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9,576 | import warnings
import cv2
import numpy as np
from annotator.uniformer.mmcv.arraymisc import dequantize, quantize
from annotator.uniformer.mmcv.image import imread, imwrite
from annotator.uniformer.mmcv.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `sparse_flow_from_bytes` function. Write a Python function `def sparse_flow_from_bytes(content)` to solve the following problem:
Read the optical flow in KITTI datasets from bytes. This function is modified from RAFT load the `KITTI datasets <https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_. Args: content (bytes): Optical flow bytes got from files or other streams. Returns: Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2) and flow valid mask with the shape (H, W).
Here is the function:
def sparse_flow_from_bytes(content):
"""Read the optical flow in KITTI datasets from bytes.
This function is modified from RAFT load the `KITTI datasets
<https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_.
Args:
content (bytes): Optical flow bytes got from files or other streams.
Returns:
Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2)
and flow valid mask with the shape (H, W).
""" # nopa
content = np.frombuffer(content, np.uint8)
flow = cv2.imdecode(content, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
flow = flow[:, :, ::-1].astype(np.float32)
# flow shape (H, W, 2) valid shape (H, W)
flow, valid = flow[:, :, :2], flow[:, :, 2]
flow = (flow - 2**15) / 64.0
return flow, valid | Read the optical flow in KITTI datasets from bytes. This function is modified from RAFT load the `KITTI datasets <https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_. Args: content (bytes): Optical flow bytes got from files or other streams. Returns: Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2) and flow valid mask with the shape (H, W). |
9,577 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
try:
# If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
# and used; otherwise, auto fp16 will adopt mmcv's implementation.
# Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
# manually, so the behavior may not be consistent with real amp.
from torch.cuda.amp import autocast
except ImportError:
pass
def cast_tensor_type(inputs, src_type, dst_type):
"""Recursively convert Tensor in inputs from src_type to dst_type.
Args:
inputs: Inputs that to be casted.
src_type (torch.dtype): Source type..
dst_type (torch.dtype): Destination type.
Returns:
The same type with inputs, but all contained Tensors have been cast.
"""
if isinstance(inputs, nn.Module):
return inputs
elif isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isinstance(inputs, abc.Mapping):
return type(inputs)({
k: cast_tensor_type(v, src_type, dst_type)
for k, v in inputs.items()
})
elif isinstance(inputs, abc.Iterable):
return type(inputs)(
cast_tensor_type(item, src_type, dst_type) for item in inputs)
else:
return inputs
The provided code snippet includes necessary dependencies for implementing the `auto_fp16` function. Write a Python function `def auto_fp16(apply_to=None, out_fp32=False)` to solve the following problem:
Decorator to enable fp16 training automatically. This decorator is useful when you write custom modules and want to support mixed precision training. If inputs arguments are fp32 tensors, they will be converted to fp16 automatically. Arguments other than fp32 tensors are ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. Args: apply_to (Iterable, optional): The argument names to be converted. `None` indicates all arguments. out_fp32 (bool): Whether to convert the output back to fp32. Example: >>> import torch.nn as nn >>> class MyModule1(nn.Module): >>> >>> # Convert x and y to fp16 >>> @auto_fp16() >>> def forward(self, x, y): >>> pass >>> import torch.nn as nn >>> class MyModule2(nn.Module): >>> >>> # convert pred to fp16 >>> @auto_fp16(apply_to=('pred', )) >>> def do_something(self, pred, others): >>> pass
Here is the function:
def auto_fp16(apply_to=None, out_fp32=False):
"""Decorator to enable fp16 training automatically.
This decorator is useful when you write custom modules and want to support
mixed precision training. If inputs arguments are fp32 tensors, they will
be converted to fp16 automatically. Arguments other than fp32 tensors are
ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
backend, otherwise, original mmcv implementation will be adopted.
Args:
apply_to (Iterable, optional): The argument names to be converted.
`None` indicates all arguments.
out_fp32 (bool): Whether to convert the output back to fp32.
Example:
>>> import torch.nn as nn
>>> class MyModule1(nn.Module):
>>>
>>> # Convert x and y to fp16
>>> @auto_fp16()
>>> def forward(self, x, y):
>>> pass
>>> import torch.nn as nn
>>> class MyModule2(nn.Module):
>>>
>>> # convert pred to fp16
>>> @auto_fp16(apply_to=('pred', ))
>>> def do_something(self, pred, others):
>>> pass
"""
def auto_fp16_wrapper(old_func):
@functools.wraps(old_func)
def new_func(*args, **kwargs):
# check if the module has set the attribute `fp16_enabled`, if not,
# just fallback to the original method.
if not isinstance(args[0], torch.nn.Module):
raise TypeError('@auto_fp16 can only be used to decorate the '
'method of nn.Module')
if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
return old_func(*args, **kwargs)
# get the arg spec of the decorated method
args_info = getfullargspec(old_func)
# get the argument names to be casted
args_to_cast = args_info.args if apply_to is None else apply_to
# convert the args that need to be processed
new_args = []
# NOTE: default args are not taken into consideration
if args:
arg_names = args_info.args[:len(args)]
for i, arg_name in enumerate(arg_names):
if arg_name in args_to_cast:
new_args.append(
cast_tensor_type(args[i], torch.float, torch.half))
else:
new_args.append(args[i])
# convert the kwargs that need to be processed
new_kwargs = {}
if kwargs:
for arg_name, arg_value in kwargs.items():
if arg_name in args_to_cast:
new_kwargs[arg_name] = cast_tensor_type(
arg_value, torch.float, torch.half)
else:
new_kwargs[arg_name] = arg_value
# apply converted arguments to the decorated method
if (TORCH_VERSION != 'parrots' and
digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
with autocast(enabled=True):
output = old_func(*new_args, **new_kwargs)
else:
output = old_func(*new_args, **new_kwargs)
# cast the results back to fp32 if necessary
if out_fp32:
output = cast_tensor_type(output, torch.half, torch.float)
return output
return new_func
return auto_fp16_wrapper | Decorator to enable fp16 training automatically. This decorator is useful when you write custom modules and want to support mixed precision training. If inputs arguments are fp32 tensors, they will be converted to fp16 automatically. Arguments other than fp32 tensors are ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. Args: apply_to (Iterable, optional): The argument names to be converted. `None` indicates all arguments. out_fp32 (bool): Whether to convert the output back to fp32. Example: >>> import torch.nn as nn >>> class MyModule1(nn.Module): >>> >>> # Convert x and y to fp16 >>> @auto_fp16() >>> def forward(self, x, y): >>> pass >>> import torch.nn as nn >>> class MyModule2(nn.Module): >>> >>> # convert pred to fp16 >>> @auto_fp16(apply_to=('pred', )) >>> def do_something(self, pred, others): >>> pass |
9,578 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
try:
# If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
# and used; otherwise, auto fp16 will adopt mmcv's implementation.
# Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
# manually, so the behavior may not be consistent with real amp.
from torch.cuda.amp import autocast
except ImportError:
pass
def cast_tensor_type(inputs, src_type, dst_type):
"""Recursively convert Tensor in inputs from src_type to dst_type.
Args:
inputs: Inputs that to be casted.
src_type (torch.dtype): Source type..
dst_type (torch.dtype): Destination type.
Returns:
The same type with inputs, but all contained Tensors have been cast.
"""
if isinstance(inputs, nn.Module):
return inputs
elif isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isinstance(inputs, abc.Mapping):
return type(inputs)({
k: cast_tensor_type(v, src_type, dst_type)
for k, v in inputs.items()
})
elif isinstance(inputs, abc.Iterable):
return type(inputs)(
cast_tensor_type(item, src_type, dst_type) for item in inputs)
else:
return inputs
The provided code snippet includes necessary dependencies for implementing the `force_fp32` function. Write a Python function `def force_fp32(apply_to=None, out_fp16=False)` to solve the following problem:
Decorator to convert input arguments to fp32 in force. This decorator is useful when you write custom modules and want to support mixed precision training. If there are some inputs that must be processed in fp32 mode, then this decorator can handle it. If inputs arguments are fp16 tensors, they will be converted to fp32 automatically. Arguments other than fp16 tensors are ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. Args: apply_to (Iterable, optional): The argument names to be converted. `None` indicates all arguments. out_fp16 (bool): Whether to convert the output back to fp16. Example: >>> import torch.nn as nn >>> class MyModule1(nn.Module): >>> >>> # Convert x and y to fp32 >>> @force_fp32() >>> def loss(self, x, y): >>> pass >>> import torch.nn as nn >>> class MyModule2(nn.Module): >>> >>> # convert pred to fp32 >>> @force_fp32(apply_to=('pred', )) >>> def post_process(self, pred, others): >>> pass
Here is the function:
def force_fp32(apply_to=None, out_fp16=False):
"""Decorator to convert input arguments to fp32 in force.
This decorator is useful when you write custom modules and want to support
mixed precision training. If there are some inputs that must be processed
in fp32 mode, then this decorator can handle it. If inputs arguments are
fp16 tensors, they will be converted to fp32 automatically. Arguments other
than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
torch.cuda.amp is used as the backend, otherwise, original mmcv
implementation will be adopted.
Args:
apply_to (Iterable, optional): The argument names to be converted.
`None` indicates all arguments.
out_fp16 (bool): Whether to convert the output back to fp16.
Example:
>>> import torch.nn as nn
>>> class MyModule1(nn.Module):
>>>
>>> # Convert x and y to fp32
>>> @force_fp32()
>>> def loss(self, x, y):
>>> pass
>>> import torch.nn as nn
>>> class MyModule2(nn.Module):
>>>
>>> # convert pred to fp32
>>> @force_fp32(apply_to=('pred', ))
>>> def post_process(self, pred, others):
>>> pass
"""
def force_fp32_wrapper(old_func):
@functools.wraps(old_func)
def new_func(*args, **kwargs):
# check if the module has set the attribute `fp16_enabled`, if not,
# just fallback to the original method.
if not isinstance(args[0], torch.nn.Module):
raise TypeError('@force_fp32 can only be used to decorate the '
'method of nn.Module')
if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
return old_func(*args, **kwargs)
# get the arg spec of the decorated method
args_info = getfullargspec(old_func)
# get the argument names to be casted
args_to_cast = args_info.args if apply_to is None else apply_to
# convert the args that need to be processed
new_args = []
if args:
arg_names = args_info.args[:len(args)]
for i, arg_name in enumerate(arg_names):
if arg_name in args_to_cast:
new_args.append(
cast_tensor_type(args[i], torch.half, torch.float))
else:
new_args.append(args[i])
# convert the kwargs that need to be processed
new_kwargs = dict()
if kwargs:
for arg_name, arg_value in kwargs.items():
if arg_name in args_to_cast:
new_kwargs[arg_name] = cast_tensor_type(
arg_value, torch.half, torch.float)
else:
new_kwargs[arg_name] = arg_value
# apply converted arguments to the decorated method
if (TORCH_VERSION != 'parrots' and
digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
with autocast(enabled=False):
output = old_func(*new_args, **new_kwargs)
else:
output = old_func(*new_args, **new_kwargs)
# cast the results back to fp32 if necessary
if out_fp16:
output = cast_tensor_type(output, torch.float, torch.half)
return output
return new_func
return force_fp32_wrapper | Decorator to convert input arguments to fp32 in force. This decorator is useful when you write custom modules and want to support mixed precision training. If there are some inputs that must be processed in fp32 mode, then this decorator can handle it. If inputs arguments are fp16 tensors, they will be converted to fp32 automatically. Arguments other than fp16 tensors are ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. Args: apply_to (Iterable, optional): The argument names to be converted. `None` indicates all arguments. out_fp16 (bool): Whether to convert the output back to fp16. Example: >>> import torch.nn as nn >>> class MyModule1(nn.Module): >>> >>> # Convert x and y to fp32 >>> @force_fp32() >>> def loss(self, x, y): >>> pass >>> import torch.nn as nn >>> class MyModule2(nn.Module): >>> >>> # convert pred to fp32 >>> @force_fp32(apply_to=('pred', )) >>> def post_process(self, pred, others): >>> pass |
9,579 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
warnings.warning(
'"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
_allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb) | null |
9,580 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
def patch_norm_fp32(module):
"""Recursively convert normalization layers from FP16 to FP32.
Args:
module (nn.Module): The modules to be converted in FP16.
Returns:
nn.Module: The converted module, the normalization layers have been
converted to FP32.
"""
if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
module.float()
if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
module.forward = patch_forward_method(module.forward, torch.half,
torch.float)
for child in module.children():
patch_norm_fp32(child)
return module
The provided code snippet includes necessary dependencies for implementing the `wrap_fp16_model` function. Write a Python function `def wrap_fp16_model(model)` to solve the following problem:
Wrap the FP32 model to FP16. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. For PyTorch >= 1.6, this function will 1. Set fp16 flag inside the model to True. Otherwise: 1. Convert FP32 model to FP16. 2. Remain some necessary layers to be FP32, e.g., normalization layers. 3. Set `fp16_enabled` flag inside the model to True. Args: model (nn.Module): Model in FP32.
Here is the function:
def wrap_fp16_model(model):
"""Wrap the FP32 model to FP16.
If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
backend, otherwise, original mmcv implementation will be adopted.
For PyTorch >= 1.6, this function will
1. Set fp16 flag inside the model to True.
Otherwise:
1. Convert FP32 model to FP16.
2. Remain some necessary layers to be FP32, e.g., normalization layers.
3. Set `fp16_enabled` flag inside the model to True.
Args:
model (nn.Module): Model in FP32.
"""
if (TORCH_VERSION == 'parrots'
or digit_version(TORCH_VERSION) < digit_version('1.6.0')):
# convert model to fp16
model.half()
# patch the normalization layers to make it work in fp32 mode
patch_norm_fp32(model)
# set `fp16_enabled` flag
for m in model.modules():
if hasattr(m, 'fp16_enabled'):
m.fp16_enabled = True | Wrap the FP32 model to FP16. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. For PyTorch >= 1.6, this function will 1. Set fp16 flag inside the model to True. Otherwise: 1. Convert FP32 model to FP16. 2. Remain some necessary layers to be FP32, e.g., normalization layers. 3. Set `fp16_enabled` flag inside the model to True. Args: model (nn.Module): Model in FP32. |
9,581 | from enum import Enum
class Priority(Enum):
"""Hook priority levels.
+--------------+------------+
| Level | Value |
+==============+============+
| HIGHEST | 0 |
+--------------+------------+
| VERY_HIGH | 10 |
+--------------+------------+
| HIGH | 30 |
+--------------+------------+
| ABOVE_NORMAL | 40 |
+--------------+------------+
| NORMAL | 50 |
+--------------+------------+
| BELOW_NORMAL | 60 |
+--------------+------------+
| LOW | 70 |
+--------------+------------+
| VERY_LOW | 90 |
+--------------+------------+
| LOWEST | 100 |
+--------------+------------+
"""
HIGHEST = 0
VERY_HIGH = 10
HIGH = 30
ABOVE_NORMAL = 40
NORMAL = 50
BELOW_NORMAL = 60
LOW = 70
VERY_LOW = 90
LOWEST = 100
The provided code snippet includes necessary dependencies for implementing the `get_priority` function. Write a Python function `def get_priority(priority)` to solve the following problem:
Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value.
Here is the function:
def get_priority(priority):
"""Get priority value.
Args:
priority (int or str or :obj:`Priority`): Priority.
Returns:
int: The priority value.
"""
if isinstance(priority, int):
if priority < 0 or priority > 100:
raise ValueError('priority must be between 0 and 100')
return priority
elif isinstance(priority, Priority):
return priority.value
elif isinstance(priority, str):
return Priority[priority.upper()].value
else:
raise TypeError('priority must be an integer or Priority enum value') | Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value. |
9,582 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.uniformer.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `get_host_info` function. Write a Python function `def get_host_info()` to solve the following problem:
Get hostname and username. Return empty string if exception raised, e.g. ``getpass.getuser()`` will lead to error in docker container
Here is the function:
def get_host_info():
"""Get hostname and username.
