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# Copyright (c) Open-MMLab.
import sys
from collections.abc import Iterable
from runpy import run_path
from shlex import split
from typing import Any, Dict, List
from unittest.mock import patch
def check_python_script(cmd):
"""Run the python cmd script with `__main__`. The difference between
`os.system` is that, this function exectues code in the current process, so
that it can be tracked by coverage tools. Currently it supports two forms:
- ./tests/data/scripts/hello.py zz
- python tests/data/scripts/hello.py zz
"""
args = split(cmd)
if args[0] == 'python':
args = args[1:]
with patch.object(sys, 'argv', args):
run_path(args[0], run_name='__main__')
def _any(judge_result):
"""Since built-in ``any`` works only when the element of iterable is not
iterable, implement the function."""
if not isinstance(judge_result, Iterable):
return judge_result
try:
for element in judge_result:
if _any(element):
return True
except TypeError:
# Maybe encounter the case: torch.tensor(True) | torch.tensor(False)
if judge_result:
return True
return False
def assert_dict_contains_subset(dict_obj: Dict[Any, Any],
expected_subset: Dict[Any, Any]) -> bool:
"""Check if the dict_obj contains the expected_subset.
Args:
dict_obj (Dict[Any, Any]): Dict object to be checked.
expected_subset (Dict[Any, Any]): Subset expected to be contained in
dict_obj.
Returns:
bool: Whether the dict_obj contains the expected_subset.
"""
for key, value in expected_subset.items():
if key not in dict_obj.keys() or _any(dict_obj[key] != value):
return False
return True
def assert_attrs_equal(obj: Any, expected_attrs: Dict[str, Any]) -> bool:
"""Check if attribute of class object is correct.
Args:
obj (object): Class object to be checked.
expected_attrs (Dict[str, Any]): Dict of the expected attrs.
Returns:
bool: Whether the attribute of class object is correct.
"""
for attr, value in expected_attrs.items():
if not hasattr(obj, attr) or _any(getattr(obj, attr) != value):
return False
return True
def assert_dict_has_keys(obj: Dict[str, Any],
expected_keys: List[str]) -> bool:
"""Check if the obj has all the expected_keys.
Args:
obj (Dict[str, Any]): Object to be checked.
expected_keys (List[str]): Keys expected to contained in the keys of
the obj.
Returns:
bool: Whether the obj has the expected keys.
"""
return set(expected_keys).issubset(set(obj.keys()))
def assert_keys_equal(result_keys: List[str], target_keys: List[str]) -> bool:
"""Check if target_keys is equal to result_keys.
Args:
result_keys (List[str]): Result keys to be checked.
target_keys (List[str]): Target keys to be checked.
Returns:
bool: Whether target_keys is equal to result_keys.
"""
return set(result_keys) == set(target_keys)
def assert_is_norm_layer(module) -> bool:
"""Check if the module is a norm layer.
Args:
module (nn.Module): The module to be checked.
Returns:
bool: Whether the module is a norm layer.
"""
from .parrots_wrapper import _BatchNorm, _InstanceNorm
from torch.nn import GroupNorm, LayerNorm
norm_layer_candidates = (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)
return isinstance(module, norm_layer_candidates)
def assert_params_all_zeros(module) -> bool:
"""Check if the parameters of the module is all zeros.
Args:
module (nn.Module): The module to be checked.
Returns:
bool: Whether the parameters of the module is all zeros.
"""
weight_data = module.weight.data
is_weight_zero = weight_data.allclose(
weight_data.new_zeros(weight_data.size()))
if hasattr(module, 'bias') and module.bias is not None:
bias_data = module.bias.data
is_bias_zero = bias_data.allclose(
bias_data.new_zeros(bias_data.size()))
else:
is_bias_zero = True
return is_weight_zero and is_bias_zero
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