/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, * software distributed under the License is distributed on an * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY * KIND, either express or implied. See the License for the * specific language governing permissions and limitations * under the License. */ /*! * \file resize.cc * \brief Image resize operators */ #include <tvm/tir/data_layout.h> #include <tvm/relay/op.h> #include <tvm/relay/attrs/image.h> #include "../op_common.h" namespace tvm { namespace relay { TVM_REGISTER_NODE_TYPE(ResizeAttrs); bool ResizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, const TypeReporter& reporter) { CHECK_EQ(types.size(), 2); const auto* data = types[0].as<TensorTypeNode>(); if (data == nullptr) return false; static const Layout kNCHW("NCHW"); const ResizeAttrs* param = attrs.as<ResizeAttrs>(); CHECK(param != nullptr); const Layout in_layout(param->layout); auto layout_converter = tir::BijectiveLayout(in_layout, kNCHW); CHECK(layout_converter.defined()) << "Resize only support input layouts that are convertible from NCHW." << " But got " << in_layout; auto oshape = layout_converter.ForwardShape(data->shape); oshape.Set(2, param->size[0]); oshape.Set(3, param->size[1]); DataType out_dtype = param->out_dtype; if (out_dtype.bits() == 0) { out_dtype = data->dtype; } // assign output type reporter->Assign(types[1], TensorType(layout_converter.BackwardShape(oshape), out_dtype)); return true; } // Positional relay function to create image operator // used by frontend FFI. Expr MakeResize(Expr data, Array<IndexExpr> size, std::string layout, std::string method, std::string coordinate_transformation_mode, DataType out_dtype) { auto attrs = make_object<ResizeAttrs>(); attrs->size = std::move(size); attrs->layout = std::move(layout); attrs->method = std::move(method); attrs->coordinate_transformation_mode = coordinate_transformation_mode; attrs->out_dtype = out_dtype; static const Op& op = Op::Get("image.resize"); return Call(op, {data}, Attrs(attrs), {}); } TVM_REGISTER_GLOBAL("relay.op.image._make.resize") .set_body_typed(MakeResize); RELAY_REGISTER_OP("image.resize") .describe(R"code(Perform resize to input array with nearest neighbour or bilinear interpolation. - **data**: data is 4D array of shape (batch_size, channels, in_height, in_width) for NCHW (batch_size, in_height, in_width, channels) for NHWC - **out**: Output is 4D array of shape for layout NCHW (batch_size, channels, size[0], size[1]) for layout NHWC (batch_size, size[0], size[1], channels) )code" TVM_ADD_FILELINE) .set_attrs_type<ResizeAttrs>() .set_num_inputs(1) .add_argument("data", "Tensor", "The input tensor.") .set_support_level(5) .add_type_rel("Resize", ResizeRel) .set_attr<TOpPattern>("TOpPattern", kInjective); TVM_REGISTER_NODE_TYPE(CropAndResizeAttrs); bool CropAndResizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, const TypeReporter& reporter) { CHECK_EQ(types.size(), 4); const auto* data = types[0].as<TensorTypeNode>(); const auto* boxes = types[1].as<TensorTypeNode>(); const auto* box_indices = types[2].as<TensorTypeNode>(); if (data == nullptr || boxes == nullptr || box_indices == nullptr) return false; const CropAndResizeAttrs* param = attrs.as<CropAndResizeAttrs>(); CHECK(param != nullptr); auto crop_size = param->crop_size; DataType out_dtype = param->out_dtype; if (out_dtype.bits() == 0) { out_dtype = data->dtype; } // 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] static const Layout kNCHW("NCHW"); const Layout in_layout(param->layout); auto layout_converter = tir::BijectiveLayout(in_layout, kNCHW); auto oshape = layout_converter.ForwardShape(data->shape); oshape.Set(0, box_indices->shape[0]); oshape.Set(2, crop_size[0]); oshape.Set(3, crop_size[1]); auto bshape = layout_converter.BackwardShape(oshape); // assign output type reporter->Assign(types[3], TensorType(layout_converter.BackwardShape(oshape), out_dtype)); return true; } Expr MakeCropAndResize(Expr data, Expr boxes, Expr box_indices, Array<IndexExpr> crop_size, std::string layout, std::string method, double extrapolation_value, DataType out_dtype) { auto attrs = make_object<CropAndResizeAttrs>(); attrs->crop_size = std::move(crop_size); attrs->layout = std::move(layout); attrs->method = std::move(method); attrs->extrapolation_value = std::move(extrapolation_value); attrs->out_dtype = out_dtype; static const Op& op = Op::Get("image.crop_and_resize"); return Call(op, {data, boxes, box_indices}, Attrs(attrs), {}); } TVM_REGISTER_GLOBAL("relay.op.image._make.crop_and_resize") .set_body_typed(MakeCropAndResize); RELAY_REGISTER_OP("image.crop_and_resize") .describe(R"code(Perform crop and resize to input array with nearest neighbour or bilinear interpolation. - **data**: data is 4D array of shape (batch_size, channels, in_height, in_width) for NCHW (batch_size, in_height, in_width, channels) for NHWC - **out**: Output is 4D array of shape for layout NCHW (batch_size, channels, crop_size[0], crop_size[1]) for layout NHWC (batch_size, crop_size[0], crop_size[1], channels) )code" TVM_ADD_FILELINE) .set_num_inputs(3) .add_argument("data", "Tensor", "The input tensor.") .add_argument("boxes", "Tensor", "The boxes tensor.") .add_argument("box_indices", "Tensor", "The box indices tensor.") .set_attrs_type<CropAndResizeAttrs>() .set_support_level(5) .add_type_rel("CropAndResize", CropAndResizeRel) .set_attr<TOpPattern>("TOpPattern", kInjective); } // namespace relay } // namespace tvm