -157,10 +157,10 @@ __device__ __forceinline__ bool PruneEndBeams(Triple* top_beam_local,
}
__device__ __forceinline__ void WriteBack(
int64_t* selected_ids, float* selected_scores, size_t* selected_offsets,
Triple* top_beam_local, const int seq_offset_start,
const int seq_offset_end, const int selected_seq_start,
const int selected_seq_length) {
int64_t* selected_ids, float* selected_scores, int* parent_idx,
size_t* selected_offsets, Triple* top_beam_local,
const int seq_offset_start, const int seq_offset_end,
const int selected_seq_start, const int selected_seq_length) {
const int tid = threadIdx.x; // use 1 thread only for each sequence
int global_index = selected_seq_start;
for (int global_offset = seq_offset_start; global_offset < seq_offset_end;
......@@ -171,6 +171,7 @@ __device__ __forceinline__ void WriteBack(
selected_ids[global_index] =
static_cast<int64_t>(top_beam_local[local_index].id);
selected_scores[global_index] = top_beam_local[local_index].score;
parent_idx[global_index] = static_cast<int>(global_offset);
global_index++;
}
}
......@@ -180,11 +181,11 @@ __device__ __forceinline__ void WriteBack(
template <int MaxLength, int MaxThreadsPerSeq, int MaxSeqs>
__device__ void BeamSearchDetails(
int64_t* selected_ids, float* selected_scores, size_t* selected_offsets,
const int64_t* pre_ids, const float* pre_scores, const int64_t* ids,
const float* scores, const int seq_offset_start, const int seq_offset_end,
const int seq_width, int beam_size, int end_id, bool is_accumulated,
int num_used_threads) {
int64_t* selected_ids, float* selected_scores, int* parent_idx,
size_t* selected_offsets, const int64_t* pre_ids, const float* pre_scores,
const int64_t* ids, const float* scores, const int seq_offset_start,
const int seq_offset_end, const int seq_width, int beam_size, int end_id,
bool is_accumulated, int num_used_threads) {
__shared__ Triple top_beam[MaxLength];
int num_items = 0;
......@@ -228,15 +229,15 @@ __device__ void BeamSearchDetails(
selected_offsets[0] = 0;
}
WriteBack(selected_ids, selected_scores, selected_offsets, top_beam_local,
seq_offset_start, seq_offset_end, selected_seq_start,
selected_seq_length);
WriteBack(selected_ids, selected_scores, parent_idx, selected_offsets,
top_beam_local, seq_offset_start, seq_offset_end,
selected_seq_start, selected_seq_length);
}
}
template <int MaxLength, int MaxThreadsPerSeq, int MaxSeqs>
__global__ void BeamSearchKernel(int64_t* selected_ids, float* selected_scores,
size_t* selected_offsets,
int* parent_idx, size_t* selected_offsets,
const int64_t* pre_ids,
const float* pre_scores, const int64_t* ids,
const float* scores, const size_t* seq_offsets,
......@@ -250,24 +251,25 @@ __global__ void BeamSearchKernel(int64_t* selected_ids, float* selected_scores,
int seq_offset_end = static_cast<int>(seq_offsets[seq_id + 1]);
BeamSearchDetails<MaxLength, MaxThreadsPerSeq, MaxSeqs>(
selected_ids, selected_scores, selected_offsets, pre_ids, pre_scores, ids,
scores, seq_offset_start, seq_offset_end, seq_width, beam_size, end_id,
is_accumulated, num_used_threads);
selected_ids, selected_scores, parent_idx, selected_offsets, pre_ids,
pre_scores, ids, scores, seq_offset_start, seq_offset_end, seq_width,
beam_size, end_id, is_accumulated, num_used_threads);
}
template <int MaxLength, int MaxThreadsPerSeq>
__global__ void BeamSearchKernelSingle(
int64_t* selected_ids, float* selected_scores, size_t* selected_offsets,
const int64_t* pre_ids, const float* pre_scores, const int64_t* ids,
const float* scores, const int seq_length, const int seq_width,
int beam_size, int end_id, bool is_accumulated, int num_used_threads) {
int64_t* selected_ids, float* selected_scores, int* parent_idx,
size_t* selected_offsets, const int64_t* pre_ids, const float* pre_scores,
const int64_t* ids, const float* scores, const int seq_length,
const int seq_width, int beam_size, int end_id, bool is_accumulated,
int num_used_threads) {
const int seq_offset_start = 0;
const int seq_offset_end = seq_length;
BeamSearchDetails<MaxLength, MaxThreadsPerSeq, 1>(
selected_ids, selected_scores, selected_offsets, pre_ids, pre_scores, ids,
scores, seq_offset_start, seq_offset_end, seq_width, beam_size, end_id,
is_accumulated, num_used_threads);
selected_ids, selected_scores, parent_idx, selected_offsets, pre_ids,
pre_scores, ids, scores, seq_offset_start, seq_offset_end, seq_width,
beam_size, end_id, is_accumulated, num_used_threads);
}
static inline int GetNumUsedThreads(const int max_threads_per_seq,
......@@ -300,8 +302,9 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
const framework::LoDTensor* ids,
const framework::LoDTensor* scores,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores, size_t level,
size_t beam_size, int end_id, bool is_accumulated) {
framework::LoDTensor* selected_scores,
framework::Tensor* parent_idx, size_t level, size_t beam_size,
int end_id, bool is_accumulated) {
auto abs_lod = framework::ToAbsOffset(scores->lod());
const int64_t* pre_ids_data = pre_ids->data<int64_t>();
......@@ -322,6 +325,8 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
selected_ids->mutable_data<int64_t>(selected_dims, context.GetPlace());
float* selected_scores_data =
selected_scores->mutable_data<float>(selected_dims, context.GetPlace());
int* parent_idx_data = parent_idx->mutable_data<int>(
{static_cast<int64_t>(num_seqs * beam_size)}, context.GetPlace());
framework::LoD selected_lod(2);
selected_lod[0].assign(abs_lod[level].begin(), abs_lod[level].end());
......@@ -339,9 +344,9 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
CUDA_LAUNCH_KERNEL_HELPER(
BeamSearchKernelSingle<kPowerOfTwoDim, kMaxThreadsPerSeq><<<
1, kMaxThreadsPerSeq, 0, context.stream()>>>(
selected_ids_data, selected_scores_data, selected_offsets,
pre_ids_data, pre_scores_data, ids_data, scores_data,
seq_length, static_cast<int>(seq_width),
selected_ids_data, selected_scores_data, parent_idx_data,
selected_offsets, pre_ids_data, pre_scores_data, ids_data,
scores_data, seq_length, static_cast<int>(seq_width),
static_cast<int>(beam_size), static_cast<int>(end_id),
is_accumulated, num_used_threads));
}
......@@ -357,9 +362,9 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
CUDA_LAUNCH_KERNEL_HELPER(
BeamSearchKernel<kPowerOfTwoDim, kMaxThreadsPerSeq, kMaxSeqs><<<
1, num_seqs * kMaxThreadsPerSeq, 0, context.stream()>>>(
selected_ids_data, selected_scores_data, selected_offsets,
pre_ids_data, pre_scores_data, ids_data, scores_data,
seq_offsets, static_cast<int>(num_seqs),
selected_ids_data, selected_scores_data, parent_idx_data,
selected_offsets, pre_ids_data, pre_scores_data, ids_data,
scores_data, seq_offsets, static_cast<int>(num_seqs),
static_cast<int>(seq_width), static_cast<int>(beam_size),
end_id, is_accumulated, num_used_threads));
}
......@@ -379,6 +384,7 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
{static_cast<int64_t>(selected_lod[1].back()), 1});
selected_ids->Resize(final_selected_dims);
selected_scores->Resize(final_selected_dims);
parent_idx->Resize({static_cast<int64_t>(selected_lod[1].back())});
}
}
};
......
paddle/fluid/operators/math/beam_search.h
浏览文件 @ 4eb44380
......@@ -104,14 +104,12 @@ class BeamSearchFunctor {
* Return false if all the input tensor is empty, in machine translation task
* that means no candidates is provided, and the task will stop running.
*/
void operator()(const DeviceContext& context,
const framework::LoDTensor* pre_ids,
const framework::LoDTensor* pre_scores,
const framework::LoDTensor* ids,
const framework::LoDTensor* scores,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores, size_t level,
size_t beam_size, int end_id, bool is_accumulated);
void operator()(
const DeviceContext& context, const framework::LoDTensor* pre_ids,
const framework::LoDTensor* pre_scores, const framework::LoDTensor* ids,
const framework::LoDTensor* scores, framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores, framework::Tensor* parent_idx,
size_t level, size_t beam_size, int end_id, bool is_accumulated);
};
} // namespace math
......
paddle/fluid/operators/math/beam_search_test.cc
浏览文件 @ 4eb44380
......@@ -93,13 +93,14 @@ void TestBeamSearch() {
paddle::framework::LoDTensor selected_ids;
paddle::framework::LoDTensor selected_scores;
paddle::framework::LoDTensor parent_idx;
size_t level = 0;
size_t beam_size = 2;
int end_id = 0;
paddle::operators::math::BeamSearchFunctor<DeviceContext, float> beamsearch;
beamsearch(*context, &pre_ids, &pre_scores, &ids, &scores, &selected_ids,
&selected_scores, level, beam_size, end_id, true);
&selected_scores, &parent_idx, level, beam_size, end_id, true);
ASSERT_EQ(selected_ids.lod(), selected_scores.lod());
......
paddle/fluid/operators/ngraph/ngraph_bridge.cc
浏览文件 @ 4eb44380
......@@ -38,6 +38,8 @@ std::map<std::string,
{"mean_grad", NG_OPS::BuildMeanGradNode},
{"mul", NG_OPS::BuildMulNode},
{"mul_grad", NG_OPS::BuildMulGradNode},
{"pool2d", NG_OPS::BuildPool2dNode},
{"pool2d_grad", NG_OPS::BuildPool2dGradNode},
{"softmax", NG_OPS::BuildSoftmaxNode},
{"softmax_grad", NG_OPS::BuildSoftmaxGradNode},
{"scale", NG_OPS::BuildScaleNode},
......
