text: # Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
  #
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  # modification, are permitted provided that the following conditions
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  #  * Redistributions of source code must retain the above copyright
  #    notice, this list of conditions and the following disclaimer.
  #  * Redistributions in binary form must reproduce the above copyright
  #    notice, this list of conditions and the following disclaimer in the
  #    documentation and/or other materials provided with the distribution.
  #  * Neither the name of NVIDIA CORPORATION nor the names of its
  #    contributors may be used to endorse or promote products derived
  #    from this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
  # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
  # PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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  # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  import sys
  sys.path.append("../common")
  
  from builtins import range
  from future.utils import iteritems
  import unittest
  import numpy as np
  import infer_util as iu
  import test_util as tu
  import os
  
  np_dtype_string = np.dtype(object)
  
  TEST_SYSTEM_SHARED_MEMORY = bool(
      int(os.environ.get('TEST_SYSTEM_SHARED_MEMORY', 0)))
  TEST_CUDA_SHARED_MEMORY = bool(int(os.environ.get('TEST_CUDA_SHARED_MEMORY',
                                                    0)))
  
  
  class InferReshapeTest(tu.TestResultCollector):
  
      def _full_reshape(self,
                        dtype,
                        input_shapes,
                        output_shapes=None,
                        no_batch=True):
          # 'shapes' is list of shapes, one for each input.
          if output_shapes is None:
              output_shapes = input_shapes
  
          # For validation assume any shape can be used...
          if tu.validate_for_tf_model(dtype, dtype, dtype, input_shapes[0],
                                      input_shapes[0], input_shapes[0]):
              # model that supports batching
              for bs in (1, 8):
                  full_shapes = [[
                      bs,
                  ] + input_shape for input_shape in input_shapes]
                  full_output_shapes = [[
                      bs,
                  ] + output_shape for output_shape in output_shapes]
                  iu.infer_zero(
                      self,
                      'graphdef',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
                  iu.infer_zero(
                      self,
                      'savedmodel',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
              # model that does not support batching
              if no_batch:
                  iu.infer_zero(
                      self,
                      'graphdef_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
                  iu.infer_zero(
                      self,
                      'savedmodel_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
          if tu.validate_for_c2_model(dtype, dtype, dtype, input_shapes[0],
                                      input_shapes[0], input_shapes[0]):
              # model that supports batching
              for bs in (1, 8):
                  full_shapes = [[
                      bs,
                  ] + input_shape for input_shape in input_shapes]
                  full_output_shapes = [[
                      bs,
                  ] + output_shape for output_shape in output_shapes]
                  iu.infer_zero(
                      self,
                      'netdef',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
              # model that does not support batching
              if no_batch:
                  iu.infer_zero(
                      self,
                      'netdef_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
          if tu.validate_for_custom_model(dtype, dtype, dtype, input_shapes[0],
                                          input_shapes[0], input_shapes[0]):
              # model that supports batching
              for bs in (1, 8):
                  full_shapes = [[
                      bs,
                  ] + input_shape for input_shape in input_shapes]
                  full_output_shapes = [[
                      bs,
                  ] + output_shape for output_shape in output_shapes]
                  iu.infer_zero(
                      self,
                      'custom',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
              # model that does not support batching
              if no_batch:
                  iu.infer_zero(
                      self,
                      'custom_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
          if tu.validate_for_onnx_model(dtype, dtype, dtype, input_shapes[0],
                                        input_shapes[0], input_shapes[0]):
              # model that supports batching
              for bs in (1, 8):
                  full_shapes = [[
                      bs,
                  ] + input_shape for input_shape in input_shapes]
                  full_output_shapes = [[
                      bs,
                  ] + output_shape for output_shape in output_shapes]
                  iu.infer_zero(
                      self,
                      'onnx',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
              # model that does not support batching
              if no_batch:
                  iu.infer_zero(
                      self,
                      'onnx_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
          if tu.validate_for_libtorch_model(dtype, dtype, dtype, input_shapes[0],
                                            input_shapes[0], input_shapes[0]):
              # skip variable size reshape on libtorch for now,
              # see "gen_qa_reshape_model.py" for detail
              if dtype != np.int32:
                  # model that supports batching
                  for bs in (1, 8):
                      full_shapes = [[
                          bs,
                      ] + input_shape for input_shape in input_shapes]
                      full_output_shapes = [[
                          bs,
                      ] + output_shape for output_shape in output_shapes]
                      iu.infer_zero(
                          self,
                          'libtorch',
                          bs,
                          dtype,
                          full_shapes,
                          full_output_shapes,
                          use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                          use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
                  # model that does not support batching
                  if no_batch:
                      iu.infer_zero(
                          self,
                          'libtorch_nobatch',
                          1,
                          dtype,
                          input_shapes,
                          output_shapes,
                          use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                          use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
          for name in ["simple_reshape", "sequence_reshape", "fan_reshape"]:
              # [TODO] Skip variable size reshape on ensemble for now.
              # Need rework on how ensemble for reshape are generated
              if dtype == np.int32:
                  break
              if tu.validate_for_ensemble_model(name, dtype, dtype, dtype,
                                                input_shapes[0], input_shapes[0],
                                                input_shapes[0]):
                  # model that supports batching
                  for bs in (1, 8):
                      full_shapes = [[
                          bs,
                      ] + input_shape for input_shape in input_shapes]
                      full_output_shapes = [[
                          bs,
                      ] + output_shape for output_shape in output_shapes]
                      iu.infer_zero(
                          self,
                          name,
                          bs,
                          dtype,
                          full_shapes,
                          full_output_shapes,
                          use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                          use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
                  # model that does not support batching
                  if no_batch:
                      iu.infer_zero(
                          self,
                          name + '_nobatch',
                          1,
                          dtype,
                          input_shapes,
                          output_shapes,
                          use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                          use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
      def _trt_reshape(self,
                       dtype,
                       input_shapes,
                       output_shapes=None,
                       no_batch=True):
          # 'shapes' is list of shapes, one for each input.
          if output_shapes is None:
              output_shapes = input_shapes
  
