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提交 3ad3635d 编写于 作者: S sneaxiy
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fix conflict

上级 3ee0a648 6537b175
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Showing with 2359 addition and 650 deletion
paddle/fluid/API.spec
浏览文件 @ 3ad3635d
......@@ -73,7 +73,6 @@ paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program',
paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))
......@@ -161,6 +160,12 @@ paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None))
paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.stanh ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None))
paddle.fluid.layers.hard_sigmoid ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None))
paddle.fluid.layers.swish ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
......@@ -277,12 +282,6 @@ paddle.fluid.layers.softsign ArgSpec(args=[], varargs='args', keywords='kwargs',
paddle.fluid.layers.brelu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.leaky_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.soft_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.elu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.relu6 ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.pow ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.stanh ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.hard_sigmoid ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.swish ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
......@@ -296,6 +295,7 @@ paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', '
paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
paddle.fluid.layers.rpn_target_assign ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True))
paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
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.iou_similarity ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
......
paddle/fluid/framework/details/cow_ptr.h
浏览文件 @ 3ad3635d
......@@ -20,41 +20,79 @@ namespace paddle {
namespace framework {
namespace details {
template <class T>
class COWPtr {
// Change it to thread safe flags if needed.
class ThreadUnsafeOwnershipFlags {
public:
typedef std::shared_ptr<T> RefPtr;
explicit ThreadUnsafeOwnershipFlags(bool flag) : flag_(flag) {}
private:
RefPtr m_sp;
ThreadUnsafeOwnershipFlags(const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags& operator=(
const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags(ThreadUnsafeOwnershipFlags&& other) = default;
void detach() {
T* tmp = m_sp.get();
if (!(tmp == nullptr || m_sp.unique())) {
m_sp = RefPtr(new T(*tmp));
void SetOwnership(bool flag) { flag_ = flag; }
// Invoke the callback if it is not owned.
template <typename Callback>
void AcquireOwnershipOnce(Callback acquire) {
if (!flag_) {
acquire();
flag_ = true;
}
}
public:
COWPtr() : m_sp(nullptr) {}
explicit COWPtr(T* t) : m_sp(t) {}
explicit COWPtr(const RefPtr& refptr) : m_sp(refptr) {}
private:
bool flag_;
};
const T& Data() const { return operator*(); }
// Copy-On-Write pointer.
// It will hold a T* pointer, and only copy once when `MutableData` is invoked.
//
// The template parameter OwnershipFlags should have:
// * a constructor takes a bool. True if own.
// * SetOwnership(bool flag).
// * AcquireOwnershipOnce(Callback). It will invoke the callback if it is not
// owned.
//
// https://en.wikipedia.org/wiki/Copy-on-write
template <typename T, typename OwnershipFlags = ThreadUnsafeOwnershipFlags>
class COWPtr {
public:
// Ctor from raw pointer.
explicit COWPtr(T* ptr) : payload_(ptr), ownership_{true} {}
T* MutableData() { return operator->(); }
// Move methods. Steal ownership from origin
COWPtr(COWPtr&& other)
: payload_(other.payload_), ownership_{std::move(other.ownership_)} {}
COWPtr& operator=(COWPtr&& origin) = default;
const T& operator*() const { return *m_sp; }
T& operator*() {
detach();
return *m_sp;
// Copy methods. Not own payload
COWPtr(const COWPtr& other) : payload_(other.payload_), ownership_{false} {}
COWPtr& operator=(const COWPtr& other) {
payload_ = other.payload_;
ownership_.SetOwnership(false);
return *this;
}
const T* operator->() const { return m_sp.operator->(); }
T* operator->() {
detach();
return m_sp.operator->();
// Access read only data.
const T& Data() const { return *payload_; }
// Access mutable data. If the data is not owned, the data will be copied
// before.
T* MutableData() {
ownership_.AcquireOwnershipOnce(
[this] { payload_.reset(new T(*payload_)); });
return payload_.get();
}
private:
// Actual data pointer.
std::shared_ptr<T> payload_;
// Ownership flag.
OwnershipFlags ownership_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/cow_ptr_test.cc
浏览文件 @ 3ad3635d
......@@ -30,14 +30,6 @@ TEST(COWPtr, all) {
ASSERT_EQ(ptr2.Data(), 10);
}
TEST(COWPtr, change_old) {
COWPtr<int> ptr(new int{0});
COWPtr<int> ptr2 = ptr;
*ptr.MutableData() = 10;
ASSERT_EQ(ptr2.Data(), 0);
ASSERT_EQ(ptr.Data(), 10);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 3ad3635d
......@@ -210,43 +210,6 @@ std::vector<std::string> MultiDevSSAGraphBuilder::FindDistTrainRecvVars(
return recv_vars;
}
bool MultiDevSSAGraphBuilder::IsDistTrainOp(
ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) const {
if (send_vars.size() == 0 || recv_vars.size() == 0) {
return false;
}
/**
* Check any of opvars contains `.block` and in sendvars
*/
auto checker = [](const std::vector<std::string> &opvars,
const std::vector<std::string> &rpc_vars) -> bool {
for (auto &var : opvars) {
// a variable name with the suffix `.block` means it's a splited
// variable by (DistributeTranspiler)
// [python/paddle/fluid/transpiler/distribute_transpiler.py]
if (var.find(".block") != std::string::npos &&
std::find(rpc_vars.begin(), rpc_vars.end(), var) != rpc_vars.end()) {
return true;
}
}
return false;
};
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
}
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
}
return checker(output_var_names, send_vars) ||
checker(input_var_names, recv_vars);
}
size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
......@@ -370,7 +333,9 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
}
}
is_dist_train = true;
} else if (IsDistTrainOp(node, send_vars, recv_vars)) {
} else if (boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kDist)) {
int op_dev_id = CreateDistTrainOp(&result, node);
if (node->Op()->Type() == "concat") {
auto origin_param_name = node->Op()->OutputArgumentNames()[0];
......@@ -736,6 +701,7 @@ int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
.emplace(varname, op_dev_id);
}
} else {
LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
PADDLE_THROW(
"the distribute training related op should be in [split_byref, "
"concat].");
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 3ad3635d
......@@ -51,12 +51,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
/**
* Is this operator as the end-point operator before/after send operator.
*/
bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) const;
std::vector<std::string> FindDistTrainSendVars(
const std::vector<ir::Node *> &nodes) const;
......
paddle/fluid/framework/details/reference_count_op_handle.h
浏览文件 @ 3ad3635d
......@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
......@@ -46,17 +47,15 @@ class ReferenceCountOpHandle : public OpHandleBase {
const std::vector<std::string> &var_names,
GarbageCollector<Tensor> *gc,
AtomicReferenceCountMap *ref_cnts)
: OpHandleBase(node),
scope_(scope),
var_names_(var_names),
gc_(gc),
ref_cnts_(ref_cnts) {
: OpHandleBase(node), scope_(scope), gc_(gc), ref_cnts_(ref_cnts) {
dev_ctx_ = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place));
if (IsStreamGarabageCollector()) {
PADDLE_ENFORCE(cudaSetDevice(place.device));
PADDLE_ENFORCE(cudaEventCreateWithFlags(&event_, cudaEventDisableTiming));
}
for (auto &name : var_names) AddVar(name);
}
~ReferenceCountOpHandle() {
......@@ -69,19 +68,35 @@ class ReferenceCountOpHandle : public OpHandleBase {
std::string Name() const override { return "reference_count"; }
void AddVar(const std::string &name) {
auto it = var_names_.find(name);
if (it != var_names_.end())
++(it->second);
else
var_names_[name] = 1;
}
protected:
void RunImpl() override {
auto *exec_scope = scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
std::vector<LoDTensor *> tensors;
for (auto &name : var_names_) {
std::vector<Tensor *> tensors;
for (auto &pair : var_names_) {
auto &name = pair.first;
auto it = ref_cnts_->find(name);
if (it == ref_cnts_->end()) continue;
auto *var = exec_scope->FindVar(name);
if (var == nullptr || !var->IsType<LoDTensor>()) continue;
if (it->second.fetch_sub(1) <= 1) {
tensors.emplace_back(var->GetMutable<LoDTensor>());
if (var == nullptr) continue;
if (var->IsType<LoDTensor>()) {
if (it->second.fetch_sub(pair.second) <= pair.second) {
tensors.emplace_back(var->GetMutable<LoDTensor>());
}
} else if (var->IsType<SelectedRows>()) {
if (it->second.fetch_sub(pair.second) <= pair.second) {
tensors.emplace_back(
var->GetMutable<SelectedRows>()->mutable_value());
}
}
}
......@@ -91,7 +106,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
}
private:
void ClearTensors(const std::vector<LoDTensor *> &tensors) {
void ClearTensors(const std::vector<Tensor *> &tensors) {
auto *gc = dynamic_cast<StreamGarbageCollector<Tensor> *>(gc_);
if (gc != nullptr) {
auto compute_stream = dev_ctx_->stream();
......@@ -112,7 +127,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
const Scope *scope_;
platform::CUDADeviceContext *dev_ctx_;
std::vector<std::string> var_names_;
std::unordered_map<std::string, int> var_names_;
GarbageCollector<Tensor> *gc_; // not own
AtomicReferenceCountMap *ref_cnts_; // not own
cudaEvent_t event_;
......
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @ 3ad3635d
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <queue>
#include <string>
#include <vector>
......@@ -23,6 +24,25 @@ namespace paddle {
namespace framework {
namespace details {
static ComputationOpHandle *FindNextComputationOpHandle(VarHandle *var_in) {
std::queue<VarHandleBase *> queue;
queue.push(var_in);
do {
auto *var = queue.front();
queue.pop();
for (auto *op : var->PendingOps()) {
auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
if (compute_op != nullptr && compute_op->GetPlace() == var_in->place_) {
return compute_op;
}
for (auto *out_var : op->Outputs()) {
queue.push(out_var);
}
}
} while (!queue.empty());
return nullptr;
}
std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
auto &ref_cnts = Get<DeviceReferenceCountMap>(kGlobalReferenceCount);
......@@ -34,6 +54,9 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
// Step 2: Find all variables in non-computation ops which refers to variables
// in computation ops
std::unordered_set<std::string> names;
std::unordered_map<OpHandleBase *, std::unique_ptr<ReferenceCountOpHandle>>
compute_ref_cnt_map;
auto get_ref_cnts_from_compute_op = [&](
const std::unique_ptr<OpHandleBase> &op,
const std::vector<VarHandleBase *> &vars) {
......@@ -54,15 +77,18 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
VarDesc *var_desc = var_handle->Node()->Var();
auto var_name = var_handle->Node()->Name();
// This is wierd but there is really some variables without var_desc
// This is weird but there is really some variables without var_desc
// in computation_op
if (var_desc == nullptr) {
if (compute_op->Node()->Op()->Block()->FindVar(var_name) == nullptr)
continue;
} else {
if (var_desc->Persistable() ||
var_desc->Proto()->type().type() != proto::VarType::LOD_TENSOR)
if (var_desc->Persistable()) continue;
auto var_type = var_desc->Proto()->type().type();
if (var_type != proto::VarType::LOD_TENSOR &&
var_type != proto::VarType::SELECTED_ROWS) {
continue;
}
}
// compute op only runs in one device
......@@ -93,12 +119,33 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
if (ref_cnts.count(place.device) &&
ref_cnts[place.device]->count(var_name)) {
++(*ref_cnts[place.device])[var_name];
auto *next_compute_op = FindNextComputationOpHandle(var_handle);
if (next_compute_op != nullptr) {
if (compute_ref_cnt_map.count(next_compute_op)) {
compute_ref_cnt_map[next_compute_op]->AddVar(var_name);
VLOG(5) << "Add reference count of " << var_name << " to Operator "
<< next_compute_op->Name();
} else {
// Create new reference_count_op_handle
ir::Node *ref_cnt_node = graph->CreateEmptyNode(
"reference_count", ir::Node::Type::kOperation);
auto *ref_cnt_handle = new ReferenceCountOpHandle(
ref_cnt_node, next_compute_op->GetScope(), place, {var_name},
gcs[place.device].get(), cur_ref_cnts[place.device].get());
if (next_compute_op->Outputs().empty()) {
auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
next_compute_op->AddOutput(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
}
ref_cnt_handle->AddInput(next_compute_op->Outputs().front());
compute_ref_cnt_map[next_compute_op].reset(ref_cnt_handle);
}
}
}
}
};
std::unordered_map<OpHandleBase *, ReferenceCountOpHandle *>
compute_ref_cnt_map;
auto &all_ops = graph->Get<GraphOps>(kGraphOps);
for (auto &op : all_ops) {
auto in_var_names = get_ref_cnts_from_compute_op(op, op->Inputs());
......@@ -113,11 +160,13 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
auto *ref_cnt_handle = new ReferenceCountOpHandle(
ref_cnt_node, compute_op->GetScope(), place, in_var_names,
gcs[place.device].get(), cur_ref_cnts[place.device].get());
auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
compute_op->AddOutput(dep_var);
ref_cnt_handle->AddInput(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
compute_ref_cnt_map[compute_op] = ref_cnt_handle;
if (compute_op->Outputs().empty()) {
auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
compute_op->AddOutput(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
}
ref_cnt_handle->AddInput(compute_op->Outputs().front());
compute_ref_cnt_map[compute_op].reset(ref_cnt_handle);
}
for (auto &op : all_ops) {
......@@ -131,7 +180,11 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
new_all_ops.emplace_back(std::move(op));
auto it = compute_ref_cnt_map.find(new_all_ops.back().get());
if (it != compute_ref_cnt_map.end()) {
new_all_ops.emplace_back(it->second);
// Add LeafNode to ReferenceCountOpHandle
auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
it->second->AddOutput(dummy_leaf);
new_all_ops.emplace_back(std::move(it->second));
}
}
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 3ad3635d
......@@ -17,12 +17,10 @@
#include <algorithm>
#include <initializer_list>
#include <memory>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/memcpy.h"
#include "glog/logging.h"
......@@ -30,401 +28,206 @@ namespace paddle {
namespace framework {
#if defined(PADDLE_WITH_CUDA)
namespace details {
struct CUDABuffer {
void *data_{nullptr};
size_t size_{0};
platform::CUDAPlace place_;
CUDABuffer() {}
CUDABuffer(platform::Place place, size_t size)
: size_(size), place_(boost::get<platform::CUDAPlace>(place)) {
data_ = memory::Alloc(place_, size);
}
~CUDABuffer() { ClearMemory(); }
CUDABuffer(const CUDABuffer &o) = delete;
CUDABuffer &operator=(const CUDABuffer &o) = delete;
void Resize(platform::Place place, size_t size) {
ClearMemory();
place_ = boost::get<platform::CUDAPlace>(place);
data_ = memory::Alloc(place_, size);
size_ = size;
}
void Swap(CUDABuffer &o) {
std::swap(data_, o.data_);
std::swap(place_, o.place_);
std::swap(size_, o.size_);
}
private:
void ClearMemory() const {
if (data_) {
memory::Free(place_, data_);
}
}
};
} // namespace details
// Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside.
