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Remove deprecated caffe2::Tensor APIs #15814

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64bc330
Remaining Tensor API fixes(CPU) - dims() -> sizes()
jerryzh168 Jan 15, 2019
f53c871
Remove deprecated caffe2::Tensor APIs (#15814)
jerryzh168 Jan 15, 2019
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2 changes: 1 addition & 1 deletion caffe2/contrib/aten/aten_op_template.h
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ class ATenOp : public Operator<Context> {
}
at::Tensor tensorWrapping(const Tensor& ten_) {
auto& ten = const_cast<Tensor&>(ten_);
return typeFor(ten).tensorFromBlob(ten.raw_mutable_data(), ten.dims());
return typeFor(ten).tensorFromBlob(ten.raw_mutable_data(), ten.sizes());
}

at::Tensor peek(size_t i, size_t N) {
Expand Down
4 changes: 2 additions & 2 deletions caffe2/contrib/tensorrt/tensorrt_op_trt.cc
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Expand Up @@ -161,7 +161,7 @@ bool TensorRTOp::RunOnDevice() {
size_t N = 0;
for (int i = 0; i < InputSize(); ++i) {
const auto& input_tensor = Input(i);
const auto tensor_dims = input_tensor.dims();
const auto tensor_dims = input_tensor.sizes();
CAFFE_ENFORCE(!tensor_dims.empty(), "Input tensor cannot be empty");
if (i == 0) {
N = tensor_dims.front();
Expand Down Expand Up @@ -198,7 +198,7 @@ bool TensorRTOp::RunOnDevice() {
// input, check input dimensions
const auto& input_tensor = Input(input_idx++);
const float* input_data = input_tensor.data<float>();
const auto tensor_dims = input_tensor.dims();
const auto tensor_dims = input_tensor.sizes();
auto chw = CheckDims(dims, tensor_dims);
bindings.push_back((void*)(input_data + offset * chw));
} else {
Expand Down
6 changes: 1 addition & 5 deletions caffe2/core/tensor.h
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Expand Up @@ -83,6 +83,7 @@ class CAFFE2_API Tensor final {
Resize(dims);
}

// TODO: remove?
explicit Tensor(const vector<int>& dims, DeviceType type)
: Tensor(type) {
Resize(dims);
Expand Down Expand Up @@ -438,11 +439,6 @@ class CAFFE2_API Tensor final {
return impl_.get()->sizes();
}

// To be deprecated
inline at::IntList dims() const {
return impl_.get()->sizes();
}

inline int64_t size_from_dim(int k) const {
return size_from_dim_(k, impl_->sizes());
}
Expand Down
7 changes: 3 additions & 4 deletions caffe2/mobile/contrib/ios/ios_caffe_predictor.cc
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Expand Up @@ -50,8 +50,7 @@ Caffe2IOSPredictor::Caffe2IOSPredictor(const caffe2::NetDef& init_net,

void Caffe2IOSPredictor::run(const Tensor& inData, Tensor& outData, std::string& errorMessage) {
FLAGS_caffe2_force_shared_col_buffer = true;
caffe2::Tensor input(caffe2::CPU);
input.Resize(inData.dims);
caffe2::Tensor input = caffe2::empty(inData.dims, at::dtype<uint8_t>().device(caffe2::CPU));
input.ShareExternalPointer(inData.data);
caffe2::Predictor::TensorList input_vec;
input_vec.emplace_back(std::move(input));
Expand All @@ -67,7 +66,7 @@ void Caffe2IOSPredictor::run(const Tensor& inData, Tensor& outData, std::string&
errorMessage.swap(error);
return;
}
caffe2::TensorCPU* output = &output_vec.front();
caffe2::Tensor* output = &output_vec.front();
outData.data = output->mutable_data<uint8_t>();
outData.dims = output->dims().vec();
outData.dims = output->sizes().vec();
}
70 changes: 35 additions & 35 deletions caffe2/mobile/contrib/ios/mpscnn/mpscnn.mm
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Expand Up @@ -256,12 +256,12 @@ void computeOutputHW(
int W,
int* OH,
int* OW) {
Tensor input(CPU), output(CPU);
input.Resize(1, 1, H, W);
Tensor input = caffe2::empty({1, 1, H, W}, at::dtype<float>().device(CPU));
Tensor output(CPU);
op->SetOutputSize(input, &output, 1);
CAFFE_ENFORCE_EQ(output.ndim(), 4);
*OH = output.dim(2);
*OW = output.dim(3);
CAFFE_ENFORCE_EQ(output.dim(), 4);
*OH = output.size(2);
*OW = output.size(3);
}