Return empty string if exception raised, e.g. ``getpass.getuser()`` will
lead to error in docker container
"""
host = ''
try:
host = f'{getuser()}@{gethostname()}'
except Exception as e:
warnings.warn(f'Host or user not found: {str(e)}')
finally:
return host | Get hostname and username. Return empty string if exception raised, e.g. ``getpass.getuser()`` will lead to error in docker container |
9,583 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.uniformer.mmcv as mmcv
def get_time_str():
return time.strftime('%Y%m%d_%H%M%S', time.localtime()) | null |
9,584 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.uniformer.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `obj_from_dict` function. Write a Python function `def obj_from_dict(info, parent=None, default_args=None)` to solve the following problem:
Initialize an object from dict. The dict must contain the key "type", which indicates the object type, it can be either a string or type, such as "list" or ``list``. Remaining fields are treated as the arguments for constructing the object. Args: info (dict): Object types and arguments. parent (:class:`module`): Module which may containing expected object classes. default_args (dict, optional): Default arguments for initializing the object. Returns: any type: Object built from the dict.
Here is the function:
def obj_from_dict(info, parent=None, default_args=None):
"""Initialize an object from dict.
The dict must contain the key "type", which indicates the object type, it
can be either a string or type, such as "list" or ``list``. Remaining
fields are treated as the arguments for constructing the object.
Args:
info (dict): Object types and arguments.
parent (:class:`module`): Module which may containing expected object
classes.
default_args (dict, optional): Default arguments for initializing the
object.
Returns:
any type: Object built from the dict.
"""
assert isinstance(info, dict) and 'type' in info
assert isinstance(default_args, dict) or default_args is None
args = info.copy()
obj_type = args.pop('type')
if mmcv.is_str(obj_type):
if parent is not None:
obj_type = getattr(parent, obj_type)
else:
obj_type = sys.modules[obj_type]
elif not isinstance(obj_type, type):
raise TypeError('type must be a str or valid type, but '
f'got {type(obj_type)}')
if default_args is not None:
for name, value in default_args.items():
args.setdefault(name, value)
return obj_type(**args) | Initialize an object from dict. The dict must contain the key "type", which indicates the object type, it can be either a string or type, such as "list" or ``list``. Remaining fields are treated as the arguments for constructing the object. Args: info (dict): Object types and arguments. parent (:class:`module`): Module which may containing expected object classes. default_args (dict, optional): Default arguments for initializing the object. Returns: any type: Object built from the dict. |
9,585 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.uniformer.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `set_random_seed` function. Write a Python function `def set_random_seed(seed, deterministic=False, use_rank_shift=False)` to solve the following problem:
Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Default: False. rank_shift (bool): Whether to add rank number to the random seed to have different random seed in different threads. Default: False.
Here is the function:
def set_random_seed(seed, deterministic=False, use_rank_shift=False):
"""Set random seed.
Args:
seed (int): Seed to be used.
deterministic (bool): Whether to set the deterministic option for
CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
to True and `torch.backends.cudnn.benchmark` to False.
Default: False.
rank_shift (bool): Whether to add rank number to the random seed to
have different random seed in different threads. Default: False.
"""
if use_rank_shift:
rank, _ = mmcv.runner.get_dist_info()
seed += rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
if deterministic:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False | Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Default: False. rank_shift (bool): Whether to add rank number to the random seed to have different random seed in different threads. Default: False. |
9,586 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
The provided code snippet includes necessary dependencies for implementing the `load_from_local` function. Write a Python function `def load_from_local(filename, map_location)` to solve the following problem:
load checkpoint by local file path. Args: filename (str): local checkpoint file path map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_local(filename, map_location):
"""load checkpoint by local file path.
Args:
filename (str): local checkpoint file path
map_location (str, optional): Same as :func:`torch.load`.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint | load checkpoint by local file path. Args: filename (str): local checkpoint file path map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
9,587 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
The provided code snippet includes necessary dependencies for implementing the `load_from_pavi` function. Write a Python function `def load_from_pavi(filename, map_location=None)` to solve the following problem:
load checkpoint through the file path prefixed with pavi. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with pavi prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_pavi(filename, map_location=None):
"""load checkpoint through the file path prefixed with pavi. In distributed
setting, this function download ckpt at all ranks to different temporary
directories.
Args:
filename (str): checkpoint file path with pavi prefix
map_location (str, optional): Same as :func:`torch.load`.
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
assert filename.startswith('pavi://'), \
f'Expected filename startswith `pavi://`, but get {filename}'
model_path = filename[7:]
try:
from pavi import modelcloud
except ImportError:
raise ImportError(
'Please install pavi to load checkpoint from modelcloud.')
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
return checkpoint | load checkpoint through the file path prefixed with pavi. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with pavi prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict: The loaded checkpoint. |
9,588 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
The provided code snippet includes necessary dependencies for implementing the `load_from_ceph` function. Write a Python function `def load_from_ceph(filename, map_location=None, backend='petrel')` to solve the following problem:
load checkpoint through the file path prefixed with s3. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with s3 prefix map_location (str, optional): Same as :func:`torch.load`. backend (str, optional): The storage backend type. Options are 'ceph', 'petrel'. Default: 'petrel'. .. warning:: :class:`mmcv.fileio.file_client.CephBackend` will be deprecated, please use :class:`mmcv.fileio.file_client.PetrelBackend` instead. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_ceph(filename, map_location=None, backend='petrel'):
"""load checkpoint through the file path prefixed with s3. In distributed
setting, this function download ckpt at all ranks to different temporary
directories.
Args:
filename (str): checkpoint file path with s3 prefix
map_location (str, optional): Same as :func:`torch.load`.
backend (str, optional): The storage backend type. Options are 'ceph',
'petrel'. Default: 'petrel'.
.. warning::
:class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
allowed_backends = ['ceph', 'petrel']
if backend not in allowed_backends:
raise ValueError(f'Load from Backend {backend} is not supported.')
if backend == 'ceph':
warnings.warn(
'CephBackend will be deprecated, please use PetrelBackend instead')
# CephClient and PetrelBackend have the same prefix 's3://' and the latter
# will be chosen as default. If PetrelBackend can not be instantiated
# successfully, the CephClient will be chosen.
try:
file_client = FileClient(backend=backend)
except ImportError:
allowed_backends.remove(backend)
file_client = FileClient(backend=allowed_backends[0])
with io.BytesIO(file_client.get(filename)) as buffer:
checkpoint = torch.load(buffer, map_location=map_location)
return checkpoint | load checkpoint through the file path prefixed with s3. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with s3 prefix map_location (str, optional): Same as :func:`torch.load`. backend (str, optional): The storage backend type. Options are 'ceph', 'petrel'. Default: 'petrel'. .. warning:: :class:`mmcv.fileio.file_client.CephBackend` will be deprecated, please use :class:`mmcv.fileio.file_client.PetrelBackend` instead. Returns: dict or OrderedDict: The loaded checkpoint. |
9,589 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
def get_torchvision_models():
model_urls = dict()
for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
if ispkg:
continue
_zoo = import_module(f'torchvision.models.{name}')
if hasattr(_zoo, 'model_urls'):
_urls = getattr(_zoo, 'model_urls')
model_urls.update(_urls)
return model_urls
def load_from_http(filename, map_location=None, model_dir=None):
"""load checkpoint through HTTP or HTTPS scheme path. In distributed
setting, this function only download checkpoint at local rank 0.
Args:
filename (str): checkpoint file path with modelzoo or
torchvision prefix
map_location (str, optional): Same as :func:`torch.load`.
model_dir (string, optional): directory in which to save the object,
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
rank, world_size = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if rank == 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
if world_size > 1:
torch.distributed.barrier()
if rank > 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
return checkpoint
The provided code snippet includes necessary dependencies for implementing the `load_from_torchvision` function. Write a Python function `def load_from_torchvision(filename, map_location=None)` to solve the following problem:
load checkpoint through the file path prefixed with modelzoo or torchvision. Args: filename (str): checkpoint file path with modelzoo or torchvision prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_torchvision(filename, map_location=None):
"""load checkpoint through the file path prefixed with modelzoo or
torchvision.
Args:
filename (str): checkpoint file path with modelzoo or
torchvision prefix
map_location (str, optional): Same as :func:`torch.load`.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
model_urls = get_torchvision_models()
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
'use "torchvision://" instead')
model_name = filename[11:]
else:
model_name = filename[14:]
return load_from_http(model_urls[model_name], map_location=map_location) | load checkpoint through the file path prefixed with modelzoo or torchvision. Args: filename (str): checkpoint file path with modelzoo or torchvision prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
9,590 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
def _get_mmcv_home():
mmcv_home = os.path.expanduser(
os.getenv(
ENV_MMCV_HOME,
os.path.join(
os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
mkdir_or_exist(mmcv_home)
return mmcv_home
def get_external_models():
mmcv_home = _get_mmcv_home()
default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
default_urls = load_file(default_json_path)
assert isinstance(default_urls, dict)
external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
if osp.exists(external_json_path):
external_urls = load_file(external_json_path)
assert isinstance(external_urls, dict)
default_urls.update(external_urls)
return default_urls
def get_deprecated_model_names():
deprecate_json_path = osp.join(mmcv.__path__[0],
'model_zoo/deprecated.json')
deprecate_urls = load_file(deprecate_json_path)
assert isinstance(deprecate_urls, dict)
return deprecate_urls
def load_from_http(filename, map_location=None, model_dir=None):
"""load checkpoint through HTTP or HTTPS scheme path. In distributed
setting, this function only download checkpoint at local rank 0.
Args:
filename (str): checkpoint file path with modelzoo or
torchvision prefix
map_location (str, optional): Same as :func:`torch.load`.
model_dir (string, optional): directory in which to save the object,
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
rank, world_size = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if rank == 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
if world_size > 1:
torch.distributed.barrier()
if rank > 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
return checkpoint
The provided code snippet includes necessary dependencies for implementing the `load_from_openmmlab` function. Write a Python function `def load_from_openmmlab(filename, map_location=None)` to solve the following problem:
load checkpoint through the file path prefixed with open-mmlab or openmmlab. Args: filename (str): checkpoint file path with open-mmlab or openmmlab prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_openmmlab(filename, map_location=None):
"""load checkpoint through the file path prefixed with open-mmlab or
openmmlab.
Args:
filename (str): checkpoint file path with open-mmlab or
openmmlab prefix
map_location (str, optional): Same as :func:`torch.load`.
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
model_urls = get_external_models()
prefix_str = 'open-mmlab://'
if filename.startswith(prefix_str):
model_name = filename[13:]
else:
model_name = filename[12:]
prefix_str = 'openmmlab://'
deprecated_urls = get_deprecated_model_names()
if model_name in deprecated_urls:
warnings.warn(f'{prefix_str}{model_name} is deprecated in favor '
f'of {prefix_str}{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
# check if is url
if model_url.startswith(('http://', 'https://')):
checkpoint = load_from_http(model_url, map_location=map_location)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint | load checkpoint through the file path prefixed with open-mmlab or openmmlab. Args: filename (str): checkpoint file path with open-mmlab or openmmlab prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict: The loaded checkpoint. |
9,591 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
def get_mmcls_models():
mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
mmcls_urls = load_file(mmcls_json_path)
return mmcls_urls
def _process_mmcls_checkpoint(checkpoint):
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if k.startswith('backbone.'):
new_state_dict[k[9:]] = v
new_checkpoint = dict(state_dict=new_state_dict)
return new_checkpoint
def load_from_http(filename, map_location=None, model_dir=None):
"""load checkpoint through HTTP or HTTPS scheme path. In distributed
setting, this function only download checkpoint at local rank 0.
Args:
filename (str): checkpoint file path with modelzoo or
torchvision prefix
map_location (str, optional): Same as :func:`torch.load`.
model_dir (string, optional): directory in which to save the object,
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
rank, world_size = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if rank == 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
if world_size > 1:
torch.distributed.barrier()
if rank > 0:
checkpoint = model_zoo.load_url(
filename, model_dir=model_dir, map_location=map_location)
return checkpoint
The provided code snippet includes necessary dependencies for implementing the `load_from_mmcls` function. Write a Python function `def load_from_mmcls(filename, map_location=None)` to solve the following problem:
load checkpoint through the file path prefixed with mmcls. Args: filename (str): checkpoint file path with mmcls prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_from_mmcls(filename, map_location=None):
"""load checkpoint through the file path prefixed with mmcls.
Args:
filename (str): checkpoint file path with mmcls prefix
map_location (str, optional): Same as :func:`torch.load`.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
model_urls = get_mmcls_models()
model_name = filename[8:]
checkpoint = load_from_http(
model_urls[model_name], map_location=map_location)
checkpoint = _process_mmcls_checkpoint(checkpoint)
return checkpoint | load checkpoint through the file path prefixed with mmcls. Args: filename (str): checkpoint file path with mmcls prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
9,592 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
def _load_checkpoint(filename, map_location=None, logger=None):
"""Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str, optional): Same as :func:`torch.load`.
Default: None.
logger (:mod:`logging.Logger`, optional): The logger for error message.
Default: None
Returns:
dict or OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.
"""
return CheckpointLoader.load_checkpoint(filename, map_location, logger)
The provided code snippet includes necessary dependencies for implementing the `_load_checkpoint_with_prefix` function. Write a Python function `def _load_checkpoint_with_prefix(prefix, filename, map_location=None)` to solve the following problem:
Load partial pretrained model with specific prefix. Args: prefix (str): The prefix of sub-module. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str | None): Same as :func:`torch.load`. Default: None. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def _load_checkpoint_with_prefix(prefix, filename, map_location=None):
"""Load partial pretrained model with specific prefix.
Args:
prefix (str): The prefix of sub-module.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`. Default: None.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = _load_checkpoint(filename, map_location=map_location)
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
if not prefix.endswith('.'):
prefix += '.'
prefix_len = len(prefix)
state_dict = {
k[prefix_len:]: v
for k, v in state_dict.items() if k.startswith(prefix)
}
assert state_dict, f'{prefix} is not in the pretrained model'
return state_dict | Load partial pretrained model with specific prefix. Args: prefix (str): The prefix of sub-module. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str | None): Same as :func:`torch.load`. Default: None. Returns: dict or OrderedDict: The loaded checkpoint. |
9,593 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annotator.uniformer.mmcv as mmcv
from ..fileio import FileClient
from ..fileio import load as load_file
from ..parallel import is_module_wrapper
from ..utils import mkdir_or_exist
from .dist_utils import get_dist_info
def weights_to_cpu(state_dict):
"""Copy a model state_dict to cpu.
Args:
state_dict (OrderedDict): Model weights on GPU.
Returns:
OrderedDict: Model weights on GPU.
"""
state_dict_cpu = OrderedDict()
for key, val in state_dict.items():
state_dict_cpu[key] = val.cpu()
# Keep metadata in state_dict
state_dict_cpu._metadata = getattr(state_dict, '_metadata', OrderedDict())
return state_dict_cpu
def get_state_dict(module, destination=None, prefix='', keep_vars=False):
"""Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
This method is modified from :meth:`torch.nn.Module.state_dict` to
recursively check parallel module in case that the model has a complicated
structure, e.g., nn.Module(nn.Module(DDP)).
Args:
module (nn.Module): The module to generate state_dict.
destination (OrderedDict): Returned dict for the state of the
module.
prefix (str): Prefix of the key.
keep_vars (bool): Whether to keep the variable property of the
parameters. Default: False.
Returns:
dict: A dictionary containing a whole state of the module.
