FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core

Name	Size	Mode	Actions
allocator.h	136	0644	edit dl rm
blob.h	4168	0644	edit dl rm
blob_serialization.h	10791	0644	edit dl rm
blob_serializer_base.h	3905	0644	edit dl rm
blob_stats.h	1127	0644	edit dl rm
common.h	4329	0644	edit dl rm
common_cudnn.h	9893	0644	edit dl rm
common_gpu.h	21414	0644	edit dl rm
common_omp.h	156	0644	edit dl rm
context.h	6174	0644	edit dl rm
context_base.h	4382	0644	edit dl rm
context_gpu.h	11014	0644	edit dl rm
cudnn_wrappers.h	6956	0644	edit dl rm
db.h	9352	0644	edit dl rm
distributions_stubs.h	2161	0644	edit dl rm
event.h	12420	0644	edit dl rm
event_cpu.h	1192	0644	edit dl rm
export_c10_op_to_caffe2.h	9487	0644	edit dl rm
export_caffe2_op_to_c10.h	11101	0644	edit dl rm
flags.h	74	0644	edit dl rm
graph.h	5258	0644	edit dl rm
init.h	6496	0644	edit dl rm
logging.h	75	0644	edit dl rm
macros.h	3426	0644	edit dl rm
memonger.h	817	0644	edit dl rm
module.h	2473	0644	edit dl rm
net.h	4634	0644	edit dl rm
net_async_base.h	7397	0644	edit dl rm
net_async_scheduling.h	993	0644	edit dl rm
net_async_task.h	833	0644	edit dl rm
net_async_task_future.h	1925	0644	edit dl rm
net_async_task_graph.h	2253	0644	edit dl rm
net_async_tracing.h	5093	0644	edit dl rm
net_dag_utils.h	2146	0644	edit dl rm
net_parallel.h	2144	0644	edit dl rm
net_simple.h	2606	0644	edit dl rm
net_simple_refcount.h	2097	0644	edit dl rm
numa.h	72	0644	edit dl rm
observer.h	3809	0644	edit dl rm
operator.h	58872	0644	edit dl rm
operator_gradient.h	10222	0644	edit dl rm
operator_schema.h	18477	0644	edit dl rm
plan_executor.h	219	0644	edit dl rm
prof_dag_counters.h	2751	0644	edit dl rm
qtensor.h	6615	0644	edit dl rm
qtensor_serialization.h	2624	0644	edit dl rm
scope_guard.h	4675	0644	edit dl rm
static_tracepoint.h	398	0644	edit dl rm
static_tracepoint_elfx86.h	5555	0644	edit dl rm
stats.h	10365	0644	edit dl rm
storage.h	733	0644	edit dl rm
tensor.h	18668	0644	edit dl rm
tensor_impl.h	351	0644	edit dl rm
tensor_int8.h	450	0644	edit dl rm
test_utils.h	6285	0644	edit dl rm
timer.h	1218	0644	edit dl rm
transform.h	5741	0644	edit dl rm
types.h	2248	0644	edit dl rm
workspace.h	11305	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/test_utils.h (6285B)

#ifndef CAFFE2_UTILS_TEST_UTILS_H_
#define CAFFE2_UTILS_TEST_UTILS_H_

#include "caffe2/core/tensor.h"
#include "caffe2/core/workspace.h"
#include "caffe2/utils/proto_utils.h"

#include <c10/macros/Macros.h>

#include <cmath>
#include <string>
#include <vector>

// Utilities that make it easier to write caffe2 C++ unit tests.
// These utils are designed to be concise and easy to use. They may sacrifice
// performance and should only be used in tests/non production code.
namespace caffe2 {
namespace testing {

// Asserts that the values of two tensors are the same.
TORCH_API void assertTensorEquals(
    const TensorCPU& tensor1,
    const TensorCPU& tensor2,
    float eps = 1e-6);

// Asserts that two float values are close within epsilon.
TORCH_API void assertNear(float value1, float value2, float epsilon);

// Asserts that the numeric values of a tensor is equal to a data vector.
template <typename T>
void assertTensorEquals(
    const TensorCPU& tensor,
    const std::vector<T>& data,
    float epsilon = 0.1f) {
  CAFFE_ENFORCE(tensor.IsType<T>());
  CAFFE_ENFORCE_EQ(tensor.numel(), data.size());
  for (auto idx = 0; idx < tensor.numel(); ++idx) {
    if (tensor.IsType<float>()) {
      assertNear(tensor.data<T>()[idx], data[idx], epsilon);
    } else {
      CAFFE_ENFORCE_EQ(tensor.data<T>()[idx], data[idx]);
    }
  }
}

// Assertion for tensor sizes and values.
template <typename T>
void assertTensor(
    const TensorCPU& tensor,
    const std::vector<int64_t>& sizes,
    const std::vector<T>& data,
    float epsilon = 0.1f) {
  CAFFE_ENFORCE_EQ(tensor.sizes(), sizes);
  assertTensorEquals(tensor, data, epsilon);
}

