/
usr
/
local
/
lib64
/
python3.6
/
site-packages
/
caffe2
/
python
/
operator_test
/
/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
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adam_test.py
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batch_sparse_to_dense_op_test.py
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bbox_transform_test.py
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blobs_queue_db_test.py
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boolean_mask_test.py
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box_with_nms_limit_op_test.py
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cast_op_test.py
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ceil_op_test.py
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channel_backprop_stats_op_test.py
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checkpoint_test.py
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collect_and_distribute_fpn_rpn_proposals_op_test.py
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concat_split_op_test.py
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conditional_test.py
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conftest.py
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conv_test.py
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conv_transpose_test.py
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copy_ops_test.py
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copy_rows_to_tensor_op_test.py
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cosine_embedding_criterion_op_test.py
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counter_ops_test.py
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crf_test.py
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cross_entropy_ops_test.py
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ctc_beam_search_decoder_op_test.py
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ctc_greedy_decoder_op_test.py
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cudnn_recurrent_test.py
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dataset_ops_test.py
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data_couple_op_test.py
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deform_conv_test.py
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dense_vector_to_id_list_op_test.py
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depthwise_3x3_conv_test.py
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detectron_keypoints.py
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distance_op_test.py
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dropout_op_test.py
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duplicate_operands_test.py
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elementwise_linear_op_test.py
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elementwise_ops_test.py
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emptysample_ops_test.py
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expand_op_test.py
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fc_operator_test.py
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feature_maps_ops_test.py
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filler_ops_test.py
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find_op_test.py
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flatten_op_test.py
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flexible_top_k_test.py
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given_tensor_byte_string_to_uint8_fill_op_test.py
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given_tensor_fill_op_test.py
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glu_op_test.py
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group_conv_test.py
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group_norm_op_test.py
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gru_test.py
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heatmap_max_keypoint_op_test.py
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histogram_test.py
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hsm_test.py
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hyperbolic_ops_test.py
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im2col_col2im_test.py
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image_input_op_test.py
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index_hash_ops_test.py
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index_ops_test.py
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instance_norm_test.py
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integral_image_ops_test.py
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jsd_ops_test.py
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key_split_ops_test.py
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lars_test.py
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layer_norm_op_test.py
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leaky_relu_test.py
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learning_rate_adaption_op_test.py
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learning_rate_op_test.py
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lengths_pad_op_test.py
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lengths_reducer_fused_nbit_rowwise_ops_test.py
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lengths_tile_op_test.py
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lengths_top_k_ops_test.py
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length_split_op_test.py
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listwise_l2r_operator_test.py
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load_save_test.py
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locally_connected_op_test.py
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loss_ops_test.py
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lpnorm_op_test.py
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map_ops_test.py
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margin_ranking_criterion_op_test.py
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math_ops_test.py
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matmul_op_test.py
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mean_op_test.py
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merge_id_lists_op_test.py
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mkl_conv_op_test.py
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mkl_packed_fc_op_test.py
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mod_op_test.py
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moments_op_test.py
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momentum_sgd_test.py
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mpi_test.py
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mul_gradient_benchmark.py
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negate_gradient_op_test.py
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ngram_ops_test.py
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normalize_op_test.py
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numpy_tile_op_test.py
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one_hot_ops_test.py
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onnx_while_test.py
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order_switch_test.py
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pack_ops_test.py
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pack_rnn_sequence_op_test.py
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pad_test.py
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partition_ops_test.py
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percentile_op_test.py
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piecewise_linear_transform_test.py
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pooling_test.py
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prepend_dim_test.py
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python_op_test.py
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quantile_test.py
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rand_quantization_op_speed_test.py
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rank_loss_operator_test.py
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rebatching_queue_test.py
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record_queue_test.py
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recurrent_network_test.py
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recurrent_net_executor_test.py
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reduce_ops_test.py