paddle/fluid/operators/ngraph/ngraph_ops.h
浏览文件 @ 4eb44380
......@@ -26,6 +26,7 @@ limitations under the License. */
#include "ops/fill_constant_op.h"
#include "ops/mean_op.h"
#include "ops/mul_op.h"
#include "ops/pool2d_op.h"
#include "ops/scale_op.h"
#include "ops/softmax_op.h"
#include "ops/top_k_op.h"
paddle/fluid/operators/ngraph/ops/pool2d_op.h 0 → 100644
浏览文件 @ 4eb44380
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed 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. */
#pragma once
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildPool2dNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto x_shape = x->get_shape();
std::string pooling_type = op_attrs.Get<std::string>("pooling_type");
std::vector<int> ksize = op_attrs.Get<std::vector<int>>("ksize");
std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_shape.size() - 2, ksize.size(),
"Handling 2d pooling only");
if (op_attrs.Get<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(x_shape.at(i + 2));
}
}
ngraph::Shape ng_padding_below{static_cast<size_t>(paddings.at(0)),
static_cast<size_t>(paddings.at(1))};
ngraph::Shape ng_padding_above{static_cast<size_t>(paddings.at(0)),
static_cast<size_t>(paddings.at(1))};
ngraph::Shape ng_ksize_shape{static_cast<size_t>(ksize.at(0)),
static_cast<size_t>(ksize.at(1))};
ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
static_cast<size_t>(strides.at(1))};
auto ComputeCeiledOutput = [](size_t in, size_t k, size_t p, size_t s) {
return (in - k + 2 * p) / s + 1;
};
if (op_attrs.Get<bool>("ceil_mode")) {
auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
auto dummpy_shape = dummy_out->get_shape();
for (size_t i = 0; i < ng_padding_above.size(); ++i) {
auto desired_size = ComputeCeiledOutput(x_shape[i + 2], ksize[i],
paddings[i], strides[i]);
if (desired_size != dummpy_shape[i + 2]) {
ng_padding_above[i] += strides[i];
}
}
}
bool padding_exclusive = op_attrs.Get<bool>("exclusive");
if (pooling_type == "max") {
auto pool2d = std::make_shared<ngraph::op::MaxPool>(
x, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above);
paddle::platform::SetOutputNode(op, "Out", pool2d, ngb_node_map);
} else if (pooling_type == "avg") {
std::shared_ptr<ngraph::Node> pool2d;
if (op_attrs.Get<bool>("adaptive")) {
auto ComputeAdaptive = [](size_t in, size_t k) {
return std::floor(in / k);
};
ng_strides[0] = x_shape.size() == 4
? ComputeAdaptive(x_shape[3], ksize[0])
: ng_strides[0];
ng_strides[1] = x_shape.size() == 4
? ComputeAdaptive(x_shape[3], ksize[0])
: ng_strides[1];
pool2d =
std::make_shared<ngraph::op::AvgPool>(x, ng_ksize_shape, ng_strides);
} else {
pool2d = std::make_shared<ngraph::op::AvgPool>(
x, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above,
!padding_exclusive);
}
paddle::platform::SetOutputNode(op, "Out", pool2d, ngb_node_map);
} else {
PADDLE_THROW("Support max and avg pooling only");
}
}
void BuildPool2dGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
auto out = paddle::platform::GetInputNode(op, "Out", ngb_node_map);
auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto x_shape = x->get_shape();
std::string pooling_type = op_attrs.Get<std::string>("pooling_type");
std::vector<int> ksize = op_attrs.Get<std::vector<int>>("ksize");
std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_shape.size() - 2, ksize.size(),
"Handling 2d pooling only");
if (op_attrs.Get<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(x_shape.at(i + 2));
}
}
ngraph::Shape ng_padding_below{static_cast<size_t>(paddings.at(0)),
static_cast<size_t>(paddings.at(1))};
ngraph::Shape ng_padding_above{static_cast<size_t>(paddings.at(0)),
static_cast<size_t>(paddings.at(1))};
ngraph::Shape ng_ksize_shape{static_cast<size_t>(ksize.at(0)),
static_cast<size_t>(ksize.at(1))};
ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
static_cast<size_t>(strides.at(1))};
bool padding_exclusive = op_attrs.Get<bool>("exclusive");
if (pooling_type == "max") {
auto pool2d_grad = std::make_shared<ngraph::op::MaxPoolBackprop>(
x, dout, out, ng_ksize_shape, ng_strides, ng_padding_below,
ng_padding_above);
paddle::platform::SetOutputNode(op, "X@GRAD", pool2d_grad, ngb_node_map);
} else if (pooling_type == "avg") {
std::shared_ptr<ngraph::Node> pool2d_grad;
if (op_attrs.Get<bool>("adaptive")) {
auto ComputeAdaptive = [](size_t in, size_t k) {
return std::floor(in / k);
};
ng_strides[0] = x_shape.size() == 4
? ComputeAdaptive(x_shape[3], ksize[0])
: ng_strides[0];
ng_strides[1] = x_shape.size() == 4
? ComputeAdaptive(x_shape[3], ksize[0])
: ng_strides[1];
pool2d_grad = std::make_shared<ngraph::op::AvgPoolBackprop>(
x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below,
ng_padding_above, !padding_exclusive);
} else {
pool2d_grad = std::make_shared<ngraph::op::AvgPoolBackprop>(
x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below,
ng_padding_above, !padding_exclusive);
}
paddle::platform::SetOutputNode(op, "X@GRAD", pool2d_grad, ngb_node_map);
} else {
PADDLE_THROW("Support max and avg pooling only");
}
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 4eb44380
......@@ -330,6 +330,7 @@ class Reshape2GradOp : public framework::OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
......@@ -356,16 +357,20 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
int64_t, ops::ReshapeKernel, plat::float16,
ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel, plat::float16,
ops::ReshapeGradKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
int64_t, ops::ReshapeKernel, plat::float16,
ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel, plat::float16,
ops::ReshapeGradKernel);
#endif
paddle/fluid/operators/slice_op.cc
浏览文件 @ 4eb44380
......@@ -54,6 +54,9 @@ class SliceOp : public framework::OperatorWithKernel {
out_dims[axes[i]] = end - start;
}
ctx->SetOutputDim("Out", out_dims);
if (axes[0] != 0) {
ctx->ShareLoD("Input", /*->*/ "Out");
}
}
protected:
......
paddle/fluid/operators/stack_op.cu
浏览文件 @ 4eb44380
......@@ -17,13 +17,16 @@
namespace plat = paddle::platform;
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(stack, ops::StackKernel<plat::CUDADeviceContext, float>,
ops::StackKernel<plat::CUDADeviceContext, double>,
ops::StackKernel<plat::CUDADeviceContext, int>,
ops::StackKernel<plat::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
stack, ops::StackKernel<plat::CUDADeviceContext, float>,
ops::StackKernel<plat::CUDADeviceContext, double>,
ops::StackKernel<plat::CUDADeviceContext, int>,
ops::StackKernel<plat::CUDADeviceContext, int64_t>,
ops::StackKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(stack_grad,
ops::StackGradKernel<plat::CUDADeviceContext, float>,
ops::StackGradKernel<plat::CUDADeviceContext, double>,
ops::StackGradKernel<plat::CUDADeviceContext, int>,
ops::StackGradKernel<plat::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
stack_grad, ops::StackGradKernel<plat::CUDADeviceContext, float>,
ops::StackGradKernel<plat::CUDADeviceContext, double>,
ops::StackGradKernel<plat::CUDADeviceContext, int>,
ops::StackGradKernel<plat::CUDADeviceContext, int64_t>,
ops::StackGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/transpose_op.cu.cc
浏览文件 @ 4eb44380
......@@ -15,19 +15,27 @@ limitations under the License. */
#include "paddle/fluid/operators/transpose_op.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
REGISTER_OP_CUDA_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
paddle/fluid/operators/yolov3_loss_op.h 已删除 100644 → 0
浏览文件 @ e402c0ec
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
using Array5 = Eigen::DSizes<int64_t, 5>;
template <typename T>
static inline bool isZero(T x) {
return fabs(x) < 1e-6;
}
template <typename T>
static inline T sigmoid(T x) {
return 1.0 / (exp(-1.0 * x) + 1.0);
}
template <typename T>
static inline T CalcMaskPointNum(const Tensor& mask) {
auto mask_t = EigenVector<int>::Flatten(mask);
T count = 0.0;
for (int i = 0; i < mask_t.dimensions()[0]; i++) {
if (mask_t(i)) {
count += 1.0;
}
}
return count;
}
template <typename T>
static inline T CalcMSEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum += pow(x_t(i) - y_t(i), 2);
points += 1;
}
}
return (error_sum / points);
}
template <typename T>
static void CalcMSEGradWithMask(Tensor* grad, const Tensor& x, const Tensor& y,
const Tensor& mask, T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = 2.0 * (x_t(i) - y_t(i)) / mf;
}
}
}
template <typename T>
static inline T CalcBCEWithMask(const Tensor& x, const Tensor& y,
const Tensor& mask) {
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
T error_sum = 0.0;
T points = 0.0;
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
error_sum +=
-1.0 * (y_t(i) * log(x_t(i)) + (1.0 - y_t(i)) * log(1.0 - x_t(i)));
points += 1;
}
}
return (error_sum / points);
}
template <typename T>
static inline void CalcBCEGradWithMask(Tensor* grad, const Tensor& x,
const Tensor& y, const Tensor& mask,
T mf) {
auto grad_t = EigenVector<T>::Flatten(*grad).setConstant(0.0);
auto x_t = EigenVector<T>::Flatten(x);
auto y_t = EigenVector<T>::Flatten(y);
auto mask_t = EigenVector<int>::Flatten(mask);
for (int i = 0; i < x_t.dimensions()[0]; i++) {
if (mask_t(i)) {
grad_t(i) = ((1.0 - y_t(i)) / (1.0 - x_t(i)) - y_t(i) / x_t(i)) / mf;
}
}
}
template <typename T>
static void CalcPredResult(const Tensor& input, Tensor* pred_conf,
Tensor* pred_class, Tensor* pred_x, Tensor* pred_y,
Tensor* pred_w, Tensor* pred_h, const int anchor_num,
const int class_num) {
const int n = input.dims()[0];
const int h = input.dims()[2];
const int w = input.dims()[3];
const int box_attr_num = 5 + class_num;
auto input_t = EigenTensor<T, 4>::From(input);
auto pred_conf_t = EigenTensor<T, 4>::From(*pred_conf);
auto pred_class_t = EigenTensor<T, 5>::From(*pred_class);
auto pred_x_t = EigenTensor<T, 4>::From(*pred_x);
auto pred_y_t = EigenTensor<T, 4>::From(*pred_y);
auto pred_w_t = EigenTensor<T, 4>::From(*pred_w);
auto pred_h_t = EigenTensor<T, 4>::From(*pred_h);
for (int i = 0; i < n; i++) {
for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
for (int j = 0; j < h; j++) {
for (int k = 0; k < w; k++) {
pred_x_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx, j, k));
pred_y_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 1, j, k));
pred_w_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 2, j, k);
pred_h_t(i, an_idx, j, k) =
input_t(i, box_attr_num * an_idx + 3, j, k);
pred_conf_t(i, an_idx, j, k) =
sigmoid(input_t(i, box_attr_num * an_idx + 4, j, k));
for (int c = 0; c < class_num; c++) {
pred_class_t(i, an_idx, j, k, c) =
sigmoid(input_t(i, box_attr_num * an_idx + 5 + c, j, k));
}
}
}
}
}
}
template <typename T>
static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
T b1_x1 = box1[0] - box1[2] / 2;
T b1_x2 = box1[0] + box1[2] / 2;
T b1_y1 = box1[1] - box1[3] / 2;
T b1_y2 = box1[1] + box1[3] / 2;
T b2_x1 = box2[0] - box2[2] / 2;
T b2_x2 = box2[0] + box2[2] / 2;
T b2_y1 = box2[1] - box2[3] / 2;
T b2_y2 = box2[1] + box2[3] / 2;
T b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1);
T b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1);
T inter_rect_x1 = std::max(b1_x1, b2_x1);
T inter_rect_y1 = std::max(b1_y1, b2_y1);
T inter_rect_x2 = std::min(b1_x2, b2_x2);
T inter_rect_y2 = std::min(b1_y2, b2_y2);
T inter_area = std::max(inter_rect_x2 - inter_rect_x1, static_cast<T>(0.0)) *
std::max(inter_rect_y2 - inter_rect_y1, static_cast<T>(0.0));
return inter_area / (b1_area + b2_area - inter_area);
}
template <typename T>
static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
const float ignore_thresh, std::vector<int> anchors,
const int grid_size, Tensor* obj_mask,
