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/",
+]
+
+
[email protected]("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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