text: #include <climits>
  #include <vector>
  
  #include "caffe/blob.hpp"
  #include "caffe/common.hpp"
  #include "caffe/syncedmem.hpp"
  #include "caffe/util/math_functions.hpp"
  
  namespace caffe {
  
  template <typename Dtype>
  void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
      const int width) {
    vector<int> shape(4);
    shape[0] = num;
    shape[1] = channels;
    shape[2] = height;
    shape[3] = width;
    Reshape(shape);
  }
  
  template <typename Dtype>
  void Blob<Dtype>::Reshape(const vector<int>& shape) {
    CHECK_LE(shape.size(), kMaxBlobAxes);
    count_ = 1;
    shape_.resize(shape.size());
    if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
      shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
    }
    int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
    for (int i = 0; i < shape.size(); ++i) {
      CHECK_GE(shape[i], 0);
      if (count_ != 0) {
        CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
      }
      count_ *= shape[i];
      shape_[i] = shape[i];
      shape_data[i] = shape[i];
    }
    if (count_ > capacity_) {
      capacity_ = count_;
      data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
      diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    }
  }
  
  template <typename Dtype>
  void Blob<Dtype>::Reshape(const BlobShape& shape) {
    CHECK_LE(shape.dim_size(), kMaxBlobAxes);
    vector<int> shape_vec(shape.dim_size());
    for (int i = 0; i < shape.dim_size(); ++i) {
      shape_vec[i] = shape.dim(i);
    }
    Reshape(shape_vec);
  }
  
  template <typename Dtype>
  void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
    Reshape(other.shape());
  }
  
  template <typename Dtype>
  Blob<Dtype>::Blob(const int num, const int channels, const int height,
      const int width)
    // capacity_ must be initialized before calling Reshape
    : capacity_(0) {
    Reshape(num, channels, height, width);
  }
  
  template <typename Dtype>
  Blob<Dtype>::Blob(const vector<int>& shape)
    // capacity_ must be initialized before calling Reshape
    : capacity_(0) {
    Reshape(shape);
  }
  
  template <typename Dtype>
  const int* Blob<Dtype>::gpu_shape() const {
    CHECK(shape_data_);
    return (const int*)shape_data_->gpu_data();
  }
  
  template <typename Dtype>
  const Dtype* Blob<Dtype>::cpu_data() const {
    CHECK(data_);
    return (const Dtype*)data_->cpu_data();
  }
  
  template <typename Dtype>
  void Blob<Dtype>::set_cpu_data(Dtype* data) {
    CHECK(data);
    // Make sure CPU and GPU sizes remain equal
    size_t size = count_ * sizeof(Dtype);
    if (data_->size() != size) {
      data_.reset(new SyncedMemory(size));
      diff_.reset(new SyncedMemory(size));
    }
    data_->set_cpu_data(data);
  }
  
  template <typename Dtype>
  const Dtype* Blob<Dtype>::gpu_data() const {
    CHECK(data_);
    return (const Dtype*)data_->gpu_data();
  }
  
  template <typename Dtype>
  void Blob<Dtype>::set_gpu_data(Dtype* data) {
    CHECK(data);
    // Make sure CPU and GPU sizes remain equal
    size_t size = count_ * sizeof(Dtype);
    if (data_->size() != size) {
      data_.reset(new SyncedMemory(size));
      diff_.reset(new SyncedMemory(size));
    }
    data_->set_gpu_data(data);
  }
  
  template <typename Dtype>
  const Dtype* Blob<Dtype>::cpu_diff() const {
    CHECK(diff_);
    return (const Dtype*)diff_->cpu_data();
  }
  
  template <typename Dtype>
  const Dtype* Blob<Dtype>::gpu_diff() const {
    CHECK(diff_);
    return (const Dtype*)diff_->gpu_data();
  }
  
  template <typename Dtype>
  Dtype* Blob<Dtype>::mutable_cpu_data() {
    CHECK(data_);
    return static_cast<Dtype*>(data_->mutable_cpu_data());
  }
  
  template <typename Dtype>
  Dtype* Blob<Dtype>::pseudo_cpu_data() const {
    CHECK(data_);
    return static_cast<Dtype*>(data_->mutable_cpu_data());
  }
  
  template <typename Dtype>
  Dtype* Blob<Dtype>::mutable_gpu_data() {
    CHECK(data_);
    return static_cast<Dtype*>(data_->mutable_gpu_data());
  }
  
  template <typename Dtype>
  Dtype* Blob<Dtype>::mutable_cpu_diff() {
    CHECK(diff_);
    return static_cast<Dtype*>(diff_->mutable_cpu_data());
  }
  
  template <typename Dtype>
  Dtype* Blob<Dtype>::mutable_gpu_diff() {
    CHECK(diff_);
    return static_cast<Dtype*>(diff_->mutable_gpu_data());
  }
  
  template <typename Dtype>
  void Blob<Dtype>::ShareData(const Blob& other) {
    CHECK_EQ(count_, other.count());
    data_ = other.data();
  }
  
  template <typename Dtype>
  void Blob<Dtype>::ShareDiff(const Blob& other) {
    CHECK_EQ(count_, other.count());
    diff_ = other.diff();
  }
  