"""
# recursively check parallel module in case that the model has a
# complicated structure, e.g., nn.Module(nn.Module(DDP))
if is_module_wrapper(module):
module = module.module
# below is the same as torch.nn.Module.state_dict()
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:-1]] = local_metadata = dict(
version=module._version)
_save_to_state_dict(module, destination, prefix, keep_vars)
for name, child in module._modules.items():
if child is not None:
get_state_dict(
child, destination, prefix + name + '.', keep_vars=keep_vars)
for hook in module._state_dict_hooks.values():
hook_result = hook(module, destination, prefix, local_metadata)
if hook_result is not None:
destination = hook_result
return destination
The provided code snippet includes necessary dependencies for implementing the `save_checkpoint` function. Write a Python function `def save_checkpoint(model, filename, optimizer=None, meta=None, file_client_args=None)` to solve the following problem:
Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. filename (str): Checkpoint filename. optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. meta (dict, optional): Metadata to be saved in checkpoint. file_client_args (dict, optional): Arguments to instantiate a FileClient. See :class:`mmcv.fileio.FileClient` for details. Default: None. `New in version 1.3.16.`
Here is the function:
def save_checkpoint(model,
filename,
optimizer=None,
meta=None,
file_client_args=None):
"""Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
`New in version 1.3.16.`
"""
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
if is_module_wrapper(model):
model = model.module
if hasattr(model, 'CLASSES') and model.CLASSES is not None:
# save class name to the meta
meta.update(CLASSES=model.CLASSES)
checkpoint = {
'meta': meta,
'state_dict': weights_to_cpu(get_state_dict(model))
}
# save optimizer state dict in the checkpoint
if isinstance(optimizer, Optimizer):
checkpoint['optimizer'] = optimizer.state_dict()
elif isinstance(optimizer, dict):
checkpoint['optimizer'] = {}
for name, optim in optimizer.items():
checkpoint['optimizer'][name] = optim.state_dict()
if filename.startswith('pavi://'):
if file_client_args is not None:
raise ValueError(
'file_client_args should be "None" if filename starts with'
f'"pavi://", but got {file_client_args}')
try:
from pavi import modelcloud
from pavi import exception
except ImportError:
raise ImportError(
'Please install pavi to load checkpoint from modelcloud.')
model_path = filename[7:]
root = modelcloud.Folder()
model_dir, model_name = osp.split(model_path)
try:
model = modelcloud.get(model_dir)
except exception.NodeNotFoundError:
model = root.create_training_model(model_dir)
with TemporaryDirectory() as tmp_dir:
checkpoint_file = osp.join(tmp_dir, model_name)
with open(checkpoint_file, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
model.create_file(checkpoint_file, name=model_name)
else:
file_client = FileClient.infer_client(file_client_args, filename)
with io.BytesIO() as f:
torch.save(checkpoint, f)
file_client.put(f.getvalue(), filename) | Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. filename (str): Checkpoint filename. optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. meta (dict, optional): Metadata to be saved in checkpoint. file_client_args (dict, optional): Arguments to instantiate a FileClient. See :class:`mmcv.fileio.FileClient` for details. Default: None. `New in version 1.3.16.` |
9,594 | import copy
import inspect
import torch
from ...utils import Registry, build_from_cfg
OPTIMIZERS = Registry('optimizer')
from torch.nn.utils import clip_grad
try:
# If PyTorch version >= 1.6.0, torch.cuda.amp.GradScaler would be imported
# and used; otherwise, auto fp16 will adopt mmcv's implementation.
from torch.cuda.amp import GradScaler
except ImportError:
pass
def register_torch_optimizers():
torch_optimizers = []
for module_name in dir(torch.optim):
if module_name.startswith('__'):
continue
_optim = getattr(torch.optim, module_name)
if inspect.isclass(_optim) and issubclass(_optim,
torch.optim.Optimizer):
OPTIMIZERS.register_module()(_optim)
torch_optimizers.append(module_name)
return torch_optimizers | null |
9,595 | import copy
import inspect
import torch
from ...utils import Registry, build_from_cfg
def build_optimizer_constructor(cfg):
return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
constructor_type = optimizer_cfg.pop('constructor',
'DefaultOptimizerConstructor')
paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
optim_constructor = build_optimizer_constructor(
dict(
type=constructor_type,
optimizer_cfg=optimizer_cfg,
paramwise_cfg=paramwise_cfg))
optimizer = optim_constructor(model)
return optimizer | null |
9,596 | import numbers
from math import cos, pi
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `get_position_from_periods` function. Write a Python function `def get_position_from_periods(iteration, cumulative_periods)` to solve the following problem:
Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_periods = [100, 200, 300, 400], if iteration == 50, return 0; if iteration == 210, return 2; if iteration == 300, return 3. Args: iteration (int): Current iteration. cumulative_periods (list[int]): Cumulative period list. Returns: int: The position of the right-closest number in the period list.
Here is the function:
def get_position_from_periods(iteration, cumulative_periods):
"""Get the position from a period list.
It will return the index of the right-closest number in the period list.
For example, the cumulative_periods = [100, 200, 300, 400],
if iteration == 50, return 0;
if iteration == 210, return 2;
if iteration == 300, return 3.
Args:
iteration (int): Current iteration.
cumulative_periods (list[int]): Cumulative period list.
Returns:
int: The position of the right-closest number in the period list.
"""
for i, period in enumerate(cumulative_periods):
if iteration < period:
return i
raise ValueError(f'Current iteration {iteration} exceeds '
f'cumulative_periods {cumulative_periods}') | Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_periods = [100, 200, 300, 400], if iteration == 50, return 0; if iteration == 210, return 2; if iteration == 300, return 3. Args: iteration (int): Current iteration. cumulative_periods (list[int]): Cumulative period list. Returns: int: The position of the right-closest number in the period list. |
9,597 | import numbers
from math import cos, pi
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `annealing_cos` function. Write a Python function `def annealing_cos(start, end, factor, weight=1)` to solve the following problem:
Calculate annealing cos learning rate. Cosine anneal from `weight * start + (1 - weight) * end` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the cosine annealing. end (float): The ending learing rate of the cosine annealing. factor (float): The coefficient of `pi` when calculating the current percentage. Range from 0.0 to 1.0. weight (float, optional): The combination factor of `start` and `end` when calculating the actual starting learning rate. Default to 1.
Here is the function:
def annealing_cos(start, end, factor, weight=1):
"""Calculate annealing cos learning rate.
Cosine anneal from `weight * start + (1 - weight) * end` to `end` as
percentage goes from 0.0 to 1.0.
Args:
start (float): The starting learning rate of the cosine annealing.
end (float): The ending learing rate of the cosine annealing.
factor (float): The coefficient of `pi` when calculating the current
percentage. Range from 0.0 to 1.0.
weight (float, optional): The combination factor of `start` and `end`
when calculating the actual starting learning rate. Default to 1.
"""
cos_out = cos(pi * factor) + 1
return end + 0.5 * weight * (start - end) * cos_out | Calculate annealing cos learning rate. Cosine anneal from `weight * start + (1 - weight) * end` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the cosine annealing. end (float): The ending learing rate of the cosine annealing. factor (float): The coefficient of `pi` when calculating the current percentage. Range from 0.0 to 1.0. weight (float, optional): The combination factor of `start` and `end` when calculating the actual starting learning rate. Default to 1. |
9,598 | import numbers
from math import cos, pi
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `annealing_linear` function. Write a Python function `def annealing_linear(start, end, factor)` to solve the following problem:
Calculate annealing linear learning rate. Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the linear annealing. end (float): The ending learing rate of the linear annealing. factor (float): The coefficient of `pi` when calculating the current percentage. Range from 0.0 to 1.0.
Here is the function:
def annealing_linear(start, end, factor):
"""Calculate annealing linear learning rate.
Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0.
Args:
start (float): The starting learning rate of the linear annealing.
end (float): The ending learing rate of the linear annealing.
factor (float): The coefficient of `pi` when calculating the current
percentage. Range from 0.0 to 1.0.
"""
return start + (end - start) * factor | Calculate annealing linear learning rate. Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the linear annealing. end (float): The ending learing rate of the linear annealing. factor (float): The coefficient of `pi` when calculating the current percentage. Range from 0.0 to 1.0. |
9,599 | import numbers
from math import cos, pi
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
def format_param(name, optim, param):
if isinstance(param, numbers.Number):
return [param] * len(optim.param_groups)
elif isinstance(param, (list, tuple)): # multi param groups
if len(param) != len(optim.param_groups):
raise ValueError(f'expected {len(optim.param_groups)} '
f'values for {name}, got {len(param)}')
return param
else: # multi optimizers
if name not in param:
raise KeyError(f'{name} is not found in {param.keys()}')
return param[name] | null |
9,600 | import functools
import os
import subprocess
from collections import OrderedDict
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _init_dist_pytorch(backend, **kwargs):
def _init_dist_mpi(backend, **kwargs):
def _init_dist_slurm(backend, port=None):
def init_dist(launcher, backend='nccl', **kwargs):
if mp.get_start_method(allow_none=True) is None:
mp.set_start_method('spawn')
if launcher == 'pytorch':
_init_dist_pytorch(backend, **kwargs)
elif launcher == 'mpi':
_init_dist_mpi(backend, **kwargs)
elif launcher == 'slurm':
_init_dist_slurm(backend, **kwargs)
else:
raise ValueError(f'Invalid launcher type: {launcher}') | null |
9,601 | import functools
import os
import subprocess
from collections import OrderedDict
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def get_dist_info():
if dist.is_available() and dist.is_initialized():
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return rank, world_size
def master_only(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
rank, _ = get_dist_info()
if rank == 0:
return func(*args, **kwargs)
return wrapper | null |
9,602 | import functools
import os
import subprocess
from collections import OrderedDict
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def get_dist_info():
if dist.is_available() and dist.is_initialized():
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return rank, world_size
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
bucket_size_bytes = bucket_size_mb * 1024 * 1024
buckets = _take_tensors(tensors, bucket_size_bytes)
else:
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.type()
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(tensor)
buckets = buckets.values()
for bucket in buckets:
flat_tensors = _flatten_dense_tensors(bucket)
dist.all_reduce(flat_tensors)
flat_tensors.div_(world_size)
for tensor, synced in zip(
bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
tensor.copy_(synced)
The provided code snippet includes necessary dependencies for implementing the `allreduce_params` function. Write a Python function `def allreduce_params(params, coalesce=True, bucket_size_mb=-1)` to solve the following problem:
Allreduce parameters. Args: params (list[torch.Parameters]): List of parameters or buffers of a model. coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1.
Here is the function:
def allreduce_params(params, coalesce=True, bucket_size_mb=-1):
"""Allreduce parameters.
Args:
params (list[torch.Parameters]): List of parameters or buffers of a
model.
coalesce (bool, optional): Whether allreduce parameters as a whole.
Defaults to True.
bucket_size_mb (int, optional): Size of bucket, the unit is MB.
Defaults to -1.
"""
_, world_size = get_dist_info()
if world_size == 1:
return
params = [param.data for param in params]
if coalesce:
_allreduce_coalesced(params, world_size, bucket_size_mb)
else:
for tensor in params:
dist.all_reduce(tensor.div_(world_size)) | Allreduce parameters. Args: params (list[torch.Parameters]): List of parameters or buffers of a model. coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1. |
9,603 | import functools
import os
import subprocess
from collections import OrderedDict
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def get_dist_info():
if dist.is_available() and dist.is_initialized():
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return rank, world_size
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
bucket_size_bytes = bucket_size_mb * 1024 * 1024
buckets = _take_tensors(tensors, bucket_size_bytes)
else:
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.type()
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(tensor)
buckets = buckets.values()
for bucket in buckets:
flat_tensors = _flatten_dense_tensors(bucket)
dist.all_reduce(flat_tensors)
flat_tensors.div_(world_size)
for tensor, synced in zip(
bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
tensor.copy_(synced)
The provided code snippet includes necessary dependencies for implementing the `allreduce_grads` function. Write a Python function `def allreduce_grads(params, coalesce=True, bucket_size_mb=-1)` to solve the following problem:
Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1.
Here is the function:
def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
"""Allreduce gradients.
Args:
params (list[torch.Parameters]): List of parameters of a model
coalesce (bool, optional): Whether allreduce parameters as a whole.
Defaults to True.
bucket_size_mb (int, optional): Size of bucket, the unit is MB.
Defaults to -1.
"""
grads = [
param.grad.data for param in params
if param.requires_grad and param.grad is not None
]
_, world_size = get_dist_info()
if world_size == 1:
return
if coalesce:
_allreduce_coalesced(grads, world_size, bucket_size_mb)
else:
for tensor in grads:
dist.all_reduce(tensor.div_(world_size)) | Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allreduce parameters as a whole. Defaults to True. bucket_size_mb (int, optional): Size of bucket, the unit is MB. Defaults to -1. |
9,604 | import copy
from ..utils import Registry
def build_runner_constructor(cfg):
def build_runner(cfg, default_args=None):
runner_cfg = copy.deepcopy(cfg)
constructor_type = runner_cfg.pop('constructor',
'DefaultRunnerConstructor')
runner_constructor = build_runner_constructor(
dict(
type=constructor_type,
runner_cfg=runner_cfg,
default_args=default_args))
runner = runner_constructor()
return runner | null |
9,605 | import importlib
import os
import pkgutil
import warnings
from collections import namedtuple
import torch
def load_ext(name, funcs):
ext = importlib.import_module('mmcv.' + name)
for fun in funcs:
assert hasattr(ext, fun), f'{fun} miss in module {name}'
return ext | null |
9,606 | import importlib
import os
import pkgutil
import warnings
from collections import namedtuple
import torch
if torch.__version__ != 'parrots':
else:
from parrots import extension
from parrots.base import ParrotsException
has_return_value_ops = [
'nms',
'softnms',
'nms_match',
'nms_rotated',
'top_pool_forward',
'top_pool_backward',
'bottom_pool_forward',
'bottom_pool_backward',
'left_pool_forward',
'left_pool_backward',
'right_pool_forward',
'right_pool_backward',
'fused_bias_leakyrelu',
'upfirdn2d',
'ms_deform_attn_forward',
'pixel_group',
'contour_expand',
]
def get_fake_func(name, e):
def fake_func(*args, **kwargs):
warnings.warn(f'{name} is not supported in parrots now')
raise e
return fake_func
def load_ext(name, funcs):
ExtModule = namedtuple('ExtModule', funcs)
ext_list = []
lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
for fun in funcs:
try:
ext_fun = extension.load(fun, name, lib_dir=lib_root)
except ParrotsException as e:
if 'No element registered' not in e.message:
warnings.warn(e.message)
ext_fun = get_fake_func(fun, e)
ext_list.append(ext_fun)
else:
if fun in has_return_value_ops:
ext_list.append(ext_fun.op)
else:
ext_list.append(ext_fun.op_)
return ExtModule(*ext_list) | null |
9,607 | import importlib
import os
import pkgutil
import warnings
from collections import namedtuple
import torch
def check_ops_exist():
ext_loader = pkgutil.find_loader('mmcv._ext')
return ext_loader is not None | null |
9,608 | import logging
import torch.distributed as dist
def get_logger(name, log_file=None, log_level=logging.INFO, file_mode='w'):
"""Initialize and get a logger by name.
If the logger has not been initialized, this method will initialize the
logger by adding one or two handlers, otherwise the initialized logger will
be directly returned. During initialization, a StreamHandler will always be
added. If `log_file` is specified and the process rank is 0, a FileHandler
will also be added.
Args:
name (str): Logger name.
log_file (str | None): The log filename. If specified, a FileHandler
will be added to the logger.
log_level (int): The logger level. Note that only the process of
rank 0 is affected, and other processes will set the level to
"Error" thus be silent most of the time.
file_mode (str): The file mode used in opening log file.
Defaults to 'w'.
Returns:
logging.Logger: The expected logger.
"""
logger = logging.getLogger(name)
if name in logger_initialized:
return logger
# handle hierarchical names
# e.g., logger "a" is initialized, then logger "a.b" will skip the
# initialization since it is a child of "a".
for logger_name in logger_initialized:
if name.startswith(logger_name):
return logger
# handle duplicate logs to the console
# Starting in 1.8.0, PyTorch DDP attaches a StreamHandler <stderr> (NOTSET)
# to the root logger. As logger.propagate is True by default, this root
# level handler causes logging messages from rank>0 processes to
# unexpectedly show up on the console, creating much unwanted clutter.
# To fix this issue, we set the root logger's StreamHandler, if any, to log
# at the ERROR level.
for handler in logger.root.handlers:
if type(handler) is logging.StreamHandler:
handler.setLevel(logging.ERROR)
stream_handler = logging.StreamHandler()
handlers = [stream_handler]
if dist.is_available() and dist.is_initialized():
rank = dist.get_rank()
else:
rank = 0
# only rank 0 will add a FileHandler
if rank == 0 and log_file is not None:
# Here, the default behaviour of the official logger is 'a'. Thus, we
# provide an interface to change the file mode to the default
# behaviour.
file_handler = logging.FileHandler(log_file, file_mode)
handlers.append(file_handler)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
for handler in handlers:
handler.setFormatter(formatter)
handler.setLevel(log_level)
logger.addHandler(handler)
if rank == 0:
logger.setLevel(log_level)
else:
logger.setLevel(logging.ERROR)
logger_initialized[name] = True
return logger
import logging
The provided code snippet includes necessary dependencies for implementing the `print_log` function. Write a Python function `def print_log(msg, logger=None, level=logging.INFO)` to solve the following problem:
Print a log message. Args: msg (str): The message to be logged. logger (logging.Logger | str | None): The logger to be used. Some special loggers are: - "silent": no message will be printed. - other str: the logger obtained with `get_root_logger(logger)`. - None: The `print()` method will be used to print log messages. level (int): Logging level. Only available when `logger` is a Logger object or "root".
