forked from mindspore-Ecosystem/mindspore
cleancode
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423791f298
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@ -148,7 +148,7 @@ def __get_compile_cache_dep_files(file_path, compile_cache_dep_files, pkg):
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whole_module = n.name
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else:
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whole_module = module_name
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if not n.name is None:
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if n.name is not None:
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whole_module += "." + n.name
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try:
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module_spec = importlib.util.find_spec(whole_module, pkg)
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@ -163,7 +163,7 @@ def __get_compile_cache_dep_files(file_path, compile_cache_dep_files, pkg):
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else:
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continue
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# Exclude the installed modules.
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if not _in_sys_path(dep_file_path) and not dep_file_path in compile_cache_dep_files:
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if not _in_sys_path(dep_file_path) and dep_file_path not in compile_cache_dep_files:
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logger.debug(f"dependent file path: {dep_file_path}")
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compile_cache_dep_files.append(dep_file_path)
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__get_compile_cache_dep_files(dep_file_path, compile_cache_dep_files, module.__package__)
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@ -184,8 +184,8 @@ def _get_compile_cache_dep_files():
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def _restore_mutable_attr(args_list, compile_args):
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"""Restore the mutable attr for every arg."""
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new_compile_args = ()
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for idx in range(len(args_list)):
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if hasattr(args_list[idx], "__ms_mutable__") and getattr(args_list[idx], "__ms_mutable__"):
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for idx, arg in enumerate(args_list):
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if hasattr(arg, "__ms_mutable__") and getattr(arg, "__ms_mutable__"):
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new_compile_args += (mutable(compile_args[idx]),)
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else:
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new_compile_args += (compile_args[idx],)
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@ -190,7 +190,7 @@ def pytype_to_dtype(obj):
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if not isinstance(obj, type):
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raise TypeError("For 'pytype_to_dtype', the argument 'obj' must be a python type object,"
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"such as int, float, str, etc. But got type {}.".format(type(obj)))
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elif obj in _simple_types:
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if obj in _simple_types:
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return _simple_types[obj]
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raise NotImplementedError(f"The python type {obj} cannot be converted to MindSpore type.")
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@ -3346,7 +3346,7 @@ class Tensor(Tensor_):
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res = tensor_operator_registry.get('reduce_sum')(prod.astype(mstype.float32), -1)
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begin = ()
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for i in range(ndim - 2):
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for _ in range(ndim - 2):
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begin += (0,)
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last_dim_begin = max(0, -offset)
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begin += (last_dim_begin,)
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@ -1449,9 +1449,9 @@ def _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even):
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tensor_slices_new_inner = []
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for j in range(ele_count):
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new_tensor = tensor_slices_new[j * tensor_strategy[dim_len - 1 - i]]
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for l in range(j * tensor_strategy[dim_len - 1 - i] + 1,
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for k in range(j * tensor_strategy[dim_len - 1 - i] + 1,
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(j + 1) * tensor_strategy[dim_len - 1 - i]):
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new_tensor = np.concatenate((new_tensor, tensor_slices_new[l]), axis=dim_len - 1 - i)
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new_tensor = np.concatenate((new_tensor, tensor_slices_new[k]), axis=dim_len - 1 - i)
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tensor_slices_new_inner.insert(len(tensor_slices_new_inner), np.array(new_tensor))
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tensor_slices_new = tensor_slices_new_inner
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merged_tensor = Tensor(tensor_slices_new[0])
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