Using tensorwatch (0.9.1) to draw network:
tw.draw_model(net, (1, 3, 512, 512))

PyTorch(1.5.1), torchvision(0.6.1)

RuntimeError                              Traceback (most recent call last)
 in 
----> 1 tw.draw_model(net, (1, 3, 512, 512))
g:\medicine\winvenv\lib\site-packages\tensorwatch_init_.py in draw_model(model, input_shape, orientation, png_filename)
33 def draw_model(model, input_shape=None, orientation=‘TB’, png_filename=None): #orientation = ‘LR’ for landscpe
34     from .model_graph.hiddenlayer import pytorch_draw_model
—> 35     g = pytorch_draw_model.draw_graph(model, input_shape)
36     return g
37
g:\medicine\winvenv\lib\site-packages\tensorwatch\model_graph\hiddenlayer\pytorch_draw_model.py in draw_graph(model, args)
33         args = torch.ones(args)
34
—> 35     dot = draw_img_classifier(model, args)
36     return DotWrapper(dot)
37
g:\medicine\winvenv\lib\site-packages\tensorwatch\model_graph\hiddenlayer\pytorch_draw_model.py in draw_img_classifier(model, dataset, display_param_nodes, rankdir, styles, input_shape)
61     try:
62         non_para_model = distiller.make_non_parallel_copy(model)
—> 63         g = SummaryGraph(non_para_model, dummy_input)
64
65         return sgraph2dot(g, display_param_nodes, rankdir, styles)
g:\medicine\winvenv\lib\site-packages\tensorwatch\model_graph\hiddenlayer\summary_graph.py in init(self, model, dummy_input, apply_scope_name_workarounds)
94                 nodes = graph.nodes()
95             elif hasattr(jit, ‘_get_trace_graph’):
—> 96                 trace, _ = jit._get_trace_graph(model_clone, dummy_input, _force_outplace=True)
97                 graph = trace
98                 nodes = graph.nodes()
g:\medicine\winvenv\lib\site-packages\torch\jit_init_.py in _get_trace_graph(f, args, kwargs, _force_outplace, return_inputs, _return_inputs_states)
276     if not isinstance(args, tuple):
277         args = (args,)
–> 278     outs = ONNXTracedModule(f, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
279     return outs
280
g:\medicine\winvenv\lib\site-packages\torch\nn\modules\module.py in call(self, *input, **kwargs)
548             result = self._slow_forward(*input, **kwargs)
549         else:
–> 550             result = self.forward(*input, **kwargs)
551         for hook in self._forward_hooks.values():
552             hook_result = hook(self, input, result)
g:\medicine\winvenv\lib\site-packages\torch\jit_init_.py in forward(self, *args)
359             in_vars + module_state,
360             _create_interpreter_name_lookup_fn(),
–> 361             self._force_outplace,
362         )
363
RuntimeError: 0 INTERNAL ASSERT FAILED at …\torch\csrc\jit\ir\alias_analysis.cpp:318, please report a bug to PyTorch. We don’t have an op for aten::uniform but it isn’t a special case.  Argument types: Tensor, float, float, None,