Here is the function:
def print_log(msg, logger=None, level=logging.INFO):
"""Print a log message.
Args:
msg (str): The message to be logged.
logger (logging.Logger | str | None): The logger to be used.
Some special loggers are:
- "silent": no message will be printed.
- other str: the logger obtained with `get_root_logger(logger)`.
- None: The `print()` method will be used to print log messages.
level (int): Logging level. Only available when `logger` is a Logger
object or "root".
"""
if logger is None:
print(msg)
elif isinstance(logger, logging.Logger):
logger.log(level, msg)
elif logger == 'silent':
pass
elif isinstance(logger, str):
_logger = get_logger(logger)
_logger.log(level, msg)
else:
raise TypeError(
'logger should be either a logging.Logger object, str, '
f'"silent" or None, but got {type(logger)}') | Print a log message. Args: msg (str): The message to be logged. logger (logging.Logger | str | None): The logger to be used. Some special loggers are: - "silent": no message will be printed. - other str: the logger obtained with `get_root_logger(logger)`. - None: The `print()` method will be used to print log messages. level (int): Logging level. Only available when `logger` is a Logger object or "root". |
9,609 | import os
from .parrots_wrapper import TORCH_VERSION
def jit(func=None,
check_input=None,
full_shape=True,
derivate=False,
coderize=False,
optimize=False):
def wrapper(func):
def wrapper_inner(*args, **kargs):
return func(*args, **kargs)
return wrapper_inner
if func is None:
return wrapper
else:
return func | null |
9,610 | import os
from .parrots_wrapper import TORCH_VERSION
def skip_no_elena(func):
def wrapper(*args, **kargs):
return func(*args, **kargs)
return wrapper | null |
9,611 | import inspect
import warnings
from functools import partial
from .misc import is_seq_of
class Registry:
"""A registry to map strings to classes.
Registered object could be built from registry.
Example:
>>> MODELS = Registry('models')
>>> @MODELS.register_module()
>>> class ResNet:
>>> pass
>>> resnet = MODELS.build(dict(type='ResNet'))
Please refer to
https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
advanced usage.
Args:
name (str): Registry name.
build_func(func, optional): Build function to construct instance from
Registry, func:`build_from_cfg` is used if neither ``parent`` or
``build_func`` is specified. If ``parent`` is specified and
``build_func`` is not given, ``build_func`` will be inherited
from ``parent``. Default: None.
parent (Registry, optional): Parent registry. The class registered in
children registry could be built from parent. Default: None.
scope (str, optional): The scope of registry. It is the key to search
for children registry. If not specified, scope will be the name of
the package where class is defined, e.g. mmdet, mmcls, mmseg.
Default: None.
"""
def __init__(self, name, build_func=None, parent=None, scope=None):
self._name = name
self._module_dict = dict()
self._children = dict()
self._scope = self.infer_scope() if scope is None else scope
# self.build_func will be set with the following priority:
# 1. build_func
# 2. parent.build_func
# 3. build_from_cfg
if build_func is None:
if parent is not None:
self.build_func = parent.build_func
else:
self.build_func = build_from_cfg
else:
self.build_func = build_func
if parent is not None:
assert isinstance(parent, Registry)
parent._add_children(self)
self.parent = parent
else:
self.parent = None
def __len__(self):
return len(self._module_dict)
def __contains__(self, key):
return self.get(key) is not None
def __repr__(self):
format_str = self.__class__.__name__ + \
f'(name={self._name}, ' \
f'items={self._module_dict})'
return format_str
def infer_scope():
"""Infer the scope of registry.
The name of the package where registry is defined will be returned.
Example:
# in mmdet/models/backbone/resnet.py
>>> MODELS = Registry('models')
>>> @MODELS.register_module()
>>> class ResNet:
>>> pass
The scope of ``ResNet`` will be ``mmdet``.
Returns:
scope (str): The inferred scope name.
"""
# inspect.stack() trace where this function is called, the index-2
# indicates the frame where `infer_scope()` is called
filename = inspect.getmodule(inspect.stack()[2][0]).__name__
split_filename = filename.split('.')
return split_filename[0]
def split_scope_key(key):
"""Split scope and key.
The first scope will be split from key.
Examples:
>>> Registry.split_scope_key('mmdet.ResNet')
'mmdet', 'ResNet'
>>> Registry.split_scope_key('ResNet')
None, 'ResNet'
Return:
scope (str, None): The first scope.
key (str): The remaining key.
"""
split_index = key.find('.')
if split_index != -1:
return key[:split_index], key[split_index + 1:]
else:
return None, key
def name(self):
return self._name
def scope(self):
return self._scope
def module_dict(self):
return self._module_dict
def children(self):
return self._children
def get(self, key):
"""Get the registry record.
Args:
key (str): The class name in string format.
Returns:
class: The corresponding class.
"""
scope, real_key = self.split_scope_key(key)
if scope is None or scope == self._scope:
# get from self
if real_key in self._module_dict:
return self._module_dict[real_key]
else:
# get from self._children
if scope in self._children:
return self._children[scope].get(real_key)
else:
# goto root
parent = self.parent
while parent.parent is not None:
parent = parent.parent
return parent.get(key)
def build(self, *args, **kwargs):
return self.build_func(*args, **kwargs, registry=self)
def _add_children(self, registry):
"""Add children for a registry.
The ``registry`` will be added as children based on its scope.
The parent registry could build objects from children registry.
Example:
>>> models = Registry('models')
>>> mmdet_models = Registry('models', parent=models)
>>> @mmdet_models.register_module()
>>> class ResNet:
>>> pass
>>> resnet = models.build(dict(type='mmdet.ResNet'))
"""
assert isinstance(registry, Registry)
assert registry.scope is not None
assert registry.scope not in self.children, \
f'scope {registry.scope} exists in {self.name} registry'
self.children[registry.scope] = registry
def _register_module(self, module_class, module_name=None, force=False):
if not inspect.isclass(module_class):
raise TypeError('module must be a class, '
f'but got {type(module_class)}')
if module_name is None:
module_name = module_class.__name__
if isinstance(module_name, str):
module_name = [module_name]
for name in module_name:
if not force and name in self._module_dict:
raise KeyError(f'{name} is already registered '
f'in {self.name}')
self._module_dict[name] = module_class
def deprecated_register_module(self, cls=None, force=False):
warnings.warn(
'The old API of register_module(module, force=False) '
'is deprecated and will be removed, please use the new API '
'register_module(name=None, force=False, module=None) instead.')
if cls is None:
return partial(self.deprecated_register_module, force=force)
self._register_module(cls, force=force)
return cls
def register_module(self, name=None, force=False, module=None):
"""Register a module.
A record will be added to `self._module_dict`, whose key is the class
name or the specified name, and value is the class itself.
It can be used as a decorator or a normal function.
Example:
>>> backbones = Registry('backbone')
>>> @backbones.register_module()
>>> class ResNet:
>>> pass
>>> backbones = Registry('backbone')
>>> @backbones.register_module(name='mnet')
>>> class MobileNet:
>>> pass
>>> backbones = Registry('backbone')
>>> class ResNet:
>>> pass
>>> backbones.register_module(ResNet)
Args:
name (str | None): The module name to be registered. If not
specified, the class name will be used.
force (bool, optional): Whether to override an existing class with
the same name. Default: False.
module (type): Module class to be registered.
"""
if not isinstance(force, bool):
raise TypeError(f'force must be a boolean, but got {type(force)}')
# NOTE: This is a walkaround to be compatible with the old api,
# while it may introduce unexpected bugs.
if isinstance(name, type):
return self.deprecated_register_module(name, force=force)
# raise the error ahead of time
if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
raise TypeError(
'name must be either of None, an instance of str or a sequence'
f' of str, but got {type(name)}')
# use it as a normal method: x.register_module(module=SomeClass)
if module is not None:
self._register_module(
module_class=module, module_name=name, force=force)
return module
# use it as a decorator: @x.register_module()
def _register(cls):
self._register_module(
module_class=cls, module_name=name, force=force)
return cls
return _register
The provided code snippet includes necessary dependencies for implementing the `build_from_cfg` function. Write a Python function `def build_from_cfg(cfg, registry, default_args=None)` to solve the following problem:
Build a module from config dict. Args: cfg (dict): Config dict. It should at least contain the key "type". registry (:obj:`Registry`): The registry to search the type from. default_args (dict, optional): Default initialization arguments. Returns: object: The constructed object.
Here is the function:
def build_from_cfg(cfg, registry, default_args=None):
"""Build a module from config dict.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
registry (:obj:`Registry`): The registry to search the type from.
default_args (dict, optional): Default initialization arguments.
Returns:
object: The constructed object.
"""
if not isinstance(cfg, dict):
raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
if 'type' not in cfg:
if default_args is None or 'type' not in default_args:
raise KeyError(
'`cfg` or `default_args` must contain the key "type", '
f'but got {cfg}\n{default_args}')
if not isinstance(registry, Registry):
raise TypeError('registry must be an mmcv.Registry object, '
f'but got {type(registry)}')
if not (isinstance(default_args, dict) or default_args is None):
raise TypeError('default_args must be a dict or None, '
f'but got {type(default_args)}')
args = cfg.copy()
if default_args is not None:
for name, value in default_args.items():
args.setdefault(name, value)
obj_type = args.pop('type')
if isinstance(obj_type, str):
obj_cls = registry.get(obj_type)
if obj_cls is None:
raise KeyError(
f'{obj_type} is not in the {registry.name} registry')
elif inspect.isclass(obj_type):
obj_cls = obj_type
else:
raise TypeError(
f'type must be a str or valid type, but got {type(obj_type)}')
try:
return obj_cls(**args)
except Exception as e:
# Normal TypeError does not print class name.
raise type(e)(f'{obj_cls.__name__}: {e}') | Build a module from config dict. Args: cfg (dict): Config dict. It should at least contain the key "type". registry (:obj:`Registry`): The registry to search the type from. default_args (dict, optional): Default initialization arguments. Returns: object: The constructed object. |
9,612 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def is_str(x):
"""Whether the input is an string instance.
Note: This method is deprecated since python 2 is no longer supported.
"""
return isinstance(x, str)
def is_filepath(x):
return is_str(x) or isinstance(x, Path) | null |
9,613 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def is_str(x):
"""Whether the input is an string instance.
Note: This method is deprecated since python 2 is no longer supported.
"""
return isinstance(x, str)
def fopen(filepath, *args, **kwargs):
if is_str(filepath):
return open(filepath, *args, **kwargs)
elif isinstance(filepath, Path):
return filepath.open(*args, **kwargs)
raise ValueError('`filepath` should be a string or a Path') | null |
9,614 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
if not osp.isfile(filename):
raise FileNotFoundError(msg_tmpl.format(filename)) | null |
9,615 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def mkdir_or_exist(dir_name, mode=0o777):
if dir_name == '':
return
dir_name = osp.expanduser(dir_name)
os.makedirs(dir_name, mode=mode, exist_ok=True) | null |
9,616 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def symlink(src, dst, overwrite=True, **kwargs):
if os.path.lexists(dst) and overwrite:
os.remove(dst)
os.symlink(src, dst, **kwargs) | null |
9,617 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
The provided code snippet includes necessary dependencies for implementing the `scandir` function. Write a Python function `def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True)` to solve the following problem:
Scan a directory to find the interested files. Args: dir_path (str | obj:`Path`): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. case_sensitive (bool, optional) : If set to False, ignore the case of suffix. Default: True. Returns: A generator for all the interested files with relative paths.
Here is the function:
def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
"""Scan a directory to find the interested files.
Args:
dir_path (str | obj:`Path`): Path of the directory.
suffix (str | tuple(str), optional): File suffix that we are
interested in. Default: None.
recursive (bool, optional): If set to True, recursively scan the
directory. Default: False.
case_sensitive (bool, optional) : If set to False, ignore the case of
suffix. Default: True.
Returns:
A generator for all the interested files with relative paths.
"""
if isinstance(dir_path, (str, Path)):
dir_path = str(dir_path)
else:
raise TypeError('"dir_path" must be a string or Path object')
if (suffix is not None) and not isinstance(suffix, (str, tuple)):
raise TypeError('"suffix" must be a string or tuple of strings')
if suffix is not None and not case_sensitive:
suffix = suffix.lower() if isinstance(suffix, str) else tuple(
item.lower() for item in suffix)
root = dir_path
def _scandir(dir_path, suffix, recursive, case_sensitive):
for entry in os.scandir(dir_path):
if not entry.name.startswith('.') and entry.is_file():
rel_path = osp.relpath(entry.path, root)
_rel_path = rel_path if case_sensitive else rel_path.lower()
if suffix is None or _rel_path.endswith(suffix):
yield rel_path
elif recursive and os.path.isdir(entry.path):
# scan recursively if entry.path is a directory
yield from _scandir(entry.path, suffix, recursive,
case_sensitive)
return _scandir(dir_path, suffix, recursive, case_sensitive) | Scan a directory to find the interested files. Args: dir_path (str | obj:`Path`): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. case_sensitive (bool, optional) : If set to False, ignore the case of suffix. Default: True. Returns: A generator for all the interested files with relative paths. |
9,618 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
The provided code snippet includes necessary dependencies for implementing the `find_vcs_root` function. Write a Python function `def find_vcs_root(path, markers=('.git', ))` to solve the following problem:
Finds the root directory (including itself) of specified markers. Args: path (str): Path of directory or file. markers (list[str], optional): List of file or directory names. Returns: The directory contained one of the markers or None if not found.
Here is the function:
def find_vcs_root(path, markers=('.git', )):
"""Finds the root directory (including itself) of specified markers.
Args:
path (str): Path of directory or file.
markers (list[str], optional): List of file or directory names.
Returns:
The directory contained one of the markers or None if not found.
"""
if osp.isfile(path):
path = osp.dirname(path)
prev, cur = None, osp.abspath(osp.expanduser(path))
while cur != prev:
if any(osp.exists(osp.join(cur, marker)) for marker in markers):
return cur
prev, cur = cur, osp.split(cur)[0]
return None | Finds the root directory (including itself) of specified markers. Args: path (str): Path of directory or file. markers (list[str], optional): List of file or directory names. Returns: The directory contained one of the markers or None if not found. |
9,619 | import warnings
import torch
from annotator.uniformer.mmcv.utils import digit_version
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def is_jit_tracing() -> bool:
if (torch.__version__ != 'parrots'
and digit_version(torch.__version__) >= digit_version('1.6.0')):
on_trace = torch.jit.is_tracing()
# In PyTorch 1.6, torch.jit.is_tracing has a bug.
# Refers to https://github.com/pytorch/pytorch/issues/42448
if isinstance(on_trace, bool):
return on_trace
else:
return torch._C._is_tracing()
else:
warnings.warn(
'torch.jit.is_tracing is only supported after v1.6.0. '
'Therefore is_tracing returns False automatically. Please '
'set on_trace manually if you are using trace.', UserWarning)
return False | null |
9,620 | import ast
import copy
import os
import os.path as osp
import platform
import shutil
import sys
import tempfile
import uuid
import warnings
from argparse import Action, ArgumentParser
from collections import abc
from importlib import import_module
from addict import Dict
from yapf.yapflib.yapf_api import FormatCode
from .misc import import_modules_from_strings
from .path import check_file_exist
def add_args(parser, cfg, prefix=''):
for k, v in cfg.items():
if isinstance(v, str):
parser.add_argument('--' + prefix + k)
elif isinstance(v, int):
parser.add_argument('--' + prefix + k, type=int)
elif isinstance(v, float):
parser.add_argument('--' + prefix + k, type=float)
elif isinstance(v, bool):
parser.add_argument('--' + prefix + k, action='store_true')
elif isinstance(v, dict):
add_args(parser, v, prefix + k + '.')
elif isinstance(v, abc.Iterable):
parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+')
else:
print(f'cannot parse key {prefix + k} of type {type(v)}')
return parser | null |
9,621 | import os.path as osp
import subprocess
import sys
from collections import defaultdict
import cv2
import torch
import annotator.uniformer.mmcv as mmcv
from .parrots_wrapper import get_build_config
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_cuda_home():
if TORCH_VERSION == 'parrots':
from parrots.utils.build_extension import CUDA_HOME
else:
if is_rocm_pytorch():
from torch.utils.cpp_extension import ROCM_HOME
CUDA_HOME = ROCM_HOME
else:
from torch.utils.cpp_extension import CUDA_HOME
return CUDA_HOME
def get_build_config():
if TORCH_VERSION == 'parrots':
from parrots.config import get_build_info
return get_build_info()
else:
return torch.__config__.show()
The provided code snippet includes necessary dependencies for implementing the `collect_env` function. Write a Python function `def collect_env()` to solve the following problem:
Collect the information of the running environments. Returns: dict: The environment information. The following fields are contained. - sys.platform: The variable of ``sys.platform``. - Python: Python version. - CUDA available: Bool, indicating if CUDA is available. - GPU devices: Device type of each GPU. - CUDA_HOME (optional): The env var ``CUDA_HOME``. - NVCC (optional): NVCC version. - GCC: GCC version, "n/a" if GCC is not installed. - PyTorch: PyTorch version. - PyTorch compiling details: The output of \ ``torch.__config__.show()``. - TorchVision (optional): TorchVision version. - OpenCV: OpenCV version. - MMCV: MMCV version. - MMCV Compiler: The GCC version for compiling MMCV ops. - MMCV CUDA Compiler: The CUDA version for compiling MMCV ops.
