Spaces:
Runtime error
Runtime error
File size: 4,289 Bytes
f7ac35e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
# 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
|