template <typename T>
class Vector {
public:
using value_type = T;
using iterator = typename std::vector<T>::iterator;
using const_iterator = typename std::vector<T>::const_iterator;
private:
// The actual class to implement vector logic
class VectorData {
public:
VectorData() : flag_(kDataInCPU) {}
VectorData(size_t count, const T &value)
: cpu_(count, value), flag_(kDataInCPU) {}
VectorData(std::initializer_list<T> init) : cpu_(init), flag_(kDataInCPU) {}
template <typename U>
explicit VectorData(const std::vector<U> &dat)
: cpu_(dat), flag_(kDataInCPU) {}
VectorData(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
}
VectorData &operator=(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
details::CUDABuffer null;
gpu_.Swap(null);
return *this;
}
T &operator[](size_t i) {
MutableCPU();
return cpu_[i];
}
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_[i];
}
size_t size() const { return cpu_.size(); }
iterator begin() {
MutableCPU();
return cpu_.begin();
}
iterator end() {
MutableCPU();
return cpu_.end();
}
T &front() {
MutableCPU();
return cpu_.front();
}
T &back() {
MutableCPU();
return cpu_.back();
}
const_iterator begin() const {
ImmutableCPU();
return cpu_.begin();
}
const_iterator end() const {
ImmutableCPU();
return cpu_.end();
}
const T &back() const {
ImmutableCPU();
return cpu_.back();
}
T *data() { return &(*this)[0]; }
const T *data() const { return &(*this)[0]; }
const T &front() const {
ImmutableCPU();
return cpu_.front();
}
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
MutableCPU();
cpu_.assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
MutableCPU();
cpu_.push_back(elem);
}
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
MutableCPU();
auto out_it = std::back_inserter<std::vector<T>>(this->cpu_);
std::copy(begin, end, out_it);
}
// resize the vector
void resize(size_t size) {
MutableCPU();
cpu_.resize(size);
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return reinterpret_cast<T *>(gpu_.data_);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
}
// clear
void clear() {
cpu_.clear();
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return cpu_.capacity(); }
// reserve data
void reserve(size_t size) { cpu_.reserve(size); }
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
ImmutableCPU();
return cpu_;
}
bool operator==(const VectorData &other) const {
ImmutableCPU();
other.ImmutableCPU();
return cpu_ == other.cpu_;
}
private:
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
void *src = gpu_.data_;
void *dst = cpu_.data();
memory::Copy(platform::CPUPlace(), dst, gpu_.place_, src, gpu_.size_,
nullptr);
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
CopyCPUDataToCUDA(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() &&
!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
CopyCUDADataToAnotherPlace(place);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
CopyCPUDataToCUDA(place);
SetFlag(kDataInCUDA);
} else if (!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
CopyCUDADataToAnotherPlace(place);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void CopyCUDADataToAnotherPlace(const platform::Place &place) const {
details::CUDABuffer tmp(place, gpu_.size_);
const void *src = gpu_.data_;
void *dst = tmp.data_;
memory::Copy(tmp.place_, dst, gpu_.place_, src, gpu_.size_, nullptr);
gpu_.Swap(tmp);
}
void CopyCPUDataToCUDA(const platform::Place &place) const {
void *src = cpu_.data();
gpu_.Resize(place, cpu_.size() * sizeof(T));
void *dst = gpu_.data_;
auto stream = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place))
->stream();
memory::Copy(gpu_.place_, dst, platform::CPUPlace(), src, gpu_.size_,
stream);
}
void ImmutableCPU() const {
if (IsDirty() && !IsInCPU()) { // If data has been changed in CUDA, or
// CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
mutable details::CUDABuffer gpu_;
mutable int flag_;
};
public:
// Default ctor. Create empty Vector
Vector() : m_(new VectorData()) {}
Vector() { InitEmpty(); }
// Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T())
: m_(new VectorData(count, value)) {}
explicit Vector(size_t count, const T &value = T()) {
InitEmpty();
if (count != 0) {
resize(count);
T *ptr = begin();
for (size_t i = 0; i < count; ++i) {
ptr[i] = value;
}
}
}
// Ctor with init_list
Vector(std::initializer_list<T> init) : m_(new VectorData(init)) {}
Vector(std::initializer_list<T> init) {
if (init.size() == 0) {
InitEmpty();
} else {
InitByIter(init.size(), init.begin(), init.end());
}
}
// implicit cast from std::vector.
template <typename U>
Vector(const std::vector<U> &dat) : m_(new VectorData(dat)) { // NOLINT
Vector(const std::vector<U> &dat) { // NOLINT
if (dat.size() == 0) {
InitEmpty();
} else {
InitByIter(dat.size(), dat.begin(), dat.end());
}
}
// Copy ctor
Vector(const Vector<T> &other) { m_ = other.m_; }
Vector(const Vector<T> &other) { this->operator=(other); }
// Copy operator
Vector<T> &operator=(const Vector<T> &other) {
m_ = other.m_;
if (other.size() != 0) {
this->InitByIter(other.size(), other.begin(), other.end());
} else {
InitEmpty();
}
return *this;
}
// Move ctor
Vector(Vector<T> &&other) { m_ = std::move(other.m_); }
Vector(Vector<T> &&other) {
this->size_ = other.size_;
this->flag_ = other.flag_;
if (other.cuda_vec_.memory_size()) {
this->cuda_vec_.ShareDataWith(other.cuda_vec_);
}
if (other.cpu_vec_.memory_size()) {
this->cpu_vec_.ShareDataWith(other.cpu_vec_);
}
}
// CPU data access method. Mutable.
T &operator[](size_t i) { return (*m_)[i]; }
T &operator[](size_t i) {
MutableCPU();
return const_cast<T *>(cpu_vec_.data<T>())[i];
}
// CPU data access method. Immutable.
const T &operator[](size_t i) const { return (*m_)[i]; }
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_vec_.data<T>()[i];
}
// std::vector iterator methods. Based on CPU data access method
size_t size() const { return m_->size(); }
size_t size() const { return size_; }
iterator begin() { return m_->begin(); }
T *begin() { return capacity() == 0 ? &EmptyDummy() : &this->operator[](0); }
iterator end() { return m_->end(); }
T *end() {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
T &front() { return m_->front(); }
T &front() { return *begin(); }
T &back() { return m_->back(); }
T &back() {
auto it = end();
--it;
return *it;
}
const_iterator begin() const { return m_->begin(); }
const T *begin() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](0);
}
const_iterator end() const { return m_->end(); }
const T *end() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
const_iterator cbegin() const { return begin(); }
const T *cbegin() const { return begin(); }
const_iterator cend() const { return end(); }
const T *cend() const { return end(); }
const T &back() const { return m_->back(); }
const T &back() const {
auto it = end();
--it;
return *it;
}
T *data() { return m_->data(); }
T *data() { return begin(); }
const T *data() const { return m_->data(); }
const T *data() const { return begin(); }
const T &front() const { return m_->front(); }
const T &front() const { return *begin(); }
// end of std::vector iterator methods
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
m_->assign(begin, end);
InitByIter(end - begin, begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) { m_->push_back(elem); }
void push_back(T elem) {
if (size_ + 1 > capacity()) {
reserve((size_ + 1) << 1);
}
*end() = elem;
++size_;
}
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
m_->Extend(begin, end);
size_t pre_size = size_;
resize(pre_size + (end - begin));
T *ptr = this->begin() + pre_size;
for (; begin < end; ++begin, ++ptr) {
*ptr = *begin;
}
}
// resize the vector
void resize(size_t size) {
if (m_.Data().size() != size) {
m_->resize(size);
if (size + 1 <= capacity()) {
size_ = size;
} else {
MutableCPU();
Tensor cpu_tensor;
platform::Place cpu = platform::CPUPlace();
T *ptr = cpu_tensor.mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
const T *old_ptr =
cpu_vec_.memory_size() == 0 ? nullptr : cpu_vec_.data<T>();
if (old_ptr != nullptr) {
std::copy(old_ptr, old_ptr + size_, ptr);
}
size_ = size;
cpu_vec_.ShareDataWith(cpu_tensor);
}
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
return m_.Data().CUDAData(place);
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return cuda_vec_.data<T>();
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
return m_->CUDAMutableData(place);
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
}
// clear
void clear() { m_->clear(); }
void clear() {
size_ = 0;
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return m_->capacity(); }
size_t capacity() const {
return cpu_vec_.memory_size() / SizeOfType(typeid(T));
}
// reserve data
void reserve(size_t size) { m_->reserve(size); }
void reserve(size_t size) {
size_t pre_size = size_;
resize(size);
resize(pre_size);
}
// the unify method to access CPU or CUDA data. immutable.
const T *Data(platform::Place place) const {
......@@ -445,7 +248,12 @@ class Vector {
}
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const { return *m_; }
operator std::vector<T>() const {
std::vector<T> result;
result.resize(size());
std::copy(begin(), end(), result.begin());
return result;
}
bool operator==(const Vector<T> &other) const {
if (size() != other.size()) return false;
......@@ -459,11 +267,118 @@ class Vector {
return true;
}
const void *Handle() const { return &m_.Data(); }
private:
// Vector is an COW object.
details::COWPtr<VectorData> m_;
void InitEmpty() {
size_ = 0;
flag_ = kDataInCPU;
}
template <typename Iter>
void InitByIter(size_t size, Iter begin, Iter end) {
platform::Place cpu = platform::CPUPlace();
T *ptr = this->cpu_vec_.template mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
for (size_t i = 0; i < size; ++i) {
*ptr++ = *begin++;
}
flag_ = kDataInCPU | kDirty;
size_ = size;
}
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
TensorCopy(cuda_vec_, platform::CPUPlace(), &cpu_vec_);
WaitPlace(cuda_vec_.place());
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() && !(place == cuda_vec_.place())) {
framework::Tensor tmp;
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
cuda_vec_.ShareDataWith(tmp);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
SetFlag(kDataInCUDA);
} else if (!(place == cuda_vec_.place())) {
framework::Tensor tmp;
WaitPlace(cuda_vec_.place());
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
WaitPlace(place);
cuda_vec_.ShareDataWith(tmp);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void ImmutableCPU() const {
if (IsDirty() &&
!IsInCPU()) { // If data has been changed in CUDA, or CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
static void WaitPlace(const platform::Place place) {
if (platform::is_gpu_place(place)) {
platform::DeviceContextPool::Instance()
.Get(boost::get<platform::CUDAPlace>(place))
->Wait();
}
}
static T &EmptyDummy() {
static T dummy = T();
return dummy;
}
mutable int flag_;
mutable Tensor cpu_vec_;
mutable Tensor cuda_vec_;
size_t size_;
};
#else // PADDLE_WITH_CUDA
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 3ad3635d
......@@ -120,6 +120,7 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
{static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kBackward),
static_cast<int>(OpRole::kOptimize), static_cast<int>(OpRole::kRPC),
static_cast<int>(OpRole::kDist), static_cast<int>(OpRole::kLRSched),
static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kLoss) |
static_cast<int>(OpRole::kBackward),
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 3ad3635d
......@@ -26,7 +26,13 @@ enum class OpRole {
kForward = 0x0000,
kBackward = 0x0001,
kOptimize = 0x0002,
// RPC role is for send/recv releated op
kRPC = 0x0003,
// Dist role is for split_byref/split_selected_rows/concat
// used for distributed training.
kDist = 0x0004,
// Tag all learning rate scheduler operators.
kLRSched = 0x0005,
kLoss = 0x0100,
// The default value of op's role. This should be only used for unittests and
......
paddle/fluid/operators/adam_op.h
浏览文件 @ 3ad3635d
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h> // for sqrt in CPU and CUDA
#include <Eigen/Dense>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
......@@ -306,26 +307,43 @@ class AdamOpKernel : public framework::OpKernel<T> {
VLOG(3) << "grad row size is 0!!";
return;
}
// merge duplicated rows if any.