Here is the function:
def collect_env():
"""Collect the information of the running environments.
Returns:
dict: The environment information. The following fields are contained.
- sys.platform: The variable of ``sys.platform``.
- Python: Python version.
- CUDA available: Bool, indicating if CUDA is available.
- GPU devices: Device type of each GPU.
- CUDA_HOME (optional): The env var ``CUDA_HOME``.
- NVCC (optional): NVCC version.
- GCC: GCC version, "n/a" if GCC is not installed.
- PyTorch: PyTorch version.
- PyTorch compiling details: The output of \
``torch.__config__.show()``.
- TorchVision (optional): TorchVision version.
- OpenCV: OpenCV version.
- MMCV: MMCV version.
- MMCV Compiler: The GCC version for compiling MMCV ops.
- MMCV CUDA Compiler: The CUDA version for compiling MMCV ops.
"""
env_info = {}
env_info['sys.platform'] = sys.platform
env_info['Python'] = sys.version.replace('\n', '')
cuda_available = torch.cuda.is_available()
env_info['CUDA available'] = cuda_available
if cuda_available:
devices = defaultdict(list)
for k in range(torch.cuda.device_count()):
devices[torch.cuda.get_device_name(k)].append(str(k))
for name, device_ids in devices.items():
env_info['GPU ' + ','.join(device_ids)] = name
from annotator.uniformer.mmcv.utils.parrots_wrapper import _get_cuda_home
CUDA_HOME = _get_cuda_home()
env_info['CUDA_HOME'] = CUDA_HOME
if CUDA_HOME is not None and osp.isdir(CUDA_HOME):
try:
nvcc = osp.join(CUDA_HOME, 'bin/nvcc')
nvcc = subprocess.check_output(
f'"{nvcc}" -V | tail -n1', shell=True)
nvcc = nvcc.decode('utf-8').strip()
except subprocess.SubprocessError:
nvcc = 'Not Available'
env_info['NVCC'] = nvcc
try:
gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
gcc = gcc.decode('utf-8').strip()
env_info['GCC'] = gcc
except subprocess.CalledProcessError: # gcc is unavailable
env_info['GCC'] = 'n/a'
env_info['PyTorch'] = torch.__version__
env_info['PyTorch compiling details'] = get_build_config()
try:
import torchvision
env_info['TorchVision'] = torchvision.__version__
except ModuleNotFoundError:
pass
env_info['OpenCV'] = cv2.__version__
env_info['MMCV'] = mmcv.__version__
try:
from annotator.uniformer.mmcv.ops import get_compiler_version, get_compiling_cuda_version
except ModuleNotFoundError:
env_info['MMCV Compiler'] = 'n/a'
env_info['MMCV CUDA Compiler'] = 'n/a'
else:
env_info['MMCV Compiler'] = get_compiler_version()
env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()
return env_info | Collect the information of the running environments. Returns: dict: The environment information. The following fields are contained. - sys.platform: The variable of ``sys.platform``. - Python: Python version. - CUDA available: Bool, indicating if CUDA is available. - GPU devices: Device type of each GPU. - CUDA_HOME (optional): The env var ``CUDA_HOME``. - NVCC (optional): NVCC version. - GCC: GCC version, "n/a" if GCC is not installed. - PyTorch: PyTorch version. - PyTorch compiling details: The output of \ ``torch.__config__.show()``. - TorchVision (optional): TorchVision version. - OpenCV: OpenCV version. - MMCV: MMCV version. - MMCV Compiler: The GCC version for compiling MMCV ops. - MMCV CUDA Compiler: The CUDA version for compiling MMCV ops. |
9,622 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse | null |
9,623 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `import_modules_from_strings` function. Write a Python function `def import_modules_from_strings(imports, allow_failed_imports=False)` to solve the following problem:
Import modules from the given list of strings. Args: imports (list | str | None): The given module names to be imported. allow_failed_imports (bool): If True, the failed imports will return None. Otherwise, an ImportError is raise. Default: False. Returns: list[module] | module | None: The imported modules. Examples: >>> osp, sys = import_modules_from_strings( ... ['os.path', 'sys']) >>> import os.path as osp_ >>> import sys as sys_ >>> assert osp == osp_ >>> assert sys == sys_
Here is the function:
def import_modules_from_strings(imports, allow_failed_imports=False):
"""Import modules from the given list of strings.
Args:
imports (list | str | None): The given module names to be imported.
allow_failed_imports (bool): If True, the failed imports will return
None. Otherwise, an ImportError is raise. Default: False.
Returns:
list[module] | module | None: The imported modules.
Examples:
>>> osp, sys = import_modules_from_strings(
... ['os.path', 'sys'])
>>> import os.path as osp_
>>> import sys as sys_
>>> assert osp == osp_
>>> assert sys == sys_
"""
if not imports:
return
single_import = False
if isinstance(imports, str):
single_import = True
imports = [imports]
if not isinstance(imports, list):
raise TypeError(
f'custom_imports must be a list but got type {type(imports)}')
imported = []
for imp in imports:
if not isinstance(imp, str):
raise TypeError(
f'{imp} is of type {type(imp)} and cannot be imported.')
try:
imported_tmp = import_module(imp)
except ImportError:
if allow_failed_imports:
warnings.warn(f'{imp} failed to import and is ignored.',
UserWarning)
imported_tmp = None
else:
raise ImportError
imported.append(imported_tmp)
if single_import:
imported = imported[0]
return imported | Import modules from the given list of strings. Args: imports (list | str | None): The given module names to be imported. allow_failed_imports (bool): If True, the failed imports will return None. Otherwise, an ImportError is raise. Default: False. Returns: list[module] | module | None: The imported modules. Examples: >>> osp, sys = import_modules_from_strings( ... ['os.path', 'sys']) >>> import os.path as osp_ >>> import sys as sys_ >>> assert osp == osp_ >>> assert sys == sys_ |
9,624 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def iter_cast(inputs, dst_type, return_type=None):
"""Cast elements of an iterable object into some type.
Args:
inputs (Iterable): The input object.
dst_type (type): Destination type.
return_type (type, optional): If specified, the output object will be
converted to this type, otherwise an iterator.
Returns:
iterator or specified type: The converted object.
"""
if not isinstance(inputs, abc.Iterable):
raise TypeError('inputs must be an iterable object')
if not isinstance(dst_type, type):
raise TypeError('"dst_type" must be a valid type')
out_iterable = map(dst_type, inputs)
if return_type is None:
return out_iterable
else:
return return_type(out_iterable)
The provided code snippet includes necessary dependencies for implementing the `list_cast` function. Write a Python function `def list_cast(inputs, dst_type)` to solve the following problem:
Cast elements of an iterable object into a list of some type. A partial method of :func:`iter_cast`.
Here is the function:
def list_cast(inputs, dst_type):
"""Cast elements of an iterable object into a list of some type.
A partial method of :func:`iter_cast`.
"""
return iter_cast(inputs, dst_type, return_type=list) | Cast elements of an iterable object into a list of some type. A partial method of :func:`iter_cast`. |
9,625 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def iter_cast(inputs, dst_type, return_type=None):
"""Cast elements of an iterable object into some type.
Args:
inputs (Iterable): The input object.
dst_type (type): Destination type.
return_type (type, optional): If specified, the output object will be
converted to this type, otherwise an iterator.
Returns:
iterator or specified type: The converted object.
"""
if not isinstance(inputs, abc.Iterable):
raise TypeError('inputs must be an iterable object')
if not isinstance(dst_type, type):
raise TypeError('"dst_type" must be a valid type')
out_iterable = map(dst_type, inputs)
if return_type is None:
return out_iterable
else:
return return_type(out_iterable)
The provided code snippet includes necessary dependencies for implementing the `tuple_cast` function. Write a Python function `def tuple_cast(inputs, dst_type)` to solve the following problem:
Cast elements of an iterable object into a tuple of some type. A partial method of :func:`iter_cast`.
Here is the function:
def tuple_cast(inputs, dst_type):
"""Cast elements of an iterable object into a tuple of some type.
A partial method of :func:`iter_cast`.
"""
return iter_cast(inputs, dst_type, return_type=tuple) | Cast elements of an iterable object into a tuple of some type. A partial method of :func:`iter_cast`. |
9,626 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def is_seq_of(seq, expected_type, seq_type=None):
"""Check whether it is a sequence of some type.
Args:
seq (Sequence): The sequence to be checked.
expected_type (type): Expected type of sequence items.
seq_type (type, optional): Expected sequence type.
Returns:
bool: Whether the sequence is valid.
"""
if seq_type is None:
exp_seq_type = abc.Sequence
else:
assert isinstance(seq_type, type)
exp_seq_type = seq_type
if not isinstance(seq, exp_seq_type):
return False
for item in seq:
if not isinstance(item, expected_type):
return False
return True
The provided code snippet includes necessary dependencies for implementing the `is_list_of` function. Write a Python function `def is_list_of(seq, expected_type)` to solve the following problem:
Check whether it is a list of some type. A partial method of :func:`is_seq_of`.
Here is the function:
def is_list_of(seq, expected_type):
"""Check whether it is a list of some type.
A partial method of :func:`is_seq_of`.
"""
return is_seq_of(seq, expected_type, seq_type=list) | Check whether it is a list of some type. A partial method of :func:`is_seq_of`. |
9,627 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def is_seq_of(seq, expected_type, seq_type=None):
"""Check whether it is a sequence of some type.
Args:
seq (Sequence): The sequence to be checked.
expected_type (type): Expected type of sequence items.
seq_type (type, optional): Expected sequence type.
Returns:
bool: Whether the sequence is valid.
"""
if seq_type is None:
exp_seq_type = abc.Sequence
else:
assert isinstance(seq_type, type)
exp_seq_type = seq_type
if not isinstance(seq, exp_seq_type):
return False
for item in seq:
if not isinstance(item, expected_type):
return False
return True
The provided code snippet includes necessary dependencies for implementing the `is_tuple_of` function. Write a Python function `def is_tuple_of(seq, expected_type)` to solve the following problem:
Check whether it is a tuple of some type. A partial method of :func:`is_seq_of`.
Here is the function:
def is_tuple_of(seq, expected_type):
"""Check whether it is a tuple of some type.
A partial method of :func:`is_seq_of`.
"""
return is_seq_of(seq, expected_type, seq_type=tuple) | Check whether it is a tuple of some type. A partial method of :func:`is_seq_of`. |
9,628 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `slice_list` function. Write a Python function `def slice_list(in_list, lens)` to solve the following problem:
Slice a list into several sub lists by a list of given length. Args: in_list (list): The list to be sliced. lens(int or list): The expected length of each out list. Returns: list: A list of sliced list.
Here is the function:
def slice_list(in_list, lens):
"""Slice a list into several sub lists by a list of given length.
Args:
in_list (list): The list to be sliced.
lens(int or list): The expected length of each out list.
Returns:
list: A list of sliced list.
"""
if isinstance(lens, int):
assert len(in_list) % lens == 0
lens = [lens] * int(len(in_list) / lens)
if not isinstance(lens, list):
raise TypeError('"indices" must be an integer or a list of integers')
elif sum(lens) != len(in_list):
raise ValueError('sum of lens and list length does not '
f'match: {sum(lens)} != {len(in_list)}')
out_list = []
idx = 0
for i in range(len(lens)):
out_list.append(in_list[idx:idx + lens[i]])
idx += lens[i]
return out_list | Slice a list into several sub lists by a list of given length. Args: in_list (list): The list to be sliced. lens(int or list): The expected length of each out list. Returns: list: A list of sliced list. |
9,629 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `concat_list` function. Write a Python function `def concat_list(in_list)` to solve the following problem:
Concatenate a list of list into a single list. Args: in_list (list): The list of list to be merged. Returns: list: The concatenated flat list.
Here is the function:
def concat_list(in_list):
"""Concatenate a list of list into a single list.
Args:
in_list (list): The list of list to be merged.
Returns:
list: The concatenated flat list.
"""
return list(itertools.chain(*in_list)) | Concatenate a list of list into a single list. Args: in_list (list): The list of list to be merged. Returns: list: The concatenated flat list. |
9,630 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def check_prerequisites(
prerequisites,
checker,
msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
'found, please install them first.'): # yapf: disable
"""A decorator factory to check if prerequisites are satisfied.
Args:
prerequisites (str of list[str]): Prerequisites to be checked.
checker (callable): The checker method that returns True if a
prerequisite is meet, False otherwise.
msg_tmpl (str): The message template with two variables.
Returns:
decorator: A specific decorator.
"""
def wrap(func):
def wrapped_func(*args, **kwargs):
requirements = [prerequisites] if isinstance(
prerequisites, str) else prerequisites
missing = []
for item in requirements:
if not checker(item):
missing.append(item)
if missing:
print(msg_tmpl.format(', '.join(missing), func.__name__))
raise RuntimeError('Prerequisites not meet.')
else:
return func(*args, **kwargs)
return wrapped_func
return wrap
def _check_py_package(package):
try:
import_module(package)
except ImportError:
return False
else:
return True
The provided code snippet includes necessary dependencies for implementing the `requires_package` function. Write a Python function `def requires_package(prerequisites)` to solve the following problem:
A decorator to check if some python packages are installed. Example: >>> @requires_package('numpy') >>> func(arg1, args): >>> return numpy.zeros(1) array([0.]) >>> @requires_package(['numpy', 'non_package']) >>> func(arg1, args): >>> return numpy.zeros(1) ImportError
Here is the function:
def requires_package(prerequisites):
"""A decorator to check if some python packages are installed.
Example:
>>> @requires_package('numpy')
>>> func(arg1, args):
>>> return numpy.zeros(1)
array([0.])
>>> @requires_package(['numpy', 'non_package'])
>>> func(arg1, args):
>>> return numpy.zeros(1)
ImportError
"""
return check_prerequisites(prerequisites, checker=_check_py_package) | A decorator to check if some python packages are installed. Example: >>> @requires_package('numpy') >>> func(arg1, args): >>> return numpy.zeros(1) array([0.]) >>> @requires_package(['numpy', 'non_package']) >>> func(arg1, args): >>> return numpy.zeros(1) ImportError |
9,631 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
def check_prerequisites(
prerequisites,
checker,
msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
'found, please install them first.'): # yapf: disable
"""A decorator factory to check if prerequisites are satisfied.
Args:
prerequisites (str of list[str]): Prerequisites to be checked.
checker (callable): The checker method that returns True if a
prerequisite is meet, False otherwise.
msg_tmpl (str): The message template with two variables.
Returns:
decorator: A specific decorator.
