EVOLUTION-MANAGER

Edit File: reshape_op.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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http://www.apache.org/licenses/LICENSE-2.0

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_RESHAPE_OP_H_
#define TENSORFLOW_CORE_KERNELS_RESHAPE_OP_H_

#include <memory>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

// Note that this op is subclassed for QuantizedReshapeOp.
class ReshapeOp : public OpKernel {
 public:
  explicit ReshapeOp(OpKernelConstruction* context) : OpKernel(context) {}

void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& sizes = context->input(1);
    // Preliminary validation of sizes.
    OP_REQUIRES(
        context,
        (TensorShapeUtils::IsVector(sizes.shape()) ||
         // TODO(rmlarsen): Disallow legacy use of scalars to represent shape.
         TensorShapeUtils::IsScalar(sizes.shape())),
        errors::InvalidArgument("sizes input must be 1-D, not ",
                                sizes.shape().DebugString()));

// Compute the output shape.  Determine product of specified
    // dimensions, and find the index of the unspecified one.
    TensorShape shape;
    int64 product = 1;
    int unknown_index = -1;
    bool sizes_has_zero_dim;
    switch (sizes.dtype()) {
      case DT_INT32:
        OP_REQUIRES_OK(context,
                       ValidateSizes<int32>(sizes, &product, &unknown_index,
                                            &shape, &sizes_has_zero_dim));
        break;
      case DT_INT64:
        OP_REQUIRES_OK(context,
                       ValidateSizes<int64>(sizes, &product, &unknown_index,
                                            &shape, &sizes_has_zero_dim));
        break;
      default:
        context->CtxFailure(errors::InvalidArgument(
            "desired shape must be a DT_INT32 or DT_INT64 vector, not a ",
            DataTypeString(sizes.dtype())));
        return;
    }
    if (unknown_index != -1) {
      int64 input_num_elements = 1;
      bool input_has_zero_dim = false;
      for (int dim = 0; dim < input.dims(); dim++) {
        // For zero dimension, we don't count it into `input_num_elements`
        // unless `sizes` has no zero dimension, so we are still able to
        // infer shapes for other dimensions.
        if (input.dim_size(dim) > 0 || !sizes_has_zero_dim) {
          input_num_elements *= input.dim_size(dim);
        } else {
          input_has_zero_dim = true;
        }
      }

const int64 missing = input_num_elements / product;
      if (!input_has_zero_dim) {
        OP_REQUIRES(
            context, product * missing == input_num_elements,
            errors::InvalidArgument(
                "Input to reshape is a tensor with ", input_num_elements,
                " values, but the requested shape requires a multiple of ",
                product));
      }
      shape.set_dim(unknown_index, missing);
    }
    OP_REQUIRES(context, shape.num_elements() == input.NumElements(),
                errors::InvalidArgument("Input to reshape is a tensor with ",
                                        input.NumElements(),
                                        " values, but the requested shape has ",
                                        shape.num_elements()));

// Actually produce the reshaped output.
    Tensor output(input.dtype());
    CHECK(output.CopyFrom(input, shape));
    context->set_output(0, output);
  }

bool IsExpensive() override { return false; }

private:
  template <typename Tshape>
  Status ValidateSizes(const Tensor& sizes, int64* product, int* unknown_index,
                       TensorShape* shape, bool* has_zero_dim) {
    *product = 1;
    *unknown_index = -1;
    *has_zero_dim = false;
    const int64 num_dims = sizes.NumElements();
    auto Svec = sizes.flat<Tshape>();
    for (int d = 0; d < num_dims; ++d) {
      const Tshape size = Svec(d);
      if (size == -1) {
        if (*unknown_index != -1) {
          return errors::InvalidArgument(
              "Only one input size may be -1, not both ", *unknown_index,
              " and ", d);
        }
        *unknown_index = d;
        shape->AddDim(1);
      } else if (size < 0) {
        return errors::InvalidArgument("Size ", d,
                                       " must be non-negative, not ", size);
      } else if (size == 0) {
        // We don't include zero-sized dimension in product, so that we can
        // still calculate number of elements for non-zero-sized dimensions and
        // therefore infer their shapes.
        shape->AddDim(size);
        *has_zero_dim = true;
      } else {
        shape->AddDim(size);
        (*product) *= size;
      }
    }
    return Status::OK();
  }
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_RESHAPE_OP_H_