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import random
import numpy as np
import torch
# from utils.action_label_to_idx import action_label_to_idx
from data_loaders.tensors import collate
from utils.misc import to_torch
import utils.rotation_conversions as geometry
class Dataset(torch.utils.data.Dataset):
def __init__(self, num_frames=1, sampling="conseq", sampling_step=1, split="train",
pose_rep="rot6d", translation=True, glob=True, max_len=-1, min_len=-1, num_seq_max=-1, **kwargs):
self.num_frames = num_frames
self.sampling = sampling
self.sampling_step = sampling_step
self.split = split
self.pose_rep = pose_rep
self.translation = translation
self.glob = glob
self.max_len = max_len
self.min_len = min_len
self.num_seq_max = num_seq_max
self.align_pose_frontview = kwargs.get('align_pose_frontview', False)
self.use_action_cat_as_text_labels = kwargs.get('use_action_cat_as_text_labels', False)
self.only_60_classes = kwargs.get('only_60_classes', False)
self.leave_out_15_classes = kwargs.get('leave_out_15_classes', False)
self.use_only_15_classes = kwargs.get('use_only_15_classes', False)
if self.split not in ["train", "val", "test"]:
raise ValueError(f"{self.split} is not a valid split")
super().__init__()
# to remove shuffling
self._original_train = None
self._original_test = None
def action_to_label(self, action):
return self._action_to_label[action]
def label_to_action(self, label):
import numbers
if isinstance(label, numbers.Integral):
return self._label_to_action[label]
else: # if it is one hot vector
label = np.argmax(label)
return self._label_to_action[label]
def get_pose_data(self, data_index, frame_ix):
pose = self._load(data_index, frame_ix)
label = self.get_label(data_index)
return pose, label
def get_label(self, ind):
action = self.get_action(ind)
return self.action_to_label(action)
def get_action(self, ind):
return self._actions[ind]
def action_to_action_name(self, action):
return self._action_classes[action]
def action_name_to_action(self, action_name):
# self._action_classes is either a list or a dictionary. If it's a dictionary, we 1st convert it to a list
all_action_names = self._action_classes
if isinstance(all_action_names, dict):
all_action_names = list(all_action_names.values())
assert list(self._action_classes.keys()) == list(range(len(all_action_names))) # the keys should be ordered from 0 to num_actions
sorter = np.argsort(all_action_names)
actions = sorter[np.searchsorted(all_action_names, action_name, sorter=sorter)]
return actions
def __getitem__(self, index):
if self.split == 'train':
data_index = self._train[index]
else:
data_index = self._test[index]
# inp, target = self._get_item_data_index(data_index)
# return inp, target
return self._get_item_data_index(data_index)
def _load(self, ind, frame_ix):
pose_rep = self.pose_rep
if pose_rep == "xyz" or self.translation:
if getattr(self, "_load_joints3D", None) is not None:
# Locate the root joint of initial pose at origin
joints3D = self._load_joints3D(ind, frame_ix)
joints3D = joints3D - joints3D[0, 0, :]
ret = to_torch(joints3D)
if self.translation:
ret_tr = ret[:, 0, :]
else:
if pose_rep == "xyz":
raise ValueError("This representation is not possible.")
if getattr(self, "_load_translation") is None:
raise ValueError("Can't extract translations.")
ret_tr = self._load_translation(ind, frame_ix)
ret_tr = to_torch(ret_tr - ret_tr[0])
if pose_rep != "xyz":
if getattr(self, "_load_rotvec", None) is None:
raise ValueError("This representation is not possible.")
