text: # Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.
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  import sys
  sys.path.append("../common")
  
  from builtins import range
  from future.utils import iteritems
  import unittest
  import numpy as np
  import os
  import tritonhttpclient as httpclient
  from tritonclientutils import InferenceServerException
  
  class PluginModelTest(unittest.TestCase):
      def _full_exact(self, batch_size, model_name, plugin_name):
          triton_client = httpclient.InferenceServerClient("localhost:8000", verbose=True)
  
          inputs = []
          outputs = []
          inputs.append(httpclient.InferInput('INPUT0', [batch_size, 16], "FP32"))
  
          input0_data = np.random.randn(batch_size, 16).astype(np.float32)
          inputs[0].set_data_from_numpy(input0_data, binary_data=True)
  
          outputs.append(httpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
  
          results = triton_client.infer(model_name + '_' + plugin_name,
                                        inputs,
                                        outputs=outputs)
  
          output0_data = results.as_numpy('OUTPUT0')
  
          # Verify values of Leaky RELU (it uses 0.1 instead of the default 0.01)
          # and for CustomClipPlugin min_clip = 0.1, max_clip = 0.5
          for b in range(batch_size):
              if plugin_name == 'LReLU_TRT':
                  test_input = np.where(input0_data > 0, input0_data, input0_data * 0.1)
                  self.assertTrue(np.isclose(output0_data, test_input).all())
              else:
                  # [TODO] Add test for CustomClip output
                  test_input = np.clip(input0_data, 0.1, 0.5)
  
      def test_raw_fff_lrelu(self):
          # model that supports batching
          for bs in (1, 8):
              self._full_exact(bs, 'plan_float32_float32_float32', 'LReLU_TRT')
  
      # add test for CustomClipPlugin after model is fixed
  
  if __name__ == '__main__':
      unittest.main()
  