| // Copyright 2019 Google LLC |
| // |
| // Licensed 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 |
| // |
| // https://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. |
| |
| // Image edge detection module generated by iree/colab/edge_detection.ipynb. |
| // |
| // Input : a single 128x128 pixel image as a tensor<1x128x128x1xf32>, with pixels in [0.0, 1.0] |
| // Output: a single image in the same format after running edge detection |
| |
| module attributes {tf_saved_model.semantics} { |
| func @__inference_edge_detect_sobel_operator_130(%arg0: tensor<1x128x128x1xf32> {tf_saved_model.index_path = [0]}) -> (tensor<1x128x128x1xf32> {tf_saved_model.index_path = []}) attributes {tf._input_shapes = ["tfshape$dim { size: 1 } dim { size: 128 } dim { size: 128 } dim { size: 1 }"], tf_saved_model.exported_names = ["edge_detect_sobel_operator"]} { |
| %0 = xla_hlo.constant dense<[[[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]], [[[-2.000000e+00]], [[0.000000e+00]], [[2.000000e+00]]], [[[-1.000000e+00]], [[0.000000e+00]], [[1.000000e+00]]]]> : tensor<3x3x1x1xf32> |
| %1 = xla_hlo.constant dense<[[[[1.000000e+00]], [[2.000000e+00]], [[1.000000e+00]]], [[[0.000000e+00]], [[0.000000e+00]], [[0.000000e+00]]], [[[-1.000000e+00]], [[-2.000000e+00]], [[-1.000000e+00]]]]> : tensor<3x3x1x1xf32> |
| %2 = "xla_hlo.conv"(%arg0, %0) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x128x128x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x128x128x1xf32> |
| %3 = xla_hlo.mul %2, %2 : tensor<1x128x128x1xf32> |
| %4 = "xla_hlo.conv"(%arg0, %1) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x128x128x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x128x128x1xf32> |
| %5 = xla_hlo.mul %4, %4 : tensor<1x128x128x1xf32> |
| %6 = xla_hlo.add %3, %5 : tensor<1x128x128x1xf32> |
| %7 = "xla_hlo.sqrt"(%6) : (tensor<1x128x128x1xf32>) -> tensor<1x128x128x1xf32> |
| return %7 : tensor<1x128x128x1xf32> |
| } |
| } |