// The rows of grad_merge have been sorted inside MergeAdd functor
scatter::MergeAdd<DeviceContext, T> merge_func;
auto& grad_merge = *(ctx.scope()
.NewScope()
.Var("sparse_adam_grad_merge")
->GetMutable<framework::SelectedRows>());
merge_func(ctx.template device_context<DeviceContext>(), grad,
&grad_merge);
std::vector<int64_t> cpu_rows(grad.rows().begin(), grad.rows().end());
bool is_strict_sorted = true;
for (size_t i = 1; i < cpu_rows.size(); ++i) {
if (cpu_rows[i - 1] >= cpu_rows[i]) {
is_strict_sorted = false;
break;
}
}
const framework::SelectedRows* grad_merge_ptr;
if (is_strict_sorted) {
grad_merge_ptr = &grad;
} else {
// merge duplicated rows if any.
// The rows of grad_merge have been sorted inside MergeAdd functor
scatter::MergeAdd<DeviceContext, T> merge_func;
auto* grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
merge_func(ctx.template device_context<DeviceContext>(), grad,
grad_merge_var);
grad_merge_ptr = grad_merge_var;
}
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
int64_t* rows = nullptr;
// When compiled without CUDA, the CUDAMutableData() interface should not be
const int64_t* rows = nullptr;
// When compiled without CUDA, the CUDAData() interface should not be
// provided.
#if defined(PADDLE_WITH_CUDA)
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = grad_merge.mutable_rows()->CUDAMutableData(ctx.GetPlace());
rows = grad_merge.rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = grad_merge.mutable_rows()->data();
rows = grad_merge.rows().data();
#if defined(PADDLE_WITH_CUDA)
}
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 3ad3635d
......@@ -31,5 +31,6 @@ 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(generate_proposals_op SRCS generate_proposals_op.cc)
detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
#Export local libraries to parent
set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)
paddle/fluid/operators/detection/roi_perspective_transform_op.cc 0 → 100644
浏览文件 @ 3ad3635d
/* 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. */
#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;
using LoDTensor = framework::LoDTensor;
static constexpr int kROISize = 4;
template <typename T>
bool GT_E(T a, T b) {
return (a > b) || fabs(a - b) < 1e-4;
}
template <typename T>
bool LT_E(T a, T b) {
return (a < b) || fabs(a - b) < 1e-4;
}
template <typename T>
bool GT(T a, T b) {
return (a - b) > 1e-4;
}
/*
*check if (x, y) is in the boundary of roi
*/
template <typename T>
bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
for (int i = 0; i < 4; i++) {
T xs = roi_x[i];
T ys = roi_y[i];
T xe = roi_x[(i + 1) % 4];
T ye = roi_y[(i + 1) % 4];
if (fabs(ys - ye) < 1e-4) {
if (fabs(y - ys) < 1e-4 && fabs(y - ye) < 1e-4 &&
GT_E<T>(x, std::min(xs, xe)) && LT_E<T>(x, std::max(xs, xe))) {
return true;
}
} else {
T intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs;
if (fabs(intersec_x - x) < 1e-4 && GT_E<T>(y, std::min(ys, ye)) &&
LT_E<T>(y, std::max(ys, ye))) {
return true;
}
}
}
int n_cross = 0;
for (int i = 0; i < 4; i++) {
T xs = roi_x[i];
T ys = roi_y[i];
T xe = roi_x[(i + 1) % 4];
T ye = roi_y[(i + 1) % 4];
if (fabs(ys - ye) < 1e-4) {
continue;
}
if (LT_E<T>(y, std::min(ys, ye)) || GT<T>(y, std::max(ys, ye))) {
continue;
}
T intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs;
if (fabs(intersec_x - x) < 1e-4) {
return true;
}
if (GT<T>(intersec_x, x)) {
n_cross++;
}
}
return (n_cross % 2 == 1);
}
/**
* Get the matrix of perspective transform.
*
* dx1 = x1 - x2
* dx2 = x3 - x2
* dx3 = x0 - x1 + x2 - x3
* dy1 = y1 - y2
* dy2 = y3 - y2
* dy3 = y0 - y1 + y2 - y3
*
* a11 = (x1 - x0 + a31 * (w - 1) * x1) / (w - 1)
* a12 = (x3 - x0 + a32 * (h - 1) * x3) / (h - 1)
* a13 = x0
* a21 = (y1 - y0 + a31 * (w - 1) * y1) / (w - 1)
* a22 = (y3 - y0 + a32 * (h - 1) * y3) / (h - 1)
* a23 = y0
* a31 = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (w - 1)
* a32 = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (h - 1)
* a33 = 1
*
*/
template <typename T>
void get_transform_matrix(const int transformed_width,
const int transformed_height, T roi_x[], T roi_y[],
T matrix[]) {
T x0 = roi_x[0];
T x1 = roi_x[1];
T x2 = roi_x[2];
T x3 = roi_x[3];
T y0 = roi_y[0];
T y1 = roi_y[1];
T y2 = roi_y[2];
T y3 = roi_y[3];
// Estimate the height and width of RoI
T len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
T len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2));
T len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3));
T len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0));
T estimated_height = (len2 + len4) / 2.0;
T estimated_width = (len1 + len3) / 2.0;
// Get the normalized height and normalized width
int normalized_height = transformed_height;
int normalized_width =
std::round(estimated_width * (normalized_height - 1) / estimated_height) +
1;
normalized_width = std::min(normalized_width, transformed_width);
T dx1 = x1 - x2;
T dx2 = x3 - x2;
T dx3 = x0 - x1 + x2 - x3;
T dy1 = y1 - y2;
T dy2 = y3 - y2;
T dy3 = y0 - y1 + y2 - y3;
matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) /
(normalized_width - 1);
matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) /
(normalized_height - 1);
matrix[8] = 1;
matrix[3] = (y1 - y0 + matrix[6] * (normalized_width - 1) * y1) /
(normalized_width - 1);
matrix[4] = (y3 - y0 + matrix[7] * (normalized_height - 1) * y3) /
(normalized_height - 1);
matrix[5] = y0;
matrix[0] = (x1 - x0 + matrix[6] * (normalized_width - 1) * x1) /
(normalized_width - 1);
matrix[1] = (x3 - x0 + matrix[7] * (normalized_height - 1) * x3) /
(normalized_height - 1);
matrix[2] = x0;
}
/**
* Get the source coordinates in the input feature map.
*
* (u, v, w)^matrix = matrix * (out_w, out_h, 1)^matrix
*
* in_w = u / w
* in_h = v / w
*
*/
template <typename T>
void get_source_coords(T matrix[], int out_w, int out_h, T* in_w, T* in_h) {
T u = matrix[0] * out_w + matrix[1] * out_h + matrix[2];
T v = matrix[3] * out_w + matrix[4] * out_h + matrix[5];
T w = matrix[6] * out_w + matrix[7] * out_h + matrix[8];
in_w[0] = u / w;
in_h[0] = v / w;
}
/**
* Perform bilinear interpolation in the input feature map.
*/
template <typename T>
void bilinear_interpolate(const T* in_data, const int channels, const int width,
const int height, int in_n, int in_c, T in_w, T in_h,
T* val) {
// Deal with cases that source coords are out of feature map boundary
if (GT<T>(-0.5, in_w) || GT<T>(in_w, width - 0.5) || GT<T>(-0.5, in_h) ||
GT<T>(in_h, height - 0.5)) {
// empty
val[0] = 0.0;
return;
}
if (GT<T>(0, in_w)) {
in_w = 0;
}
if (GT<T>(0, in_h)) {
in_h = 0;
}
int in_w_floor = floor(in_w);
int in_h_floor = floor(in_h);
int in_w_ceil;
int in_h_ceil;
if (GT_E<T>(in_w_floor, width - 1)) {
in_w_ceil = in_w_floor = width - 1;
in_w = static_cast<T>(in_w_floor);
} else {
in_w_ceil = in_w_floor + 1;
}
if (GT_E<T>(in_h_floor, height - 1)) {
in_h_ceil = in_h_floor = height - 1;
in_h = static_cast<T>(in_h_floor);
} else {
in_h_ceil = in_h_floor + 1;
}
T w_floor = in_w - in_w_floor;
T h_floor = in_h - in_h_floor;
T w_ceil = 1 - w_floor;
T h_ceil = 1 - h_floor;
const T* data = in_data + (in_n * channels + in_c) * height * width;
// Do bilinear interpolation
T v1 = data[in_h_floor * width + in_w_floor];
T v2 = data[in_h_ceil * width + in_w_floor];
T v3 = data[in_h_ceil * width + in_w_ceil];
T v4 = data[in_h_floor * width + in_w_ceil];
T w1 = w_ceil * h_ceil;
T w2 = w_ceil * h_floor;
T w3 = w_floor * h_floor;
T w4 = w_floor * h_ceil;
val[0] = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4;
}
template <typename T>
class CPUROIPerspectiveTransformOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
auto* out = ctx.Output<framework::Tensor>("Out");
auto transformed_height = ctx.Attr<int>("transformed_height");
auto transformed_width = ctx.Attr<int>("transformed_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
int channels = in_dims[1];
int in_height = in_dims[2];
int in_width = in_dims[3];
int rois_num = rois->dims()[0];
const T* input_data = in->data<T>();
framework::Tensor roi2image;
roi2image.Resize({rois_num});
int* roi2image_data = roi2image.mutable_data<int>(ctx.GetPlace());
auto lod = rois->lod().back();
for (int i = 0; i < lod.size() - 1; ++i) {
for (int j = lod[i]; j < lod[i + 1]; ++j) {
roi2image_data[j] = i;
}
}
T* output_data = out->mutable_data<T>(ctx.GetPlace());
const T* rois_data = rois->data<T>();
for (int n = 0; n < rois_num; ++n) {
const T* n_rois = rois_data + n * 8;
T roi_x[4];
T roi_y[4];
for (int k = 0; k < 4; ++k) {
roi_x[k] = n_rois[2 * k] * spatial_scale;
roi_y[k] = n_rois[2 * k + 1] * spatial_scale;
}
int image_id = roi2image_data[n];
// Get transform matrix
T transform_matrix[9];
get_transform_matrix<T>(transformed_width, transformed_height, roi_x,
roi_y, transform_matrix);
for (int c = 0; c < channels; ++c) {
for (int out_h = 0; out_h < transformed_height; ++out_h) {
for (int out_w = 0; out_w < transformed_width; ++out_w) {
int out_index =
n * channels * transformed_height * transformed_width +
c * transformed_height * transformed_width +
out_h * transformed_width + out_w;
T in_w, in_h;
get_source_coords<T>(transform_matrix, out_w, out_h, &in_w, &in_h);
if (in_quad<T>(in_w, in_h, roi_x, roi_y)) {
if (GT<T>(-0.5, in_w) ||
GT<T>(in_w, static_cast<T>(in_width - 0.5)) ||
GT<T>(-0.5, in_h) ||
GT<T>(in_h, static_cast<T>(in_height - 0.5))) {
output_data[out_index] = 0.0;
} else {
bilinear_interpolate(input_data, channels, in_width, in_height,
image_id, c, in_w, in_h,
output_data + out_index);
}
} else {
output_data[out_index] = 0.0;
}
}
}
}
}
}
};
template <typename T>
T get_feature_gradient(T xs, T ys, int w, int h, const int width,
const int height) {
if (GT<T>(-0.5, xs) || GT<T>(xs, width - 0.5) || GT<T>(-0.5, ys) ||
GT<T>(ys, height - 0.5)) {
return 0;
}
if (GT<T>(0, xs)) {
xs = 0;
}
if (GT<T>(0, ys)) {
ys = 0;
}
int xs_floor = floor(xs);
int ys_floor = floor(ys);