          if tu.validate_for_trt_model(dtype, dtype, dtype, input_shapes[0],
                                       input_shapes[0], input_shapes[0]):
              # model that supports batching
              for bs in (1, 8):
                  full_shapes = [[
                      bs,
                  ] + input_shape for input_shape in input_shapes]
                  full_output_shapes = [[
                      bs,
                  ] + output_shape for output_shape in output_shapes]
                  iu.infer_zero(
                      self,
                      'plan',
                      bs,
                      dtype,
                      full_shapes,
                      full_output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
              # model that does not support batching
              if no_batch:
                  iu.infer_zero(
                      self,
                      'plan_nobatch',
                      1,
                      dtype,
                      input_shapes,
                      output_shapes,
                      use_system_shared_memory=TEST_SYSTEM_SHARED_MEMORY,
                      use_cuda_shared_memory=TEST_CUDA_SHARED_MEMORY)
  
      def test_ff1(self):
          self._full_reshape(np.float32, input_shapes=([1],), no_batch=False)
  
      def test_ff2(self):
          self._full_reshape(np.float32, input_shapes=([1], [8]), no_batch=False)
          self._trt_reshape(np.float32, input_shapes=([1], [8]))
  
      def test_ff3(self):
          self._full_reshape(np.float32, input_shapes=([4, 4], [2], [2, 2, 3]))
  
      def test_ff4(self):
          self._full_reshape(np.float32,
                             input_shapes=([4, 4], [2], [2, 2, 3], [1]),
                             output_shapes=([16], [1, 2], [3, 2, 2], [1]))
          self._trt_reshape(np.float32,
                            input_shapes=([4, 4], [2], [2, 2, 3], [1]),
                            output_shapes=([2, 2, 4], [1, 2, 1], [3, 2,
                                                                  2], [1, 1, 1]))
  
      def test_ii1(self):
          self._full_reshape(np.int32, input_shapes=([2, 4, 5, 6],))
  
      def test_ii2(self):
          self._full_reshape(np.int32,
                             input_shapes=([4, 1], [2]),
                             output_shapes=([1, 4], [1, 2]))
  
      def test_ii3(self):
          self._full_reshape(np.int32, input_shapes=([1, 4, 1], [8], [2, 2, 3]))
  
      def test_oo1(self):
          self._full_reshape(np.object, input_shapes=([1],), no_batch=False)
  
  
  if __name__ == '__main__':
      unittest.main()
  