Tensor* noobj_mask, Tensor* tx, Tensor* ty,
Tensor* tw, Tensor* th, Tensor* tconf,
Tensor* tclass) {
const int n = gt_box.dims()[0];
const int b = gt_box.dims()[1];
const int anchor_num = anchors.size() / 2;
auto gt_box_t = EigenTensor<T, 3>::From(gt_box);
auto gt_label_t = EigenTensor<int, 2>::From(gt_label);
auto obj_mask_t = EigenTensor<int, 4>::From(*obj_mask).setConstant(0);
auto noobj_mask_t = EigenTensor<int, 4>::From(*noobj_mask).setConstant(1);
auto tx_t = EigenTensor<T, 4>::From(*tx).setConstant(0.0);
auto ty_t = EigenTensor<T, 4>::From(*ty).setConstant(0.0);
auto tw_t = EigenTensor<T, 4>::From(*tw).setConstant(0.0);
auto th_t = EigenTensor<T, 4>::From(*th).setConstant(0.0);
auto tconf_t = EigenTensor<T, 4>::From(*tconf).setConstant(0.0);
auto tclass_t = EigenTensor<T, 5>::From(*tclass).setConstant(0.0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < b; j++) {
if (isZero<T>(gt_box_t(i, j, 0)) && isZero<T>(gt_box_t(i, j, 1)) &&
isZero<T>(gt_box_t(i, j, 2)) && isZero<T>(gt_box_t(i, j, 3))) {
continue;
}
int cur_label = gt_label_t(i, j);
T gx = gt_box_t(i, j, 0) * grid_size;
T gy = gt_box_t(i, j, 1) * grid_size;
T gw = gt_box_t(i, j, 2) * grid_size;
T gh = gt_box_t(i, j, 3) * grid_size;
int gi = static_cast<int>(gx);
int gj = static_cast<int>(gy);
T max_iou = static_cast<T>(0);
T iou;
int best_an_index = -1;
std::vector<T> gt_box_shape({0, 0, gw, gh});
for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
std::vector<T> anchor_shape({0, 0, static_cast<T>(anchors[2 * an_idx]),
static_cast<T>(anchors[2 * an_idx + 1])});
iou = CalcBoxIoU<T>(gt_box_shape, anchor_shape);
if (iou > max_iou) {
max_iou = iou;
best_an_index = an_idx;
}
if (iou > ignore_thresh) {
noobj_mask_t(i, an_idx, gj, gi) = 0;
}
}
obj_mask_t(i, best_an_index, gj, gi) = 1;
noobj_mask_t(i, best_an_index, gj, gi) = 0;
tx_t(i, best_an_index, gj, gi) = gx - gi;
ty_t(i, best_an_index, gj, gi) = gy - gj;
tw_t(i, best_an_index, gj, gi) = log(gw / anchors[2 * best_an_index]);
th_t(i, best_an_index, gj, gi) = log(gh / anchors[2 * best_an_index + 1]);
tclass_t(i, best_an_index, gj, gi, cur_label) = 1;
tconf_t(i, best_an_index, gj, gi) = 1;
}
}
}
static void ExpandObjMaskByClassNum(Tensor* obj_mask_expand,
const Tensor& obj_mask) {
const int n = obj_mask_expand->dims()[0];
const int an_num = obj_mask_expand->dims()[1];
const int h = obj_mask_expand->dims()[2];
const int w = obj_mask_expand->dims()[3];
const int class_num = obj_mask_expand->dims()[4];
auto obj_mask_expand_t = EigenTensor<int, 5>::From(*obj_mask_expand);
auto obj_mask_t = EigenTensor<int, 4>::From(obj_mask);
obj_mask_expand_t = obj_mask_t.reshape(Array5(n, an_num, h, w, 1))
.broadcast(Array5(1, 1, 1, 1, class_num));
}
template <typename T>
static void AddAllGradToInputGrad(
Tensor* grad, T loss, const Tensor& pred_x, const Tensor& pred_y,
const Tensor& pred_conf, const Tensor& pred_class, const Tensor& grad_x,
const Tensor& grad_y, const Tensor& grad_w, const Tensor& grad_h,
const Tensor& grad_conf_target, const Tensor& grad_conf_notarget,
const Tensor& grad_class, const int class_num, const float loss_weight_xy,
const float loss_weight_wh, const float loss_weight_conf_target,
const float loss_weight_conf_notarget, const float loss_weight_class) {
const int n = pred_x.dims()[0];
const int an_num = pred_x.dims()[1];
const int h = pred_x.dims()[2];
const int w = pred_x.dims()[3];
const int attr_num = class_num + 5;
auto grad_t = EigenTensor<T, 4>::From(*grad).setConstant(0.0);
auto pred_x_t = EigenTensor<T, 4>::From(pred_x);
auto pred_y_t = EigenTensor<T, 4>::From(pred_y);
auto pred_conf_t = EigenTensor<T, 4>::From(pred_conf);
auto pred_class_t = EigenTensor<T, 5>::From(pred_class);
auto grad_x_t = EigenTensor<T, 4>::From(grad_x);
auto grad_y_t = EigenTensor<T, 4>::From(grad_y);
auto grad_w_t = EigenTensor<T, 4>::From(grad_w);
auto grad_h_t = EigenTensor<T, 4>::From(grad_h);
auto grad_conf_target_t = EigenTensor<T, 4>::From(grad_conf_target);
auto grad_conf_notarget_t = EigenTensor<T, 4>::From(grad_conf_notarget);
auto grad_class_t = EigenTensor<T, 5>::From(grad_class);
for (int i = 0; i < n; i++) {
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
grad_t(i, j * attr_num, k, l) =
grad_x_t(i, j, k, l) * pred_x_t(i, j, k, l) *
(1.0 - pred_x_t(i, j, k, l)) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 1, k, l) =
grad_y_t(i, j, k, l) * pred_y_t(i, j, k, l) *
(1.0 - pred_y_t(i, j, k, l)) * loss * loss_weight_xy;
grad_t(i, j * attr_num + 2, k, l) =
grad_w_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 3, k, l) =
grad_h_t(i, j, k, l) * loss * loss_weight_wh;
grad_t(i, j * attr_num + 4, k, l) =
grad_conf_target_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss * loss_weight_conf_target;
grad_t(i, j * attr_num + 4, k, l) +=
grad_conf_notarget_t(i, j, k, l) * pred_conf_t(i, j, k, l) *
(1.0 - pred_conf_t(i, j, k, l)) * loss *
loss_weight_conf_notarget;
for (int c = 0; c < class_num; c++) {
grad_t(i, j * attr_num + 5 + c, k, l) =
grad_class_t(i, j, k, l, c) * pred_class_t(i, j, k, l, c) *
(1.0 - pred_class_t(i, j, k, l, c)) * loss * loss_weight_class;
}
}
}
}
}
}
template <typename T>
class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* loss = ctx.Output<Tensor>("Loss");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
float loss_weight_conf_target = ctx.Attr<float>("loss_weight_conf_target");
float loss_weight_conf_notarget =
ctx.Attr<float>("loss_weight_conf_notarget");
float loss_weight_class = ctx.Attr<float>("loss_weight_class");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
Tensor pred_x, pred_y, pred_w, pred_h;
Tensor pred_conf, pred_class;
pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
T loss_x = CalcMSEWithMask<T>(pred_x, tx, obj_mask);
T loss_y = CalcMSEWithMask<T>(pred_y, ty, obj_mask);
T loss_w = CalcMSEWithMask<T>(pred_w, tw, obj_mask);
T loss_h = CalcMSEWithMask<T>(pred_h, th, obj_mask);
T loss_conf_target = CalcBCEWithMask<T>(pred_conf, tconf, obj_mask);
T loss_conf_notarget = CalcBCEWithMask<T>(pred_conf, tconf, noobj_mask);
T loss_class = CalcBCEWithMask<T>(pred_class, tclass, obj_mask_expand);
auto* loss_data = loss->mutable_data<T>({1}, ctx.GetPlace());
loss_data[0] = loss_weight_xy * (loss_x + loss_y) +
loss_weight_wh * (loss_w + loss_h) +
loss_weight_conf_target * loss_conf_target +
loss_weight_conf_notarget * loss_conf_notarget +
loss_weight_class * loss_class;
}
};
template <typename T>
class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
const T loss = output_grad->data<T>()[0];
float loss_weight_xy = ctx.Attr<float>("loss_weight_xy");
float loss_weight_wh = ctx.Attr<float>("loss_weight_wh");
float loss_weight_conf_target = ctx.Attr<float>("loss_weight_conf_target");
float loss_weight_conf_notarget =
ctx.Attr<float>("loss_weight_conf_notarget");
float loss_weight_class = ctx.Attr<float>("loss_weight_class");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int an_num = anchors.size() / 2;
Tensor pred_x, pred_y, pred_w, pred_h;
Tensor pred_conf, pred_class;
pred_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_conf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
pred_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
CalcPredResult<T>(*input, &pred_conf, &pred_class, &pred_x, &pred_y,
&pred_w, &pred_h, an_num, class_num);
Tensor obj_mask, noobj_mask;
Tensor tx, ty, tw, th, tconf, tclass;
obj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
noobj_mask.mutable_data<int>({n, an_num, h, w}, ctx.GetPlace());
tx.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
ty.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tw.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
th.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tconf.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
tclass.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
PreProcessGTBox<T>(*gt_box, *gt_label, ignore_thresh, anchors, h, &obj_mask,
&noobj_mask, &tx, &ty, &tw, &th, &tconf, &tclass);
Tensor obj_mask_expand;
obj_mask_expand.mutable_data<int>({n, an_num, h, w, class_num},
ctx.GetPlace());
ExpandObjMaskByClassNum(&obj_mask_expand, obj_mask);
Tensor grad_x, grad_y, grad_w, grad_h;
Tensor grad_conf_target, grad_conf_notarget, grad_class;
grad_x.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_y.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_w.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_h.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_conf_target.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_conf_notarget.mutable_data<T>({n, an_num, h, w}, ctx.GetPlace());
grad_class.mutable_data<T>({n, an_num, h, w, class_num}, ctx.GetPlace());
T obj_mf = CalcMaskPointNum<int>(obj_mask);
T noobj_mf = CalcMaskPointNum<int>(noobj_mask);
T obj_expand_mf = CalcMaskPointNum<int>(obj_mask_expand);
CalcMSEGradWithMask<T>(&grad_x, pred_x, tx, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_y, pred_y, ty, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_w, pred_w, tw, obj_mask, obj_mf);
CalcMSEGradWithMask<T>(&grad_h, pred_h, th, obj_mask, obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_target, pred_conf, tconf, obj_mask,
obj_mf);
CalcBCEGradWithMask<T>(&grad_conf_notarget, pred_conf, tconf, noobj_mask,
noobj_mf);
CalcBCEGradWithMask<T>(&grad_class, pred_class, tclass, obj_mask_expand,
obj_expand_mf);
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
AddAllGradToInputGrad<T>(
input_grad, loss, pred_x, pred_y, pred_conf, pred_class, grad_x, grad_y,
grad_w, grad_h, grad_conf_target, grad_conf_notarget, grad_class,
class_num, loss_weight_xy, loss_weight_wh, loss_weight_conf_target,
loss_weight_conf_notarget, loss_weight_class);
}
};
} // namespace operators
} // namespace paddle
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 4eb44380
......@@ -22,6 +22,8 @@ from . import op_frequence
from .op_frequence import *
from . import quantize
from .quantize import *
from . import int8_inference
from .int8_inference import *
from . import reader
from .reader import *
from . import slim
......@@ -34,6 +36,7 @@ __all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += quantize.__all__
__all__ += int8_inference.__all__
__all__ += reader.__all__
__all__ += slim.__all__
__all__ += utils.__all__
python/paddle/fluid/contrib/int8_inference/__init__.py
浏览文件 @ 4eb44380
......@@ -11,3 +11,10 @@
# 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
from . import utility
from .utility import *
__all__ = utility.__all__
python/paddle/fluid/contrib/int8_inference/utility.py
浏览文件 @ 4eb44380
......@@ -11,11 +11,15 @@
# 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.
import paddle.fluid.core as core
from paddle.fluid import core
import numpy as np
import math
import os
import paddle.fluid as fluid
from paddle.fluid.executor import global_scope
from paddle.fluid import io
__all__ = ['Calibrator']
class Calibrator(object):
......@@ -76,8 +80,7 @@ class Calibrator(object):
'''
for i in self.sampling_program.list_vars():
if i.name in self.sampling_vars:
np_data = np.array(fluid.global_scope().find_var(i.name)
.get_tensor())
np_data = np.array(global_scope().find_var(i.name).get_tensor())
if i.name not in self._sampling_data:
self._sampling_data[i.name] = []
self._sampling_data[i.name].append(np_data)
......@@ -86,9 +89,9 @@ class Calibrator(object):
'''
Save the quantized model to the disk.
'''
fluid.io.save_inference_model(self.output, self.feed_var_names,
self.fetch_list, self.exe,
self.sampling_program)
io.save_inference_model(self.output, self.feed_var_names,
self.fetch_list, self.exe,
self.sampling_program)
def __display_debug(self):
if self.debug:
......