  // The "update" method is used for parameter blobs in a Net, which are stored
  // as Blob<float> or Blob<double> -- hence we do not define it for
  // Blob<int> or Blob<unsigned int>.
  template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
  template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
  
  template <typename Dtype>
  void Blob<Dtype>::Update() {
    // We will perform update based on where the data is located.
    switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      // perform computation on CPU
      caffe_axpy<Dtype>(count_, Dtype(-1),
          static_cast<const Dtype*>(diff_->cpu_data()),
          static_cast<Dtype*>(data_->mutable_cpu_data()));
      break;
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
      // perform computation on GPU
      caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
          static_cast<const Dtype*>(diff_->gpu_data()),
          static_cast<Dtype*>(data_->mutable_gpu_data()));
  #else
      NO_GPU;
  #endif
      break;
    default:
      LOG(FATAL) << "Syncedmem not initialized.";
    }
  }
  
  template <> unsigned int Blob<unsigned int>::asum_data() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <> int Blob<int>::asum_data() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <typename Dtype>
  Dtype Blob<Dtype>::asum_data() const {
    if (!data_) { return 0; }
    switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      return caffe_cpu_asum(count_, cpu_data());
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
    {
      Dtype asum;
      caffe_gpu_asum(count_, gpu_data(), &asum);
      return asum;
    }
  #else
      NO_GPU;
  #endif
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
    }
    return 0;
  }
  
  template <> unsigned int Blob<unsigned int>::asum_diff() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <> int Blob<int>::asum_diff() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <typename Dtype>
  Dtype Blob<Dtype>::asum_diff() const {
    if (!diff_) { return 0; }
    switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      return caffe_cpu_asum(count_, cpu_diff());
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
    {
      Dtype asum;
      caffe_gpu_asum(count_, gpu_diff(), &asum);
      return asum;
    }
  #else
      NO_GPU;
  #endif
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
    }
    return 0;
  }
  
  template <> unsigned int Blob<unsigned int>::sumsq_data() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <> int Blob<int>::sumsq_data() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <typename Dtype>
  Dtype Blob<Dtype>::sumsq_data() const {
    Dtype sumsq;
    const Dtype* data;
    if (!data_) { return 0; }
    switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      data = cpu_data();
      sumsq = caffe_cpu_dot(count_, data, data);
      break;
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
      data = gpu_data();
      caffe_gpu_dot(count_, data, data, &sumsq);
  #else
      NO_GPU;
  #endif
      break;
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
    }
    return sumsq;
  }
  
  template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <> int Blob<int>::sumsq_diff() const {
    NOT_IMPLEMENTED;
    return 0;
  }
  
  template <typename Dtype>
  Dtype Blob<Dtype>::sumsq_diff() const {
    Dtype sumsq;
    const Dtype* diff;
    if (!diff_) { return 0; }
    switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      diff = cpu_diff();
      sumsq = caffe_cpu_dot(count_, diff, diff);
      break;
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
      diff = gpu_diff();
      caffe_gpu_dot(count_, diff, diff, &sumsq);
      break;
  #else
      NO_GPU;
  #endif
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
    }
    return sumsq;
  }
  
  template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
    NOT_IMPLEMENTED;
  }
  
  template <> void Blob<int>::scale_data(int scale_factor) {
    NOT_IMPLEMENTED;
  }
  
  template <typename Dtype>
  void Blob<Dtype>::scale_data(Dtype scale_factor) {
    Dtype* data;
    if (!data_) { return; }
    switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      data = mutable_cpu_data();
      caffe_scal(count_, scale_factor, data);
      return;
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
      data = mutable_gpu_data();
      caffe_gpu_scal(count_, scale_factor, data);
      return;
  #else
      NO_GPU;
  #endif
    case SyncedMemory::UNINITIALIZED:
      return;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
    }
  }
  
  template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
    NOT_IMPLEMENTED;
  }
  
  template <> void Blob<int>::scale_diff(int scale_factor) {
    NOT_IMPLEMENTED;
  }
  
  template <typename Dtype>
  void Blob<Dtype>::scale_diff(Dtype scale_factor) {
    Dtype* diff;
    if (!diff_) { return; }
    switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      diff = mutable_cpu_diff();
      caffe_scal(count_, scale_factor, diff);
      return;
    case SyncedMemory::HEAD_AT_GPU:
    case SyncedMemory::SYNCED:
  #ifndef CPU_ONLY
      diff = mutable_gpu_diff();
      caffe_gpu_scal(count_, scale_factor, diff);
      return;
  #else
      NO_GPU;
  #endif
    case SyncedMemory::UNINITIALIZED:
      return;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
    }
  }
  
