torch.fill_ can not apply after general function #1924
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| # update the mapping | ||
| node_output = node.outputs[0] | ||
| val = tensor_to_node_sequence_mapping[node_input] | ||
| tensor_to_node_sequence_mapping[node_output] = val |
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Could you please share your reasoning on why this would be generally applicable for nodes in addition to to?
Also, what is the reason to delete line del tensor_to_node_sequence_mapping[node_input]? With that line, it makes tensor_to_node_sequence_mapping[node_output] to be ordered as latest
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what is the reason to delete line del tensor_to_node_sequence_mapping[node_input]
with del tensor_to_node_sequence_mapping[node_input], if cast source is referenced more than once, ValueError will be raised.
class Net(torch.nn.Module):
def forward(self, x):
y = torch.empty(x.shape)
z = y.to(torch.int32)
w = y.to(torch.int32)
return z, w
stacktrace
Traceback (most recent call last):
File "/Users/ryosukefukatani/work/coremltools/onth11.py", line 21, in <module>
ct.TensorType("x", shape=(ct.RangeDim(), ct.RangeDim())),
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/_converters_entry.py", line 542, in convert
main_pipeline=pass_pipeline,
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 188, in mil_convert
return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 217, in _mil_convert
**kwargs
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
prog = frontend_converter(model, **kwargs)
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/converter.py", line 108, in __call__
return load(*args, **kwargs)
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/load.py", line 61, in load
specification_version,
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/converter.py", line 335, in __init__
p(self.graph)
File "/Users/ryosukefukatani/work/coremltools/coremltools/converters/mil/frontend/torch/torchir_passes.py", line 141, in generate_tensor_assignment_ops
raise ValueError("No matching select or slice.")
ValueError: No matching select or slice.
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Could you please share your reasoning on why this would be generally applicable for nodes in addition to to?
We need general solution of #1917
ex.
class Net(torch.nn.Module):
def forward(self, x):
y = torch.empty(x.shape) + 1
y.fill_(0.0)
return y
class Net(torch.nn.Module):
def forward(self, x):
y = torch.empty(x.shape) - 1
y.fill_(0.0)
return y
class Net(torch.nn.Module):
def forward(self, x):
y = torch.empty(x.shape) .flatten()
y.fill_(0.0)
return y
and all other operations.
| ) | ||
| y_cm = ct_model.predict({'x': x})['y'] | ||
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| assert((y_cm == np.zeros(shape)).all()) |
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Maybe remove the outmost parentheses to be assert all(y_cm == np.zeros(shape))?
Resolves #1920