"""
def wrap(func):
def wrapped_func(*args, **kwargs):
requirements = [prerequisites] if isinstance(
prerequisites, str) else prerequisites
missing = []
for item in requirements:
if not checker(item):
missing.append(item)
if missing:
print(msg_tmpl.format(', '.join(missing), func.__name__))
raise RuntimeError('Prerequisites not meet.')
else:
return func(*args, **kwargs)
return wrapped_func
return wrap
def _check_executable(cmd):
if subprocess.call(f'which {cmd}', shell=True) != 0:
return False
else:
return True
The provided code snippet includes necessary dependencies for implementing the `requires_executable` function. Write a Python function `def requires_executable(prerequisites)` to solve the following problem:
A decorator to check if some executable files are installed. Example: >>> @requires_executable('ffmpeg') >>> func(arg1, args): >>> print(1) 1
Here is the function:
def requires_executable(prerequisites):
"""A decorator to check if some executable files are installed.
Example:
>>> @requires_executable('ffmpeg')
>>> func(arg1, args):
>>> print(1)
1
"""
return check_prerequisites(prerequisites, checker=_check_executable) | A decorator to check if some executable files are installed. Example: >>> @requires_executable('ffmpeg') >>> func(arg1, args): >>> print(1) 1 |
9,632 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `deprecated_api_warning` function. Write a Python function `def deprecated_api_warning(name_dict, cls_name=None)` to solve the following problem:
A decorator to check if some arguments are deprecate and try to replace deprecate src_arg_name to dst_arg_name. Args: name_dict(dict): key (str): Deprecate argument names. val (str): Expected argument names. Returns: func: New function.
Here is the function:
def deprecated_api_warning(name_dict, cls_name=None):
"""A decorator to check if some arguments are deprecate and try to replace
deprecate src_arg_name to dst_arg_name.
Args:
name_dict(dict):
key (str): Deprecate argument names.
val (str): Expected argument names.
Returns:
func: New function.
"""
def api_warning_wrapper(old_func):
@functools.wraps(old_func)
def new_func(*args, **kwargs):
# get the arg spec of the decorated method
args_info = getfullargspec(old_func)
# get name of the function
func_name = old_func.__name__
if cls_name is not None:
func_name = f'{cls_name}.{func_name}'
if args:
arg_names = args_info.args[:len(args)]
for src_arg_name, dst_arg_name in name_dict.items():
if src_arg_name in arg_names:
warnings.warn(
f'"{src_arg_name}" is deprecated in '
f'`{func_name}`, please use "{dst_arg_name}" '
'instead')
arg_names[arg_names.index(src_arg_name)] = dst_arg_name
if kwargs:
for src_arg_name, dst_arg_name in name_dict.items():
if src_arg_name in kwargs:
assert dst_arg_name not in kwargs, (
f'The expected behavior is to replace '
f'the deprecated key `{src_arg_name}` to '
f'new key `{dst_arg_name}`, but got them '
f'in the arguments at the same time, which '
f'is confusing. `{src_arg_name} will be '
f'deprecated in the future, please '
f'use `{dst_arg_name}` instead.')
warnings.warn(
f'"{src_arg_name}" is deprecated in '
f'`{func_name}`, please use "{dst_arg_name}" '
'instead')
kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
# apply converted arguments to the decorated method
output = old_func(*args, **kwargs)
return output
return new_func
return api_warning_wrapper | A decorator to check if some arguments are deprecate and try to replace deprecate src_arg_name to dst_arg_name. Args: name_dict(dict): key (str): Deprecate argument names. val (str): Expected argument names. Returns: func: New function. |
9,633 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `is_method_overridden` function. Write a Python function `def is_method_overridden(method, base_class, derived_class)` to solve the following problem:
Check if a method of base class is overridden in derived class. Args: method (str): the method name to check. base_class (type): the class of the base class. derived_class (type | Any): the class or instance of the derived class.
Here is the function:
def is_method_overridden(method, base_class, derived_class):
"""Check if a method of base class is overridden in derived class.
Args:
method (str): the method name to check.
base_class (type): the class of the base class.
derived_class (type | Any): the class or instance of the derived class.
"""
assert isinstance(base_class, type), \
"base_class doesn't accept instance, Please pass class instead."
if not isinstance(derived_class, type):
derived_class = derived_class.__class__
base_method = getattr(base_class, method)
derived_method = getattr(derived_class, method)
return derived_method != base_method | Check if a method of base class is overridden in derived class. Args: method (str): the method name to check. base_class (type): the class of the base class. derived_class (type | Any): the class or instance of the derived class. |
9,634 | import collections.abc
import functools
import itertools
import subprocess
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec
from itertools import repeat
The provided code snippet includes necessary dependencies for implementing the `has_method` function. Write a Python function `def has_method(obj: object, method: str) -> bool` to solve the following problem:
Check whether the object has a method. Args: method (str): The method name to check. obj (object): The object to check. Returns: bool: True if the object has the method else False.
Here is the function:
def has_method(obj: object, method: str) -> bool:
"""Check whether the object has a method.
Args:
method (str): The method name to check.
obj (object): The object to check.
Returns:
bool: True if the object has the method else False.
"""
return hasattr(obj, method) and callable(getattr(obj, method)) | Check whether the object has a method. Args: method (str): The method name to check. obj (object): The object to check. Returns: bool: True if the object has the method else False. |
9,635 | from time import time
class Timer:
"""A flexible Timer class.
:Example:
>>> import time
>>> import annotator.uniformer.mmcv as mmcv
>>> with mmcv.Timer():
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
1.000
>>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'):
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
it takes 1.0 seconds
>>> timer = mmcv.Timer()
>>> time.sleep(0.5)
>>> print(timer.since_start())
0.500
>>> time.sleep(0.5)
>>> print(timer.since_last_check())
0.500
>>> print(timer.since_start())
1.000
"""
def __init__(self, start=True, print_tmpl=None):
self._is_running = False
self.print_tmpl = print_tmpl if print_tmpl else '{:.3f}'
if start:
self.start()
def is_running(self):
"""bool: indicate whether the timer is running"""
return self._is_running
def __enter__(self):
self.start()
return self
def __exit__(self, type, value, traceback):
print(self.print_tmpl.format(self.since_last_check()))
self._is_running = False
def start(self):
"""Start the timer."""
if not self._is_running:
self._t_start = time()
self._is_running = True
self._t_last = time()
def since_start(self):
"""Total time since the timer is started.
Returns (float): Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
self._t_last = time()
return self._t_last - self._t_start
def since_last_check(self):
"""Time since the last checking.
Either :func:`since_start` or :func:`since_last_check` is a checking
operation.
Returns (float): Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
dur = time() - self._t_last
self._t_last = time()
return dur
_g_timers = {}
The provided code snippet includes necessary dependencies for implementing the `check_time` function. Write a Python function `def check_time(timer_id)` to solve the following problem:
Add check points in a single line. This method is suitable for running a task on a list of items. A timer will be registered when the method is called for the first time. :Example: >>> import time >>> import annotator.uniformer.mmcv as mmcv >>> for i in range(1, 6): >>> # simulate a code block >>> time.sleep(i) >>> mmcv.check_time('task1') 2.000 3.000 4.000 5.000 Args: timer_id (str): Timer identifier.
Here is the function:
def check_time(timer_id):
"""Add check points in a single line.
This method is suitable for running a task on a list of items. A timer will
be registered when the method is called for the first time.
:Example:
>>> import time
>>> import annotator.uniformer.mmcv as mmcv
>>> for i in range(1, 6):
>>> # simulate a code block
>>> time.sleep(i)
>>> mmcv.check_time('task1')
2.000
3.000
4.000
5.000
Args:
timer_id (str): Timer identifier.
"""
if timer_id not in _g_timers:
_g_timers[timer_id] = Timer()
return 0
else:
return _g_timers[timer_id].since_last_check() | Add check points in a single line. This method is suitable for running a task on a list of items. A timer will be registered when the method is called for the first time. :Example: >>> import time >>> import annotator.uniformer.mmcv as mmcv >>> for i in range(1, 6): >>> # simulate a code block >>> time.sleep(i) >>> mmcv.check_time('task1') 2.000 3.000 4.000 5.000 Args: timer_id (str): Timer identifier. |
9,636 | from functools import partial
import torch
TORCH_VERSION = torch.__version__
_ConvNd, _ConvTransposeMixin = _get_conv()
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_conv():
if TORCH_VERSION == 'parrots':
from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin
else:
from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin
return _ConvNd, _ConvTransposeMixin | null |
9,637 | from functools import partial
import torch
TORCH_VERSION = torch.__version__
DataLoader, PoolDataLoader = _get_dataloader()
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_dataloader():
if TORCH_VERSION == 'parrots':
from torch.utils.data import DataLoader, PoolDataLoader
else:
from torch.utils.data import DataLoader
PoolDataLoader = DataLoader
return DataLoader, PoolDataLoader | null |
9,638 | from functools import partial
import torch
TORCH_VERSION = torch.__version__
BuildExtension, CppExtension, CUDAExtension = _get_extension()
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_extension():
if TORCH_VERSION == 'parrots':
from parrots.utils.build_extension import BuildExtension, Extension
CppExtension = partial(Extension, cuda=False)
CUDAExtension = partial(Extension, cuda=True)
else:
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
return BuildExtension, CppExtension, CUDAExtension | null |
9,639 | from functools import partial
import torch
TORCH_VERSION = torch.__version__
_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool()
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_pool():
if TORCH_VERSION == 'parrots':
from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd,
_AdaptiveMaxPoolNd, _AvgPoolNd,
_MaxPoolNd)
else:
from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd,
_AdaptiveMaxPoolNd, _AvgPoolNd,
_MaxPoolNd)
return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd | null |
9,640 | from functools import partial
import torch
TORCH_VERSION = torch.__version__
_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm()
class SyncBatchNorm(SyncBatchNorm_):
def _check_input_dim(self, input):
if TORCH_VERSION == 'parrots':
if input.dim() < 2:
raise ValueError(
f'expected at least 2D input (got {input.dim()}D input)')
else:
super()._check_input_dim(input)
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_list', 'check_prerequisites', 'requires_package',
'requires_executable', 'is_filepath', 'fopen', 'check_file_exist',
'mkdir_or_exist', 'symlink', 'scandir', 'ProgressBar',
'track_progress', 'track_iter_progress', 'track_parallel_progress',
'Timer', 'TimerError', 'check_time', 'deprecated_api_warning',
'digit_version', 'get_git_hash', 'import_modules_from_strings',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'check_python_script',
'to_1tuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'to_ntuple',
'is_method_overridden', 'has_method'
]
else:
from .env import collect_env
from .logging import get_logger, print_log
from .parrots_jit import jit, skip_no_elena
from .parrots_wrapper import (
TORCH_VERSION, BuildExtension, CppExtension, CUDAExtension, DataLoader,
PoolDataLoader, SyncBatchNorm, _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd,
_AvgPoolNd, _BatchNorm, _ConvNd, _ConvTransposeMixin, _InstanceNorm,
_MaxPoolNd, get_build_config, is_rocm_pytorch, _get_cuda_home)
from .registry import Registry, build_from_cfg
from .trace import is_jit_tracing
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'collect_env', 'get_logger',
'print_log', 'is_str', 'iter_cast', 'list_cast', 'tuple_cast',
'is_seq_of', 'is_list_of', 'is_tuple_of', 'slice_list', 'concat_list',
'check_prerequisites', 'requires_package', 'requires_executable',
'is_filepath', 'fopen', 'check_file_exist', 'mkdir_or_exist',
'symlink', 'scandir', 'ProgressBar', 'track_progress',
'track_iter_progress', 'track_parallel_progress', 'Registry',
'build_from_cfg', 'Timer', 'TimerError', 'check_time', 'SyncBatchNorm',
'_AdaptiveAvgPoolNd', '_AdaptiveMaxPoolNd', '_AvgPoolNd', '_BatchNorm',
'_ConvNd', '_ConvTransposeMixin', '_InstanceNorm', '_MaxPoolNd',
'get_build_config', 'BuildExtension', 'CppExtension', 'CUDAExtension',
'DataLoader', 'PoolDataLoader', 'TORCH_VERSION',
'deprecated_api_warning', 'digit_version', 'get_git_hash',
'import_modules_from_strings', 'jit', 'skip_no_elena',
'assert_dict_contains_subset', 'assert_attrs_equal',
'assert_dict_has_keys', 'assert_keys_equal', 'assert_is_norm_layer',
'assert_params_all_zeros', 'check_python_script',
'is_method_overridden', 'is_jit_tracing', 'is_rocm_pytorch',
'_get_cuda_home', 'has_method'
]
def _get_norm():
if TORCH_VERSION == 'parrots':
from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
else:
from torch.nn.modules.instancenorm import _InstanceNorm
from torch.nn.modules.batchnorm import _BatchNorm
SyncBatchNorm_ = torch.nn.SyncBatchNorm
return _BatchNorm, _InstanceNorm, SyncBatchNorm_ | null |
9,641 | import sys
from collections.abc import Iterable
from multiprocessing import Pool
from shutil import get_terminal_size
from .timer import Timer
class ProgressBar:
"""A progress bar which can print the progress."""
def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):
self.task_num = task_num
self.bar_width = bar_width
self.completed = 0
self.file = file
if start:
self.start()
def terminal_width(self):
width, _ = get_terminal_size()
return width
def start(self):
if self.task_num > 0:
self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, '
'elapsed: 0s, ETA:')
else:
self.file.write('completed: 0, elapsed: 0s')
self.file.flush()
self.timer = Timer()
def update(self, num_tasks=1):
assert num_tasks > 0
self.completed += num_tasks
elapsed = self.timer.since_start()
if elapsed > 0:
fps = self.completed / elapsed
else:
fps = float('inf')
if self.task_num > 0:
percentage = self.completed / float(self.task_num)
eta = int(elapsed * (1 - percentage) / percentage + 0.5)
msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \
f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \
f'ETA: {eta:5}s'
bar_width = min(self.bar_width,
int(self.terminal_width - len(msg)) + 2,
int(self.terminal_width * 0.6))
bar_width = max(2, bar_width)
mark_width = int(bar_width * percentage)
bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width)
self.file.write(msg.format(bar_chars))
else:
self.file.write(
f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,'
f' {fps:.1f} tasks/s')
self.file.flush()
The provided code snippet includes necessary dependencies for implementing the `track_progress` function. Write a Python function `def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs)` to solve the following problem:
Track the progress of tasks execution with a progress bar. Tasks are done with a simple for-loop. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). bar_width (int): Width of progress bar. Returns: list: The task results.
Here is the function:
def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs):
"""Track the progress of tasks execution with a progress bar.
Tasks are done with a simple for-loop.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
(tasks, total num).
bar_width (int): Width of progress bar.
Returns:
list: The task results.