constexpr int computeMPSAlignOffset(int kernel, int pad) {
Expand All @@ -285,7 +285,7 @@ constexpr int computeMPSAlignOffset(int kernel, int pad) {
size_t ComputeStartIndex(
const TensorCPU& tensor,
const std::vector<int>& index) {
DCHECK_EQ(index.size(), tensor.ndim());
DCHECK_EQ(index.size(), tensor.dim());

size_t ret = 0;
for (int i = 0; i < index.size(); i++) {
Expand All @@ -306,12 +306,12 @@ size_t ComputeStartIndex(
return utils::ConstTensorView<T>(nullptr, {});
}

std::vector<int> start_dims(tensor.ndim(), 0);
std::vector<int> start_dims(tensor.dim(), 0);
start_dims.at(0) = dim0_start_index;
auto st_idx = ComputeStartIndex(tensor, start_dims);
auto ptr = tensor.data<T>() + st_idx;

auto input_dims = tensor.dims();
auto input_dims = tensor.sizes();
std::vector<int> ret_dims(input_dims.begin() + 1, input_dims.end());

utils::ConstTensorView<T> ret(ptr, ret_dims);
Expand All @@ -328,9 +328,9 @@ bool RunOnDevice() override {
std::vector<MPSImageWrapper> wrappers(Inputs().size());
for (auto i = 0; i < Inputs().size(); ++i) {
const auto& X = Input(i);
CAFFE_ENFORCE(X.ndim() > 0 && X.ndim() <= 4);
CAFFE_ENFORCE(X.dim() > 0 && X.dim() <= 4);
std::vector<int64_t> XDims = {1, 1, 1, 1};
XDims.assign(X.dims().begin(), X.dims().end());
XDims.assign(X.sizes().begin(), X.sizes().end());

caffe2::Timer t;
const auto n = XDims[0];
Expand Down Expand Up @@ -476,11 +476,11 @@ bool RunOnDevice() override {
const auto& X = Input(0);
const auto& mean = Input(1);
CAFFE_ENFORCE_EQ(mean.size(), 3);
CAFFE_ENFORCE_EQ(X.ndim(), 4);
CAFFE_ENFORCE_EQ(X.dim(0), 1);
CAFFE_ENFORCE_EQ(X.dim(3), 4);
const auto H = X.dim(1);
const auto W = X.dim(2);
CAFFE_ENFORCE_EQ(X.dim(), 4);
CAFFE_ENFORCE_EQ(X.size(0), 1);
CAFFE_ENFORCE_EQ(X.size(3), 4);
const auto H = X.size(1);
const auto W = X.size(2);

caffe2::Timer t;

Expand Down Expand Up @@ -749,15 +749,15 @@ bool RunOnDeviceWithOrderNCHW() override {

auto& filter = Input(FILTER);
auto& bias = Input(BIAS);
CAFFE_ENFORCE_EQ(filter.ndim(), 4);
CAFFE_ENFORCE_EQ(filter.dim(), 4);
// For NCHW, X.dim32(1), inputChannels
const int C = X.featureChannels;
const int M = filter.dim32(0);
const int Cf = filter.dim32(1);

CAFFE_ENFORCE(filter.dim32(2) == kernel_h(), "");
CAFFE_ENFORCE(filter.dim32(3) == kernel_w(), "");
CAFFE_ENFORCE(bias.ndim() == 1, "");
CAFFE_ENFORCE(bias.dim() == 1, "");
CAFFE_ENFORCE(bias.dim32(0) == M, "");

const auto kH = kernel_h();
Expand Down Expand Up @@ -986,9 +986,9 @@ bool RunOnDevice() override {

const auto& X1 = Input(1);
CAFFE_ENFORCE_EQ(
X1.ndim(),
X1.dim(),
1,
"MPSCNNMulOp: Only ndim == 1 for Input(1) is supported for now");
"MPSCNNMulOp: Only dim == 1 for Input(1) is supported for now");

auto X1_ = [getMPSCNNContext().device
newBufferWithBytes:X1.template data<float>()
Expand Down Expand Up @@ -1054,9 +1054,9 @@ bool RunOnDevice() override {