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ 4eb44380
......@@ -19,15 +19,12 @@ import sys
import random
import paddle
import paddle.fluid as fluid
import argparse
import functools
import contextlib
import paddle.fluid.profiler as profiler
from paddle.dataset.common import download
from PIL import Image, ImageEnhance
import math
sys.path.append('..')
import int8_inference.utility as int8_utility
import paddle.fluid.contrib.int8_inference.utility as int8_utility
random.seed(0)
np.random.seed(0)
......@@ -43,7 +40,7 @@ img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
# TODO(guomingz): Remove duplicated code from line 45 ~ line 114
# TODO(guomingz): Remove duplicated code from resize_short, crop_image, process_image, _reader_creator
def resize_short(img, target_size):
percent = float(target_size) / min(img.size[0], img.size[1])
resized_width = int(round(img.size[0] * percent))
......@@ -123,16 +120,37 @@ class TestCalibrationForResnet50(unittest.TestCase):
self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
self.int8_download)
data_url = 'http://paddle-inference-dist.cdn.bcebos.com/int8/calibration_test_data.tar.gz'
data_md5 = '1b6c1c434172cca1bf9ba1e4d7a3157d'
self.data_cache_folder = self.download_data(data_url, data_md5, "data")
data_urls = []
data_md5s = []
self.data_cache_folder = ''
if os.environ.get('DATASET') == 'full':
data_urls.append(
'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partaa'
)
data_md5s.append('60f6525b0e1d127f345641d75d41f0a8')
data_urls.append(
'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partab'
)
data_md5s.append('1e9f15f64e015e58d6f9ec3210ed18b5')
self.data_cache_folder = self.download_data(data_urls, data_md5s,
"full_data", False)
else:
data_urls.append(
'http://paddle-inference-dist.cdn.bcebos.com/int8/calibration_test_data.tar.gz'
)
data_md5s.append('1b6c1c434172cca1bf9ba1e4d7a3157d')
self.data_cache_folder = self.download_data(data_urls, data_md5s,
"small_data", False)
# reader/decorator.py requires the relative path to the data folder
cmd = 'rm -rf {0} && ln -s {1} {0}'.format("data",
self.data_cache_folder)
os.system(cmd)
self.iterations = 50
self.batch_size = 1
self.sample_iterations = 50
self.infer_iterations = 50000 if os.environ.get(
'DATASET') == 'full' else 50
def cache_unzipping(self, target_folder, zip_path):
if not os.path.exists(target_folder):
......@@ -140,20 +158,44 @@ class TestCalibrationForResnet50(unittest.TestCase):
zip_path)
os.system(cmd)
def download_data(self, data_url, data_md5, folder_name):
download(data_url, self.int8_download, data_md5)
def download_data(self, data_urls, data_md5s, folder_name, is_model=True):
data_cache_folder = os.path.join(self.cache_folder, folder_name)
file_name = data_url.split('/')[-1]
zip_path = os.path.join(self.cache_folder, file_name)
zip_path = ''
if os.environ.get('DATASET') == 'full':
file_names = []
for i in range(0, len(data_urls)):
download(data_urls[i], self.int8_download, data_md5s[i])
file_names.append(data_urls[i].split('/')[-1])
zip_path = os.path.join(self.cache_folder,
'full_imagenet_val.tar.gz')
if not os.path.exists(zip_path):
cat_command = 'cat'
for file_name in file_names:
cat_command += ' ' + os.path.join(self.cache_folder,
file_name)
cat_command += ' > ' + zip_path
os.system(cat_command)
if os.environ.get('DATASET') != 'full' or is_model:
download(data_urls[0], self.int8_download, data_md5s[0])
file_name = data_urls[0].split('/')[-1]
zip_path = os.path.join(self.cache_folder, file_name)
print('Data is downloaded at {0}').format(zip_path)
self.cache_unzipping(data_cache_folder, zip_path)
return data_cache_folder
def download_resnet50_model(self):
def download_model(self):
# resnet50 fp32 data
data_url = 'http://paddle-inference-dist.cdn.bcebos.com/int8/resnet50_int8_model.tar.gz'
data_md5 = '4a5194524823d9b76da6e738e1367881'
self.model_cache_folder = self.download_data(data_url, data_md5,
data_urls = [
'http://paddle-inference-dist.cdn.bcebos.com/int8/resnet50_int8_model.tar.gz'
]
data_md5s = ['4a5194524823d9b76da6e738e1367881']
self.model_cache_folder = self.download_data(data_urls, data_md5s,
"resnet50_fp32")
self.model = "ResNet-50"
self.algo = "direct"
def run_program(self, model_path, generate_int8=False, algo='direct'):
image_shape = [3, 224, 224]
......@@ -169,17 +211,17 @@ class TestCalibrationForResnet50(unittest.TestCase):
t = fluid.transpiler.InferenceTranspiler()
t.transpile(infer_program, fluid.CPUPlace())
val_reader = paddle.batch(val(), batch_size=1)
val_reader = paddle.batch(val(), self.batch_size)
iterations = self.infer_iterations
if generate_int8:
int8_model = os.path.join(os.getcwd(), "calibration_out")
iterations = self.sample_iterations
if os.path.exists(int8_model):
os.system("rm -rf " + int8_model)
os.system("mkdir " + int8_model)
print("Start calibration ...")
calibrator = int8_utility.Calibrator(
program=infer_program,
pretrained_model=model_path,
......@@ -191,6 +233,7 @@ class TestCalibrationForResnet50(unittest.TestCase):
test_info = []
cnt = 0
periods = []
for batch_id, data in enumerate(val_reader()):
image = np.array(
[x[0].reshape(image_shape) for x in data]).astype("float32")
......@@ -202,21 +245,28 @@ class TestCalibrationForResnet50(unittest.TestCase):
if op.has_attr("use_mkldnn"):
op._set_attr("use_mkldnn", True)
t1 = time.time()
_, acc1, _ = exe.run(
running_program,
feed={feed_dict[0]: image,
feed_dict[1]: label},
fetch_list=fetch_targets)
t2 = time.time()
period = t2 - t1
periods.append(period)
if generate_int8:
calibrator.sample_data()
test_info.append(np.mean(acc1) * len(data))
cnt += len(data)
if batch_id != self.iterations - 1:
continue
if (batch_id + 1) % 100 == 0:
print("{0} images,".format(batch_id + 1))
sys.stdout.flush()
break
if (batch_id + 1) == iterations:
break
if generate_int8:
calibrator.save_int8_model()
......@@ -225,32 +275,49 @@ class TestCalibrationForResnet50(unittest.TestCase):
"Calibration is done and the corresponding files are generated at {}".
format(os.path.abspath("calibration_out")))
else:
return np.sum(test_info) / cnt
throughput = cnt / np.sum(periods)
latency = np.average(periods)
acc1 = np.sum(test_info) / cnt
return (throughput, latency, acc1)
def test_calibration(self):
self.download_resnet50_model()
fp32_acc1 = self.run_program(self.model_cache_folder + "/model")
self.run_program(self.model_cache_folder + "/model", True)
int8_acc1 = self.run_program("calibration_out")
self.download_model()
print("Start FP32 inference for {0} on {1} images ...").format(
self.model, self.infer_iterations)
(fp32_throughput, fp32_latency,
fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
print("Start INT8 calibration for {0} on {1} images ...").format(
self.model, self.sample_iterations)
self.run_program(
self.model_cache_folder + "/model", True, algo=self.algo)
print("Start INT8 inference for {0} on {1} images ...").format(
self.model, self.infer_iterations)
(int8_throughput, int8_latency,
int8_acc1) = self.run_program("calibration_out")
delta_value = np.abs(fp32_acc1 - int8_acc1)
self.assertLess(delta_value, 0.01)
print(
"FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
format(self.model, self.batch_size, fp32_throughput, fp32_latency,
fp32_acc1))
print(
"INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
format(self.model, self.batch_size, int8_throughput, int8_latency,
int8_acc1))
sys.stdout.flush()
class TestCalibrationForMobilenetv1(TestCalibrationForResnet50):
def download_mobilenetv1_model(self):
def download_model(self):
# mobilenetv1 fp32 data
data_url = 'http://paddle-inference-dist.cdn.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
data_md5 = '13892b0716d26443a8cdea15b3c6438b'
self.model_cache_folder = self.download_data(data_url, data_md5,
data_urls = [
'http://paddle-inference-dist.cdn.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
]
data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
self.model_cache_folder = self.download_data(data_urls, data_md5s,
"mobilenetv1_fp32")
def test_calibration(self):
self.download_mobilenetv1_model()
fp32_acc1 = self.run_program(self.model_cache_folder + "/model")
self.run_program(self.model_cache_folder + "/model", True, algo='KL')
int8_acc1 = self.run_program("calibration_out")
delta_value = np.abs(fp32_acc1 - int8_acc1)
self.assertLess(delta_value, 0.01)
self.model = "MobileNet-V1"
self.algo = "KL"
if __name__ == '__main__':
......
python/paddle/fluid/initializer.py
浏览文件 @ 4eb44380
......@@ -366,17 +366,40 @@ class TruncatedNormalInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16:
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(".".join(
['truncated_gaussian_random', 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
op = block._prepend_op(
type="truncated_gaussian_random",
outputs={"Out": var},
outputs={"Out": out_var},
attrs={
"shape": var.shape,
"dtype": int(var.dtype),
"dtype": out_dtype,
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed
},
stop_gradient=True)
if var.dtype == VarDesc.VarType.FP16:
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
var.op = op
return op
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 4eb44380
......@@ -347,19 +347,107 @@ def box_coder(prior_box,
target_box,
code_type="encode_center_size",
box_normalized=True,
name=None):
name=None,
axis=0):
"""
${comment}
**Box Coder Layer**
Encode/Decode the target bounding box with the priorbox information.
The Encoding schema described below:
.. math::
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = \log(\abs(tw / pw)) / pwv
oh = \log(\abs(th / ph)) / phv
The Decoding schema described below:
.. math::
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = \exp(pwv * tw) * pw + tw / 2
oh = \exp(phv * th) * ph + th / 2
where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates,
width and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote
the priorbox's (anchor) center coordinates, width and height. `pxv`,
`pyv`, `pwv`, `phv` denote the variance of the priorbox and `ox`, `oy`,
`ow`, `oh` denote the encoded/decoded coordinates, width and height.
During Box Decoding, two modes for broadcast are supported. Say target
box has shape [N, M, 4], and the shape of prior box can be [N, 4] or
[M, 4]. Then prior box will broadcast to target box along the
assigned axis.
Args:
prior_box(${prior_box_type}): ${prior_box_comment}
prior_box_var(${prior_box_var_type}): ${prior_box_var_comment}
target_box(${target_box_type}): ${target_box_comment}
code_type(${code_type_type}): ${code_type_comment}
box_normalized(${box_normalized_type}): ${box_normalized_comment}
prior_box(Variable): Box list prior_box is a 2-D Tensor with shape
[M, 4] holds M boxes, each box is represented as
[xmin, ymin, xmax, ymax], [xmin, ymin] is the
left top coordinate of the anchor box, if the
input is image feature map, they are close to
the origin of the coordinate system. [xmax, ymax]
is the right bottom coordinate of the anchor box.
prior_box_var(Variable|list): prior_box_var supports two types of input.
One is variable with shape [M, 4] holds M group.
The other one is list consist of 4 elements
shared by all boxes.
target_box(Variable): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'.
This input also can be a 3-D Tensor with shape
[N, M, 4] when code_type is 'decode_center_size'.
Each box is represented as
[xmin, ymin, xmax, ymax]. This tensor can
contain LoD information to represent a batch
of inputs.
code_type(string): The code type used with the target box. It can be
encode_center_size or decode_center_size
box_normalized(int): Whether treat the priorbox as a noramlized box.
Set true by default.
name(string): The name of box coder.
axis(int): Which axis in PriorBox to broadcast for box decode,
for example, if axis is 0 and TargetBox has shape
[N, M, 4] and PriorBox has shape [M, 4], then PriorBox
will broadcast to [N, M, 4] for decoding. It is only valid
when code type is decode_center_size. Set 0 by default.
Returns:
output_box(${output_box_type}): ${output_box_comment}
output_box(Variable): When code_type is 'encode_center_size', the
output tensor of box_coder_op with shape
[N, M, 4] representing the result of N target
boxes encoded with M Prior boxes and variances.
When code_type is 'decode_center_size',
N represents the batch size and M represents
the number of deocded boxes.
