[GitHub] [tvm] electriclilies commented on a change in pull request #7720: [ONNX] Onnx node tests

[GitBox <gi...@apache.org>]

electriclilies commented on a change in pull request #7720:
URL: https://github.com/apache/tvm/pull/7720#discussion_r599033812



##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -444,9 +448,15 @@ class ConvTranspose(OnnxOpConverter):
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
         # get number of channels
-        channels = infer_channels(inputs[1], True)
+        out_type = infer_type(inputs[1])
+        out_shapes = [get_const_tuple(out_type.checked_type.shape)]
+        channels = out_shapes[0][1]

Review comment:
       Does this need to work for layouts other than NCHW? It looks like the ONNX op doesn't specify layout in the ConvTranspose operator

##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -4090,6 +4090,170 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
     verify_cumsum(data, 1, 1, 1, type="int32")
 
 
+from onnx import numpy_helper
+
+f = onnx.__file__
+import glob
+
+onnx_test_folders = sorted(glob.glob("/".join(f.split("/")[0:-1]) + "/backend/test/data/node/*/"))
+
+unsupported_onnx_tests = [
+    "test_basic_convinteger/",
+    "test_bitshift_left_uint16/",
+    "test_bitshift_left_uint32/",
+    "test_bitshift_left_uint64/",
+    "test_bitshift_left_uint8/",
+    "test_bitshift_right_uint16/",
+    "test_bitshift_right_uint32/",
+    "test_bitshift_right_uint64/",
+    "test_bitshift_right_uint8/",
+    "test_cast_DOUBLE_to_FLOAT16/",
+    "test_cast_FLOAT16_to_DOUBLE/",
+    "test_cast_FLOAT16_to_FLOAT/",
+    "test_cast_FLOAT_to_FLOAT16/",
+    "test_cast_FLOAT_to_STRING/",
+    "test_cast_STRING_to_FLOAT/",
+    "test_compress_0/",
+    "test_compress_1/",
+    "test_compress_default_axis/",
+    "test_compress_negative_axis/",
+    "test_convinteger_with_padding/",
+    "test_convtranspose_dilations/",
+    "test_convtranspose_output_shape/",
+    "test_cumsum_1d/",
+    "test_cumsum_1d_exclusive/",
+    "test_cumsum_1d_reverse/",
+    "test_cumsum_1d_reverse_exclusive/",
+    "test_cumsum_2d_axis_0/",
+    "test_cumsum_2d_axis_1/",
+    "test_cumsum_2d_negative_axis/",
+    "test_dequantizelinear/",
+    "test_det_2d/",
+    "test_det_nd/",
+    "test_dynamicquantizelinear/",
+    "test_dynamicquantizelinear_expanded/",
+    "test_dynamicquantizelinear_max_adjusted/",
+    "test_dynamicquantizelinear_max_adjusted_expanded/",
+    "test_dynamicquantizelinear_min_adjusted/",
+    "test_dynamicquantizelinear_min_adjusted_expanded/",
+    "test_eyelike_populate_off_main_diagonal/",
+    "test_eyelike_with_dtype/",
+    "test_eyelike_without_dtype/",
+    "test_hardmax_axis_0/",
+    "test_hardmax_axis_1/",
+    "test_hardmax_axis_2/",
+    "test_hardmax_default_axis/",
+    "test_hardmax_example/",
+    "test_hardmax_negative_axis/",
+    "test_hardmax_one_hot/",
+    "test_isinf_negative/",
+    "test_isinf_positive/",
+    "test_lstm_defaults/",
+    "test_lstm_with_initial_bias/",
+    "test_lstm_with_peepholes/",
+    "test_matmulinteger/",
+    "test_maxpool_2d_dilations/",
+    "test_maxpool_2d_same_lower/",
+    "test_maxpool_2d_same_upper/",
+    "test_maxpool_with_argmax_2d_precomputed_pads/",
+    "test_maxpool_with_argmax_2d_precomputed_strides/",
+    "test_maxunpool_export_with_output_shape/",
+    "test_mvn/",
+    "test_nonmaxsuppression_center_point_box_format/",
+    "test_qlinearconv/",
+    "test_qlinearmatmul_2D/",
+    "test_qlinearmatmul_3D/",
+    "test_quantizelinear/",
+    "test_range_float_type_positive_delta_expanded/",
