- Sort Score
- Result 10 results
- Languages All
Results 1 - 10 of 50 for input_type (0.15 sec)
-
tensorflow/compiler/mlir/quantization/common/ir/UniformSupport.cc
return ExpressedToQuantizedConverter{input_type, element_type}; } // Supported primitive type (which just is the expressed type). if (isQuantizablePrimitiveType(input_type)) return ExpressedToQuantizedConverter{input_type, input_type}; // Unsupported. return ExpressedToQuantizedConverter{input_type, nullptr}; } Type ExpressedToQuantizedConverter::convert(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/cost_model.cc
if (input_op && input_op == from_graph.getOperation()) { auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType()); if (input_type == nullptr || !input_type.hasStaticShape()) continue; // Quantized type does not support getSizeInBits. if (IsQUI8Type(input_type) || IsQI8Type(input_type)) { total_size_transferred += input_type.getNumElements() * 8; } else {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/arithmetic_count_util.h
int64_t total_count = 0; for (auto input : op->getOperands()) { auto input_type = mlir::dyn_cast_or_null<mlir::RankedTensorType>(input.getType()); if (!input_type || !input_type.hasStaticShape()) { return false; } total_count += input_type.getNumElements(); } *count = total_count; return true; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/modify_io_nodes.cc
public: MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ModifyIONodesPass) explicit ModifyIONodesPass() {} explicit ModifyIONodesPass(mlir::Type input_type, mlir::Type output_type) { this->input_type = input_type; this->output_type = output_type; } void runOnOperation() override; private: // Assign the io types from the command line flag. This is only required for // tests.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/c/kernels/ops/bitcast.cc
TF_ShapeHandle* shape, TF_DataType input_type, TF_DataType output_type, TF_Status* status) { size_t input_type_size = TF_DataTypeSize(input_type); size_t output_type_size = TF_DataTypeSize(output_type); if (input_type_size == 0 || output_type_size == 0) { std::ostringstream err; err << "Cannot bitcast type " << input_type << " to " << output_type
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 07:51:50 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/common/utils.cc
bool uint8_type_observed = false; for (auto& input : op->getOpOperands()) { auto input_type = input.get().getType(); if (IsF32ShapedType(input_type)) { float_type_observed = true; } else if (IsQI8Type(input_type)) { int8_type_observed = true; } else if (IsQUI8Type(input_type)) { uint8_type_observed = true; } } // We should not observe both uint8 & int8.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 06 05:37:07 UTC 2024 - 2.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform.cc
SmallVector<Value, 4> dequantized_inputs; for (auto& input : op->getOpOperands()) { auto input_type = input.get().getType(); if (IsQI8Type(input_type) || IsQUI8Type(input_type) || IsQI32Type(input_type)) { auto dequantized_input_type = mlir::quant::QuantizedType::castToExpressedType(input_type); builder->setInsertionPoint(op); auto dequantize_op = builder->create<TFL::DequantizeOp>(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/perception_ops_utils_test.cc
auto input_type = RankedTensorType::get({1, 2, 2, 1}, builder_->getF32Type()); auto output_type = RankedTensorType::get({1, 2, 1, 1}, builder_->getF32Type()); SmallVector<mlir::Type, 1> input_types{input_type}; SmallVector<mlir::Type, 1> output_types{output_type}; auto max_unpooling_func = createMaxUnpoolingFunc<1, 1>(builder_.get(), input_types, output_types);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 29 21:02:21 UTC 2022 - 7.4K bytes - Viewed (0) -
tensorflow/cc/gradients/image_grad.cc
DataType input_type; string method; TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "method", &method)); TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "T", &input_type)); auto image_shape = Shape(scope, op.input(0)); grad_outputs->push_back(CropAndResizeGradImage( scope, grad_inputs[0], op.input(1), op.input(2), image_shape, input_type, CropAndResizeGradImage::Method(method)));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 11 00:29:23 UTC 2021 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_op_order.cc
// can have smaller memory usage. auto input_type = mlir::dyn_cast<RankedTensorType>(dequantize_op.getOutput().getType()); auto output_type = mlir::dyn_cast<RankedTensorType>( passthrough_op->getResult(0).getType()); if (!input_type || !output_type || get_num_elements(input_type) <= get_num_elements(output_type)) { return failure(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.1K bytes - Viewed (0)