int xs_ceil;
int ys_ceil;
if (GT_E(xs_floor, width - 1)) {
xs_ceil = xs_floor = width - 1;
xs = static_cast<T>(xs_floor);
} else {
xs_ceil = xs_floor + 1;
}
if (GT_E(ys_floor, height - 1)) {
ys_ceil = ys_floor = height - 1;
ys = static_cast<T>(ys_floor);
} else {
ys_ceil = ys_floor + 1;
}
T weight = 0;
if (w == xs_floor) {
if (h == ys_floor) {
weight = (w + 1 - xs) * (h + 1 - ys);
} else if (h == ys_ceil) {
weight = (w + 1 - xs) * (ys + 1 - h);
}
} else if (w == xs_ceil) {
if (h == ys_floor) {
weight = (xs + 1 - w) * (h + 1 - ys);
} else if (h == ys_ceil) {
weight = (xs + 1 - w) * (ys + 1 - h);
}
}
return weight;
}
template <typename T>
class CPUROIPerspectiveTransformGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto transformed_height = ctx.Attr<int>("transformed_height");
auto transformed_width = ctx.Attr<int>("transformed_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
int batch_size = in_dims[0];
int channels = in_dims[1];
int in_height = in_dims[2];
int in_width = in_dims[3];
int rois_num = rois->dims()[0];
T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
const T* out_grad_data = out_grad->data<T>();
const T* rois_data = rois->data<T>();
framework::Tensor roi2image;
roi2image.Resize({rois_num});
int* roi2image_data = roi2image.mutable_data<int>(ctx.GetPlace());
auto lod = rois->lod().back();
for (int i = 0; i < lod.size() - 1; ++i) {
for (int j = lod[i]; j < lod[i + 1]; ++j) {
roi2image_data[j] = i;
}
}
for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < channels; ++c) {
for (int in_h = 0; in_h < in_height; ++in_h) {
for (int in_w = 0; in_w < in_width; ++in_w) {
T gradient = 0.0;
for (int roi_idx = lod[n]; roi_idx < lod[n + 1]; ++roi_idx) {
const T* rois = rois_data + roi_idx * 8;
T roi_x[4];
T roi_y[4];
for (int k = 0; k < 4; ++k) {
roi_x[k] = rois[2 * k] * spatial_scale;
roi_y[k] = rois[2 * k + 1] * spatial_scale;
}
// Get transform matrix
T matrix[9];
get_transform_matrix<T>(transformed_width, transformed_height,
roi_x, roi_y, matrix);
const T* out_grad_ptr = out_grad_data +
(roi_idx * channels + c) *
transformed_height *
transformed_width;
for (int out_h = 0; out_h < transformed_height; ++out_h) {
for (int out_w = 0; out_w < transformed_width; ++out_w) {
T src_w;
T src_h;
get_source_coords<T>(matrix, out_w, out_h, &src_w, &src_h);
if (in_quad<T>(src_w, src_h, roi_x, roi_y)) {
if (GT<T>(-0.5, src_w) ||
GT<T>(src_w, static_cast<T>(in_width - 0.5)) ||
GT<T>(-0.5, src_h) ||
GT<T>(src_h, static_cast<T>(in_height - 0.5))) {
continue;
}
T weight = get_feature_gradient<T>(src_w, src_h, in_w, in_h,
in_width, in_height);
gradient +=
out_grad_ptr[out_h * transformed_width + out_w] *
weight;
}
}
}
}
int out_idx = (n * channels + c) * in_height * in_width +
in_h * in_width + in_w;
in_grad_data[out_idx] = gradient;
}
}
}
}
}
};
class ROIPerspectiveTransformOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ROIPerspectiveTransformOp should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("ROIs"),
"Input(ROIs) of ROIPerspectiveTransformOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of ROIPerspectiveTransformOp should not be null.");
auto input_dims = ctx->GetInputDim("X");
auto rois_dims = ctx->GetInputDim("ROIs");
PADDLE_ENFORCE(input_dims.size() == 4,
"The format of input tensor is NCHW.");
PADDLE_ENFORCE(rois_dims.size() == 2,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 8)"
"given as [[x0, y0, x1, y1, x2, y2, x3, y3], ...]");
PADDLE_ENFORCE(rois_dims[1] == 8,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 8)"
"given as [[x0, y0, x1, y1, x2, y2, x3, y3], ...].");
int transformed_height = ctx->Attrs().Get<int>("transformed_height");
int transformed_width = ctx->Attrs().Get<int>("transformed_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
PADDLE_ENFORCE_GT(transformed_height, 0,
"The transformed output height must greater than 0");
PADDLE_ENFORCE_GT(transformed_width, 0,
"The transformed output width must greater than 0");
PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
"The spatial scale must greater than 0");
std::vector<int64_t> out_dims_v({rois_dims[0], // num_rois
input_dims[1], // channels
static_cast<int64_t>(transformed_height),
static_cast<int64_t>(transformed_width)});
auto out_dims = framework::make_ddim(out_dims_v);
ctx->SetOutputDim("Out", out_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class ROIPerspectiveTransformGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
"The gradient of X should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class ROIPerspectiveTransformOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor), "
"the input of ROIPerspectiveTransformOp. "
"The format of input tensor is NCHW. Where N is batch size, "
"C is the number of input channels, "
"H is the height of the feature, and "
"W is the width of the feature.");
AddInput("ROIs",
"(LoDTensor), "
"ROIs (Regions of Interest) to be transformed. "
"should be a 2-D LoDTensor of shape (num_rois, 8)"
"given as [[x1, y1, x2, y2, x3, y3, x4, y4], ...]."
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the top right coordinates, and"
"(x3, y3) is the bottom right coordinates, and"
"(x4, y4) is the bottom left coordinates.");
AddOutput(
"Out",
"(Tensor), "
"The output of ROIPerspectiveTransformOp is a 4-D tensor with shape "
"(num_rois, channels, transformed_h, transformed_w).");
AddAttr<float>("spatial_scale",
"(float, default 1.0), "
"Spatial scale factor to scale ROI coords.")
.SetDefault(1.0);
AddAttr<int>("transformed_height",
"(int, default 1), "
"The height of transformed output.")
.SetDefault(1);
AddAttr<int>("transformed_width",
"(int, default 1), "
"The width of transformed output.")
.SetDefault(1);
AddComment(R"DOC(
**ROIPerspectiveTransform Operator**
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_perspective_transform, ops::ROIPerspectiveTransformOp,
ops::ROIPerspectiveTransformOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(roi_perspective_transform_grad,
ops::ROIPerspectiveTransformGradOp);
REGISTER_OP_CPU_KERNEL(roi_perspective_transform,
ops::CPUROIPerspectiveTransformOpKernel<float>);
REGISTER_OP_CPU_KERNEL(roi_perspective_transform_grad,
ops::CPUROIPerspectiveTransformGradOpKernel<float>);
paddle/fluid/operators/detection/roi_perspective_transform_op.cu 0 → 100644
浏览文件 @ 3ad3635d
/* 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. */
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace paddle {
namespace operators {
// CUDA: index helpers
#define idx4_4(index, d1, d2, d3, d4) (index % d4)
#define idx4_3(index, d1, d2, d3, d4) ((index / d4) % d3)
#define idx4_2(index, d1, d2, d3, d4) ((index / d4 / d3) % d2)
#define idx4_1(index, d1, d2, d3, d4) ((index / d4 / d3 / d2) % d1)
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T>
__device__ bool GT_E(T a, T b) {
return (a > b) || fabs(a - b) < 1e-4;
}
template <typename T>
__device__ bool LT_E(T a, T b) {
return (a < b) || fabs(a - b) < 1e-4;
}
template <typename T>
__device__ bool GT(T a, T b) {
return (a - b) > 1e-4;
}
template <typename T>
__device__ T max(T a, T b) {
return a > b ? a : b;
}
template <typename T>
__device__ T min(T a, T b) {
return a < b ? a : b;
}
/*
* check if (x, y) is in the boundary of roi
*/
template <typename T>
__device__ bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
for (int i = 0; i < 4; i++) {
T start_w = roi_x[i];
T start_h = roi_y[i];
T end_w = roi_x[(i + 1) % 4];
T end_h = roi_y[(i + 1) % 4];
if (fabs(start_h - end_h) < 1e-4) {
if (fabs(y - start_h) < 1e-4 && fabs(y - end_h) < 1e-4 &&
GT_E<T>(x, min<T>(start_w, end_w)) &&
LT_E<T>(x, max<T>(start_w, end_w))) {
return true;
}
} else {
T intersec_x =
(y - start_h) * (end_w - start_w) / (end_h - start_h) + start_w;
if (fabs(intersec_x - x) < 1e-4 && GT_E(y, min<T>(start_h, end_h)) &&
LT_E<T>(y, max<T>(start_h, end_h))) {
return true;
}
}
}
int n_cross = 0;
for (int i = 0; i < 4; i++) {
T start_w = roi_x[i];
T start_h = roi_y[i];
T end_w = roi_x[(i + 1) % 4];
T end_h = roi_y[(i + 1) % 4];
if (fabs(start_h - end_h) < 1e-4) {
continue;
}
if (LT_E<T>(y, min<T>(start_h, end_h)) ||
GT<T>(y, max<T>(start_h, end_h))) {
continue;
}
T intersec_x =
(y - start_h) * (end_w - start_w) / (end_h - start_h) + start_w;
if (fabs(intersec_x - x) < 1e-4) {
return true;
}
if (GT<T>(intersec_x, x)) {
n_cross++;
}
}
return (n_cross % 2 == 1);
}
/**
* Perform bilinear interpolation in the input feature map.
*/
template <typename T>
__device__ void bilinear_interpolate(const T* in_data, const int channels,
const int width, const int height,
int in_n, int in_c, T in_w, T in_h,
T* val) {
// Deal with cases that source coords are out of feature map boundary
if (GT<T>(-0.5, in_w) || GT<T>(in_w, width - 0.5) || GT<T>(-0.5, in_h) ||
GT<T>(in_h, height - 0.5)) {
val[0] = 0.0;
return;
}
if (GT<T>(0, in_w)) {
in_w = 0;
}
if (GT<T>(0, in_h)) {
in_h = 0;
}
int in_w_floor = floor(in_w);
int in_h_floor = floor(in_h);
int in_w_ceil;
int in_h_ceil;
if (GT_E<T>(in_w_floor, width - 1)) {
in_w_ceil = in_w_floor = width - 1;
in_w = static_cast<T>(in_w_floor);
} else {
in_w_ceil = in_w_floor + 1;
}
if (GT_E<T>(in_h_floor, height - 1)) {
in_h_ceil = in_h_floor = height - 1;
in_h = static_cast<T>(in_h_floor);
} else {
in_h_ceil = in_h_floor + 1;
}
T w_floor = in_w - in_w_floor;
T h_floor = in_h - in_h_floor;
T w_ceil = 1 - w_floor;
T h_ceil = 1 - h_floor;
const T* data = in_data + (in_n * channels + in_c) * height * width;
// Do bilinear interpolation
T v1 = data[in_h_floor * width + in_w_floor];
T v2 = data[in_h_ceil * width + in_w_floor];
T v3 = data[in_h_ceil * width + in_w_ceil];
T v4 = data[in_h_floor * width + in_w_ceil];
T w1 = w_ceil * h_ceil;
T w2 = w_ceil * h_floor;
T w3 = w_floor * h_floor;
T w4 = w_floor * h_ceil;
val[0] = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4;
}
/**
* Get the source coordinates in the input feature map.
