Add Shapes operator returning sample shapes. #1223
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| output_desc.resize(1); | ||
| output_desc[0].type = TypeTable::GetTypeInfo(output_type_); | ||
| decltype(auto) shape = GetInputShape(ws); | ||
| output_desc[0].shape = ShapeShape(shape);; |
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I guess when CI is in better shape...
| assert(out.shape().num_samples() == shape.num_samples()); | ||
| for (int i = 0; i < shape.num_samples(); i++) { | ||
| type *data = out.mutable_tensor<type>(i); | ||
| auto tshape = shape.tensor_shape_span(i); |
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Maybe t_shape or tensor_shape for a bit more clarity?
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| } | ||
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| static kernels::TensorListShape<1> ShapeShape(const kernels::TensorListShape<> &shape) { |
| auto &output_tensor = output[i]; | ||
| auto *dest = output_tensor.raw_mutable_data(); | ||
| auto *src = tmp_.raw_mutable_tensor(i); | ||
| std::memcpy(dest, src, output_tensor.nbytes()); |
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Why do the batch_size memcpys instead of writing directly to output in case of CPU op?
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Because TensorVector is not really interchangeable with TensorList and I didn't want to write the conversion twice.
| void ConvertShape(TensorList<CPUBackend> &out, const kernels::TensorListShape<> &shape) { | ||
| assert(out.shape().num_samples() == shape.num_samples()); | ||
| for (int i = 0; i < shape.num_samples(); i++) { | ||
| type *data = out.mutable_tensor<type>(i); |
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In case of CPU you will do the allocation at this point implicitly.
Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
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Signed-off-by: Michal Zientkiewicz michalz@nvidia.com
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JIRA TASK: [DALI-XXXX]