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use tract_hir::internal::*; use crate::tfpb::tensorflow::tensor_shape_proto::Dim; use crate::tfpb::tensorflow::{TensorProto, TensorShapeProto}; use crate::tfpb::tensorflow::DataType; use std::convert::TryFrom; impl TryFrom<DataType> for DatumType { type Error = TractError; fn try_from(t: DataType) -> TractResult<DatumType> { match t { DataType::DtBool => Ok(DatumType::Bool), DataType::DtUint8 => Ok(DatumType::U8), DataType::DtUint16 => Ok(DatumType::U16), DataType::DtUint32 => Ok(DatumType::U32), DataType::DtUint64 => Ok(DatumType::U64), DataType::DtInt8 => Ok(DatumType::I8), DataType::DtInt16 => Ok(DatumType::I16), DataType::DtInt32 => Ok(DatumType::I32), DataType::DtInt64 => Ok(DatumType::I64), DataType::DtHalf => Ok(DatumType::F16), DataType::DtFloat => Ok(DatumType::F32), DataType::DtDouble => Ok(DatumType::F64), DataType::DtString => Ok(DatumType::Blob), _ => Err(format_err!("Unknown DatumType {:?}", t))?, } } } impl<'a> TryFrom<&'a TensorShapeProto> for TVec<isize> { type Error = TractError; fn try_from(t: &'a TensorShapeProto) -> TractResult<TVec<isize>> { Ok(t.dim.iter().map(|d| d.size as isize).collect::<TVec<_>>()) } } impl<'a> TryFrom<&'a TensorShapeProto> for TVec<usize> { type Error = TractError; fn try_from(t: &'a TensorShapeProto) -> TractResult<TVec<usize>> { if t.dim.iter().any(|d| d.size < 0) { bail!("Negative dim found") } Ok(t.dim.iter().map(|d| d.size as usize).collect::<TVec<_>>()) } } impl TryFrom<DatumType> for DataType { type Error = TractError; fn try_from(dt: DatumType) -> TractResult<DataType> { match dt { DatumType::Bool => Ok(DataType::DtBool), DatumType::U8 => Ok(DataType::DtUint8), DatumType::U16 => Ok(DataType::DtUint16), DatumType::U32 => Ok(DataType::DtUint32), DatumType::U64 => Ok(DataType::DtUint64), DatumType::I8 => Ok(DataType::DtInt8), DatumType::I16 => Ok(DataType::DtInt16), DatumType::I32 => Ok(DataType::DtInt32), DatumType::I64 => Ok(DataType::DtInt64), DatumType::F16 => Ok(DataType::DtHalf), DatumType::F32 => Ok(DataType::DtFloat), DatumType::F64 => Ok(DataType::DtDouble), DatumType::Blob => Ok(DataType::DtString), DatumType::String => Ok(DataType::DtString), DatumType::TDim => bail!("Dimension is not translatable in protobuf"), } } } fn tensor_from_repeated_field<T: Datum>(shape: &[usize], data: Vec<T>) -> TractResult<Tensor> { let t = if data.len() == 1 { tract_ndarray::ArrayD::from_elem(shape, data[0].clone()).into() } else { tract_ndarray::ArrayD::from_shape_vec(shape, data.to_vec())?.into() }; Ok(t) } impl<'a> TryFrom<&'a TensorProto> for Tensor { type Error = TractError; fn try_from(t: &TensorProto) -> TractResult<Tensor> { let dims: TVec<usize> = t.tensor_shape.as_ref().unwrap().dim.iter().map(|x| x.size as _).collect(); let rank = dims.len(); let content = &t.tensor_content; let dtype = DataType::from_i32(t.dtype).unwrap(); let mat: Tensor = if content.len() != 0 { unsafe { match dtype { DataType::DtFloat => Self::from_raw::<f32>(&dims, content)?, DataType::DtDouble => Self::from_raw::<f64>(&dims, content)?, DataType::DtInt32 => Self::from_raw::<i32>(&dims, content)?, DataType::DtInt64 => Self::from_raw::<i64>(&dims, content)?, _ => unimplemented!("missing type (for get_tensor_content) {:?}", dtype), } } } else { match dtype { DataType::DtInt32 => tensor_from_repeated_field(&*dims, t.int_val.to_vec())?, DataType::DtInt64 => tensor_from_repeated_field(&*dims, t.int64_val.to_vec())?, DataType::DtFloat => tensor_from_repeated_field(&*dims, t.float_val.to_vec())?, DataType::DtDouble => tensor_from_repeated_field(&*dims, t.double_val.to_vec())?, DataType::DtString => { let strings = t.string_val.iter().map(|s| Blob(s.to_owned())).collect::<Vec<Blob>>(); tensor_from_repeated_field(&*dims, strings)? } _ => unimplemented!("missing type (for _val()) {:?}", t.dtype), } }; assert_eq!(rank, mat.shape().len()); Ok(mat) } } fn empty_tensor_proto() -> TensorProto { TensorProto { dtype: 0, tensor_shape: None, version_number: 0, tensor_content: vec![], half_val: vec![], float_val: vec![], double_val: vec![], int_val: vec![], string_val: vec![], scomplex_val: vec![], dcomplex_val: vec![], resource_handle_val: vec![], variant_val: vec![], uint32_val: vec![], uint64_val: vec![], int64_val: vec![], bool_val: vec![], } } impl<'a> TryFrom<&'a Tensor> for TensorProto { type Error = TractError; fn try_from(from: &Tensor) -> TractResult<TensorProto> { let mut tensor = empty_tensor_proto(); let shape = TensorShapeProto { dim: from.shape().iter().map(|d| Dim { size: *d as _, name: String::new() }).collect(), unknown_rank: false, }; tensor.tensor_shape = Some(shape); let dt = DataType::try_from(from.datum_type())?; tensor.dtype = dt.into(); match from.datum_type() { DatumType::F32 => { tensor.float_val = from.to_array_view::<f32>()?.iter().cloned().collect(); } DatumType::F64 => { tensor.double_val = from.to_array_view::<f64>()?.iter().cloned().collect(); } DatumType::I32 => { tensor.int_val = from.to_array_view::<i32>()?.iter().cloned().collect(); } DatumType::I64 => { tensor.int64_val = from.to_array_view::<i64>()?.iter().cloned().collect(); } _ => unimplemented!("missing type {:?}", from.datum_type()), } Ok(tensor) } }