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f8c9a9a1c9
Improve the performance of bevformer * add hybrid brach (#232) Co-authored-by: yhq <yanhaiqiang.yhq@alibaba-inc.com>
124 lines
3.5 KiB
Python
124 lines
3.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import functools
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import inspect
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import logging
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import pickle
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import warnings
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import mmcv
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import numpy as np
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import torch
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from easycv.framework.errors import ValueError
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def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
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num_imgs = tensor.size(0)
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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imgs = []
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for img_id in range(num_imgs):
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img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
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img = mmcv.imdenormalize(
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img, mean, std, to_bgr=to_rgb).astype(np.uint8)
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imgs.append(np.ascontiguousarray(img))
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return imgs
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def unmap(data, count, inds, fill=0):
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""" Unmap a subset of item (data) back to the original set of items (of
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size count) """
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if data.dim() == 1:
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ret = data.new_full((count, ), fill)
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ret[inds] = data
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else:
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new_size = (count, ) + data.size()[1:]
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ret = data.new_full(new_size, fill)
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ret[inds, :] = data
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return ret
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def add_prefix(inputs, prefix):
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"""Add prefix for dict key.
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Args:
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inputs (dict): The input dict with str keys.
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prefix (str): The prefix add to key name.
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Returns:
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dict: The dict with keys wrapped with ``prefix``.
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"""
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outputs = dict()
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for name, value in inputs.items():
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outputs[f'{prefix}.{name}'] = value
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return outputs
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def reparameterize_models(model):
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""" reparameterize model for inference, especially forf
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1. rep conv block : merge 3x3 weight 1x1 weights
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call module switch_to_deploy recursively
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Args:
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model: nn.Module
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"""
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from easycv.models.backbones.repvgg_yolox_backbone import RepVGGBlock
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reparameterize_count = 0
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for layer in model.modules():
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if isinstance(layer, RepVGGBlock):
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reparameterize_count += 1
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layer.switch_to_deploy()
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logging.info(
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'export : PAI-export reparameterize_count(RepVGGBlock, ) switch to deploy with {} blocks'
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.format(reparameterize_count))
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print('reparam:', reparameterize_count)
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return model
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def deprecated(reason):
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"""
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This is a decorator which can be used to mark functions
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as deprecated. It will result in a warning being emitted
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when the function is used.
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"""
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def decorator(func1):
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if inspect.isclass(func1):
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fmt1 = 'Call to deprecated class {name} ({reason}).'
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else:
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fmt1 = 'Call to deprecated function {name} ({reason}).'
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@functools.wraps(func1)
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def new_func1(*args, **kwargs):
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warnings.simplefilter('always', DeprecationWarning)
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warnings.warn(
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fmt1.format(name=func1.__name__, reason=reason),
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category=DeprecationWarning,
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stacklevel=2)
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warnings.simplefilter('default', DeprecationWarning)
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return func1(*args, **kwargs)
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return new_func1
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return decorator
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def encode_str_to_tensor(obj):
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if isinstance(obj, str):
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return torch.tensor(bytearray(pickle.dumps(obj)), dtype=torch.uint8)
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elif isinstance(obj, torch.Tensor):
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return obj
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else:
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raise ValueError(f'Not support type {type(obj)}')
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def decode_tensor_to_str(obj):
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if isinstance(obj, torch.Tensor):
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return pickle.loads(obj.cpu().numpy().tobytes())
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elif isinstance(obj, str):
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return obj
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else:
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raise ValueError(f'Not support type {type(obj)}')
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