121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
import math
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import random
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import custom_mmpkg.custom_mmcv as mmcv
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import numpy as np
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from ..builder import PIPELINES
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import torch
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from typing import Optional, Tuple, Union
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@PIPELINES.register_module()
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class Crop(object):
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r"""Crop motion sequences.
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Args:
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crop_size (int): The size of the cropped motion sequence.
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"""
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def __init__(self,
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crop_size: Optional[Union[int, None]] = None):
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self.crop_size = crop_size
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assert self.crop_size is not None
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def __call__(self, results):
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motion = results['motion']
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length = len(motion)
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if length >= self.crop_size:
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idx = random.randint(0, length - self.crop_size)
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motion = motion[idx: idx + self.crop_size]
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results['motion_length'] = self.crop_size
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else:
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padding_length = self.crop_size - length
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D = motion.shape[1:]
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padding_zeros = np.zeros((padding_length, *D), dtype=np.float32)
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motion = np.concatenate([motion, padding_zeros], axis=0)
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results['motion_length'] = length
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assert len(motion) == self.crop_size
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results['motion'] = motion
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results['motion_shape'] = motion.shape
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if length >= self.crop_size:
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results['motion_mask'] = torch.ones(self.crop_size).numpy()
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else:
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results['motion_mask'] = torch.cat(
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(torch.ones(length), torch.zeros(self.crop_size - length))).numpy()
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return results
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def __repr__(self):
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repr_str = self.__class__.__name__ + f'(crop_size={self.crop_size})'
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return repr_str
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@PIPELINES.register_module()
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class RandomCrop(object):
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r"""Random crop motion sequences. Each sequence will be padded with zeros to the maximum length.
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Args:
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min_size (int or None): The minimum size of the cropped motion sequence (inclusive).
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max_size (int or None): The maximum size of the cropped motion sequence (inclusive).
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"""
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def __init__(self,
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min_size: Optional[Union[int, None]] = None,
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max_size: Optional[Union[int, None]] = None):
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self.min_size = min_size
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self.max_size = max_size
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assert self.min_size is not None
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assert self.max_size is not None
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def __call__(self, results):
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motion = results['motion']
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length = len(motion)
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crop_size = random.randint(self.min_size, self.max_size)
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if length > crop_size:
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idx = random.randint(0, length - crop_size)
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motion = motion[idx: idx + crop_size]
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results['motion_length'] = crop_size
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else:
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results['motion_length'] = length
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padding_length = self.max_size - min(crop_size, length)
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if padding_length > 0:
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D = motion.shape[1:]
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padding_zeros = np.zeros((padding_length, *D), dtype=np.float32)
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motion = np.concatenate([motion, padding_zeros], axis=0)
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results['motion'] = motion
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results['motion_shape'] = motion.shape
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if length >= self.max_size and crop_size == self.max_size:
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results['motion_mask'] = torch.ones(self.max_size).numpy()
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else:
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results['motion_mask'] = torch.cat((
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torch.ones(min(length, crop_size)),
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torch.zeros(self.max_size - min(length, crop_size))), dim=0).numpy()
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assert len(motion) == self.max_size
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return results
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def __repr__(self):
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repr_str = self.__class__.__name__ + f'(min_size={self.min_size}'
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repr_str += f', max_size={self.max_size})'
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return repr_str
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@PIPELINES.register_module()
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class Normalize(object):
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"""Normalize motion sequences.
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Args:
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mean_path (str): Path of mean file.
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std_path (str): Path of std file.
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"""
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def __init__(self, mean_path, std_path, eps=1e-9):
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self.mean = np.load(mean_path)
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self.std = np.load(std_path)
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self.eps = eps
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def __call__(self, results):
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motion = results['motion']
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motion = (motion - self.mean) / (self.std + self.eps)
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results['motion'] = motion
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results['motion_norm_mean'] = self.mean
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results['motion_norm_std'] = self.std
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return results
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