Examples:
.. code-block:: python
prior_box = fluid.layers.data(name='prior_box',
shape=[512, 4],
dtype='float32',
append_batch_size=False)
target_box = fluid.layers.data(name='target_box',
shape=[512,81,4],
dtype='float32',
append_batch_size=False)
output = fluid.layers.box_coder(prior_box=prior_box,
prior_box_var=[0.1,0.1,0.2,0.2],
target_box=target_box,
code_type="decode_center_size",
box_normalized=False,
axis=1)
"""
helper = LayerHelper("box_coder", **locals())
......@@ -370,15 +458,22 @@ def box_coder(prior_box,
output_box = helper.create_variable(
name=name, dtype=prior_box.dtype, persistable=False)
inputs = {"PriorBox": prior_box, "TargetBox": target_box}
attrs = {
"code_type": code_type,
"box_normalized": box_normalized,
"axis": axis
}
if isinstance(prior_box_var, Variable):
inputs['PriorBoxVar'] = prior_box_var
elif isinstance(prior_box_var, list):
attrs['variance'] = prior_box_var
else:
raise TypeError("Input variance of box_coder must be Variable or lisz")
helper.append_op(
type="box_coder",
inputs={
"PriorBox": prior_box,
"PriorBoxVar": prior_box_var,
"TargetBox": target_box
},
attrs={"code_type": code_type,
"box_normalized": box_normalized},
inputs=inputs,
attrs=attrs,
outputs={"OutputBox": output_box})
return output_box
......@@ -414,13 +509,10 @@ def yolov3_loss(x,
gtbox,
gtlabel,
anchors,
anchor_mask,
class_num,
ignore_thresh,
loss_weight_xy=None,
loss_weight_wh=None,
loss_weight_conf_target=None,
loss_weight_conf_notarget=None,
loss_weight_class=None,
downsample_ratio,
name=None):
"""
${comment}
......@@ -432,16 +524,13 @@ def yolov3_loss(x,
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
gtlabel (Variable): class id of ground truth boxes, shoud be in shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
Returns:
......@@ -461,9 +550,10 @@ def yolov3_loss(x,
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
"""
helper = LayerHelper('yolov3_loss', **locals())
......@@ -475,6 +565,8 @@ def yolov3_loss(x,
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
if not isinstance(anchor_mask, list) and not isinstance(anchor_mask, tuple):
raise TypeError("Attr anchor_mask of yolov3_loss must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
......@@ -487,31 +579,29 @@ def yolov3_loss(x,
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
objectness_mask = helper.create_variable_for_type_inference(dtype='int32')
gt_match_mask = helper.create_variable_for_type_inference(dtype='int32')
attrs = {
"anchors": anchors,
"anchor_mask": anchor_mask,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
"downsample_ratio": downsample_ratio,
}
if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
self.attrs['loss_weight_xy'] = loss_weight_xy
if loss_weight_wh is not None and isinstance(loss_weight_wh, float):
self.attrs['loss_weight_wh'] = loss_weight_wh
if loss_weight_conf_target is not None and isinstance(
loss_weight_conf_target, float):
self.attrs['loss_weight_conf_target'] = loss_weight_conf_target
if loss_weight_conf_notarget is not None and isinstance(
loss_weight_conf_notarget, float):
self.attrs['loss_weight_conf_notarget'] = loss_weight_conf_notarget
if loss_weight_class is not None and isinstance(loss_weight_class, float):
self.attrs['loss_weight_class'] = loss_weight_class
helper.append_op(
type='yolov3_loss',
inputs={"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel},
outputs={'Loss': loss},
inputs={
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel,
},
outputs={
'Loss': loss,
'ObjectnessMask': objectness_mask,
'GTMatchMask': gt_match_mask
},
attrs=attrs)
return loss
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4eb44380
......@@ -3877,7 +3877,8 @@ def beam_search(pre_ids,
end_id,
level=0,
is_accumulated=True,
name=None):
name=None,
return_parent_idx=False):
"""
Beam search is a classical algorithm for selecting candidate words in a
machine translation task.
......@@ -3933,10 +3934,16 @@ def beam_search(pre_ids,
accumulated scores.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
return_parent_idx(bool): Whether to return an extra Tensor variable
preserving the selected_ids' parent indice in pre_ids
in output, which can be used to gather cell states at
the next time step.
Returns:
Variable: The LodTensor pair containing the selected ids and the \
corresponding scores.
Variable: The LodTensor tuple containing the selected ids and the \
corresponding scores. If :attr:`return_parent_idx` is :attr:`True`, \
an extra Tensor variable preserving the selected_ids' parent indice \
is included.
Examples:
.. code-block:: python
......@@ -3969,6 +3976,11 @@ def beam_search(pre_ids,
selected_scores = helper.create_variable_for_type_inference(
dtype=score_type)
selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
# parent_idx is a tensor used to gather cell states at the next time
# step. Though lod in selected_ids can also be used to gather by
# sequence_expand, it is not efficient.
# gather_op's index input only supports int32 dtype currently
parent_idx = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='beam_search',
......@@ -3976,6 +3988,7 @@ def beam_search(pre_ids,
outputs={
'selected_ids': selected_ids,
'selected_scores': selected_scores,
'parent_idx': parent_idx
},
attrs={
# TODO(ChunweiYan) to assure other value support
......@@ -3984,8 +3997,10 @@ def beam_search(pre_ids,
'end_id': end_id,
'is_accumulated': is_accumulated,
})
return selected_ids, selected_scores
if return_parent_idx:
return selected_ids, selected_scores, parent_idx
else:
return selected_ids, selected_scores
def beam_search_decode(ids, scores, beam_size, end_id, name=None):
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 4eb44380
......@@ -135,7 +135,7 @@ def thresholded_relu(x, threshold=None):
if val is not None:
kwargs[name] = val
_thresholded_relu_(**kwargs)
return _thresholded_relu_(**kwargs)
thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 4eb44380
......@@ -50,6 +50,19 @@ class TestDetection(unittest.TestCase):
self.assertEqual(out.shape[-1], 6)
print(str(program))
def test_box_coder_api(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
bcoder = layers.box_coder(
prior_box=x,
prior_box_var=[0.1, 0.2, 0.1, 0.2],
target_box=y,
code_type='encode_center_size')
self.assertIsNotNone(bcoder)
print(str(program))
def test_detection_api(self):
program = Program()
with program_guard(program):
......@@ -463,8 +476,8 @@ class TestYoloDetection(unittest.TestCase):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13], 10,
0.5)
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13],
[0, 1], 10, 0.7, 32)
self.assertIsNotNone(loss)
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 4eb44380
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# The MKLDNN tests are skiped when the MKLDNN flag is OFF
if(NOT WITH_MKLDNN)
foreach(src ${TEST_OPS})
if(${src} MATCHES ".*_mkldnn_op$")
list(REMOVE_ITEM TEST_OPS ${src})
endif()
endforeach()
endif(NOT WITH_MKLDNN)
if(NOT WITH_DISTRIBUTE)
list(REMOVE_ITEM TEST_OPS test_recv_op)
list(REMOVE_ITEM TEST_OPS test_dist_transpiler)
......@@ -123,3 +114,7 @@ endif()
if (WITH_NGRAPH)
add_subdirectory(ngraph)
endif()
if (WITH_MKLDNN)
add_subdirectory(mkldnn)
endif()
python/paddle/fluid/tests/unittests/mkldnn/CMakeLists.txt 0 → 100644
浏览文件 @ 4eb44380
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
python/paddle/fluid/tests/unittests/mkldnn/__init__.py 0 → 100644
浏览文件 @ 4eb44380
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed 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.
python/paddle/fluid/tests/unittests/test_activation_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -17,9 +17,9 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
from scipy.special import expit
from test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
class TestMKLDNNReluDim2(TestRelu):
......
python/paddle/fluid/tests/unittests/test_batch_norm_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_batch_norm_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -19,9 +19,9 @@ import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.framework import grad_var_name
from test_batch_norm_op import TestBatchNormOpInference, TestBatchNormOpTraining, _reference_training, _reference_grad
from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpInference, TestBatchNormOpTraining, _reference_training, _reference_grad
class TestMKLDNNBatchNormOpTraining(TestBatchNormOpTraining):
......
python/paddle/fluid/tests/unittests/test_concat_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_concat_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -15,7 +15,7 @@
from __future__ import print_function
import unittest
from test_concat_op import TestConcatOp, TestConcatOp2, TestConcatOp3
from paddle.fluid.tests.unittests.test_concat_op import TestConcatOp, TestConcatOp2, TestConcatOp3
class TestMKLDNNConcatOp(TestConcatOp):
......
python/paddle/fluid/tests/unittests/test_conv2d_int8_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -18,8 +18,8 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from test_conv2d_op import conv2d_forward_naive, TestConv2dOp
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp
def conv2d_forward_refer(input, filter, group, conv_param):
......
python/paddle/fluid/tests/unittests/test_conv2d_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
class TestMKLDNN(TestConv2dOp):
......
python/paddle/fluid/tests/unittests/test_conv2d_transpose_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_conv2d_transpose_op import TestConv2dTransposeOp, TestWithPad, TestWithStride
from paddle.fluid.tests.unittests.test_conv2d_transpose_op import TestConv2dTransposeOp, TestWithPad, TestWithStride
class TestMKLDNN(TestConv2dTransposeOp):
......
python/paddle/fluid/tests/unittests/test_conv3d_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_conv3d_op import TestConv3dOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1
from paddle.fluid.tests.unittests.test_conv3d_op import TestConv3dOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1
class TestMKLDNN(TestConv3dOp):
......
python/paddle/fluid/tests/unittests/test_dequantize_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
class TestDeQuantizeOp(OpTest):
......
python/paddle/fluid/tests/unittests/test_elementwise_add_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_add_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,8 +16,8 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from test_elementwise_add_op import *
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_elementwise_add_op import *
'''
Some tests differ from the tests defined in test_elementwise_add_op.py
because MKLDNN does not support tensors of number of dimensions 3.
......
python/paddle/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_mul_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -15,10 +15,10 @@
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from test_elementwise_mul_op import *
from paddle.fluid.tests.unittests.test_elementwise_mul_op import *
class TestElementwiseMulMKLDNNOp_BroadcastNCHW16c(ElementwiseMulOp):
......
python/paddle/fluid/tests/unittests/test_fc_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_fc_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
def fully_connected_naive(input, weights, bias_data=None):
......
python/paddle/fluid/tests/unittests/test_gaussian_random_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_gaussian_random_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_gaussian_random_op import TestGaussianRandomOp
from paddle.fluid.tests.unittests.test_gaussian_random_op import TestGaussianRandomOp
class TestMKLDNN(TestGaussianRandomOp):
......
python/paddle/fluid/tests/unittests/test_lrn_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_lrn_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -15,7 +15,7 @@
from __future__ import print_function
import unittest
from test_lrn_op import TestLRNOp
from paddle.fluid.tests.unittests.test_lrn_op import TestLRNOp
class TestLRNMKLDNNOp(TestLRNOp):
......
python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -19,8 +19,8 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive
class TestPool2dMKLDNNInt8_Op(TestPool2D_Op):
......
python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -15,7 +15,7 @@
from __future__ import print_function
import unittest
from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
def create_test_mkldnn_class(parent):
......
python/paddle/fluid/tests/unittests/test_quantize_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_quantize_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid.tests.unittests.op_test import OpTest
class TestQuantizeOp(OpTest):
......