else:
pose = self._load_rotvec(ind, frame_ix)
if not self.glob:
pose = pose[:, 1:, :]
pose = to_torch(pose)
if self.align_pose_frontview:
first_frame_root_pose_matrix = geometry.axis_angle_to_matrix(pose[0][0])
all_root_poses_matrix = geometry.axis_angle_to_matrix(pose[:, 0, :])
aligned_root_poses_matrix = torch.matmul(torch.transpose(first_frame_root_pose_matrix, 0, 1),
all_root_poses_matrix)
pose[:, 0, :] = geometry.matrix_to_axis_angle(aligned_root_poses_matrix)
if self.translation:
ret_tr = torch.matmul(torch.transpose(first_frame_root_pose_matrix, 0, 1).float(),
torch.transpose(ret_tr, 0, 1))
ret_tr = torch.transpose(ret_tr, 0, 1)
if pose_rep == "rotvec":
ret = pose
elif pose_rep == "rotmat":
ret = geometry.axis_angle_to_matrix(pose).view(*pose.shape[:2], 9)
elif pose_rep == "rotquat":
ret = geometry.axis_angle_to_quaternion(pose)
elif pose_rep == "rot6d":
ret = geometry.matrix_to_rotation_6d(geometry.axis_angle_to_matrix(pose))
if pose_rep != "xyz" and self.translation:
padded_tr = torch.zeros((ret.shape[0], ret.shape[2]), dtype=ret.dtype)
padded_tr[:, :3] = ret_tr
ret = torch.cat((ret, padded_tr[:, None]), 1)
ret = ret.permute(1, 2, 0).contiguous()
return ret.float()
def _get_item_data_index(self, data_index):
nframes = self._num_frames_in_video[data_index]
if self.num_frames == -1 and (self.max_len == -1 or nframes <= self.max_len):
frame_ix = np.arange(nframes)
else:
if self.num_frames == -2:
if self.min_len <= 0:
raise ValueError("You should put a min_len > 0 for num_frames == -2 mode")
if self.max_len != -1:
max_frame = min(nframes, self.max_len)
else:
max_frame = nframes
num_frames = random.randint(self.min_len, max(max_frame, self.min_len))
else:
num_frames = self.num_frames if self.num_frames != -1 else self.max_len
if num_frames > nframes:
fair = False # True
if fair:
# distills redundancy everywhere
choices = np.random.choice(range(nframes),
num_frames,
replace=True)
frame_ix = sorted(choices)
else:
# adding the last frame until done
ntoadd = max(0, num_frames - nframes)
lastframe = nframes - 1
padding = lastframe * np.ones(ntoadd, dtype=int)
frame_ix = np.concatenate((np.arange(0, nframes),
padding))
elif self.sampling in ["conseq", "random_conseq"]:
step_max = (nframes - 1) // (num_frames - 1)
if self.sampling == "conseq":
if self.sampling_step == -1 or self.sampling_step * (num_frames - 1) >= nframes:
step = step_max
else:
step = self.sampling_step
elif self.sampling == "random_conseq":
step = random.randint(1, step_max)
lastone = step * (num_frames - 1)
shift_max = nframes - lastone - 1
shift = random.randint(0, max(0, shift_max - 1))
frame_ix = shift + np.arange(0, lastone + 1, step)
elif self.sampling == "random":
choices = np.random.choice(range(nframes),
num_frames,
replace=False)
frame_ix = sorted(choices)
else:
raise ValueError("Sampling not recognized.")
inp, action = self.get_pose_data(data_index, frame_ix)
output = {'inp': inp, 'action': action}
if hasattr(self, '_actions') and hasattr(self, '_action_classes'):
output['action_text'] = self.action_to_action_name(self.get_action(data_index))
return output
def get_mean_length_label(self, label):
if self.num_frames != -1:
return self.num_frames
if self.split == 'train':
index = self._train
else:
index = self._test
action = self.label_to_action(label)
choices = np.argwhere(self._actions[index] == action).squeeze(1)
lengths = self._num_frames_in_video[np.array(index)[choices]]
if self.max_len == -1:
return np.mean(lengths)
else:
# make the lengths less than max_len
lengths[lengths > self.max_len] = self.max_len
return np.mean(lengths)
def __len__(self):
num_seq_max = getattr(self, "num_seq_max", -1)
if num_seq_max == -1:
from math import inf
num_seq_max = inf
if self.split == 'train':
return min(len(self._train), num_seq_max)
else:
return min(len(self._test), num_seq_max)
def shuffle(self):
if self.split == 'train':
random.shuffle(self._train)
else:
random.shuffle(self._test)
def reset_shuffle(self):
if self.split == 'train':
if self._original_train is None:
self._original_train = self._train
else:
self._train = self._original_train
else:
if self._original_test is None:
self._original_test = self._test
else:
self._test = self._original_test
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