// Asserts a list of tensors presented in two workspaces are equal.
TORCH_API void assertTensorListEquals(
    const std::vector<std::string>& tensorNames,
    const Workspace& workspace1,
    const Workspace& workspace2);

// Read a tensor from the workspace.
TORCH_API const caffe2::Tensor& getTensor(
    const caffe2::Workspace& workspace,
    const std::string& name);

// Create a new tensor in the workspace.
TORCH_API caffe2::Tensor* createTensor(
    const std::string& name,
    caffe2::Workspace* workspace);

// Create a new operator in the net.
TORCH_API caffe2::OperatorDef* createOperator(
    const std::string& type,
    const std::vector<std::string>& inputs,
    const std::vector<std::string>& outputs,
    caffe2::NetDef* net);

// Fill a buffer with randomly generated numbers given range [min, max)
// T can only be float, double or long double
template <typename RealType = float>
void randomFill(
    RealType* data,
    size_t size,
    const double min = 0.0,
    const double max = 1.0) {
  std::mt19937 gen(42);
  std::uniform_real_distribution<RealType> dis(
      static_cast<RealType>(min), static_cast<RealType>(max));
  for (size_t i = 0; i < size; i++) {
    data[i] = dis(gen);
  }
}

// Fill data from a vector to a tensor.
template <typename T>
void fillTensor(
    const std::vector<int64_t>& shape,
    const std::vector<T>& data,
    TensorCPU* tensor) {
  tensor->Resize(shape);
  CAFFE_ENFORCE_EQ(data.size(), tensor->numel());
  auto ptr = tensor->mutable_data<T>();
  for (int i = 0; i < tensor->numel(); ++i) {
    ptr[i] = data[i];
  }
}

// Create a tensor and fill data.
template <typename T>
caffe2::Tensor* createTensorAndFill(
    const std::string& name,
    const std::vector<int64_t>& shape,
    const std::vector<T>& data,
    Workspace* workspace) {
  auto* tensor = createTensor(name, workspace);
  fillTensor<T>(shape, data, tensor);
  return tensor;
}

template <typename T>
caffe2::Tensor createTensorAndFill(
    const std::vector<int64_t>& shape,
    const std::vector<T>& data) {
  Tensor tensor(caffe2::CPU);
  fillTensor<T>(shape, data, &tensor);
  return tensor;
}

// Fill a constant to a tensor.
template <typename T>
void constantFillTensor(
    const vector<int64_t>& shape,
    const T& data,
    TensorCPU* tensor) {
  tensor->Resize(shape);
  auto ptr = tensor->mutable_data<T>();
  for (int i = 0; i < tensor->numel(); ++i) {
    ptr[i] = data;
  }
}

// Create a tensor and fill a constant.
template <typename T>
caffe2::Tensor* createTensorAndConstantFill(
    const std::string& name,
    const std::vector<int64_t>& shape,
    const T& data,
    Workspace* workspace) {
  auto* tensor = createTensor(name, workspace);
  constantFillTensor<T>(shape, data, tensor);
  return tensor;
}

// Concise util class to mutate a net in a chaining fashion.
class TORCH_API NetMutator {
 public:
  // NOLINTNEXTLINE(clang-analyzer-optin.cplusplus.UninitializedObject)
  explicit NetMutator(caffe2::NetDef* net) : net_(net) {}

NetMutator& newOp(
      const std::string& type,
      const std::vector<std::string>& inputs,
      const std::vector<std::string>& outputs);

NetMutator& externalInputs(const std::vector<std::string>& externalInputs);

NetMutator& externalOutputs(const std::vector<std::string>& externalOutputs);

// Add argument to the last created op.
  template <typename T>
  NetMutator& addArgument(const std::string& name, const T& value) {
    CAFFE_ENFORCE(lastCreatedOp_ != nullptr);
    AddArgument(name, value, lastCreatedOp_);
    return *this;
  }

// Set device name for the last created op.
  NetMutator& setDeviceOptionName(const std::string& name);

private:
  caffe2::NetDef* net_;
  caffe2::OperatorDef* lastCreatedOp_;
};

// Concise util class to mutate a workspace in a chaining fashion.
class TORCH_API WorkspaceMutator {
 public:
  explicit WorkspaceMutator(caffe2::Workspace* workspace)
      : workspace_(workspace) {}

// New tensor filled by a data vector.
  template <typename T>
  WorkspaceMutator& newTensor(
      const std::string& name,
      const std::vector<int64_t>& shape,
      const std::vector<T>& data) {
    createTensorAndFill<T>(name, shape, data, workspace_);
    return *this;
  }

// New tensor filled by a constant.
  template <typename T>
  WorkspaceMutator& newTensorConst(
      const std::string& name,
      const std::vector<int64_t>& shape,
      const T& data) {
    createTensorAndConstantFill<T>(name, shape, data, workspace_);
    return *this;
  }

private:
  caffe2::Workspace* workspace_;
};

} // namespace testing
} // namespace caffe2

#endif // CAFFE2_UTILS_TEST_UTILS_H_

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