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_sum_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_sum_op import TestSumOp
from paddle.fluid.tests.unittests.test_sum_op import TestSumOp
class TestMKLDNN(TestSumOp):
......
python/paddle/fluid/tests/unittests/test_transpose_mkldnn_op.py → python/paddle/fluid/tests/unittests/mkldnn/test_transpose_mkldnn_op.py
浏览文件 @ 4eb44380
......@@ -16,7 +16,7 @@ from __future__ import print_function
import unittest
from test_transpose_op import TestTransposeOp
from paddle.fluid.tests.unittests.test_transpose_op import TestTransposeOp
class TestTransposeMKLDNN(TestTransposeOp):
......
python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py 0 → 100644
浏览文件 @ 4eb44380
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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
from paddle.fluid.tests.unittests.test_pool2d_op import *
class TestNGRAPHPool2D_Op(TestPool2D_Op):
def init_test_case(self):
super(TestNGRAPHPool2D_Op, self).init_test_case()
class TestNGRAPHCase1(TestCase1):
def init_test_case(self):
super(TestNGRAPHCase1, self).init_test_case()
class TestNGRAPHCase2(TestCase2):
def init_test_case(self):
super(TestNGRAPHCase2, self).init_test_case()
class TestNGRAPHCase3(TestCase3):
def init_pool_type(self):
super(TestNGRAPHCase3, self).init_pool_type()
class TestNGRAPHCase4(TestCase4):
def init_pool_type(self):
super(TestNGRAPHCase4, self).init_pool_type()
class TestNGRAPHCase5(TestCase5):
def init_pool_type(self):
super(TestNGRAPHCase5, self).init_pool_type()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_beam_search_op.py
浏览文件 @ 4eb44380
......@@ -38,6 +38,7 @@ class BeamSearchOpTester(unittest.TestCase):
self._create_pre_ids()
self.scope.var('selected_ids')
self.scope.var('selected_scores')
self.scope.var('parent_idx')
def test_run(self):
op = Operator(
......@@ -48,12 +49,14 @@ class BeamSearchOpTester(unittest.TestCase):
scores='scores',
selected_ids='selected_ids',
selected_scores='selected_scores',
parent_idx='parent_idx',
level=0,
beam_size=2,
end_id=0, )
op.run(self.scope, core.CPUPlace())
selected_ids = self.scope.find_var("selected_ids").get_tensor()
selected_scores = self.scope.find_var("selected_scores").get_tensor()
parent_idx = self.scope.find_var("parent_idx").get_tensor()
self.assertTrue(
np.allclose(
np.array(selected_ids), np.array([4, 2, 3, 8])[:, np.newaxis]))
......@@ -62,6 +65,8 @@ class BeamSearchOpTester(unittest.TestCase):
np.array(selected_scores),
np.array([0.5, 0.6, 0.9, 0.7])[:, np.newaxis]))
self.assertEqual(selected_ids.lod(), [[0, 2, 4], [0, 1, 2, 3, 4]])
self.assertTrue(
np.allclose(np.array(parent_idx), np.array([0, 1, 2, 3])))
def _create_pre_ids(self):
np_data = np.array([[1, 2, 3, 4]], dtype='int64')
......
python/paddle/fluid/tests/unittests/test_box_coder_op.py
浏览文件 @ 4eb44380
......@@ -21,80 +21,80 @@ import math
from op_test import OpTest
def box_coder(target_box, prior_box, prior_box_var, output_box, code_type,
box_normalized):
prior_box_x = (
(prior_box[:, 2] + prior_box[:, 0]) / 2).reshape(1, prior_box.shape[0])
prior_box_y = (
(prior_box[:, 3] + prior_box[:, 1]) / 2).reshape(1, prior_box.shape[0])
prior_box_width = (
(prior_box[:, 2] - prior_box[:, 0])).reshape(1, prior_box.shape[0])
prior_box_height = (
(prior_box[:, 3] - prior_box[:, 1])).reshape(1, prior_box.shape[0])
prior_box_var = prior_box_var.reshape(1, prior_box_var.shape[0],
prior_box_var.shape[1])
if not box_normalized:
prior_box_height = prior_box_height + 1
prior_box_width = prior_box_width + 1
if (code_type == "EncodeCenterSize"):
target_box_x = ((target_box[:, 2] + target_box[:, 0]) / 2).reshape(
target_box.shape[0], 1)
target_box_y = ((target_box[:, 3] + target_box[:, 1]) / 2).reshape(
target_box.shape[0], 1)
target_box_width = ((target_box[:, 2] - target_box[:, 0])).reshape(
target_box.shape[0], 1)
target_box_height = ((target_box[:, 3] - target_box[:, 1])).reshape(
target_box.shape[0], 1)
if not box_normalized:
target_box_height = target_box_height + 1
target_box_width = target_box_width + 1
output_box[:,:,0] = (target_box_x - prior_box_x) / prior_box_width / \
prior_box_var[:,:,0]
output_box[:,:,1] = (target_box_y - prior_box_y) / prior_box_height / \
prior_box_var[:,:,1]
output_box[:,:,2] = np.log(np.fabs(target_box_width / prior_box_width)) / \
prior_box_var[:,:,2]
output_box[:,:,3] = np.log(np.fabs(target_box_height / prior_box_height)) / \
prior_box_var[:,:,3]
elif (code_type == "DecodeCenterSize"):
target_box_x = prior_box_var[:,:,0] * target_box[:,:,0] * \
prior_box_width + prior_box_x
target_box_y = prior_box_var[:,:,1] * target_box[:,:,1] * \
prior_box_height + prior_box_y
target_box_width = np.exp(prior_box_var[:,:,2] * target_box[:,:,2]) * \
prior_box_width
target_box_height = np.exp(prior_box_var[:,:,3] * target_box[:,:,3]) * \
prior_box_height
output_box[:, :, 0] = target_box_x - target_box_width / 2
output_box[:, :, 1] = target_box_y - target_box_height / 2
output_box[:, :, 2] = target_box_x + target_box_width / 2
output_box[:, :, 3] = target_box_y + target_box_height / 2
if not box_normalized:
output_box[:, :, 2] = output_box[:, :, 2] - 1
output_box[:, :, 3] = output_box[:, :, 3] - 1
def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type,
box_normalized):
n = target_box.shape[0]
m = prior_box.shape[0]
def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False)
pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False)
pb_x = pb_w * 0.5 + p_box[:, 0]
pb_y = pb_h * 0.5 + p_box[:, 1]
shape = (1, p_box.shape[0]) if axis == 0 else (p_box.shape[0], 1)
pb_w = pb_w.reshape(shape)
pb_h = pb_h.reshape(shape)
pb_x = pb_x.reshape(shape)
pb_y = pb_y.reshape(shape)
if pb_v.ndim == 2:
pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
if pb_v.ndim == 1:
tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x
tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y
tb_w = np.exp(pb_v[2] * t_box[:, :, 2]) * pb_w
tb_h = np.exp(pb_v[3] * t_box[:, :, 3]) * pb_h
else:
tb_x = pb_v[:, :, 0] * t_box[:, :, 0] * pb_w + pb_x
tb_y = pb_v[:, :, 1] * t_box[:, :, 1] * pb_h + pb_y
tb_w = np.exp(pb_v[:, :, 2] * t_box[:, :, 2]) * pb_w
tb_h = np.exp(pb_v[:, :, 3] * t_box[:, :, 3]) * pb_h
output_box[:, :, 0] = tb_x - tb_w / 2
output_box[:, :, 1] = tb_y - tb_h / 2
output_box[:, :, 2] = tb_x + tb_w / 2 - (not norm)
output_box[:, :, 3] = tb_y + tb_h / 2 - (not norm)
def box_encoder(t_box, p_box, pb_v, output_box, norm):
pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False)
pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False)
pb_x = pb_w * 0.5 + p_box[:, 0]
pb_y = pb_h * 0.5 + p_box[:, 1]
shape = (1, p_box.shape[0])
pb_w = pb_w.reshape(shape)
pb_h = pb_h.reshape(shape)
pb_x = pb_x.reshape(shape)
pb_y = pb_y.reshape(shape)
if pb_v.ndim == 2:
pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
tb_x = ((t_box[:, 2] + t_box[:, 0]) / 2).reshape(t_box.shape[0], 1)
tb_y = ((t_box[:, 3] + t_box[:, 1]) / 2).reshape(t_box.shape[0], 1)
tb_w = (t_box[:, 2] - t_box[:, 0]).reshape(t_box.shape[0], 1) + (not norm)
tb_h = (t_box[:, 3] - t_box[:, 1]).reshape(t_box.shape[0], 1) + (not norm)
if pb_v.ndim == 1:
output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[0]
output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[1]
output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[2]
output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[3]
else:
output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[:, :, 0]
output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[:, :, 1]
output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[:, :, 2]
output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[:, :, 3]
def batch_box_coder(p_box, pb_v, t_box, lod, code_type, norm, axis=0):
n = t_box.shape[0]
m = p_box.shape[0]
if code_type == "DecodeCenterSize":
m = t_box.shape[1]
output_box = np.zeros((n, m, 4), dtype=np.float32)
cur_offset = 0
for i in range(len(lod)):
if (code_type == "EncodeCenterSize"):
box_coder(target_box[cur_offset:(cur_offset + lod[i]), :],
prior_box, prior_box_var,
output_box[cur_offset:(cur_offset + lod[i]), :, :],
code_type, box_normalized)
box_encoder(t_box[cur_offset:(cur_offset + lod[i]), :], p_box, pb_v,
output_box[cur_offset:(cur_offset + lod[i]), :, :],
norm)
elif (code_type == "DecodeCenterSize"):
box_coder(target_box[cur_offset:(cur_offset + lod[i]), :, :],
prior_box, prior_box_var,
output_box[cur_offset:(cur_offset + lod[i]), :, :],
code_type, box_normalized)
box_decoder(t_box, p_box, pb_v, output_box, norm, axis)
cur_offset += lod[i]
return output_box
......@@ -106,9 +106,35 @@ class TestBoxCoderOp(OpTest):
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((10, 4)).astype('float32')
prior_box_var = np.random.random((10, 4)).astype('float32')
target_box = np.random.random((5, 10, 4)).astype('float32')
prior_box = np.random.random((81, 4)).astype('float32')
prior_box_var = np.random.random((81, 4)).astype('float32')
target_box = np.random.random((20, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
lod[0], code_type, box_normalized)
self.inputs = {
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': target_box,
}
self.attrs = {
'code_type': 'decode_center_size',
'box_normalized': False
}
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithOneRankVar(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((81, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
target_box = np.random.random((20, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
......@@ -133,9 +159,9 @@ class TestBoxCoderOpWithoutBoxVar(OpTest):
def setUp(self):
self.op_type = "box_coder"
lod = [[0, 1, 2, 3, 4, 5]]
prior_box = np.random.random((10, 4)).astype('float32')
prior_box_var = np.ones((10, 4)).astype('float32')
target_box = np.random.random((5, 10, 4)).astype('float32')
prior_box = np.random.random((81, 4)).astype('float32')
prior_box_var = np.ones((81, 4)).astype('float32')
target_box = np.random.random((20, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
......@@ -158,10 +184,10 @@ class TestBoxCoderOpWithLoD(OpTest):
def setUp(self):
self.op_type = "box_coder"
lod = [[4, 8, 8]]
prior_box = np.random.random((10, 4)).astype('float32')
prior_box_var = np.random.random((10, 4)).astype('float32')
target_box = np.random.random((20, 4)).astype('float32')
lod = [[10, 20, 20]]
prior_box = np.random.random((20, 4)).astype('float32')
prior_box_var = np.random.random((20, 4)).astype('float32')
target_box = np.random.random((50, 4)).astype('float32')
code_type = "EncodeCenterSize"
box_normalized = True
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
......@@ -176,5 +202,63 @@ class TestBoxCoderOpWithLoD(OpTest):
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithAxis(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((30, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
target_box = np.random.random((30, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
axis = 1
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
lod[0], code_type, box_normalized, axis)
self.inputs = {
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': target_box,
}
self.attrs = {
'code_type': 'decode_center_size',
'box_normalized': False,
'axis': axis
}
self.outputs = {'OutputBox': output_box}
class TestBoxCoderOpWithVariance(OpTest):
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((30, 4)).astype('float32')
prior_box_var = np.random.random((4)).astype('float32')
target_box = np.random.random((30, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
axis = 1
output_box = batch_box_coder(prior_box, prior_box_var, target_box,
lod[0], code_type, box_normalized, axis)
self.inputs = {
'PriorBox': prior_box,
'TargetBox': target_box,
}
self.attrs = {
'code_type': 'decode_center_size',
'box_normalized': False,
'variance': prior_box_var.astype(np.float).flatten(),
'axis': axis
}
self.outputs = {'OutputBox': output_box}
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ 4eb44380
......@@ -268,9 +268,6 @@ class TestImperativePtbRnn(unittest.TestCase):
sgd.minimize(dy_loss)
for param in ptb_model.parameters():
dy_param_updated[param.name] = param._numpy()
# print("dy_loss is {}".format(dy_loss._numpy()))
# print("last_hidden is {}".format(last_hidden._numpy()))
# print("last_cell is {}".format(last_cell._numpy()))
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @ 4eb44380
......@@ -16,174 +16,179 @@ from __future__ import division
import unittest
import numpy as np
from scipy.special import logit
from scipy.special import expit
from op_test import OpTest
from paddle.fluid import core
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def l2loss(x, y):
return 0.5 * (y - x) * (y - x)
def mse(x, y, num):
return ((y - x)**2).sum() / num
def sce(x, label):
sigmoid_x = expit(x)
term1 = label * np.log(sigmoid_x)
term2 = (1.0 - label) * np.log(1.0 - sigmoid_x)
return -term1 - term2
def bce(x, y, mask):
x = x.reshape((-1))
y = y.reshape((-1))
mask = mask.reshape((-1))
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
error_sum = 0.0
count = 0
for i in range(x.shape[0]):
if mask[i] > 0:
error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
count += 1
return error_sum / (-1.0 * count)
def batch_xywh_box_iou(box1, box2):
b1_left = box1[:, :, 0] - box1[:, :, 2] / 2
b1_right = box1[:, :, 0] + box1[:, :, 2] / 2
b1_top = box1[:, :, 1] - box1[:, :, 3] / 2
b1_bottom = box1[:, :, 1] + box1[:, :, 3] / 2
def box_iou(box1, box2):
b1_x1 = box1[0] - box1[2] / 2
b1_x2 = box1[0] + box1[2] / 2
b1_y1 = box1[1] - box1[3] / 2
b1_y2 = box1[1] + box1[3] / 2
b2_x1 = box2[0] - box2[2] / 2
b2_x2 = box2[0] + box2[2] / 2
b2_y1 = box2[1] - box2[3] / 2
b2_y2 = box2[1] + box2[3] / 2
b2_left = box2[:, :, 0] - box2[:, :, 2] / 2
b2_right = box2[:, :, 0] + box2[:, :, 2] / 2
b2_top = box2[:, :, 1] - box2[:, :, 3] / 2
b2_bottom = box2[:, :, 1] + box2[:, :, 3] / 2
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
left = np.maximum(b1_left[:, :, np.newaxis], b2_left[:, np.newaxis, :])
right = np.minimum(b1_right[:, :, np.newaxis], b2_right[:, np.newaxis, :])
top = np.maximum(b1_top[:, :, np.newaxis], b2_top[:, np.newaxis, :])
bottom = np.minimum(b1_bottom[:, :, np.newaxis],
b2_bottom[:, np.newaxis, :])
inter_rect_x1 = max(b1_x1, b2_x1)
inter_rect_y1 = max(b1_y1, b2_y1)
inter_rect_x2 = min(b1_x2, b2_x2)
inter_rect_y2 = min(b1_y2, b2_y2)
inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
inter_rect_y2 - inter_rect_y1, 0)
inter_w = np.clip(right - left, 0., 1.)