*
* (u, v, w)^matrix = T * (out_w, out_h, 1)^matrix
*
* in_w = u / w
* in_h = v / w
*
*/
template <typename T>
__device__ void get_source_coords(T matrix[], int out_w, int out_h, T* in_w,
T* in_h) {
T u = matrix[0] * out_w + matrix[1] * out_h + matrix[2];
T v = matrix[3] * out_w + matrix[4] * out_h + matrix[5];
T w = matrix[6] * out_w + matrix[7] * out_h + matrix[8];
in_w[0] = u / w;
in_h[0] = v / w;
}
/**
* Get the matrix of perspective transform.
*
* dx1 = x1 - x2
* dx2 = x3 - x2
* dx3 = x0 - x1 + x2 - x3
* dy1 = y1 - y2
* dy2 = y3 - y2
* dy3 = y0 - y1 + y2 - y3
*
* a11 = (x1 - x0 + a31 * (w - 1) * x1) / (w - 1)
* a12 = (x3 - x0 + a32 * (h - 1) * x3) / (h - 1)
* a13 = x0
* a21 = (y1 - y0 + a31 * (w - 1) * y1) / (w - 1)
* a22 = (y3 - y0 + a32 * (h - 1) * y3) / (h - 1)
* a23 = y0
* a31 = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (w - 1)
* a32 = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (h - 1)
* a33 = 1
*
*/
template <typename T>
__device__ void get_transform_matrix(const int transformed_width,
const int transformed_height, T roi_x[],
T roi_y[], T matrix[]) {
T x0 = roi_x[0];
T x1 = roi_x[1];
T x2 = roi_x[2];
T x3 = roi_x[3];
T y0 = roi_y[0];
T y1 = roi_y[1];
T y2 = roi_y[2];
T y3 = roi_y[3];
// Estimate the height and width of RoI
T len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
T len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2));
T len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3));
T len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0));
T estimated_height = (len2 + len4) / 2.0;
T estimated_width = (len1 + len3) / 2.0;
// Get the normalized height and normalized width
int normalized_height = transformed_height;
int normalized_width =
round(estimated_width * (normalized_height - 1) / estimated_height) + 1;
normalized_width = min(normalized_width, transformed_width);
T dx1 = x1 - x2;
T dx2 = x3 - x2;
T dx3 = x0 - x1 + x2 - x3;
T dy1 = y1 - y2;
T dy2 = y3 - y2;
T dy3 = y0 - y1 + y2 - y3;
matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) /
(normalized_width - 1);
matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) /
(normalized_height - 1);
matrix[8] = 1;
matrix[3] = (y1 - y0 + matrix[6] * (normalized_width - 1) * y1) /
(normalized_width - 1);
matrix[4] = (y3 - y0 + matrix[7] * (normalized_height - 1) * y3) /
(normalized_height - 1);
matrix[5] = y0;
matrix[0] = (x1 - x0 + matrix[6] * (normalized_width - 1) * x1) /
(normalized_width - 1);
matrix[1] = (x3 - x0 + matrix[7] * (normalized_height - 1) * x3) /
(normalized_height - 1);
matrix[2] = x0;
}
template <typename T>
__global__ void RoiTransformKernel(const float* input_data,
const float* rois_data,
const int* roi2image_data, int num_rois,
int in_height, int in_width, int channels,
int transformed_height,
int transformed_width, float spatial_scale,
T* output_data) {
int output_size =
num_rois * transformed_height * transformed_width * channels;
CUDA_1D_KERNEL_LOOP(index, output_size) {
// (n, c, out_h, out_w) is an element in the transformed output
int out_w = idx4_4(index, num_rois, channels, transformed_height,
transformed_width);
int out_h = idx4_3(index, num_rois, channels, transformed_height,
transformed_width);
int c = idx4_2(index, num_rois, channels, transformed_height,
transformed_width);
int n = idx4_1(index, num_rois, channels, transformed_height,
transformed_width);
auto bottom_rois = rois_data + n * 8;
int roi_batch_ind = bottom_rois[0];
T roi_x[4];
T roi_y[4];
for (int k = 0; k < 4; ++k) {
roi_x[k] = bottom_rois[2 * k] * spatial_scale;
roi_y[k] = bottom_rois[2 * k + 1] * spatial_scale;
}
// Get transform matrix
T matrix[9];
get_transform_matrix<T>(transformed_width, transformed_height, roi_x, roi_y,
matrix);
// Get source coords
T in_w;
T in_h;
get_source_coords<T>(matrix, out_w, out_h, &in_w, &in_h);
if (in_quad<T>(in_w, in_h, roi_x, roi_y)) {
if (GT<T>(-0.5, in_w) || GT<T>(in_w, static_cast<T>(in_width - 0.5)) ||
GT<T>(-0.5, in_h) || GT<T>(in_h, static_cast<T>(in_height - 0.5))) {
// Skip if source coords is not in input image
output_data[index] = 0.0;
} else {
// Perform bilinear interpolation
int in_n = roi2image_data[n];
bilinear_interpolate<T>(input_data, channels, in_width, in_height, in_n,
c, in_w, in_h, output_data + index);
}
} else {
// Skip if source coords is not in quad
output_data[index] = 0.0;
}
}
}
template <typename T>
class CUDAROIPerspectiveTransformOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
auto* out = ctx.Output<framework::Tensor>("Out");
auto transformed_height = ctx.Attr<int>("transformed_height");
auto transformed_width = ctx.Attr<int>("transformed_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
int batch_size = in_dims[0];
int channels = in_dims[1];
int in_height = in_dims[2];
int in_width = in_dims[3];
int rois_num = rois->dims()[0];
const T* input_data = in->data<T>();
T* output_data = out->mutable_data<T>(ctx.GetPlace());
const T* rois_data = rois->data<T>();
framework::Tensor roi2image;
framework::Tensor roi2image_dev;
roi2image.Resize({rois_num});
int* roi2image_data = roi2image.mutable_data<int>(platform::CPUPlace());
auto lod = rois->lod().back();
for (int i = 0; i < lod.size() - 1; ++i) {
for (int j = lod[i]; j < lod[i + 1]; ++j) {
roi2image_data[j] = i;
}
}
TensorCopySync(roi2image, ctx.GetPlace(), &roi2image_dev);
int out_size = rois_num * transformed_height * transformed_width * channels;
auto stream = ctx.cuda_device_context().stream();
int block = 512;
int grid = (out_size + block - 1) / block;
RoiTransformKernel<T><<<grid, block, 0, stream>>>(
input_data, rois_data, roi2image_dev.data<int>(), rois_num, in_height,
in_width, channels, transformed_height, transformed_width,
spatial_scale, output_data);
}
};
template <typename T>
__device__ T get_feature_gradient(T xs, T ys, int w, int h, const int width,
const int height) {
if (GT<T>(-0.5, xs) || GT<T>(xs, width - 0.5) || GT<T>(-0.5, ys) ||
GT<T>(ys, height - 0.5)) {
return 0;
}
if (GT<T>(0, xs)) {
xs = 0;
}
if (GT<T>(0, ys)) {
ys = 0;
}
int xs_floor = floor(xs);
int ys_floor = floor(ys);
int xs_ceil;
int ys_ceil;
if (GT_E<T>(xs_floor, width - 1)) {
xs_ceil = xs_floor = width - 1;
xs = static_cast<T>(xs_floor);
} else {
xs_ceil = xs_floor + 1;
}
if (GT_E(ys_floor, height - 1)) {
ys_ceil = ys_floor = height - 1;
ys = static_cast<T>(ys_floor);
} else {
ys_ceil = ys_floor + 1;
}
T weight = 0;
if (w == xs_floor) {
if (h == ys_floor) {
weight = (w + 1 - xs) * (h + 1 - ys);
} else if (h == ys_ceil) {
weight = (w + 1 - xs) * (ys + 1 - h);
}
} else if (w == xs_ceil) {
if (h == ys_floor) {
weight = (xs + 1 - w) * (h + 1 - ys);
} else if (h == ys_ceil) {
weight = (xs + 1 - w) * (ys + 1 - h);
}
}
return weight;
}
template <typename T>
__global__ void RoiTransformGradKernel(
const size_t* lod, const T* rois_data, int batch_size, int num_rois,
int in_height, int in_width, int channels, int transformed_height,
int transformed_width, float spatial_scale, const T* out_grad_data,
T* in_grad_data) {
int input_size = batch_size * in_height * in_width * channels;
CUDA_1D_KERNEL_LOOP(index, input_size) {
// (n, c, h, w) coords in input
int in_w = idx4_4(index, batch_size, channels, in_height, in_width);
int in_h = idx4_3(index, batch_size, channels, in_height, in_width);
int c = idx4_2(index, batch_size, channels, in_height, in_width);
int n = idx4_1(index, batch_size, channels, in_height, in_width);
T gradient = 0.0;
// Accumulate gradient over all RoIs that interpolated this element
for (int roi_idx = lod[n]; roi_idx < lod[n + 1]; ++roi_idx) {
const T* rois = rois_data + roi_idx * 8;
T roi_x[4];
T roi_y[4];
for (int k = 0; k < 4; ++k) {
roi_x[k] = rois[2 * k] * spatial_scale;
roi_y[k] = rois[2 * k + 1] * spatial_scale;
}
// Get transform matrix
T matrix[9];
get_transform_matrix<T>(transformed_width, transformed_height, roi_x,
roi_y, matrix);
const T* out_grad_ptr =
out_grad_data +
(roi_idx * channels + c) * transformed_height * transformed_width;
for (int out_h = 0; out_h < transformed_height; ++out_h) {
for (int out_w = 0; out_w < transformed_width; ++out_w) {
T src_w;
T src_h;
get_source_coords<T>(matrix, out_w, out_h, &src_w, &src_h);
if (in_quad<T>(src_w, src_h, roi_x, roi_y)) {
if (GT<T>(-0.5, src_w) ||
GT<T>(src_w, static_cast<T>(in_width - 0.5)) ||
GT<T>(-0.5, src_h) ||
GT<T>(src_h, static_cast<T>(in_height - 0.5))) {
continue;
}
T weight = get_feature_gradient<T>(src_w, src_h, in_w, in_h,
in_width, in_height);
gradient +=
out_grad_ptr[out_h * transformed_width + out_w] * weight;
}
}
}
}
in_grad_data[index] = gradient;
}
}
template <typename T>
class CUDAROIPerspectiveTransformGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto transformed_height = ctx.Attr<int>("transformed_height");
auto transformed_width = ctx.Attr<int>("transformed_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
int batch_size = in_dims[0];
int channels = in_dims[1];
int in_height = in_dims[2];
int in_width = in_dims[3];
int rois_num = rois->dims()[0];
T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
const T* out_grad_data = out_grad->data<T>();
const T* rois_data = rois->data<T>();
auto lod = rois->lod().back();
auto lod_data = lod.CUDAData(ctx.GetPlace());
int in_size = in->numel();
auto stream = ctx.cuda_device_context().stream();
int block = 512;
int grid = (in_size + block - 1) / block;
RoiTransformGradKernel<T><<<grid, block, 0, stream>>>(
lod_data, rois_data, batch_size, rois_num, in_height, in_width,
channels, transformed_height, transformed_width, spatial_scale,
out_grad_data, in_grad_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(roi_perspective_transform,
ops::CUDAROIPerspectiveTransformOpKernel<float>);
REGISTER_OP_CUDA_KERNEL(roi_perspective_transform_grad,
ops::CUDAROIPerspectiveTransformGradOpKernel<float>);
paddle/fluid/operators/detection_map_op.h
浏览文件 @ 3ad3635d
......@@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num");
auto& label_lod = in_label->lod();
auto& detect_lod = in_detect->lod();
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
......@@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto& label_lod = input_label.lod();
auto& detect_lod = input_detect.lod();
auto label_lod = input_label.lod();
auto detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto& label_index = label_lod[0];
auto label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
......@@ -274,6 +274,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
output_true_pos->set_lod(true_pos_lod);
output_false_pos->set_lod(false_pos_lod);
return;
}
void GetInputPos(const framework::Tensor& input_pos_count,
......@@ -291,7 +292,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>();
auto& pos_data_lod = pos_tensor.lod()[0];
auto pos_data_lod = pos_tensor.lod()[0];
for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) {
for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) {
T score = pos_data[j * 2];
......@@ -316,23 +317,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
std::map<int, std::vector<std::pair<T, int>>>* false_pos) const {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto& image_gt_boxes = gt_boxes[n];
for (auto& image_gt_box : image_gt_boxes) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
size_t count = 0;
auto& labeled_bboxes = image_gt_box.second;
auto labeled_bboxes = it->second;
if (evaluate_difficult) {
count = labeled_bboxes.size();
} else {
for (auto& box : labeled_bboxes) {
if (!box.is_difficult) {
++count;
}
}
for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!(labeled_bboxes[i].is_difficult)) ++count;
}
if (count == 0) {
continue;
}
int label = image_gt_box.first;
int label = it->first;
if (label_pos_count->find(label) == label_pos_count->end()) {
(*label_pos_count)[label] = count;
} else {
......
paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ 3ad3635d
......@@ -92,9 +92,14 @@ bool VariableResponse::CopyLodTensorData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, const framework::DDim& dims,
int length) {
auto server_var = GetVar();
if (!server_var) {
LOG(ERROR) << "recved var should not on current server: "
<< meta_.varname();
return false;
}
auto* tensor = GetVar()->GetMutable<framework::LoDTensor>();
tensor->Resize(dims);
framework::LoD lod;
for (int i = 0; i < meta_.lod_level(); ++i) {
framework::Vector<size_t> v;
......@@ -107,7 +112,6 @@ bool VariableResponse::CopyLodTensorData(
void* tensor_data =
tensor->mutable_data(ctx.GetPlace(), ToTypeIndex(meta_.data_type()));
if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
return false;
}
......
paddle/fluid/operators/extract_rows_op.cc
浏览文件 @ 3ad3635d
......@@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto &in_rows = in.rows();
auto in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 3ad3635d
......@@ -60,9 +60,11 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
auto out_place = context.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(out_place));
memory::Copy(boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T), context.stream());
memory::Copy(
boost::get<platform::CUDAPlace>(out_place), out_data,
boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
auto* in2_data = in2_value.data<T>();
memory::Copy(boost::get<platform::CUDAPlace>(out_place),
......@@ -107,7 +109,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(in1_height, out_dims[0]);
auto& in1_value = input1.value();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2.numel() / in1_height);
......@@ -146,7 +148,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value();
......@@ -206,7 +208,7 @@ struct SelectedRowsAddToTensor<platform::CUDADeviceContext, T> {
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
auto& in1_value = input1.value();
framework::Vector<int64_t> in1_rows(input1.rows());
auto& in1_rows = input1.rows();
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
......
paddle/fluid/operators/math/selected_rows_functor_test.cu
浏览文件 @ 3ad3635d
......@@ -20,7 +20,9 @@ limitations under the License. */
TEST(selected_rows_functor, gpu_add) {
paddle::platform::CUDAPlace gpu_place(0);
paddle::platform::CPUPlace cpu_place;
paddle::platform::CUDADeviceContext ctx(gpu_place);
paddle::platform::CUDADeviceContext& ctx =
*reinterpret_cast<paddle::platform::CUDADeviceContext*>(
paddle::platform::DeviceContextPool::Instance().Get(gpu_place));
paddle::operators::math::SetConstant<paddle::platform::CUDADeviceContext,
float>
functor;
......@@ -132,7 +134,9 @@ TEST(selected_rows_functor, gpu_add) {
TEST(selected_rows_functor, gpu_add_to) {
paddle::platform::CUDAPlace gpu_place(0);
paddle::platform::CPUPlace cpu_place;
paddle::platform::CUDADeviceContext ctx(gpu_place);
paddle::platform::CUDADeviceContext& ctx =
*reinterpret_cast<paddle::platform::CUDADeviceContext*>(
paddle::platform::DeviceContextPool::Instance().Get(gpu_place));
paddle::operators::math::SetConstant<paddle::platform::CUDADeviceContext,
float>
functor;
......
paddle/fluid/operators/sum_op.h
浏览文件 @ 3ad3635d
......@@ -123,6 +123,7 @@ class SumKernel : public framework::OpKernel<T> {
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
......
paddle/fluid/pybind/const_value.cc
浏览文件 @ 3ad3635d
......@@ -36,7 +36,9 @@ void BindConstValue(pybind11::module* m) {
.value("Backward", framework::OpRole::kBackward)
.value("Optimize", framework::OpRole::kOptimize)
.value("Loss", framework::OpRole::kLoss)
.value("RPC", framework::OpRole::kRPC);
.value("RPC", framework::OpRole::kRPC)
.value("Dist", framework::OpRole::kDist)
.value("LRSched", framework::OpRole::kLRSched);
op_proto_and_checker_maker.def(
"kOpRoleAttrName", framework::OpProtoAndCheckerMaker::OpRoleAttrName);
......
python/paddle/fluid/__init__.py
浏览文件 @ 3ad3635d
......@@ -46,7 +46,7 @@ from . import transpiler
from .param_attr import ParamAttr, WeightNormParamAttr
from .data_feeder import DataFeeder
from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
from .transpiler import DistributeTranspiler, InferenceTranspiler, \
from .transpiler import DistributeTranspiler, \
memory_optimize, release_memory, DistributeTranspilerConfig
from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
from . import clip
......
python/paddle/fluid/framework.py
浏览文件 @ 3ad3635d
......@@ -1510,6 +1510,30 @@ class Program(object):
self._op_role_var = []
self._current_role = OpRole.Forward
@contextlib.contextmanager
def _lr_schedule_guard(self):
"""
A with guard to set :code:`LRSched` :code:`OpRole` and
:code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
set to the target learning rate.
Notes: This is a very low level API. Users should not use it directly.
Examples:
>>> p, g = backward(...)
>>> with program.lr_schedule_guard():
>>> lr = lr * decay
"""
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.LRSched
# TODO(typhoonzero): how to set target learning rate var
self._op_role_var = []
yield
self._op_role_var = []
self._current_role = OpRole.Forward
def __str__(self):
"""
Get the protobuf debug string of this Program.
......
python/paddle/fluid/initializer.py
浏览文件 @ 3ad3635d
......@@ -74,7 +74,7 @@ class Initializer(object):
directly, but need to use one of its implementations.
"""
def __init_(self):
def __init__(self):
pass
def __call__(self, param, block):
......@@ -293,7 +293,7 @@ class TruncatedNormalInitializer(Initializer):
assert loc is not None
assert scale is not None
assert seed is not None
super(NormalInitializer, self).__init__()
super(TruncatedNormalInitializer, self).__init__()
self._mean = loc
self._std_dev = scale
self._seed = seed
......
python/paddle/fluid/io.py
浏览文件 @ 3ad3635d
......@@ -27,8 +27,7 @@ from . import core
__all__ = [
'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
'load_persistables', 'save_inference_model', 'load_inference_model',
'get_inference_program'
'load_persistables', 'save_inference_model', 'load_inference_model'
]
......@@ -504,23 +503,6 @@ def load_persistables(executor, dirname, main_program=None, filename=None):
filename=filename)
def get_inference_program(target_vars, main_program=None):
if main_program is None:
main_program = default_main_program()
if not isinstance(target_vars, list):
target_vars = [target_vars]
vars = []
for var in target_vars:
if isinstance(var, Evaluator):
vars.extend(var.states)
vars.extend(var.metrics)
else:
vars.append(var)
pruned_program = main_program._prune(targets=vars)
inference_program = pruned_program._inference_optimize()
return inference_program
def prepend_feed_ops(inference_program,
feed_target_names,
feed_holder_name='feed'):
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 3ad3635d
......@@ -39,6 +39,7 @@ __all__ = [
'detection_map',
'rpn_target_assign',
'anchor_generator',
'roi_perspective_transform',
'generate_proposal_labels',
'generate_proposals',
]
......@@ -1262,6 +1263,54 @@ def anchor_generator(input,
return anchor, var
def roi_perspective_transform(input,
rois,
transformed_height,
transformed_width,
spatial_scale=1.0):
"""
ROI perspective transform op.
Args:
input (Variable): The input of ROIPerspectiveTransformOp. The format of
input tensor is NCHW. Where N is batch size, C is the
number of input channels, H is the height of the feature,
and W is the width of the feature.
rois (Variable): ROIs (Regions of Interest) to be transformed. It should be
a 2-D LoDTensor of shape (num_rois, 8). Given as
[[x1, y1, x2, y2, x3, y3, x4, y4], ...], (x1, y1) is the
top left coordinates, and (x2, y2) is the top right
coordinates, and (x3, y3) is the bottom right coordinates,
and (x4, y4) is the bottom left coordinates.
transformed_height (integer): The height of transformed output.
transformed_height (integer): The width of transformed output.
spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0
Returns:
Variable: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape
(num_rois, channels, transformed_h, transformed_w).
Examples:
.. code-block:: python
out = fluid.layers.roi_perspective_transform(input, rois, 7, 7, 1.0)
"""
helper = LayerHelper('roi_perspective_transform', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="roi_perspective_transform",
inputs={"X": input,
"ROIs": rois},
outputs={"Out": out},
attrs={
"transformed_height": transformed_height,
"transformed_width": transformed_width,
"spatial_scale": spatial_scale
})
return out
def generate_proposal_labels(rpn_rois,
gt_classes,
is_crowd,
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 3ad3635d
......@@ -27,7 +27,7 @@ from . import nn
from . import ops
from . import tensor
from ..initializer import init_on_cpu
from ..framework import default_main_program, Parameter
from ..framework import default_main_program, Parameter, unique_name
__all__ = [
'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
......@@ -63,11 +63,12 @@ def noam_decay(d_model, warmup_steps):
Returns:
The decayed learning rate.
"""
global_step = _decay_step_counter(1)
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter(1)
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * nn.elementwise_min(a, b)
a = global_step**-0.5
b = (warmup_steps**-1.5) * global_step
lr_value = (d_model**-0.5) * nn.elementwise_min(a, b)
return lr_value
......@@ -108,14 +109,15 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
return decayed_lr
return decayed_lr
def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
......@@ -136,14 +138,15 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Returns:
The decayed learning rate
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
return decayed_lr
return decayed_lr
def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
......@@ -181,15 +184,16 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
global_step = _decay_step_counter()
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate / (1 + decay_rate * div_res)
decayed_lr = learning_rate / (1 + decay_rate * div_res)
return decayed_lr
return decayed_lr
def polynomial_decay(learning_rate,
......@@ -220,25 +224,28 @@ def polynomial_decay(learning_rate,
Returns:
Variable: The decayed learning rate
"""
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(shape=[1], dtype='float32', value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = nn.elementwise_min(x=global_step, y=decay_steps_var)
with default_main_program()._lr_schedule_guard():
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = tensor.fill_constant(
shape=[1], dtype='float32', value=1.0)
with control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
tensor.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = nn.elementwise_min(x=global_step, y=decay_steps_var)
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
decayed_lr = (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
return decayed_lr
def piecewise_decay(boundaries, values):
......@@ -266,34 +273,36 @@ def piecewise_decay(boundaries, values):
"""
with default_main_program()._lr_schedule_guard():
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
global_step = _decay_step_counter()
global_step = _decay_step_counter()
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
lr = tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
with control_flow.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1],
dtype='float32',
value=float(boundaries[i]),
force_cpu=True)
value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
tensor.assign(value_var, lr)
last_value_var = tensor.fill_constant(
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
value=float(values[len(values) - 1]))
with switch.default():
tensor.assign(last_value_var, lr)
return lr
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 3ad3635d
......@@ -107,6 +107,12 @@ __all__ = [
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'flatten',
'sequence_mask',
......@@ -5903,6 +5909,148 @@ def pad2d(input,
return out
@templatedoc()
def elu(x, alpha=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
alpha(${alpha_type}|1.0): ${alpha_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('elu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='elu',
inputs={'X': x},
outputs={'Out': out},
attrs={'alpha': alpha})
return out
@templatedoc()
def relu6(x, threshold=6.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
threshold(${threshold_type}|6.0): ${threshold_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('relu6', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='relu6',
inputs={'X': x},
outputs={'Out': out},
attrs={'threshold': threshold})
return out
@templatedoc()
def pow(x, factor=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
factor(${factor_type}|1.0): ${factor_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('pow', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='pow',
inputs={'X': x},
outputs={'Out': out},
attrs={'factor': factor})
return out
@templatedoc()
def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment}
scale_b(${scale_b_type}|1.7159): ${scale_b_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('stanh', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='stanh',
inputs={'X': x},
outputs={'Out': out},
attrs={'scale_a': scale_a,
'scale_b': scale_b})
return out
@templatedoc()
def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
slope(${slope_type}|0.2): ${slope_comment}
offset(${offset_type}|0.5): ${offset_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('hard_sigmoid', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='hard_sigmoid',
inputs={'X': x},
outputs={'Out': out},
attrs={'slope': slope,
'offset': offset})
return out
@templatedoc()
def swish(x, beta=1.0, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
beta(${beta_type}|1.0): ${beta_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('swish', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type='swish',
inputs={'X': x},
outputs={'Out': out},
attrs={'slope': beta})
return out
def prelu(x, mode, param_attr=None, name=None):
"""
Equation:
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 3ad3635d
......@@ -36,12 +36,6 @@ __activations__ = [
'brelu',
'leaky_relu',
'soft_relu',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
]
__all__ = [
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 3ad3635d
......@@ -80,7 +80,8 @@ if(WITH_DISTRIBUTE)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
#FIXME(gongwb): random fails.