  template <typename Dtype>
  bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
    if (other.has_num() || other.has_channels() ||
        other.has_height() || other.has_width()) {
      // Using deprecated 4D Blob dimensions --
      // shape is (num, channels, height, width).
      // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
      // methods as these index from the beginning of the blob shape, where legacy
      // parameter blobs were indexed from the end of the blob shape (e.g., bias
      // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
      return shape_.size() <= 4 &&
             LegacyShape(-4) == other.num() &&
             LegacyShape(-3) == other.channels() &&
             LegacyShape(-2) == other.height() &&
             LegacyShape(-1) == other.width();
    }
    vector<int> other_shape(other.shape().dim_size());
    for (int i = 0; i < other.shape().dim_size(); ++i) {
      other_shape[i] = other.shape().dim(i);
    }
    return shape_ == other_shape;
  }
  
  template <typename Dtype>
  void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
    if (source.count() != count_ || source.shape() != shape_) {
      if (reshape) {
        ReshapeLike(source);
      } else {
        LOG(FATAL) << "Trying to copy blobs of different sizes.";
      }
    }
    switch (Caffe::mode()) {
    case Caffe::GPU:
      if (copy_diff) {
        caffe_copy(count_, source.gpu_diff(),
            static_cast<Dtype*>(diff_->mutable_gpu_data()));
      } else {
        caffe_copy(count_, source.gpu_data(),
            static_cast<Dtype*>(data_->mutable_gpu_data()));
      }
      break;
    case Caffe::CPU:
      if (copy_diff) {
        caffe_copy(count_, source.cpu_diff(),
            static_cast<Dtype*>(diff_->mutable_cpu_data()));
      } else {
        caffe_copy(count_, source.cpu_data(),
            static_cast<Dtype*>(data_->mutable_cpu_data()));
      }
      break;
    default:
      LOG(FATAL) << "Unknown caffe mode.";
    }
  }
  
  template <typename Dtype>
  void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
    if (reshape) {
      vector<int> shape;
      if (proto.has_num() || proto.has_channels() ||
          proto.has_height() || proto.has_width()) {
        // Using deprecated 4D Blob dimensions --
        // shape is (num, channels, height, width).
        shape.resize(4);
        shape[0] = proto.num();
        shape[1] = proto.channels();
        shape[2] = proto.height();
        shape[3] = proto.width();
      } else {
        shape.resize(proto.shape().dim_size());
        for (int i = 0; i < proto.shape().dim_size(); ++i) {
          shape[i] = proto.shape().dim(i);
        }
      }
      Reshape(shape);
    } else {
      CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
    }
    // copy data
    Dtype* data_vec = mutable_cpu_data();
    if (proto.double_data_size() > 0) {
      CHECK_EQ(count_, proto.double_data_size());
      for (int i = 0; i < count_; ++i) {
        data_vec[i] = proto.double_data(i);
      }
    } else {
      CHECK_EQ(count_, proto.data_size());
      for (int i = 0; i < count_; ++i) {
        data_vec[i] = proto.data(i);
      }
    }
    if (proto.double_diff_size() > 0) {
      CHECK_EQ(count_, proto.double_diff_size());
      Dtype* diff_vec = mutable_cpu_diff();
      for (int i = 0; i < count_; ++i) {
        diff_vec[i] = proto.double_diff(i);
      }
    } else if (proto.diff_size() > 0) {
      CHECK_EQ(count_, proto.diff_size());
      Dtype* diff_vec = mutable_cpu_diff();
      for (int i = 0; i < count_; ++i) {
        diff_vec[i] = proto.diff(i);
      }
    }
  }
  
  template <>
  void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
    proto->clear_shape();
    for (int i = 0; i < shape_.size(); ++i) {
      proto->mutable_shape()->add_dim(shape_[i]);
    }
    proto->clear_double_data();
    proto->clear_double_diff();
    const double* data_vec = cpu_data();
    for (int i = 0; i < count_; ++i) {
      proto->add_double_data(data_vec[i]);
    }
    if (write_diff) {
      const double* diff_vec = cpu_diff();
      for (int i = 0; i < count_; ++i) {
        proto->add_double_diff(diff_vec[i]);
      }
    }
  }
  
  template <>
  void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
    proto->clear_shape();
    for (int i = 0; i < shape_.size(); ++i) {
      proto->mutable_shape()->add_dim(shape_[i]);
    }
    proto->clear_data();
    proto->clear_diff();
    const float* data_vec = cpu_data();
    for (int i = 0; i < count_; ++i) {
      proto->add_data(data_vec[i]);
    }
    if (write_diff) {
      const float* diff_vec = cpu_diff();
      for (int i = 0; i < count_; ++i) {
        proto->add_diff(diff_vec[i]);
      }
    }
  }
  
  INSTANTIATE_CLASS(Blob);
  template class Blob<int>;
  template class Blob<unsigned int>;
  
  }  // namespace caffe
  
  