"""
if isinstance(tasks, tuple):
assert len(tasks) == 2
assert isinstance(tasks[0], Iterable)
assert isinstance(tasks[1], int)
task_num = tasks[1]
tasks = tasks[0]
elif isinstance(tasks, Iterable):
task_num = len(tasks)
else:
raise TypeError(
'"tasks" must be an iterable object or a (iterator, int) tuple')
prog_bar = ProgressBar(task_num, bar_width, file=file)
results = []
for task in tasks:
results.append(func(task, **kwargs))
prog_bar.update()
prog_bar.file.write('\n')
return results | Track the progress of tasks execution with a progress bar. Tasks are done with a simple for-loop. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). bar_width (int): Width of progress bar. Returns: list: The task results. |
9,642 | import sys
from collections.abc import Iterable
from multiprocessing import Pool
from shutil import get_terminal_size
from .timer import Timer
class ProgressBar:
"""A progress bar which can print the progress."""
def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):
self.task_num = task_num
self.bar_width = bar_width
self.completed = 0
self.file = file
if start:
self.start()
def terminal_width(self):
width, _ = get_terminal_size()
return width
def start(self):
if self.task_num > 0:
self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, '
'elapsed: 0s, ETA:')
else:
self.file.write('completed: 0, elapsed: 0s')
self.file.flush()
self.timer = Timer()
def update(self, num_tasks=1):
assert num_tasks > 0
self.completed += num_tasks
elapsed = self.timer.since_start()
if elapsed > 0:
fps = self.completed / elapsed
else:
fps = float('inf')
if self.task_num > 0:
percentage = self.completed / float(self.task_num)
eta = int(elapsed * (1 - percentage) / percentage + 0.5)
msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \
f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \
f'ETA: {eta:5}s'
bar_width = min(self.bar_width,
int(self.terminal_width - len(msg)) + 2,
int(self.terminal_width * 0.6))
bar_width = max(2, bar_width)
mark_width = int(bar_width * percentage)
bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width)
self.file.write(msg.format(bar_chars))
else:
self.file.write(
f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,'
f' {fps:.1f} tasks/s')
self.file.flush()
def init_pool(process_num, initializer=None, initargs=None):
if initializer is None:
return Pool(process_num)
elif initargs is None:
return Pool(process_num, initializer)
else:
if not isinstance(initargs, tuple):
raise TypeError('"initargs" must be a tuple')
return Pool(process_num, initializer, initargs)
The provided code snippet includes necessary dependencies for implementing the `track_parallel_progress` function. Write a Python function `def track_parallel_progress(func, tasks, nproc, initializer=None, initargs=None, bar_width=50, chunksize=1, skip_first=False, keep_order=True, file=sys.stdout)` to solve the following problem:
Track the progress of parallel task execution with a progress bar. The built-in :mod:`multiprocessing` module is used for process pools and tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). nproc (int): Process (worker) number. initializer (None or callable): Refer to :class:`multiprocessing.Pool` for details. initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for details. chunksize (int): Refer to :class:`multiprocessing.Pool` for details. bar_width (int): Width of progress bar. skip_first (bool): Whether to skip the first sample for each worker when estimating fps, since the initialization step may takes longer. keep_order (bool): If True, :func:`Pool.imap` is used, otherwise :func:`Pool.imap_unordered` is used. Returns: list: The task results.
Here is the function:
def track_parallel_progress(func,
tasks,
nproc,
initializer=None,
initargs=None,
bar_width=50,
chunksize=1,
skip_first=False,
keep_order=True,
file=sys.stdout):
"""Track the progress of parallel task execution with a progress bar.
The built-in :mod:`multiprocessing` module is used for process pools and
tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`.
Args:
func (callable): The function to be applied to each task.
tasks (list or tuple[Iterable, int]): A list of tasks or
(tasks, total num).
nproc (int): Process (worker) number.
initializer (None or callable): Refer to :class:`multiprocessing.Pool`
for details.
initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for
details.
chunksize (int): Refer to :class:`multiprocessing.Pool` for details.
bar_width (int): Width of progress bar.
skip_first (bool): Whether to skip the first sample for each worker
when estimating fps, since the initialization step may takes
longer.
keep_order (bool): If True, :func:`Pool.imap` is used, otherwise
:func:`Pool.imap_unordered` is used.
Returns:
list: The task results.
"""
if isinstance(tasks, tuple):
assert len(tasks) == 2
assert isinstance(tasks[0], Iterable)
assert isinstance(tasks[1], int)
task_num = tasks[1]
tasks = tasks[0]
elif isinstance(tasks, Iterable):
task_num = len(tasks)
else:
raise TypeError(
'"tasks" must be an iterable object or a (iterator, int) tuple')
pool = init_pool(nproc, initializer, initargs)
start = not skip_first
task_num -= nproc * chunksize * int(skip_first)
prog_bar = ProgressBar(task_num, bar_width, start, file=file)
results = []
if keep_order:
gen = pool.imap(func, tasks, chunksize)
else:
gen = pool.imap_unordered(func, tasks, chunksize)
for result in gen:
results.append(result)
if skip_first:
if len(results) < nproc * chunksize:
continue
elif len(results) == nproc * chunksize:
prog_bar.start()
continue
prog_bar.update()
prog_bar.file.write('\n')
pool.close()
pool.join()
return results | Track the progress of parallel task execution with a progress bar. The built-in :mod:`multiprocessing` module is used for process pools and tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). nproc (int): Process (worker) number. initializer (None or callable): Refer to :class:`multiprocessing.Pool` for details. initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for details. chunksize (int): Refer to :class:`multiprocessing.Pool` for details. bar_width (int): Width of progress bar. skip_first (bool): Whether to skip the first sample for each worker when estimating fps, since the initialization step may takes longer. keep_order (bool): If True, :func:`Pool.imap` is used, otherwise :func:`Pool.imap_unordered` is used. Returns: list: The task results. |
9,643 | import sys
from collections.abc import Iterable
from multiprocessing import Pool
from shutil import get_terminal_size
from .timer import Timer
class ProgressBar:
"""A progress bar which can print the progress."""
def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):
self.task_num = task_num
self.bar_width = bar_width
self.completed = 0
self.file = file
if start:
self.start()
def terminal_width(self):
width, _ = get_terminal_size()
return width
def start(self):
if self.task_num > 0:
self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, '
'elapsed: 0s, ETA:')
else:
self.file.write('completed: 0, elapsed: 0s')
self.file.flush()
self.timer = Timer()
def update(self, num_tasks=1):
assert num_tasks > 0
self.completed += num_tasks
elapsed = self.timer.since_start()
if elapsed > 0:
fps = self.completed / elapsed
else:
fps = float('inf')
if self.task_num > 0:
percentage = self.completed / float(self.task_num)
eta = int(elapsed * (1 - percentage) / percentage + 0.5)
msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \
f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \
f'ETA: {eta:5}s'
bar_width = min(self.bar_width,
int(self.terminal_width - len(msg)) + 2,
int(self.terminal_width * 0.6))
bar_width = max(2, bar_width)
mark_width = int(bar_width * percentage)
bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width)
self.file.write(msg.format(bar_chars))
else:
self.file.write(
f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,'
f' {fps:.1f} tasks/s')
self.file.flush()
The provided code snippet includes necessary dependencies for implementing the `track_iter_progress` function. Write a Python function `def track_iter_progress(tasks, bar_width=50, file=sys.stdout)` to solve the following problem:
Track the progress of tasks iteration or enumeration with a progress bar. Tasks are yielded with a simple for-loop. Args: tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). bar_width (int): Width of progress bar. Yields: list: The task results.
Here is the function:
def track_iter_progress(tasks, bar_width=50, file=sys.stdout):
"""Track the progress of tasks iteration or enumeration with a progress
bar.
Tasks are yielded with a simple for-loop.
Args:
tasks (list or tuple[Iterable, int]): A list of tasks or
(tasks, total num).
bar_width (int): Width of progress bar.
Yields:
list: The task results.
"""
if isinstance(tasks, tuple):
assert len(tasks) == 2
assert isinstance(tasks[0], Iterable)
assert isinstance(tasks[1], int)
task_num = tasks[1]
tasks = tasks[0]
elif isinstance(tasks, Iterable):
task_num = len(tasks)
else:
raise TypeError(
'"tasks" must be an iterable object or a (iterator, int) tuple')
prog_bar = ProgressBar(task_num, bar_width, file=file)
for task in tasks:
yield task
prog_bar.update()
prog_bar.file.write('\n') | Track the progress of tasks iteration or enumeration with a progress bar. Tasks are yielded with a simple for-loop. Args: tasks (list or tuple[Iterable, int]): A list of tasks or (tasks, total num). bar_width (int): Width of progress bar. Yields: list: The task results. |
9,644 | import os
import subprocess
import warnings
from packaging.version import parse
The provided code snippet includes necessary dependencies for implementing the `digit_version` function. Write a Python function `def digit_version(version_str: str, length: int = 4)` to solve the following problem:
Convert a version string into a tuple of integers. This method is usually used for comparing two versions. For pre-release versions: alpha < beta < rc. Args: version_str (str): The version string. length (int): The maximum number of version levels. Default: 4. Returns: tuple[int]: The version info in digits (integers).
Here is the function:
def digit_version(version_str: str, length: int = 4):
"""Convert a version string into a tuple of integers.
This method is usually used for comparing two versions. For pre-release
versions: alpha < beta < rc.
Args:
version_str (str): The version string.
length (int): The maximum number of version levels. Default: 4.
Returns:
tuple[int]: The version info in digits (integers).
"""
assert 'parrots' not in version_str
version = parse(version_str)
assert version.release, f'failed to parse version {version_str}'
release = list(version.release)
release = release[:length]
if len(release) < length:
release = release + [0] * (length - len(release))
if version.is_prerelease:
mapping = {'a': -3, 'b': -2, 'rc': -1}
val = -4
# version.pre can be None
if version.pre:
if version.pre[0] not in mapping:
warnings.warn(f'unknown prerelease version {version.pre[0]}, '
'version checking may go wrong')
else:
val = mapping[version.pre[0]]
release.extend([val, version.pre[-1]])
else:
release.extend([val, 0])
elif version.is_postrelease:
release.extend([1, version.post])
else:
release.extend([0, 0])
return tuple(release) | Convert a version string into a tuple of integers. This method is usually used for comparing two versions. For pre-release versions: alpha < beta < rc. Args: version_str (str): The version string. length (int): The maximum number of version levels. Default: 4. Returns: tuple[int]: The version info in digits (integers). |
9,645 | import os
import subprocess
import warnings
from packaging.version import parse
def _minimal_ext_cmd(cmd):
# construct minimal environment
env = {}
for k in ['SYSTEMROOT', 'PATH', 'HOME']:
v = os.environ.get(k)
if v is not None:
env[k] = v
# LANGUAGE is used on win32
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
out = subprocess.Popen(
cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
return out
The provided code snippet includes necessary dependencies for implementing the `get_git_hash` function. Write a Python function `def get_git_hash(fallback='unknown', digits=None)` to solve the following problem:
Get the git hash of the current repo. Args: fallback (str, optional): The fallback string when git hash is unavailable. Defaults to 'unknown'. digits (int, optional): kept digits of the hash. Defaults to None, meaning all digits are kept. Returns: str: Git commit hash.
Here is the function:
def get_git_hash(fallback='unknown', digits=None):
"""Get the git hash of the current repo.
Args:
fallback (str, optional): The fallback string when git hash is
unavailable. Defaults to 'unknown'.
digits (int, optional): kept digits of the hash. Defaults to None,
meaning all digits are kept.
Returns:
str: Git commit hash.
"""
if digits is not None and not isinstance(digits, int):
raise TypeError('digits must be None or an integer')
try:
out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
sha = out.strip().decode('ascii')
if digits is not None:
sha = sha[:digits]
except OSError:
sha = fallback
return sha | Get the git hash of the current repo. Args: fallback (str, optional): The fallback string when git hash is unavailable. Defaults to 'unknown'. digits (int, optional): kept digits of the hash. Defaults to None, meaning all digits are kept. Returns: str: Git commit hash. |
9,646 | import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
from torch.nn import functional as F
import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.fileio import FileClient
from annotator.uniformer.mmcv.fileio import load as load_file
from annotator.uniformer.mmcv.parallel import is_module_wrapper
from annotator.uniformer.mmcv.utils import mkdir_or_exist
from annotator.uniformer.mmcv.runner import get_dist_info
def load_state_dict(module, state_dict, strict=False, logger=None):
"""Load state_dict to a module.
This method is modified from :meth:`torch.nn.Module.load_state_dict`.
Default value for ``strict`` is set to ``False`` and the message for
param mismatch will be shown even if strict is False.
Args:
module (Module): Module that receives the state_dict.
state_dict (OrderedDict): Weights.
strict (bool): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
logger (:obj:`logging.Logger`, optional): Logger to log the error
message. If not specified, print function will be used.
"""
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
# use _load_from_state_dict to enable checkpoint version control
def load(module, prefix=''):
# recursively check parallel module in case that the model has a
# complicated structure, e.g., nn.Module(nn.Module(DDP))
if is_module_wrapper(module):
module = module.module
local_metadata = {} if metadata is None else metadata.get(
prefix[:-1], {})
module._load_from_state_dict(state_dict, prefix, local_metadata, True,
all_missing_keys, unexpected_keys,
err_msg)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(module)
load = None # break load->load reference cycle
# ignore "num_batches_tracked" of BN layers
missing_keys = [
key for key in all_missing_keys if 'num_batches_tracked' not in key
]
if unexpected_keys:
err_msg.append('unexpected key in source '
f'state_dict: {", ".join(unexpected_keys)}\n')
if missing_keys:
err_msg.append(
f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
rank, _ = get_dist_info()
if len(err_msg) > 0 and rank == 0:
err_msg.insert(
0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif logger is not None:
logger.warning(err_msg)
else:
print(err_msg)
def _load_checkpoint(filename, map_location=None):
"""Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`. Default: None.
Returns:
dict | OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.
"""
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
'use "torchvision://" instead')
model_urls = get_torchvision_models()
model_name = filename[11:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('torchvision://'):
model_urls = get_torchvision_models()
model_name = filename[14:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_urls = get_external_models()
model_name = filename[13:]
deprecated_urls = get_deprecated_model_names()
if model_name in deprecated_urls:
warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
f'of open-mmlab://{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
# check if is url
if model_url.startswith(('http://', 'https://')):
checkpoint = load_url_dist(model_url)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
elif filename.startswith('mmcls://'):
model_urls = get_mmcls_models()
model_name = filename[8:]
checkpoint = load_url_dist(model_urls[model_name])
checkpoint = _process_mmcls_checkpoint(checkpoint)
elif filename.startswith(('http://', 'https://')):
checkpoint = load_url_dist(filename)
elif filename.startswith('pavi://'):
model_path = filename[7:]
checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
elif filename.startswith('s3://'):
checkpoint = load_fileclient_dist(
filename, backend='ceph', map_location=map_location)
else:
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint
The provided code snippet includes necessary dependencies for implementing the `load_checkpoint` function. Write a Python function `def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None)` to solve the following problem:
Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str): Same as :func:`torch.load`. strict (bool): Whether to allow different params for the model and checkpoint. logger (:mod:`logging.Logger` or None): The logger for error message. Returns: dict or OrderedDict: The loaded checkpoint.
Here is the function:
def load_checkpoint(model,
filename,
map_location='cpu',
strict=False,
logger=None):
"""Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = _load_checkpoint(filename, map_location)
# OrderedDict is a subclass of dict
if not isinstance(checkpoint, dict):
raise RuntimeError(
f'No state_dict found in checkpoint file {filename}')
# get state_dict from checkpoint
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
elif 'model' in checkpoint:
state_dict = checkpoint['model']
else:
state_dict = checkpoint
# strip prefix of state_dict
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for k, v in state_dict.items()}
# for MoBY, load model of online branch
if sorted(list(state_dict.keys()))[0].startswith('encoder'):
state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
# reshape absolute position embedding
if state_dict.get('absolute_pos_embed') is not None:
absolute_pos_embed = state_dict['absolute_pos_embed']
N1, L, C1 = absolute_pos_embed.size()
N2, C2, H, W = model.absolute_pos_embed.size()
if N1 != N2 or C1 != C2 or L != H*W:
logger.warning("Error in loading absolute_pos_embed, pass")
else:
state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
# interpolate position bias table if needed
relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
for table_key in relative_position_bias_table_keys:
table_pretrained = state_dict[table_key]
table_current = model.state_dict()[table_key]
L1, nH1 = table_pretrained.size()
L2, nH2 = table_current.size()
if nH1 != nH2:
logger.warning(f"Error in loading {table_key}, pass")
else:
if L1 != L2:
S1 = int(L1 ** 0.5)
S2 = int(L2 ** 0.5)
table_pretrained_resized = F.interpolate(
table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
size=(S2, S2), mode='bicubic')
state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
# load state_dict
load_state_dict(model, state_dict, strict, logger)
return checkpoint | Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str): Same as :func:`torch.load`. strict (bool): Whether to allow different params for the model and checkpoint. logger (:mod:`logging.Logger` or None): The logger for error message. Returns: dict or OrderedDict: The loaded checkpoint. |
9,647 | import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
from torch.nn import functional as F
import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.fileio import FileClient
from annotator.uniformer.mmcv.fileio import load as load_file
from annotator.uniformer.mmcv.parallel import is_module_wrapper
from annotator.uniformer.mmcv.utils import mkdir_or_exist
from annotator.uniformer.mmcv.runner import get_dist_info
def weights_to_cpu(state_dict):
"""Copy a model state_dict to cpu.
Args:
state_dict (OrderedDict): Model weights on GPU.
