# Copyright (c) Alibaba, Inc. and its affiliates.
import functools
import inspect
import logging
import pickle
import warnings
import mmcv
import numpy as np
import torch
from easycv.framework.errors import ValueError
[docs]def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
num_imgs = tensor.size(0)
mean = np.array(mean, dtype=np.float32)
std = np.array(std, dtype=np.float32)
imgs = []
for img_id in range(num_imgs):
img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
img = mmcv.imdenormalize(
img, mean, std, to_bgr=to_rgb).astype(np.uint8)
imgs.append(np.ascontiguousarray(img))
return imgs
[docs]def unmap(data, count, inds, fill=0):
""" Unmap a subset of item (data) back to the original set of items (of
size count) """
if data.dim() == 1:
ret = data.new_full((count, ), fill)
ret[inds] = data
else:
new_size = (count, ) + data.size()[1:]
ret = data.new_full(new_size, fill)
ret[inds, :] = data
return ret
[docs]def add_prefix(inputs, prefix):
"""Add prefix for dict key.
Args:
inputs (dict): The input dict with str keys.
prefix (str): The prefix add to key name.
Returns:
dict: The dict with keys wrapped with ``prefix``.
"""
outputs = dict()
for name, value in inputs.items():
outputs[f'{prefix}.{name}'] = value
return outputs
[docs]def reparameterize_models(model):
""" reparameterize model for inference, especially forf
1. rep conv block : merge 3x3 weight 1x1 weights
call module switch_to_deploy recursively
Args:
model: nn.Module
"""
from easycv.models.backbones.repvgg_yolox_backbone import RepVGGBlock
reparameterize_count = 0
for layer in model.modules():
if isinstance(layer, RepVGGBlock):
reparameterize_count += 1
layer.switch_to_deploy()
logging.info(
'export : PAI-export reparameterize_count(RepVGGBlock, ) switch to deploy with {} blocks'
.format(reparameterize_count))
print('reparam:', reparameterize_count)
return model
[docs]def deprecated(reason):
"""
This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.
"""
def decorator(func1):
if inspect.isclass(func1):
fmt1 = 'Call to deprecated class {name} ({reason}).'
else:
fmt1 = 'Call to deprecated function {name} ({reason}).'
@functools.wraps(func1)
def new_func1(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
warnings.warn(
fmt1.format(name=func1.__name__, reason=reason),
category=DeprecationWarning,
stacklevel=2)
warnings.simplefilter('default', DeprecationWarning)
return func1(*args, **kwargs)
return new_func1
return decorator
[docs]def encode_str_to_tensor(obj):
if isinstance(obj, str):
return torch.tensor(bytearray(pickle.dumps(obj)), dtype=torch.uint8)
elif isinstance(obj, torch.Tensor):
return obj
else:
raise ValueError(f'Not support type {type(obj)}')
[docs]def decode_tensor_to_str(obj):
if isinstance(obj, torch.Tensor):
return pickle.loads(obj.cpu().numpy().tobytes())
elif isinstance(obj, str):
return obj
else:
raise ValueError(f'Not support type {type(obj)}')