text: 
  
  
  
  
  from caffe2.python import core
  from hypothesis import given
  import caffe2.python.hypothesis_test_util as hu
  import caffe2.python.serialized_test.serialized_test_util as serial
  import hypothesis.strategies as st
  import numpy as np
  
  
  class TestLengthsTopKOps(serial.SerializedTestCase):
      @serial.given(N=st.integers(min_value=0, max_value=10),
             K=st.integers(min_value=1, max_value=10),
             **hu.gcs_cpu_only)
      def test_lengths_top_k_op(self, N, K, gc, dc):
          lens = np.random.randint(low=1, high=2 * K + 1, size=N).astype(np.int32)
          X = []
          for i in lens:
              X.extend(x / 100.0 for x in range(0, 6 * i, 6))
          X = np.array(X, dtype=np.float32)
          op = core.CreateOperator("LengthsTopK", ["X", "Y"], ["values", "indices"], k=K)
  
          def lengths_top_k(X, lens):
              N, si = lens.shape[0], 0
              values, indices = [], []
              for i in range(N):
                  cur_indices = X[si:si + lens[i]].argsort()[-K:][::-1]
                  cur_values = X[si:si + lens[i]][cur_indices]
                  values.extend(cur_values)
                  indices.extend(cur_indices)
                  si += lens[i]
                  if lens[i] < K:
                      values.extend([0] * (K - lens[i]))
                      indices.extend([-1] * (K - lens[i]))
  
              return (np.array(values, dtype=np.float32).reshape(-1, K),
                      np.array(indices, dtype=np.int32).reshape(-1, K))
  
          self.assertDeviceChecks(dc, op, [X, lens], [0, 1])
          self.assertReferenceChecks(gc, op, [X, lens], lengths_top_k)
          self.assertGradientChecks(gc, op, [X, lens], 0, [0])
  
      @given(N=st.integers(min_value=0, max_value=10),
             K=st.integers(min_value=1, max_value=10),
             **hu.gcs_cpu_only)
      def test_lengths_top_k_empty_op(self, N, K, gc, dc):
          lens = np.zeros((N, ), dtype=np.int32)
          X = np.array([], dtype=np.float32)
          op = core.CreateOperator("LengthsTopK", ["X", "Y"], ["values", "indices"], k=K)
  
          def lengths_top_k(X, lens):
              return (np.zeros((N, K), dtype=np.float32),
                      -1 * np.ones((N, K), dtype=np.int32))
  
          self.assertDeviceChecks(dc, op, [X, lens], [0, 1])
          self.assertReferenceChecks(gc, op, [X, lens], lengths_top_k)
          self.assertGradientChecks(gc, op, [X, lens], 0, [0])
  