#py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ 3ad3635d
......@@ -437,13 +437,8 @@ def split_data(data, num_part):
]
def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
def test_context(test_program, avg_cost, train_exe, dev_count, data_input_names,
sum_cost, token_num):
# Context to do validation.
test_program = train_progm.clone()
with fluid.program_guard(test_program):
test_program = fluid.io.get_inference_program([avg_cost])
val_data = DataReader(
src_vocab_fpath=TrainTaskConfig.src_vocab_fpath,
trg_vocab_fpath=TrainTaskConfig.trg_vocab_fpath,
......@@ -505,7 +500,7 @@ def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
token_num, predict):
token_num, predict, test_program):
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
lr_scheduler.current_steps = TrainTaskConfig.start_step
......@@ -554,7 +549,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
-1] + label_data_input_fields
if TrainTaskConfig.val_file_pattern is not None:
test = test_context(train_progm, avg_cost, train_exe, dev_count,
test = test_context(test_program, avg_cost, train_exe, dev_count,
data_input_names, sum_cost, token_num)
# the best cross-entropy value with label smoothing
......@@ -1647,6 +1642,8 @@ def get_model(is_dist, is_async):
local_lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps,
TrainTaskConfig.learning_rate)
# Context to do validation.
test_program = fluid.default_main_program().clone(for_test=True)
if not is_dist:
optimizer = fluid.optimizer.Adam(
......@@ -1671,7 +1668,7 @@ def get_model(is_dist, is_async):
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
return sum_cost, avg_cost, predict, token_num, local_lr_scheduler
return sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program
def update_args():
......@@ -1705,7 +1702,7 @@ class DistTransformer2x2(TestDistRunnerBase):
def run_trainer(self, use_cuda, args):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
TrainTaskConfig.use_gpu = use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......@@ -1726,7 +1723,7 @@ class DistTransformer2x2(TestDistRunnerBase):
TrainTaskConfig.local = not args.is_dist
train_loop(startup_exe, trainer_prog, 1, sum_cost, avg_cost,
local_lr_scheduler, token_num, predict)
local_lr_scheduler, token_num, predict, test_program)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 3ad3635d
......@@ -345,7 +345,7 @@ class OpTest(unittest.TestCase):
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t) + " in class " + self.__class__.__name__)
str(actual_t))
if isinstance(expect, tuple):
self.assertListEqual(actual.recursive_sequence_lengths(),
expect[1], "Output (" + out_name +
......
python/paddle/fluid/tests/unittests/test_detection_map_op.py
浏览文件 @ 3ad3635d
......@@ -20,7 +20,6 @@ import six
import sys
import collections
import math
import paddle.fluid as fluid
from op_test import OpTest
......@@ -33,7 +32,7 @@ class TestDetectionMAPOp(OpTest):
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
if len(self.class_pos_count) > 0:
if (len(self.class_pos_count) > 0):
self.class_pos_count = np.array(self.class_pos_count).astype(
'int32')
self.true_pos = np.array(self.true_pos).astype('float32')
......@@ -274,7 +273,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp):
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1, 0]
self.class_pos_count = [0, 2, 1]
self.true_pos_lod = [[0, 3, 2]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 3, 2]]
......
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ 3ad3635d
......@@ -22,7 +22,7 @@ class TestDistMnist2x2(TestDistBase):
self._sync_mode = True
self._use_reduce = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
......@@ -31,7 +31,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
self._sync_mode = True
self._mem_opt = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=1e-7)
......@@ -40,7 +40,7 @@ class TestDistMnistAsync(TestDistBase):
self._sync_mode = False
self._use_reduce = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=200)
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 3ad3635d
......@@ -21,7 +21,16 @@ class TestDistSeResneXt2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
class TestDistseResnXt2x2WithMemopt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=1e-7)
......@@ -29,7 +38,7 @@ class TestDistSeResneXt2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_transformer.py
浏览文件 @ 3ad3635d
......@@ -59,7 +59,7 @@ class TestDistTransformer2x2Sync(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_transformer(self):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1e-5)
......@@ -68,7 +68,7 @@ class TestDistTransformer2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_transformer(self):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1.0)
......
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @ 3ad3635d
......@@ -17,19 +17,28 @@ import unittest
from test_dist_base import TestDistBase
class TestDistSeResneXt2x2(TestDistBase):
class TestDistW2V2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1e-4)
class TestDistSeResneXt2x2Async(TestDistBase):
class TestDistW2V2x2WithMemOpt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1e-4)
class TestDistW2V2x2Async(TestDistBase):
def _setup_config(self):
self._sync_mode = False
def test_se_resnext(self):
def test_dist_train(self):
self.check_with_place("dist_word2vec.py", delta=1)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 3ad3635d
......@@ -573,6 +573,16 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_roi_perspective_transform(self):
program = Program()
with program_guard(program):
x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
rois = layers.data(
name="rois", shape=[8], dtype="float32", lod_level=1)
output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6)
self.assertIsNotNone(output)
print(str(program))
def test_sequence_enumerate(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_roi_perspective_transform_op.py 0 → 100644
浏览文件 @ 3ad3635d
# 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,
# WITHOUWARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import math
import sys
import paddle.compat as cpt
from op_test import OpTest
from math import sqrt
from math import floor
def gt_e(a, b):
return a > b or abs(a - b) < 1e-4
def gt(a, b):
return (a - b) > 1e-4
def lt_e(a, b):
return a < b or abs(a - b) < 1e-4
def in_quad(x, y, roi_x, roi_y):
# check if (x, y) is in the boundary of roi
for i in range(4):
xs = roi_x[i]
ys = roi_y[i]
xe = roi_x[(i + 1) % 4]
ye = roi_y[(i + 1) % 4]
if abs(ys - ye) < 1e-4:
if abs(y - ys) < 1e-4 and abs(y - ye) < 1e-4 and gt_e(
x, min(xs, xe)) and lt_e(x, max(xs, xe)):
return True
else:
intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs
if abs(intersec_x - x) < 1e-4 and gt_e(y, min(ys, ye)) and lt_e(
y, max(ys, ye)):
return True
n_cross = 0
for i in range(4):
xs = roi_x[i]
ys = roi_y[i]
xe = roi_x[(i + 1) % 4]
ye = roi_y[(i + 1) % 4]
if abs(ys - ye) < 1e-4:
continue
if lt_e(y, min(ys, ye)) or gt(y, max(ys, ye)):
continue
intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs
if abs(intersec_x - x) < 1e-4:
return True
if gt(intersec_x, x):
n_cross += 1
return (n_cross % 2 == 1)
def get_transform_matrix(transformed_width, transformed_height, roi_x, roi_y):
x0 = roi_x[0]
x1 = roi_x[1]
x2 = roi_x[2]
x3 = roi_x[3]
y0 = roi_y[0]
y1 = roi_y[1]
y2 = roi_y[2]
y3 = roi_y[3]
len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1))
len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))
len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3))
len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0))
estimated_height = (len2 + len4) / 2.0
estimated_width = (len1 + len3) / 2.0
normalized_height = transformed_height
normalized_width = round(estimated_width *
(normalized_height - 1) / estimated_height) + 1
normalized_width = min(normalized_width, transformed_width)
dx1 = x1 - x2
dx2 = x3 - x2
dx3 = x0 - x1 + x2 - x3
dy1 = y1 - y2
dy2 = y3 - y2
dy3 = y0 - y1 + y2 - y3
matrix = np.zeros([9])
matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (
normalized_width - 1)
matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (
normalized_height - 1)
matrix[8] = 1
matrix[3] = (y1 - y0 + matrix[6] *
(normalized_width - 1) * y1) / (normalized_width - 1)
matrix[4] = (y3 - y0 + matrix[7] *
(normalized_height - 1) * y3) / (normalized_height - 1)
matrix[5] = y0
matrix[0] = (x1 - x0 + matrix[6] *
(normalized_width - 1) * x1) / (normalized_width - 1)
matrix[1] = (x3 - x0 + matrix[7] *
(normalized_height - 1) * x3) / (normalized_height - 1)
matrix[2] = x0
return matrix
def get_source_coords(matrix, out_w, out_h):
u = matrix[0] * out_w + matrix[1] * out_h + matrix[2]
v = matrix[3] * out_w + matrix[4] * out_h + matrix[5]
w = matrix[6] * out_w + matrix[7] * out_h + matrix[8]
in_w = u / w
in_h = v / w
return in_w, in_h
def bilinear_interpolate(in_data, in_n, in_c, in_w, in_h):
batch_size = in_data.shape[0]
channels = in_data.shape[1]
height = in_data.shape[2]
width = in_data.shape[3]
if gt(-0.5, in_w) or gt(in_w, width - 0.5) or gt(-0.5, in_h) or gt(
in_h, height - 0.5):
return 0.0
if gt(0, in_w):
in_w = 0
if gt(0, in_h):
in_h = 0
in_w_floor = floor(in_w)
in_h_floor = floor(in_h)
if gt_e(in_w_floor, width - 1):
in_w_ceil = width - 1
in_w_floor = width - 1
in_w = in_w_floor
else:
in_w_ceil = in_w_floor + 1
if gt_e(in_h_floor, height - 1):
in_h_ceil = height - 1
in_h_floor = height - 1
in_h = in_h_floor
else:
in_h_ceil = in_h_floor + 1
w_floor = in_w - in_w_floor
h_floor = in_h - in_h_floor
w_ceil = 1 - w_floor
h_ceil = 1 - h_floor
v1 = in_data[in_n][in_c][int(in_h_floor)][int(in_w_floor)]
v2 = in_data[in_n][in_c][int(in_h_ceil)][int(in_w_floor)]
v3 = in_data[in_n][in_c][int(in_h_ceil)][int(in_w_ceil)]
v4 = in_data[in_n][in_c][int(in_h_floor)][int(in_w_ceil)]
w1 = w_ceil * h_ceil
w2 = w_ceil * h_floor
w3 = w_floor * h_floor
w4 = w_floor * h_ceil
val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
return val
def lod_convert(lod):
ret = [0]
for count in lod:
ret.append(ret[-1] + count)
return ret
def roi_transform(in_data, rois, rois_lod, transformed_height,
transformed_width, spatial_scale):
channels = in_data.shape[1]
in_height = in_data.shape[2]
in_width = in_data.shape[3]
rois_num = rois.shape[0]
roi2image = [0] * rois_num
rois_lod = lod_convert(rois_lod[0])
for i in range(len(rois_lod) - 1):
for j in range(rois_lod[i], rois_lod[i + 1]):
roi2image[j] = i
out = np.zeros([rois_num, channels, transformed_height, transformed_width])
for n in range(rois_num):
roi_x = []
roi_y = []
for k in range(4):
roi_x.append(rois[n][2 * k] * spatial_scale)
roi_y.append(rois[n][2 * k + 1] * spatial_scale)
image_id = roi2image[n]
transform_matrix = get_transform_matrix(
transformed_width, transformed_height, roi_x, roi_y)
for c in range(channels):
for out_h in range(transformed_height):
for out_w in range(transformed_width):
in_w, in_h = get_source_coords(transform_matrix, out_w,
out_h)
if in_quad(in_w, in_h, roi_x, roi_y) and gt_e(
in_w, -0.5) and lt_e(in_w, in_width - 0.5) and gt_e(
in_h, -0.5) and lt_e(in_h, in_height - 0.5):
out[n][c][out_h][out_w] = bilinear_interpolate(
in_data, image_id, c, in_w, in_h)
else:
out[n][c][out_h][out_w] = 0.0
return out.astype("float32")
class TestROIPoolOp(OpTest):
def set_data(self):
self.init_test_case()
self.make_rois()
self.inputs = {'X': self.x, 'ROIs': (self.rois, self.rois_lod)}
self.attrs = {
'spatial_scale': self.spatial_scale,
'transformed_height': self.transformed_height,
'transformed_width': self.transformed_width
}
out = roi_transform(self.x, self.rois, self.rois_lod,
self.transformed_height, self.transformed_width,
self.spatial_scale)
self.outputs = {'Out': out}
def init_test_case(self):
self.batch_size = 2
self.channels = 2
self.height = 8
self.width = 8
# n, c, h, w
self.x_dim = (self.batch_size, self.channels, self.height, self.width)
self.spatial_scale = 1.0 / 2.0
self.transformed_height = 2
self.transformed_width = 3
self.x = np.random.random(self.x_dim).astype('float32')
def make_rois(self):
rois = []
self.rois_lod = [[]]
for bno in range(self.batch_size):
self.rois_lod[0].append(bno + 1)
for i in range(bno + 1):
x1 = np.random.randint(
0,
self.width // self.spatial_scale - self.transformed_width)
y1 = np.random.randint(
0,
self.height // self.spatial_scale - self.transformed_height)