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reduction_ops_test.py
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reshape_ops_test.py
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resize_op_test.py
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rmac_regions_op_test.py
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rms_norm_op_test.py
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rnn_cell_test.py
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roi_align_rotated_op_test.py
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rowwise_counter_test.py
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scale_op_test.py
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segment_ops_test.py
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self_binning_histogram_test.py
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selu_op_test.py
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sequence_ops_test.py
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shape_inference_test.py
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sinusoid_position_encoding_op_test.py
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softmax_ops_test.py
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softplus_op_test.py
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sparse_dropout_with_replacement_op_test.py
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sparse_gradient_checker_test.py
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sparse_itemwise_dropout_with_replacement_op_test.py
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sparse_lengths_sum_benchmark.py
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sparse_lp_regularizer_test.py
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sparse_to_dense_mask_op_test.py
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spatial_bn_op_test.py
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specialized_segment_ops_test.py
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split_op_cost_test.py
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square_root_divide_op_test.py
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stats_ops_test.py
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stats_put_ops_test.py
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storm_test.py
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string_ops_test.py
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text_file_reader_test.py
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thresholded_relu_op_test.py
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tile_op_test.py
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top_k_test.py
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torch_integration_test.py
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transpose_op_test.py
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trigonometric_op_test.py
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unique_ops_test.py
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unique_uniform_fill_op_test.py
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unsafe_coalesce_test.py
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upsample_op_test.py
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utility_ops_test.py
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video_input_op_test.py
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weighted_multi_sample_test.py
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weighted_sample_test.py
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weighted_sum_test.py
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weight_scale_test.py
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wngrad_test.py
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__init__.py
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Edit:
/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/top_k_test.py
(9113B)
import hypothesis.strategies as st import numpy as np from caffe2.python import core from hypothesis import given, settings import caffe2.python.hypothesis_test_util as hu import caffe2.python.serialized_test.serialized_test_util as serial class TestTopK(serial.SerializedTestCase): def top_k_ref(self, X, k, flatten_indices, axis=-1): in_dims = X.shape out_dims = list(in_dims) out_dims[axis] = k out_dims = tuple(out_dims) if axis == -1: axis = len(in_dims) - 1 prev_dims = 1 next_dims = 1 for i in range(axis): prev_dims *= in_dims[i] for i in range(axis + 1, len(in_dims)): next_dims *= in_dims[i] n = in_dims[axis] X_flat = X.reshape((prev_dims, n, next_dims)) values_ref = np.ndarray( shape=(prev_dims, k, next_dims), dtype=np.float32) values_ref.fill(0) indices_ref = np.ndarray( shape=(prev_dims, k, next_dims), dtype=np.int64) indices_ref.fill(-1) flatten_indices_ref = np.ndarray( shape=(prev_dims, k, next_dims), dtype=np.int64) flatten_indices_ref.fill(-1) for i in range(prev_dims): for j in range(next_dims): kv = [] for x in range(n): val = X_flat[i, x, j] y = x * next_dims + i * in_dims[axis] * next_dims + j kv.append((val, x, y)) cnt = 0 for val, x, y in sorted( kv, key=lambda x: (x[0], -x[1]), reverse=True): values_ref[i, cnt, j] = val indices_ref[i, cnt, j] = x flatten_indices_ref[i, cnt, j] = y cnt += 1 if cnt >= k or cnt >= n: break values_ref = values_ref.reshape(out_dims) indices_ref = indices_ref.reshape(out_dims) flatten_indices_ref = flatten_indices_ref.flatten() if flatten_indices: return (values_ref, indices_ref, flatten_indices_ref) else: return (values_ref, indices_ref) @serial.given( X=hu.tensor(), flatten_indices=st.booleans(), seed=st.integers(0, 10), **hu.gcs ) def test_top_k(self, X, flatten_indices, seed, gc, dc): X = X.astype(dtype=np.float32) np.random.seed(seed) # `k` can be larger than the total size k = np.random.randint(1, X.shape[-1] + 4) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(1, 1), k=st.integers(1, 1), flatten_indices=st.booleans(), **hu.gcs) def test_top_k_1(self, bs, n, k, flatten_indices, gc, dc): X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(1, 10000), k=st.integers(1, 1), flatten_indices=st.booleans(), **hu.gcs) def test_top_k_2(self, bs, n, k, flatten_indices, gc, dc): X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(1, 10000), k=st.integers(1, 1024), flatten_indices=st.booleans(), **hu.gcs) def test_top_k_3(self, bs, n, k, flatten_indices, gc, dc): X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(100, 10000), flatten_indices=st.booleans(), **hu.gcs) @settings(deadline=10000) def test_top_k_4(self, bs, n, flatten_indices, gc, dc): k = np.random.randint(n // 3, 3 * n // 4) X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(1, 1024), flatten_indices=st.booleans(), **hu.gcs) def test_top_k_5(self, bs, n, flatten_indices, gc, dc): k = n X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(bs=st.integers(1, 3), n=st.integers(1, 5000), flatten_indices=st.booleans(), **hu.gcs) @settings(deadline=10000) def test_top_k_6(self, bs, n, flatten_indices, gc, dc): k = n X = np.random.rand(bs, n).astype(dtype=np.float32) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator("TopK", ["X"], output_list, k=k, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(X=hu.tensor(dtype=np.float32), k=st.integers(1, 5), axis=st.integers(-1, 5), flatten_indices=st.booleans(), **hu.gcs) def test_top_k_axis(self, X, k, axis, flatten_indices, gc, dc): dims = X.shape if axis >= len(dims): axis %= len(dims) output_list = ["Values", "Indices"] if flatten_indices: output_list.append("FlattenIndices") op = core.CreateOperator( "TopK", ["X"], output_list, k=k, axis=axis, device_option=gc) def bind_ref(X_loc): return self.top_k_ref(X_loc, k, flatten_indices, axis) self.assertReferenceChecks(gc, op, [X], bind_ref) self.assertDeviceChecks(dc, op, [X], [0]) @given(X=hu.tensor(dtype=np.float32), k=st.integers(1, 5), axis=st.integers(-1, 5), **hu.gcs) @settings(deadline=10000) def test_top_k_grad(self, X, k, axis, gc, dc): dims = X.shape if axis >= len(dims): axis %= len(dims) input_axis = len(dims) - 1 if axis == -1 else axis prev_dims = 1 next_dims = 1 for i in range(input_axis): prev_dims *= dims[i] for i in range(input_axis + 1, len(dims)): next_dims *= dims[i] X_flat = X.reshape((prev_dims, dims[input_axis], next_dims)) for i in range(prev_dims): for j in range(next_dims): # this try to make sure adding stepsize (0.05) # will not change TopK selections at all X_flat[i, :, j] = np.arange(dims[axis], dtype=np.float32) / 5 np.random.shuffle(X_flat[i, :, j]) X = X_flat.reshape(dims) op = core.CreateOperator( "TopK", ["X"], ["Values", "Indices"], k=k, axis=axis, device_option=gc) self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=0.05)
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