line 928, in _graph_op torch._C._jit_pass_onnx_node_shape_type_inference(n, _params_dict, opset_version) RuntimeError: input_shape.size() > 0 || reshape.size() > 0INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/onnx/shape_type_inference.cpp":448, please report a bug to ...
position = tensor_dict[node.inputs[2]]iflen(node.inputs) >2elsetf.shape(input_sequence.to_sparse())[0]# check whether position is in-bounds and assert if notresult = cls.chk_pos_in_bounds(input_sequence, position) assert_pos = tf.Assert(tf.equal(result,True), [result])withtf.cont...
RuntimeError: input_shapes.size() >= n->inputs().size() INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/symbolic_shape_analysis.cpp":741, please report a bug to PyTorch. input_shapes size: 1 n inputs size: 2 The error is reproducible with the nightly-build version2.5.0.dev2024...
deftest_saving_multiple_metrics_outputs():inputs = Input(shape=(5,)) x = Dense(5)(inputs) output1 = Dense(1, name='output1')(x) output2 = Dense(1, name='output2')(x) model = Model(inputs=inputs, outputs=[output1, output2]) metrics = {'output1': ['mse','binary_accuracy...
inputs[1]] position = tensor_dict[node.inputs[2]] if len(node.inputs) > 2 else tf.shape(input_sequence.to_sparse())[0] # check whether position is in-bounds and assert if not result = cls.chk_pos_in_bounds(input_sequence, position) assert_pos = tf.Assert(tf.equal(result, ...
generate(ds)) assert_equal(len(splits), 2) for split in splits: assert_true(split.samples.base is ds.samples) assert_equal(len(split.fa['roi'].unique), 1) assert_equal(split.shape, (100, 5)) # and finally test chained splitters cspl = ChainNode([spl2, spl3, spl1]) splits ...
value = d d["a"] = 5 nt.assert_equal(d, c.value) nt.assert_true(c.value is d) 浏览完整代码 来源:test_traitlets.py 项目:hemanthkumar323/ipython 示例27 def test_metadata(): test_metadata = {'dtype': 'float64', 'shape': [1024], 'others': [], } received_metadata = pack_...
这是由于torch.tensor()声明没有在训练步骤中传输到GPU：
2. 3. 4. 5. 说明是有数据超出了边界值,并且最终报错的位置是出在utils.py 文件里的build_targets 函数中。 YOLOv3标注格式 训练自己的数据集只需获取训练集的图片位置信息以及每张图片的标注信息,json文件实际上是用不到的。具体格式如下: 图片位置信息: ...
assert_array_almost_equal(# allow a rounding error at the last decimal placeXd.astype(dtype), X2.toarray(),15) assert_array_equal(y, y2) 开发者ID:yzhy,项目名称:scikit-learn,代码行数:32,代码来源:test_svmlight_format.py 示例4: test_big_input ...