inter_h = np.clip(bottom - top, 0., 1.)
inter_area = inter_w * inter_h
return inter_area / (b1_area + b2_area + inter_area)
b1_area = (b1_right - b1_left) * (b1_bottom - b1_top)
b2_area = (b2_right - b2_left) * (b2_bottom - b2_top)
union = b1_area[:, :, np.newaxis] + b2_area[:, np.newaxis, :] - inter_area
return inter_area / union
def build_target(gtboxs, gtlabel, attrs, grid_size):
n, b, _ = gtboxs.shape
ignore_thresh = attrs["ignore_thresh"]
anchors = attrs["anchors"]
class_num = attrs["class_num"]
an_num = len(anchors) // 2
obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tcls = np.zeros(
(n, an_num, grid_size, grid_size, class_num)).astype('float32')
def YOLOv3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
b = gtbox.shape[1]
anchors = attrs['anchors']
an_num = len(anchors) // 2
anchor_mask = attrs['anchor_mask']
mask_num = len(anchor_mask)
class_num = attrs["class_num"]
ignore_thresh = attrs['ignore_thresh']
downsample = attrs['downsample']
input_size = downsample * h
x = x.reshape((n, mask_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
loss = np.zeros((n)).astype('float32')
pred_box = x[:, :, :, :, :4].copy()
grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
x[:, :, :, :, 5:] = np.where(x[:, :, :, :, 5:] < -0.5, x[:, :, :, :, 5:],
np.ones_like(x[:, :, :, :, 5:]) * 1.0 /
class_num)
mask_anchors = []
for m in anchor_mask:
mask_anchors.append((anchors[2 * m], anchors[2 * m + 1]))
anchors_s = np.array(
[(an_w / input_size, an_h / input_size) for an_w, an_h in mask_anchors])
anchor_w = anchors_s[:, 0:1].reshape((1, mask_num, 1, 1))
anchor_h = anchors_s[:, 1:2].reshape((1, mask_num, 1, 1))
pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) * anchor_h
pred_box = pred_box.reshape((n, -1, 4))
pred_obj = x[:, :, :, :, 4].reshape((n, -1))
objness = np.zeros(pred_box.shape[:2]).astype('float32')
ious = batch_xywh_box_iou(pred_box, gtbox)
ious_max = np.max(ious, axis=-1)
objness = np.where(ious_max > ignore_thresh, -np.ones_like(objness),
objness)
gtbox_shift = gtbox.copy()
gtbox_shift[:, :, 0] = 0
gtbox_shift[:, :, 1] = 0
anchors = [(anchors[2 * i], anchors[2 * i + 1]) for i in range(0, an_num)]
anchors_s = np.array(
[(an_w / input_size, an_h / input_size) for an_w, an_h in anchors])
anchor_boxes = np.concatenate(
[np.zeros_like(anchors_s), anchors_s], axis=-1)
anchor_boxes = np.tile(anchor_boxes[np.newaxis, :, :], (n, 1, 1))
ious = batch_xywh_box_iou(gtbox_shift, anchor_boxes)
iou_matches = np.argmax(ious, axis=-1)
gt_matches = iou_matches.copy()
for i in range(n):
for j in range(b):
if gtboxs[i, j, :].sum() == 0:
if gtbox[i, j, 2:].sum() == 0:
gt_matches[i, j] = -1
continue
if iou_matches[i, j] not in anchor_mask:
gt_matches[i, j] = -1
continue
an_idx = anchor_mask.index(iou_matches[i, j])
gt_matches[i, j] = an_idx
gi = int(gtbox[i, j, 0] * w)
gj = int(gtbox[i, j, 1] * h)
gt_label = gtlabel[i, j]
gx = gtboxs[i, j, 0] * grid_size
gy = gtboxs[i, j, 1] * grid_size
gw = gtboxs[i, j, 2] * grid_size
gh = gtboxs[i, j, 3] * grid_size
gi = int(gx)
gj = int(gy)
gtbox = [0, 0, gw, gh]
max_iou = 0
for k in range(an_num):
anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
iou = box_iou(gtbox, anchor_box)
if iou > max_iou:
max_iou = iou
best_an_index = k
if iou > ignore_thresh:
noobj_mask[i, best_an_index, gj, gi] = 0
obj_mask[i, best_an_index, gj, gi] = 1
noobj_mask[i, best_an_index, gj, gi] = 0
tx[i, best_an_index, gj, gi] = gx - gi
ty[i, best_an_index, gj, gi] = gy - gj
tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
best_an_index])
th[i, best_an_index, gj, gi] = np.log(
gh / anchors[2 * best_an_index + 1])
tconf[i, best_an_index, gj, gi] = 1
tcls[i, best_an_index, gj, gi, gt_label] = 1
return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
def YoloV3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
an_num = len(attrs['anchors']) // 2
class_num = attrs["class_num"]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
pred_x = sigmoid(x[:, :, :, :, 0])
pred_y = sigmoid(x[:, :, :, :, 1])
pred_w = x[:, :, :, :, 2]
pred_h = x[:, :, :, :, 3]
pred_conf = sigmoid(x[:, :, :, :, 4])
pred_cls = sigmoid(x[:, :, :, :, 5:])
tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
gtbox, gtlabel, attrs, x.shape[2])
obj_mask_expand = np.tile(
np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
loss_conf_target = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
loss_conf_notarget = bce(pred_conf * noobj_mask, tconf * noobj_mask,
noobj_mask)
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+ attrs['loss_weight_wh'] * (loss_w + loss_h) \
+ attrs['loss_weight_conf_target'] * loss_conf_target \
+ attrs['loss_weight_conf_notarget'] * loss_conf_notarget \
+ attrs['loss_weight_class'] * loss_class
tx = gtbox[i, j, 0] * w - gi
ty = gtbox[i, j, 1] * w - gj
tw = np.log(gtbox[i, j, 2] * input_size / mask_anchors[an_idx][0])
th = np.log(gtbox[i, j, 3] * input_size / mask_anchors[an_idx][1])
scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3])
loss[i] += sce(x[i, an_idx, gj, gi, 0], tx) * scale
loss[i] += sce(x[i, an_idx, gj, gi, 1], ty) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 2], tw) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 3], th) * scale
objness[i, an_idx * h * w + gj * w + gi] = 1.0
for label_idx in range(class_num):
loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
float(label_idx == gtlabel[i, j]))
for j in range(mask_num * h * w):
if objness[i, j] > 0:
loss[i] += sce(pred_obj[i, j], 1.0)
elif objness[i, j] == 0:
loss[i] += sce(pred_obj[i, j], 0.0)
return (loss, objness.reshape((n, mask_num, h, w)).astype('float32'), \
gt_matches.astype('int32'))
class TestYolov3LossOp(OpTest):
def setUp(self):
self.loss_weight_xy = 1.0
self.loss_weight_wh = 1.0
self.loss_weight_conf_target = 1.0
self.loss_weight_conf_notarget = 1.0
self.loss_weight_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
x = logit(np.random.uniform(0, 1, self.x_shape).astype('float32'))
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num,
self.gtbox_shape[:2]).astype('int32')
gtlabel = np.random.randint(0, self.class_num, self.gtbox_shape[:2])
gtmask = np.random.randint(0, 2, self.gtbox_shape[:2])
gtbox = gtbox * gtmask[:, :, np.newaxis]
gtlabel = gtlabel * gtmask
self.attrs = {
"anchors": self.anchors,
"anchor_mask": self.anchor_mask,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"loss_weight_xy": self.loss_weight_xy,
"loss_weight_wh": self.loss_weight_wh,
"loss_weight_conf_target": self.loss_weight_conf_target,
"loss_weight_conf_notarget": self.loss_weight_conf_notarget,
"loss_weight_class": self.loss_weight_class,
"downsample": self.downsample,
}
self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
self.inputs = {
'X': x,
'GTBox': gtbox.astype('float32'),
'GTLabel': gtlabel.astype('int32'),
}
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, self.attrs)
self.outputs = {
'Loss': np.array(
[YoloV3Loss(x, gtbox, gtlabel, self.attrs)]).astype('float32')
'Loss': loss,
'ObjectnessMask': objness,
"GTMatchMask": gt_matches
}
def test_check_output(self):
......@@ -196,19 +201,16 @@ class TestYolov3LossOp(OpTest):
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.06)
max_relative_error=0.3)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
self.class_num = 10
self.anchors = [10, 13, 16, 30, 33, 23]
self.anchor_mask = [1, 2]
self.class_num = 5
self.ignore_thresh = 0.5
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 10, 4)
self.loss_weight_xy = 2.5
self.loss_weight_wh = 0.8
self.loss_weight_conf_target = 1.5
self.loss_weight_conf_notarget = 0.5
self.loss_weight_class = 1.2
self.downsample = 32
self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
self.gtbox_shape = (3, 5, 4)
if __name__ == "__main__":
......
python/paddle/fluid/transpiler/details/__init__.py
浏览文件 @ 4eb44380
......@@ -17,3 +17,4 @@ from __future__ import print_function
from .program_utils import *
from .ufind import *
from .checkport import *
from .vars_distributed import *
python/paddle/fluid/transpiler/details/vars_distributed.py 0 → 100644
浏览文件 @ 4eb44380
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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
from paddle.fluid.framework import Variable
class VarStruct(object):
"""
record part properties of a Variable in python.
"""
def __init__(self, name, shape, dtype, type, lod_level, persistable):
self.name = name
self.shape = shape
self.dtype = dtype
self.type = type
self.lod_level = lod_level
self.persistable = persistable
class VarDistributed(object):
"""
a class to record the var distributed on parameter servers.
the class will record the relationship between origin var and slice var.
the slice var's properties, such as type/shape/offset/endpoint.
"""
def __init__(self,
origin_var,
slice_var,
is_slice=None,
block_id=None,
offset=None,
vtype=None,
endpoint=None):
"""
Args:
origin_var(Variable|VarStruct): origin var properties
slice_var(Variable|VarStruct): slice var properties
is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
block_id(int|None): the number about the slice var.
offset(int|None): if the slice var is sliced, offset is the numel before the var.
vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
"""
if isinstance(origin_var, Variable):
self.origin = self.__create_var_struct(origin_var)
else:
self.origin = origin_var
if isinstance(slice_var, Variable):
self.slice = self.__create_var_struct(slice_var)
else:
self.slice = slice_var
if self.equal(self.origin, self.slice):
self.is_slice = False
self.block_id = 0
self.offset = 0
else:
self.is_slice = True
self.block_id = 0
self.offset = 0
if is_slice is not None:
self.is_slice = is_slice
if block_id is not None:
self.block_id = block_id
if offset is not None:
self.offset = offset
self.vtype = vtype
self.endpoint = endpoint
@staticmethod
def __create_var_struct(var):
return VarStruct(var.name, var.shape, var.dtype, var.type,
var.lod_level, var.persistable)
@staticmethod
def equal(var1, var2):
"""
the two var is equal or not.