Returns:
OrderedDict: Model weights on GPU.
"""
state_dict_cpu = OrderedDict()
for key, val in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu
def get_state_dict(module, destination=None, prefix='', keep_vars=False):
"""Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
This method is modified from :meth:`torch.nn.Module.state_dict` to
recursively check parallel module in case that the model has a complicated
structure, e.g., nn.Module(nn.Module(DDP)).
Args:
module (nn.Module): The module to generate state_dict.
destination (OrderedDict): Returned dict for the state of the
module.
prefix (str): Prefix of the key.
keep_vars (bool): Whether to keep the variable property of the
parameters. Default: False.
Returns:
dict: A dictionary containing a whole state of the module.
"""
# recursively check parallel module in case that the model has a
# complicated structure, e.g., nn.Module(nn.Module(DDP))
if is_module_wrapper(module):
module = module.module
# below is the same as torch.nn.Module.state_dict()
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:-1]] = local_metadata = dict(
version=module._version)
_save_to_state_dict(module, destination, prefix, keep_vars)
for name, child in module._modules.items():
if child is not None:
get_state_dict(
child, destination, prefix + name + '.', keep_vars=keep_vars)
for hook in module._state_dict_hooks.values():
hook_result = hook(module, destination, prefix, local_metadata)
if hook_result is not None:
destination = hook_result
return destination
The provided code snippet includes necessary dependencies for implementing the `save_checkpoint` function. Write a Python function `def save_checkpoint(model, filename, optimizer=None, meta=None)` to solve the following problem:
Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. filename (str): Checkpoint filename. optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. meta (dict, optional): Metadata to be saved in checkpoint.
Here is the function:
def save_checkpoint(model, filename, optimizer=None, meta=None):
"""Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.
"""
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
if is_module_wrapper(model):
model = model.module
if hasattr(model, 'CLASSES') and model.CLASSES is not None:
# save class name to the meta
meta.update(CLASSES=model.CLASSES)
checkpoint = {
'meta': meta,
'state_dict': weights_to_cpu(get_state_dict(model))
}
# save optimizer state dict in the checkpoint
if isinstance(optimizer, Optimizer):
checkpoint['optimizer'] = optimizer.state_dict()
elif isinstance(optimizer, dict):
checkpoint['optimizer'] = {}
for name, optim in optimizer.items():
checkpoint['optimizer'][name] = optim.state_dict()
if filename.startswith('pavi://'):
try:
from pavi import modelcloud
from pavi.exception import NodeNotFoundError
except ImportError:
raise ImportError(
'Please install pavi to load checkpoint from modelcloud.')
model_path = filename[7:]
root = modelcloud.Folder()
model_dir, model_name = osp.split(model_path)
try:
model = modelcloud.get(model_dir)
except NodeNotFoundError:
model = root.create_training_model(model_dir)
with TemporaryDirectory() as tmp_dir:
checkpoint_file = osp.join(tmp_dir, model_name)
with open(checkpoint_file, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
model.create_file(checkpoint_file, name=model_name)
else:
mmcv.mkdir_or_exist(osp.dirname(filename))
# immediately flush buffer
with open(filename, 'wb') as f:
torch.save(checkpoint, f)
f.flush() | Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. filename (str): Checkpoint filename. optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. meta (dict, optional): Metadata to be saved in checkpoint. |
9,648 | import gradio as gr
from annotator.util import resize_image, HWC3
model_canny = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class CannyDetector:
def __call__(self, img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)
def canny(img, res, l, h):
img = resize_image(HWC3(img), res)
global model_canny
if model_canny is None:
from annotator.canny import CannyDetector
model_canny = CannyDetector()
result = model_canny(img, l, h)
return [result] | null |
9,649 | import gradio as gr
from annotator.util import resize_image, HWC3
model_hed = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class HEDdetector:
def __init__(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
self.netNetwork = ControlNetHED_Apache2().float().cuda().eval()
self.netNetwork.load_state_dict(torch.load(modelpath))
def __call__(self, input_image):
assert input_image.ndim == 3
H, W, C = input_image.shape
with torch.no_grad():
image_hed = torch.from_numpy(input_image.copy()).float().cuda()
image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
edges = self.netNetwork(image_hed)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
return edge
def hed(img, res):
img = resize_image(HWC3(img), res)
global model_hed
if model_hed is None:
from annotator.hed import HEDdetector
model_hed = HEDdetector()
result = model_hed(img)
return [result] | null |
9,650 | import gradio as gr
from annotator.util import resize_image, HWC3
model_mlsd = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class MLSDdetector:
def __init__(self):
model_path = os.path.join(annotator_ckpts_path, "mlsd_large_512_fp32.pth")
if not os.path.exists(model_path):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
model = MobileV2_MLSD_Large()
model.load_state_dict(torch.load(model_path), strict=True)
self.model = model.cuda().eval()
def __call__(self, input_image, thr_v, thr_d):
assert input_image.ndim == 3
img = input_image
img_output = np.zeros_like(img)
try:
with torch.no_grad():
lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d)
for line in lines:
x_start, y_start, x_end, y_end = [int(val) for val in line]
cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
except Exception as e:
pass
return img_output[:, :, 0]
def mlsd(img, res, thr_v, thr_d):
img = resize_image(HWC3(img), res)
global model_mlsd
if model_mlsd is None:
from annotator.mlsd import MLSDdetector
model_mlsd = MLSDdetector()
result = model_mlsd(img, thr_v, thr_d)
return [result] | null |
9,651 | import gradio as gr
from annotator.util import resize_image, HWC3
model_midas = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class MidasDetector:
def __init__(self):
self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1):
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
image_depth = torch.from_numpy(image_depth).float().cuda()
image_depth = image_depth / 127.5 - 1.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = self.model(image_depth)[0]
depth_pt = depth.clone()
depth_pt -= torch.min(depth_pt)
depth_pt /= torch.max(depth_pt)
depth_pt = depth_pt.cpu().numpy()
depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
depth_np = depth.cpu().numpy()
x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
z = np.ones_like(x) * a
x[depth_pt < bg_th] = 0
y[depth_pt < bg_th] = 0
normal = np.stack([x, y, z], axis=2)
normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
return depth_image, normal_image
def midas(img, res, a):
img = resize_image(HWC3(img), res)
global model_midas
if model_midas is None:
from annotator.midas import MidasDetector
model_midas = MidasDetector()
results = model_midas(img, a)
return results | null |
9,652 | import gradio as gr
from annotator.util import resize_image, HWC3
model_openpose = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class OpenposeDetector:
def __init__(self):
body_modelpath = os.path.join(annotator_ckpts_path, "body_pose_model.pth")
hand_modelpath = os.path.join(annotator_ckpts_path, "hand_pose_model.pth")
if not os.path.exists(hand_modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
self.body_estimation = Body(body_modelpath)
self.hand_estimation = Hand(hand_modelpath)
def __call__(self, oriImg, hand=False):
oriImg = oriImg[:, :, ::-1].copy()
with torch.no_grad():
candidate, subset = self.body_estimation(oriImg)
canvas = np.zeros_like(oriImg)
canvas = util.draw_bodypose(canvas, candidate, subset)
if hand:
hands_list = util.handDetect(candidate, subset, oriImg)
all_hand_peaks = []
for x, y, w, is_left in hands_list:
peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
all_hand_peaks.append(peaks)
canvas = util.draw_handpose(canvas, all_hand_peaks)
return canvas, dict(candidate=candidate.tolist(), subset=subset.tolist())
def openpose(img, res, has_hand):
img = resize_image(HWC3(img), res)
global model_openpose
if model_openpose is None:
from annotator.openpose import OpenposeDetector
model_openpose = OpenposeDetector()
result, _ = model_openpose(img, has_hand)
return [result] | null |
9,653 | import gradio as gr
from annotator.util import resize_image, HWC3
model_uniformer = None
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
class UniformerDetector:
def __init__(self):
modelpath = os.path.join(annotator_ckpts_path, "upernet_global_small.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(checkpoint_file, model_dir=annotator_ckpts_path)
config_file = os.path.join(os.path.dirname(annotator_ckpts_path), "uniformer", "exp", "upernet_global_small", "config.py")
self.model = init_segmentor(config_file, modelpath).cuda()
def __call__(self, img):
result = inference_segmentor(self.model, img)
res_img = show_result_pyplot(self.model, img, result, get_palette('ade'), opacity=1)
return res_img
def uniformer(img, res):
img = resize_image(HWC3(img), res)
global model_uniformer
if model_uniformer is None:
from annotator.uniformer import UniformerDetector
model_uniformer = UniformerDetector()
result = model_uniformer(img)
return [result] | null |
9,659 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
class CheckpointFunction(torch.autograd.Function):
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
"dtype": torch.get_autocast_gpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled()}
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad(), \
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
The provided code snippet includes necessary dependencies for implementing the `checkpoint` function. Write a Python function `def checkpoint(func, inputs, params, flag)` to solve the following problem:
Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing.
Here is the function:
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs) | Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. |
9,669 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
The provided code snippet includes necessary dependencies for implementing the `get_timestep_embedding` function. Write a Python function `def get_timestep_embedding(timesteps, embedding_dim)` to solve the following problem:
This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need".
Here is the function:
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0,1,0,0))
return emb | This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". |
9,670 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
def nonlinearity(x):
# swish
return x*torch.sigmoid(x) | null |
9,671 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) | null |
9,672 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
class MemoryEfficientAttnBlock(nn.Module):
"""
Uses xformers efficient implementation,
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
Note: this is a single-head self-attention operation
"""
#
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.attention_op: Optional[Any] = None
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
B, C, H, W = q.shape
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(B, t.shape[1], 1, C)
.permute(0, 2, 1, 3)
.reshape(B * 1, t.shape[1], C)
.contiguous(),
(q, k, v),
)
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
out = (
out.unsqueeze(0)
.reshape(B, 1, out.shape[1], C)
.permute(0, 2, 1, 3)
.reshape(B, out.shape[1], C)
)
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
out = self.proj_out(out)
return x+out
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
def forward(self, x, context=None, mask=None):
b, c, h, w = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
out = super().forward(x, context=context, mask=mask)
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
return x + out
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
attn_type = "vanilla-xformers"
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
assert attn_kwargs is None
return AttnBlock(in_channels)
elif attn_type == "vanilla-xformers":
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
return MemoryEfficientAttnBlock(in_channels)
elif type == "memory-efficient-cross-attn":
attn_kwargs["query_dim"] = in_channels
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
raise NotImplementedError() | null |
9,673 | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from ldm.util import exists
def convert_module_to_f16(x):
pass | null |
9,674 | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from ldm.util import exists
def convert_module_to_f32(x):
pass | null |
9,675 | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from ldm.util import exists
The provided code snippet includes necessary dependencies for implementing the `count_flops_attn` function. Write a Python function `def count_flops_attn(model, _x, y)` to solve the following problem:
A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, )
Here is the function:
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops]) | A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, ) |
9,676 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
import os
def uniq(arr):
return{el: True for el in arr}.keys() | null |
9,677 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
import os
def max_neg_value(t):
return -torch.finfo(t.dtype).max | null |
9,678 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
import os
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor | null |
9,679 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
import os
The provided code snippet includes necessary dependencies for implementing the `zero_module` function. Write a Python function `def zero_module(module)` to solve the following problem:
Zero out the parameters of a module and return it.
Here is the function:
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module | Zero out the parameters of a module and return it. |
9,680 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
import os
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | null |
9,684 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
The provided code snippet includes necessary dependencies for implementing the `srmd_degradation` function. Write a Python function `def srmd_degradation(x, k, sf=3)` to solve the following problem:
blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} }
Here is the function:
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x | blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } |
9,685 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
The provided code snippet includes necessary dependencies for implementing the `dpsr_degradation` function. Write a Python function `def dpsr_degradation(x, k, sf=3)` to solve the following problem:
bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} }
Here is the function:
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x | bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } |
9,686 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
The provided code snippet includes necessary dependencies for implementing the `classical_degradation` function. Write a Python function `def classical_degradation(x, k, sf=3)` to solve the following problem:
blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image
Here is the function:
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...] | blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image |
9,691 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan` function. Write a Python function `def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
9,692 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan_variant` function. Write a Python function `def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
# elif i == 1:
# image = add_blur(image, sf=sf)
if i == 0:
pass
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
#
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
if up:
image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then
example = {"image": image}
return example | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
9,696 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def mkdir(path):
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path) | null |
9,697 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def get_timestamp():
return datetime.now().strftime('%y%m%d-%H%M%S')
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path) | null |
9,734 | import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a Python function `def disabled_train(self, mode=True)` to solve the following problem:
Overwrite model.train with this function to make sure train/eval mode does not change anymore.
Here is the function:
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
9,735 | import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
class Resize(object):
"""Resize sample to given size (width, height).
"""
def __init__(
self,
width,
height,
resize_target=True,
keep_aspect_ratio=False,
ensure_multiple_of=1,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_AREA,
):
"""Init.
Args:
width (int): desired output width
height (int): desired output height
resize_target (bool, optional):
True: Resize the full sample (image, mask, target).
False: Resize image only.
Defaults to True.
keep_aspect_ratio (bool, optional):
True: Keep the aspect ratio of the input sample.
Output sample might not have the given width and height, and
resize behaviour depends on the parameter 'resize_method'.
Defaults to False.
ensure_multiple_of (int, optional):
Output width and height is constrained to be multiple of this parameter.
Defaults to 1.
resize_method (str, optional):
"lower_bound": Output will be at least as large as the given size.
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
Defaults to "lower_bound".
"""
self.__width = width
self.__height = height
self.__resize_target = resize_target
self.__keep_aspect_ratio = keep_aspect_ratio
self.__multiple_of = ensure_multiple_of
self.__resize_method = resize_method
self.__image_interpolation_method = image_interpolation_method
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
if max_val is not None and y > max_val:
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
if y < min_val:
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
return y
def get_size(self, width, height):
# determine new height and width
scale_height = self.__height / height
scale_width = self.__width / width
if self.__keep_aspect_ratio:
if self.__resize_method == "lower_bound":
# scale such that output size is lower bound
if scale_width > scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "upper_bound":
# scale such that output size is upper bound
if scale_width < scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "minimal":
# scale as least as possbile
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
else:
raise ValueError(
f"resize_method {self.__resize_method} not implemented"
)
if self.__resize_method == "lower_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, min_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, min_val=self.__width
)
elif self.__resize_method == "upper_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, max_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, max_val=self.__width
)
elif self.__resize_method == "minimal":
new_height = self.constrain_to_multiple_of(scale_height * height)
new_width = self.constrain_to_multiple_of(scale_width * width)
else:
raise ValueError(f"resize_method {self.__resize_method} not implemented")
return (new_width, new_height)
def __call__(self, sample):
width, height = self.get_size(
sample["image"].shape[1], sample["image"].shape[0]
)
# resize sample
sample["image"] = cv2.resize(
sample["image"],
(width, height),
interpolation=self.__image_interpolation_method,
)
if self.__resize_target:
if "disparity" in sample:
sample["disparity"] = cv2.resize(
sample["disparity"],
(width, height),
interpolation=cv2.INTER_NEAREST,
)
if "depth" in sample:
sample["depth"] = cv2.resize(
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
(width, height),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return sample
class NormalizeImage(object):
"""Normlize image by given mean and std.
"""
def __init__(self, mean, std):
self.__mean = mean
self.__std = std
def __call__(self, sample):
sample["image"] = (sample["image"] - self.__mean) / self.__std
return sample
class PrepareForNet(object):
"""Prepare sample for usage as network input.
"""
def __init__(self):
pass
def __call__(self, sample):
image = np.transpose(sample["image"], (2, 0, 1))
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
if "mask" in sample:
sample["mask"] = sample["mask"].astype(np.float32)
sample["mask"] = np.ascontiguousarray(sample["mask"])
if "disparity" in sample:
disparity = sample["disparity"].astype(np.float32)
sample["disparity"] = np.ascontiguousarray(disparity)
if "depth" in sample:
depth = sample["depth"].astype(np.float32)
sample["depth"] = np.ascontiguousarray(depth)
return sample
def load_midas_transform(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load transform only
if model_type == "dpt_large": # DPT-Large
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
elif model_type == "midas_v21_small":
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
else:
assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return transform | null |
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