const auto& X1 = Input(1);
CAFFE_ENFORCE_EQ(
X1.ndim(),
X1.dim(),
1,
"MPSCNNSubOp: Only ndim == 1 for Input(1) is supported for now");
"MPSCNNSubOp: Only dim == 1 for Input(1) is supported for now");

auto X1_ = [getMPSCNNContext().device
newBufferWithBytes:X1.template data<float>()
Expand Down Expand Up @@ -1454,14 +1454,14 @@ bool RunOnDeviceWithOrderNCHW() override {

auto& filter = Input(FILTER);
auto& bias = Input(BIAS);
CAFFE_ENFORCE(filter.ndim(), 4);
CAFFE_ENFORCE(filter.dim(), 4);
const int output_channels = filter.dim32(1);
const int input_channels = filter.dim32(0);

CAFFE_ENFORCE(X.featureChannels == input_channels, "");
CAFFE_ENFORCE(filter.dim32(2) == kernel_h(), "");
CAFFE_ENFORCE(filter.dim32(3) == kernel_w(), "");
CAFFE_ENFORCE(bias.ndim() == 1, "");
CAFFE_ENFORCE(bias.dim() == 1, "");
CAFFE_ENFORCE(bias.dim32(0) == output_channels, "");

const auto kH = kernel_h();
Expand Down Expand Up @@ -1964,7 +1964,7 @@ bool RunOnDevice() override {
auto X = inputWrapper.getImage();
CAFFE_ENFORCE_EQ(X.numberOfImages, 1);
const auto& R = Input(1);
CAFFE_ENFORCE_EQ(R.ndim(), 2);
CAFFE_ENFORCE_EQ(R.dim(), 2);
CAFFE_ENFORCE(R.dim32(1) == 4 || R.dim32(1) == 5);
const auto roiBytes = R.dim32(0) * 4 * sizeof(float16_t);
if (!roiBuffer_ || roiBuffer_.length != roiBytes) {
Expand Down Expand Up @@ -2249,25 +2249,25 @@ bool RunOnDevice() override {
auto* out_rois = Output(0);
auto* out_rois_probs = Output(1);

CAFFE_ENFORCE_EQ(scores.ndim(), 4, scores.ndim());
CAFFE_ENFORCE_EQ(scores.dim(), 4, scores.dim());
CAFFE_ENFORCE(scores.template IsType<float>(), scores.meta().name());
const auto num_images = scores.dim(0);
const auto A = scores.dim(1);
const auto height = scores.dim(2);
const auto width = scores.dim(3);
const auto num_images = scores.size(0);
const auto A = scores.size(1);
const auto height = scores.size(2);
const auto width = scores.size(3);
const auto K = height * width;

// bbox_deltas: (num_images, A * 4, H, W)
CAFFE_ENFORCE_EQ(
bbox_deltas.dims(), (vector<int64_t>{num_images, 4 * A, height, width}));
bbox_deltas.sizes(), (vector<int64_t>{num_images, 4 * A, height, width}));

// im_info_tensor: (num_images, 3), format [height, width, scale; ...]
CAFFE_ENFORCE_EQ(im_info_tensor.dims(), (vector<int64_t>{num_images, 3}));
CAFFE_ENFORCE_EQ(im_info_tensor.sizes(), (vector<int64_t>{num_images, 3}));
CAFFE_ENFORCE(
im_info_tensor.template IsType<float>(), im_info_tensor.meta().name());

// anchors: (A, 4)
CAFFE_ENFORCE_EQ(anchors.dims(), (vector<int64_t>{A, 4}));
CAFFE_ENFORCE_EQ(anchors.sizes(), (vector<int64_t>{A, 4}));
CAFFE_ENFORCE(anchors.template IsType<float>(), anchors.meta().name());
// Broadcast the anchors to all pixels
auto all_anchors_vec =
Expand All @@ -2276,8 +2276,8 @@ bool RunOnDevice() override {

Eigen::Map<const ERArrXXf> im_info(
im_info_tensor.data<float>(),
im_info_tensor.dim(0),
im_info_tensor.dim(1));
im_info_tensor.size(0),
im_info_tensor.size(1));

const int roi_col_count = 5;
out_rois->Resize(0, roi_col_count);
Expand All @@ -2300,7 +2300,7 @@ bool RunOnDevice() override {
&im_i_probs);

int csz = im_i_boxes.rows();
int cur_start_idx = out_rois->dim(0);
int cur_start_idx = out_rois->size(0);

out_rois->Extend(csz, 50);
out_rois_probs->Extend(csz, 50);
Expand Down
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