Returns:
bool: equal will return True else False
"""
assert isinstance(var1, VarStruct) and isinstance(var2, VarStruct)
return var1.name == var2.name and \
var1.type == var2.type and \
var1.shape == var2.shape and \
var1.dtype == var2.dtype and \
var1.lod_level == var2.lod_level and \
var1.persistable == var2.persistable
def __str__(self):
origin_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})". \
format(i="{", e="}", name=self.origin.name, type=self.origin.type,
shape=self.origin.shape, dtype=self.origin.dtype)
slice_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})" \
".slice({is_slice}).block({block_id}).offset({offset})". \
format(i="{", e="}", name=self.slice.name, type=self.slice.type,
shape=self.slice.shape, dtype=self.slice.dtype,
is_slice=self.is_slice, block_id=self.block_id, offset=self.offset)
return "var owned: {}, origin var: ( {} ), slice var: ( {} ), endpoint: {} ".format(
self.vtype, origin_var_str, slice_var_str, self.endpoint)
class VarsDistributed(object):
"""
a gather about VarDistributed with many methods to find distributed vars.
through the class, we can get overview about the distributed parameters on parameter servers.
this class may centralized and convenient for developer to manage and get variable's distribute.
other module can also use this to find variables such io.py.
"""
def __init__(self):
self.distributed_vars = []
def add_distributed_var(self,
origin_var,
slice_var,
is_slice=None,
block_id=None,
offset=None,
vtype=None,
endpoint=None):
"""
add distributed var in this.
Args:
origin_var(Variable|VarStruct): origin var properties
slice_var(Variable|VarStruct): slice var properties
is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
block_id(int|None): the number about the slice var.
offset(int|None): if the slice var is sliced, offset is the numel before the var.
vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
Returns:
None
"""
self.distributed_vars.append(
VarDistributed(origin_var, slice_var, is_slice, block_id, offset,
vtype, endpoint))
def get_distributed_var_by_slice(self, var_name):
"""
get distributed var by conditions.
Args:
var_name(str): slice var name, such as "w.traier0.block1"
Returns:
VarDistributed: distributed var.
"""
for dist_var in self.distributed_vars:
if dist_var.slice.name == var_name:
return dist_var
return None
@staticmethod
def equal(var1, var2):
"""
the two var is equal or not.
Returns:
bool: equal will return True else False
"""
return var1.name == var2.name and \
var1.type == var2.type and \
var1.shape == var2.shape and \
var1.dtype == var2.dtype and \
var1.lod_level == var2.lod_level and \
var1.persistable == var2.persistable
def get_distributed_var_by_origin_and_ep(self, origin_var_name, endpoint):
"""
get distributed var by conditions.
Args:
origin_var_name(str):
endpoint(str): the parameter endpoint, such as "127.0.0.1:1001"
Returns:
VarDistributed: distributed var.
"""
for dist_var in self.distributed_vars:
if dist_var.origin.name == origin_var_name and dist_var.endpoint == endpoint:
return dist_var
return None
def get_distributed_vars_by_vtypes(self, vtypes, groupby=False):
"""
get distributed vars by conditions.
Args:
vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
groupby(bool|False): group by origin var or not.
Returns:
list: distributed var list.
dict: distributed var map when groupby=True
"""
vtype_vars = []
for var in self.distributed_vars:
if var.vtype in vtypes:
vtype_vars.append(var)
if not groupby:
return vtype_vars
params_map = {}
for var in vtype_vars:
origin_var_name = var.origin.name
if origin_var_name in params_map.keys():
optimizers = params_map.get(origin_var_name)
else:
optimizers = []
optimizers.append(var)
params_map[origin_var_name] = optimizers
return params_map
def get_distributed_vars_by_ep(self, endpoint, vtype=None):
"""
get distributed vars by conditions.
Args:
endpoint(str): the parameter server endpoint, such as "127.0.0.1:2001"
vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
Returns:
list: distributed var list.
"""
endpoint_vars = []
for var in self.distributed_vars:
if var.endpoint == endpoint:
endpoint_vars.append(var)
if not vtype:
return endpoint_vars
vtype_vars = []
for var in endpoint_vars:
if var.vtype == vtype:
vtype_vars.append(var)
return vtype_vars
def overview(self):
"""
get the overview string about all params on all parameter servers.
Returns:
Str: overview string.
"""
vars_str = []
for var in self.distributed_vars:
vars_str.append(str(var))
return "\n".join(vars_str)
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 4eb44380
......@@ -30,19 +30,23 @@ Steps to transpile pserver:
5. add listen_and_serv op
"""
import sys
import math
import numpy as np
from functools import reduce
import collections
import six
import logging
import numpy as np
from .ps_dispatcher import RoundRobin, PSDispatcher
from .. import core, framework, unique_name
from ..framework import Program, default_main_program, \
default_startup_program, Block, \
Parameter, Variable, grad_var_name
from .details import *
default_startup_program, Block, Parameter, grad_var_name
from .details import wait_server_ready, UnionFind, VarStruct, VarsDistributed
from .details import delete_ops, find_op_by_output_arg
from ..distribute_lookup_table import find_distributed_lookup_table
from functools import reduce
LOOKUP_TABLE_TYPE = "lookup_table"
LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
......@@ -62,260 +66,6 @@ def log(*args):
print(args)
class VarStruct(object):
"""
record part properties of a Variable in python.
"""
def __init__(self, name, shape, dtype, type, lod_level, persistable):
self.name = name
self.shape = shape
self.dtype = dtype
self.type = type
self.lod_level = lod_level
self.persistable = persistable
class VarDistributed(object):
"""
a class to record the var distributed on parameter servers.
the class will record the relationship between origin var and slice var.
the slice var's properties, such as type/shape/offset/endpoint.
"""
def __init__(self,
origin_var,
slice_var,
is_slice=None,
block_id=None,
offset=None,
vtype=None,
endpoint=None):
"""
Args:
origin_var(Variable|VarStruct): origin var properties
slice_var(Variable|VarStruct): slice var properties
is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
block_id(int|None): the number about the slice var.
offset(int|None): if the slice var is sliced, offset is the numel before the var.
vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
"""
if isinstance(origin_var, Variable):
self.origin = self.__create_var_struct(origin_var)
else:
self.origin = origin_var
if isinstance(slice_var, Variable):
self.slice = self.__create_var_struct(slice_var)
else:
self.slice = slice_var
if self.equal(self.origin, self.slice):
self.is_slice = False
self.block_id = 0
self.offset = 0
else:
self.is_slice = True
self.block_id = 0
self.offset = 0
if is_slice is not None:
self.is_slice = is_slice
if block_id is not None:
self.block_id = block_id
if offset is not None:
self.offset = offset
self.vtype = vtype
self.endpoint = endpoint
@staticmethod
def __create_var_struct(var):
return VarStruct(var.name, var.shape, var.dtype, var.type,
var.lod_level, var.persistable)
@staticmethod
def equal(var1, var2):
"""
the two var is equal or not.
Returns:
bool: equal will return True else False
"""
assert isinstance(var1, VarStruct) and isinstance(var2, VarStruct)
return var1.name == var2.name and \
var1.type == var2.type and \
var1.shape == var2.shape and \
var1.dtype == var2.dtype and \
var1.lod_level == var2.lod_level and \
var1.persistable == var2.persistable
def __str__(self):
origin_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})". \
format(i="{", e="}", name=self.origin.name, type=self.origin.type,
shape=self.origin.shape, dtype=self.origin.dtype)
slice_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})" \
".slice({is_slice}).block({block_id}).offset({offset})". \
format(i="{", e="}", name=self.slice.name, type=self.slice.type,
shape=self.slice.shape, dtype=self.slice.dtype,
is_slice=self.is_slice, block_id=self.block_id, offset=self.offset)
return "var owned: {}, origin var: ( {} ), slice var: ( {} ), endpoint: {} ".format(
self.vtype, origin_var_str, slice_var_str, self.endpoint)
class VarsDistributed(object):
"""
a gather about VarDistributed with many methods to find distributed vars.
through the class, we can get overview about the distributed parameters on parameter servers.
this class may centralized and convenient for developer to manage and get variable's distribute.
other module can also use this to find variables such io.py.
"""
def __init__(self):
self.distributed_vars = []
def add_distributed_var(self,
origin_var,
slice_var,
is_slice=None,
block_id=None,
offset=None,
vtype=None,
endpoint=None):
"""
add distributed var in this.
Args:
origin_var(Variable|VarStruct): origin var properties
slice_var(Variable|VarStruct): slice var properties
is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
block_id(int|None): the number about the slice var.
offset(int|None): if the slice var is sliced, offset is the numel before the var.
vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
Returns:
None
"""
self.distributed_vars.append(
VarDistributed(origin_var, slice_var, is_slice, block_id, offset,
vtype, endpoint))
def get_distributed_var_by_slice(self, var_name):
"""
get distributed var by conditions.
Args:
var_name(str): slice var name, such as "w.traier0.block1"
Returns:
VarDistributed: distributed var.
"""
for dist_var in self.distributed_vars:
if dist_var.slice.name == var_name:
return dist_var
return None
@staticmethod
def equal(var1, var2):
"""
the two var is equal or not.
Returns:
bool: equal will return True else False
"""
return var1.name == var2.name and \
var1.type == var2.type and \
var1.shape == var2.shape and \
var1.dtype == var2.dtype and \
var1.lod_level == var2.lod_level and \
var1.persistable == var2.persistable
def get_distributed_var_by_origin_and_ep(self, origin_var_name, endpoint):
"""
get distributed var by conditions.
Args:
origin_var_name(str):
endpoint(str): the parameter endpoint, such as "127.0.0.1:1001"
Returns:
VarDistributed: distributed var.
"""
for dist_var in self.distributed_vars:
if dist_var.origin.name == origin_var_name and dist_var.endpoint == endpoint:
return dist_var
return None
def get_distributed_vars_by_vtypes(self, vtypes, groupby=False):
"""
get distributed vars by conditions.
Args:
vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
groupby(bool|False): group by origin var or not.
Returns:
list: distributed var list.
dict: distributed var map when groupby=True
"""
vtype_vars = []
for var in self.distributed_vars:
if var.vtype in vtypes:
vtype_vars.append(var)
if not groupby:
return vtype_vars
params_map = {}
for var in vtype_vars:
origin_var_name = var.origin.name
if origin_var_name in params_map.keys():
optimizers = params_map.get(origin_var_name)
else:
optimizers = []
optimizers.append(var)
params_map[origin_var_name] = optimizers
return params_map
def get_distributed_vars_by_ep(self, endpoint, vtype=None):
"""
get distributed vars by conditions.
Args:
endpoint(str): the parameter server endpoint, such as "127.0.0.1:2001"
vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
Returns:
list: distributed var list.
"""
endpoint_vars = []
for var in self.distributed_vars:
if var.endpoint == endpoint:
endpoint_vars.append(var)
if not vtype:
return endpoint_vars
vtype_vars = []
for var in endpoint_vars:
if var.vtype == vtype:
vtype_vars.append(var)
return vtype_vars
def overview(self):
"""
get the overview string about all params on all parameter servers.
Returns:
Str: overview string.
"""
vars_str = []
for var in self.distributed_vars:
vars_str.append(str(var))
return "\n".join(vars_str)
class VarBlock:
def __init__(self, varname, offset, size):
self.varname = varname
......
python/setup.py.in
浏览文件 @ 4eb44380
......@@ -109,6 +109,7 @@ packages=['paddle',
'paddle.fluid.contrib',
'paddle.fluid.contrib.decoder',
'paddle.fluid.contrib.quantize',
'paddle.fluid.contrib.int8_inference',
'paddle.fluid.contrib.reader',
'paddle.fluid.contrib.slim',
'paddle.fluid.contrib.slim.core',
......
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