x2 = np.random.randint(x1 + self.transformed_width,
self.width // self.spatial_scale)
y2 = np.random.randint(
0,
self.height // self.spatial_scale - self.transformed_height)
x3 = np.random.randint(x1 + self.transformed_width,
self.width // self.spatial_scale)
y3 = np.random.randint(y1 + self.transformed_height,
self.height // self.spatial_scale)
x4 = np.random.randint(
0,
self.width // self.spatial_scale - self.transformed_width)
y4 = np.random.randint(y1 + self.transformed_height,
self.height // self.spatial_scale)
roi = [x1, y1, x2, y2, x3, y3, x4, y4]
rois.append(roi)
self.rois_num = len(rois)
self.rois = np.array(rois).astype("float32")
def setUp(self):
self.op_type = "roi_perspective_transform"
self.set_data()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 3ad3635d
......@@ -21,13 +21,12 @@ import paddle
def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block._remove_op(start) for _ in six.moves.range(end - start + 1)]
except Exception as e:
raise e
block.program._sync_with_cpp()
for op in ops:
try:
idx = list(block.ops).index(op)
block._remove_op(idx)
except Exception as e:
print(e)
def find_op_by_input_arg(block, arg_name):
......@@ -37,10 +36,18 @@ def find_op_by_input_arg(block, arg_name):
return -1
def find_op_by_output_arg(block, arg_name):
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
def find_op_by_output_arg(block, arg_name, reverse=False):
if reverse:
pos = len(block.ops) - 1
while pos >= 0:
op = block.ops[pos]
if arg_name in op.output_arg_names:
return pos
pos -= 1
else:
for index, op in enumerate(block.ops):
if arg_name in op.output_arg_names:
return index
return -1
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 3ad3635d
......@@ -50,6 +50,15 @@ OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
)
RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
DIST_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Dist
LR_SCHED_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.LRSched
PRINT_LOG = False
def log(*args):
if PRINT_LOG:
print(args)
class VarBlock:
......@@ -127,6 +136,7 @@ class DistributeTranspilerConfig(object):
slice_var_up = True
split_method = None
min_block_size = 8192
print_log = False
class DistributeTranspiler(object):
......@@ -174,6 +184,9 @@ class DistributeTranspiler(object):
if self.config.split_method is None:
self.config.split_method = RoundRobin
global PRINT_LOG
if self.config.print_log:
PRINT_LOG = True
assert (self.config.min_block_size >= 8192)
assert (self.config.split_method.__bases__[0] == PSDispatcher)
......@@ -257,12 +270,12 @@ class DistributeTranspiler(object):
splited_grad_varname = grad_varname
if len(splited_vars) == 1:
splited_grad_varname = splited_vars[0].name
index = find_op_by_output_arg(program.global_block(),
splited_grad_varname)
index = find_op_by_output_arg(
program.global_block(), splited_grad_varname, reverse=True)
elif len(splited_vars) > 1:
orig_var = program.global_block().vars[splited_grad_varname]
index = find_op_by_output_arg(program.global_block(),
splited_grad_varname)
index = find_op_by_output_arg(
program.global_block(), splited_grad_varname, reverse=True)
self._insert_split_op(program, orig_var, index, splited_vars)
index += 1
else:
......@@ -301,7 +314,7 @@ class DistributeTranspiler(object):
self.grad_name_to_send_dummy_out[
self.table_name] = program.global_block().create_var(
name=framework.generate_control_dev_var_name())
input_deps = self.grad_name_to_send_dummy_out.values()
input_deps = list(self.grad_name_to_send_dummy_out.values())
program.global_block().append_op(
type="send_barrier",
......@@ -377,7 +390,10 @@ class DistributeTranspiler(object):
type="concat",
inputs={"X": splited_var},
outputs={"Out": [orig_param]},
attrs={"axis": 0})
attrs={
"axis": 0,
RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE
})
self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist)
......@@ -496,9 +512,9 @@ class DistributeTranspiler(object):
# NOTE: assume blocks of the same variable is not distributed
# on the same pserver, only change param/grad varnames for
# trainers to fetch.
sys.stderr.write("get_pserver_program() is deprecated, call\
get_pserver_programs() to get pserver main and startup\
in a single call.")
sys.stderr.write("get_pserver_program() is deprecated, call \
get_pserver_programs() to get pserver main and startup \
in a single call.")
# step1
pserver_program = Program()
pserver_program.random_seed = self.origin_program.random_seed
......@@ -615,22 +631,31 @@ class DistributeTranspiler(object):
for idx, opt_op in enumerate(opt_op_on_pserver):
per_opt_block = pserver_program._create_block(pre_block_idx)
optimize_blocks.append(per_opt_block)
optimize_target_param_name = opt_op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
# append grad merging ops before clip and weight decay
# cases may like:
# L2Decay op -> clip op -> optimize
# e.g. merge grad -> L2Decay op -> clip op -> optimize
merged_var = None
for _, op in enumerate(self.optimize_ops):
# find the origin @GRAD var before clipping
grad_varname_for_block = __op_have_grad_input__(op)
if ufind.is_connected(op, opt_op) and grad_varname_for_block:
# find the origin grad var before clipping/L2Decay,
# merged_var should be the input var name of L2Decaybuil
grad_varname_for_block = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
if op.attr(OP_ROLE_VAR_ATTR_NAME)[
0] == optimize_target_param_name:
merged_var = self._append_pserver_grad_merge_ops(
per_opt_block, grad_varname_for_block, endpoint,
grad_to_block_id, self.origin_program)
break # append optimize op once then append other ops.
for _, op in enumerate(self.optimize_ops):
# optimizer is connected to itself
if ufind.is_connected(op, opt_op) and op not in global_ops:
__append_optimize_op__(op, per_opt_block, grad_to_block_id,
merged_var, lr_ops)
if merged_var:
break # append optimize op once then append other ops.
if merged_var:
for _, op in enumerate(self.optimize_ops):
# optimizer is connected to itself
if op.attr(OP_ROLE_VAR_ATTR_NAME)[0] == optimize_target_param_name and \
op not in global_ops:
log("append opt op: ", op.type, op.input_arg_names,
merged_var)
__append_optimize_op__(op, per_opt_block,
grad_to_block_id, merged_var,
lr_ops)
# dedup grad to ids list
grad_to_block_id = list(set(grad_to_block_id))
......@@ -726,17 +751,17 @@ class DistributeTranspiler(object):
Returns:
Program: parameter server side startup program.
"""
sys.stderr.write("get_startup_program() is deprecated, call\
get_pserver_programs() to get pserver main and startup\
in a single call.")
sys.stderr.write("get_startup_program() is deprecated, call \
get_pserver_programs() to get pserver main and startup \
in a single call.")
if pserver_program != None:
sys.stderr.write("passing pserver_program to get_startup_program()\
is deprecated, you can use new API get_pserver_programs() to\
get both pserver main program and startup program.")
sys.stderr.write("passing pserver_program to get_startup_program() \
is deprecated, you can use new API get_pserver_programs() to \
get both pserver main program and startup program.")
if startup_program != None:
sys.stderr.write("passing startup_program to get_startup_program()\
is deprecated, use fluid.program_guard() or pass this argument\
to transpile() call.")
sys.stderr.write("passing startup_program to get_startup_program() \
is deprecated, use fluid.program_guard() or pass this argument \
to transpile() call.")
s_prog = Program()
orig_s_prog = self.startup_program
......@@ -1302,7 +1327,10 @@ class DistributeTranspiler(object):
type="split_selected_rows",
inputs={"X": orig_var},
outputs={"Out": splited_vars},
attrs={"height_sections": height_sections})
attrs={
"height_sections": height_sections,
RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE
})
elif orig_var.type == core.VarDesc.VarType.LOD_TENSOR:
sections = []
for v in splited_vars:
......@@ -1312,8 +1340,10 @@ class DistributeTranspiler(object):
type="split_byref",
inputs={"X": orig_var},
outputs={"Out": splited_vars},
attrs={"sections": sections} # assume split evenly
)
attrs={
"sections": sections,
RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE
})
else:
AssertionError("Variable type should be in set "
"[LOD_TENSOR, SELECTED_ROWS]")
......@@ -1381,15 +1411,15 @@ class DistributeTranspiler(object):
if not grad_block:
# do not append this op if current endpoint
# is not dealing with this grad block
return
return None
orig_varname, block_name, trainer_name = self._get_varname_parts(
grad_block.name)
if block_name:
merged_var_name = '.'.join([orig_varname, block_name])
else:
merged_var_name = orig_varname
merged_var = \
pserver_block.vars[merged_var_name]
merged_var = pserver_block.vars[merged_var_name]
grad_to_block_id.append(merged_var.name + ":" + str(optimize_block.idx))
if self.sync_mode and self.trainer_num > 1:
vars2merge = []
......@@ -1473,7 +1503,6 @@ class DistributeTranspiler(object):
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, opt_op)
outputs["ParamOut"] = new_inputs["Param"]
optimize_block.append_op(
type=opt_op.type,
inputs=new_inputs,
......@@ -1618,6 +1647,16 @@ class DistributeTranspiler(object):
return iomap
def _get_lr_ops(self):
lr_ops = []
block = self.origin_program.global_block()
for op in block.ops:
if int(op.attr(RPC_OP_ROLE_ATTR_NAME)) == int(
LR_SCHED_OP_ROLE_ATTR_VALUE):
lr_ops.append(op)
log("append lr op: ", op.type)
return lr_ops
def _get_lr_ops_deprecated(self):
lr_ops = []
# find learning rate variables by optimize op
lr_vars = set()
......@@ -1670,20 +1709,21 @@ class DistributeTranspiler(object):
block = self.origin_program.global_block()
opt_ops = []
params_grads = []
# tmp set to dedup
optimize_params = set()
origin_var_dict = self.origin_program.global_block().vars
for op in block.ops:
if self._is_opt_role_op(op):
opt_ops.append(op)
# HACK(wuyi): if we find grad vars from input of optimize
# ops, we may get the output of clip op. Use syntax "@GRAD"
# and op_role_var to get the pair.
for input_name in op.input_arg_names:
if input_name.find("@GRAD") != -1 and \
op.attr(RPC_OP_ROLE_ATTR_NAME):
param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
if op.attr(OP_ROLE_VAR_ATTR_NAME):
param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
grad_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
if not param_name in optimize_params:
optimize_params.add(param_name)
log("adding param_grad pair: ", param_name, grad_name)
params_grads.append([
origin_var_dict[param_name],
origin_var_dict[input_name]
origin_var_dict[grad_name]
])
else:
pass
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 3ad3635d
......@@ -14,10 +14,10 @@
from __future__ import print_function
from collections import defaultdict
from collections import defaultdict, OrderedDict, Callable
from .. import core
from ... import compat as cpt
from ..framework import Program, default_main_program, Parameter
from ..framework import Program, default_main_program, Parameter, Variable
from ..backward import _rename_arg_
from functools import reduce
from six.moves import range
......@@ -113,8 +113,10 @@ class ControlFlowGraph(object):
def _fill_pool(self, i, is_forward):
block_desc = self._ops[i].block()
in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
# NOTE: must sort the in_diff set for cases that get different cache var.
# FIXME(typhoonzero): maybe use a "sorted set" is better than this.
can_optimize = [
x for x in in_diff
x for x in sorted(list(in_diff))
if self._check_var_validity(block_desc, x, is_forward)
]
if can_optimize:
......@@ -220,8 +222,9 @@ class ControlFlowGraph(object):
block_desc = op.block()
is_forward = i < self._forward_num
if self.pool:
# NOTE: must sort the in_diff set for cases that get different cache var.
defs_can_optimize = [
x for x in self._defs[i]
x for x in sorted(list(self._defs[i]))
if self._check_var_validity(block_desc, x, is_forward)
]
out_pair = [
......@@ -271,6 +274,8 @@ class ControlFlowGraph(object):
self._program.block(block_desc.id).var(cpt.to_text(
x)).desc = self._find_var(block_desc, cache_var,
is_forward)
self._program.block(block_desc.id).vars[cpt.to_text(x)] = \
Variable(self._program.block(block_desc.id), name=cpt.to_text(x))
self._update_graph(x, cache_var, begin_idx=i)
break
self._fill_pool(i, is_forward)
......
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