+    "test_range_int32_type_negative_delta_expanded/",
+    "test_resize_downsample_scales_cubic/",
+    "test_resize_downsample_scales_cubic_A_n0p5_exclude_outside/",
+    "test_resize_downsample_scales_cubic_align_corners/",
+    "test_resize_downsample_scales_linear/",
+    "test_resize_downsample_scales_nearest/",
+    "test_resize_downsample_sizes_cubic/",
+    "test_resize_downsample_sizes_linear_pytorch_half_pixel/",
+    "test_resize_downsample_sizes_nearest/",
+    "test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn/",
+    "test_resize_tf_crop_and_resize/",
+    "test_resize_upsample_scales_cubic/",
+    "test_resize_upsample_scales_cubic_A_n0p5_exclude_outside/",
+    "test_resize_upsample_scales_cubic_align_corners/",
+    "test_resize_upsample_scales_cubic_asymmetric/",
+    "test_resize_upsample_scales_linear/",
+    "test_resize_upsample_sizes_cubic/",
+    "test_resize_upsample_sizes_nearest_ceil_half_pixel/",
+    "test_resize_upsample_sizes_nearest_floor_align_corners/",
+    "test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/",
+    "test_reversesequence_batch/",
+    "test_reversesequence_time/",
+    "test_rnn_seq_length/",
+    "test_roialign/",
+    "test_round/",
+    "test_scan9_sum/",
+    "test_scan_sum/",
+    "test_scatternd/",
+    "test_selu_default/",
+    "test_shrink_hard/",
+    "test_shrink_soft/",
+    "test_simple_rnn_defaults/",
+    "test_simple_rnn_with_initial_bias/",
+    "test_slice_neg_steps/",
+    "test_slice_start_out_of_bounds/",
+    "test_strnormalizer_export_monday_casesensintive_lower/",
+    "test_strnormalizer_export_monday_casesensintive_nochangecase/",
+    "test_strnormalizer_export_monday_casesensintive_upper/",
+    "test_strnormalizer_export_monday_empty_output/",
+    "test_strnormalizer_export_monday_insensintive_upper_twodim/",
+    "test_strnormalizer_nostopwords_nochangecase/",
+    "test_tfidfvectorizer_tf_batch_onlybigrams_skip0/",
+    "test_tfidfvectorizer_tf_batch_onlybigrams_skip5/",
+    "test_tfidfvectorizer_tf_batch_uniandbigrams_skip5/",
+    "test_tfidfvectorizer_tf_only_bigrams_skip0/",
+    "test_tfidfvectorizer_tf_onlybigrams_levelempty/",
+    "test_tfidfvectorizer_tf_onlybigrams_skip5/",
+    "test_tfidfvectorizer_tf_uniandbigrams_skip5/",
+    "test_top_k_smallest/",
+    "test_unique_not_sorted_without_axis/",
+    "test_unique_sorted_with_axis/",
+    "test_unique_sorted_with_axis_3d/",
+    "test_unique_sorted_with_negative_axis/",
+    "test_unique_sorted_without_axis/",
+    "test_unsqueeze_unsorted_axes/",
+    "test_upsample_nearest/",
+]
+
+
+@pytest.mark.parametrize("test", onnx_test_folders)
+def test_onnx_nodes(test):
+    for failure in unsupported_onnx_tests:
+        if failure in test:
+            pytest.skip()
+            break
+    onnx_model = onnx.load(test + "/model.onnx")
+    inputs = []
+    outputs = []
+    for dataset in glob.glob(test + "/*/"):
+        tensors = sorted(glob.glob(dataset + "/*.pb"))
+        for tensor in tensors:
+            new_tensor = onnx.TensorProto()
+            with open(tensor, "rb") as f:
+                new_tensor.ParseFromString(f.read())
+            if "input" in tensor.split("/")[-1]:
+                inputs.append(numpy_helper.to_array(new_tensor))
+            elif "output" in tensor.split("/")[-1]:
+                outputs.append(numpy_helper.to_array(new_tensor))
+            else:
+                print(tensor)
+                raise

Review comment:
       Can you put an error message here? Maybe something like "Expected tensor to be either an input or output"




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