| # Copyright 2021 The TensorFlow Authors. All Rights Reserved. |
| # |
| # 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 |
| # |
| # 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. |
| # ============================================================================== |
| """Tests for analyzer package.""" |
| |
| from tensorflow.lite.python import analyzer |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.platform import resource_loader |
| from tensorflow.python.platform import test |
| |
| |
| class ConvertTest(test_util.TensorFlowTestCase): |
| |
| def testTxt(self): |
| model_path = resource_loader.get_path_to_datafile( |
| "testdata/permute_float.tflite") |
| txt = analyzer.ModelAnalyzer.analyze(model_path, "txt") |
| self.assertIn("Subgraph#0 main(T#0) -> [T#2]", txt) |
| self.assertIn("Op#0 FULLY_CONNECTED(T#0, T#1, T#-1) -> [T#2]", txt) |
| |
| def testHtml(self): |
| model_path = resource_loader.get_path_to_datafile( |
| "testdata/permute_float.tflite") |
| html = analyzer.ModelAnalyzer.analyze(model_path, "html") |
| self.assertIn("<html>\n<head>", html) |
| self.assertIn("FULLY_CONNECTED (0)", html) |
| |
| def testMlir(self): |
| model_path = resource_loader.get_path_to_datafile( |
| "testdata/permute_float.tflite") |
| mlir = analyzer.ModelAnalyzer.analyze(model_path, "mlir") |
| self.assertIn( |
| "func @main(%arg0: tensor<1x4xf32>) -> tensor<1x4xf32> attributes " |
| '{tf.entry_function = {inputs = "input", outputs = "output"}}', mlir) |
| self.assertIn( |
| '%1 = "tfl.fully_connected"(%arg0, %0, %cst) {fused_activation_function' |
| ' = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : ' |
| "(tensor<1x4xf32>, tensor<4x4xf32>, none) -> tensor<1x4xf32>", mlir) |
| |
| def testMlirHugeConst(self): |
| model_path = resource_loader.get_path_to_datafile( |
| "../testdata/conv_huge_im2col.bin") |
| mlir = analyzer.ModelAnalyzer.analyze(model_path, "mlir") |
| self.assertIn( |
| '%1 = "tfl.pseudo_const"() {value = opaque<"_", "0xDEADBEEF"> : ' |
| "tensor<3x3x3x8xf32>} : () -> tensor<3x3x3x8xf32>", mlir) |
| |
| |
| if __name__ == "__main__": |
| test.main() |
