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import os
from typing import Any, Dict, List, Optional
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision.transforms.transforms import Compose
import pickle
import re
__all__ = ["ActionSegmentationDataset", "collate_fn"]
dataset_names = ["MCFS-22", "MCFS-130", "PKU-subject", "PKU-view", "LARA","BABEL3","BABEL1","BABEL2"]
modes = ["training", "validation", "trainval", "test"]
def get_displacements(sample):
# input: C, T, V, M
C, T, V, M = sample.shape
final_sample = np.zeros((C, T, V, M))
validFrames = (sample != 0).sum(axis=3).sum(axis=2).sum(axis=0) > 0
start = validFrames.argmax()
end = len(validFrames) - validFrames[::-1].argmax()
sample = sample[:, start:end, :, :]
t = sample.shape[1]
# Shape: C, t-1, V, M
disps = sample[:, 1:, :, :] - sample[:, :-1, :, :]
# Shape: C, T, V, M
final_sample[:, start:end-1, :, :] = disps
return final_sample
def get_relative_coordinates(sample,
references=(0)):
# input: C, T, V, M
# references=(4, 8, 12, 16)
C, T, V, M = sample.shape
final_sample = np.zeros((C, T, V, M))
validFrames = (sample != 0).sum(axis=3).sum(axis=2).sum(axis=0) > 0
start = validFrames.argmax()
end = len(validFrames) - validFrames[::-1].argmax()
sample = sample[:, start:end, :, :]
C, t, V, M = sample.shape
rel_coords = []
#for i in range(len(references)):
ref_loc = sample[:, :, references, :]
coords_diff = (sample.transpose((2, 0, 1, 3)) - ref_loc).transpose((1, 2, 0, 3))
rel_coords.append(coords_diff)
# Shape: C, t, V, M
rel_coords = np.vstack(rel_coords)
# Shape: C, T, V, M
final_sample[:, start:end, :, :] = rel_coords
return final_sample
class ActionSegmentationDataset(Dataset):
""" Action Segmentation Dataset """
def __init__(
self,
dataset: str,
transform: Optional[Compose] = None,
mode: str = "training",
split: int = 1,
dataset_dir: str = "./dataset",
csv_dir: str = "./csv",
) -> None:
super().__init__()
"""
Args:
dataset: the name of dataset
transform: torchvision.transforms.Compose([...])
mode: training, validation, test
split: which split of train, val and test do you want to use in csv files.(default:1)
csv_dir: the path to the directory where the csv files are saved
"""
assert (
dataset in dataset_names
), "You have to choose dataset."
self.transform = transform
self.dataset = dataset
print(dataset)
self.babel = r"BABEL.*"
if not re.match(self.babel, dataset):
if mode == "training":
self.df = pd.read_csv(
os.path.join(csv_dir, dataset, "train{}.csv".format(split))
) #Get the (NUM, 3) file, where three are features, labels, and boundary labels
elif mode == "validation":
self.df = pd.read_csv(
os.path.join(csv_dir, dataset, "val{}.csv".format(split))
)
elif mode == "trainval":
df1 = pd.read_csv(
os.path.join(csv_dir, dataset, "train{}.csv".format(split))
)
df2 = pd.read_csv(os.path.join(csv_dir, dataset, "val{}.csv".format(split)))
self.df = pd.concat([df1, df2])
elif mode == "test":
self.df = pd.read_csv(
os.path.join(csv_dir, dataset, "test{}.csv".format(split))
)
else:
assert (
mode in modes
), "You have to choose 'training', 'trainval', 'validation' or 'test' as the dataset mode."
# <libs.transformer.ToTensor object at 0x7f3ed3ff3550>和<libs.transformer.TempDownSamp object at 0x7f3ed402abb0>
else:
if mode == "training":
with open('./dataset/'+str(self.dataset) +'/train_split'+str(self.dataset)[-1] +'.pkl',"rb") as f:
self.df = pickle.load(f,encoding="latin1")
else:
with open('./dataset/'+str(self.dataset) +'/val_split'+str(self.dataset)[-1] +'.pkl',"rb") as f:
self.df = pickle.load(f,encoding="latin1")
def __len__(self) -> int:
if re.match(self.babel, self.dataset):
return len(self.df["X"])
else:
return len(self.df)
def __getitem__(self, idx: int) -> Dict[str, Any]:
if not re.match(self.babel, self.dataset):
feature_path = self.df.iloc[idx]["feature"]
label_path = self.df.iloc[idx]["label"]
boundary_path = self.df.iloc[idx]["boundary"]
# feature.shape = 617,150 -> 3, 617,25,2
feature = np.load(feature_path, allow_pickle=True).astype(np.float32) #特征（C，24000,19,M）
label = np.load(label_path).astype(np.int64)
boundary = np.load(boundary_path).astype(np.float32)
else:
feature = self.df["X"][idx]
label = self.df["L"][idx]
boundary = np.zeros_like(label)
boundary[1:] = label[1:] != label[:-1]
boundary[0]=1
feature_path = None
if (self.dataset == 'MCFS-22') or (self.dataset == 'MCFS-130'):
feature = feature[:,:,:2] # t,v,c
feature[:,:,0] = feature[:,:,0]/1280 - 0.5
feature[:,:,1] = feature[:,:,1]/720 - 0.5
feature = feature - feature[:,8:9,:]
feature = feature.transpose(2, 1, 0) # t,v,c--->c,v,t
elif (self.dataset == 'PKU-subject') or (self.dataset == 'PKU-view'):
feature = feature.reshape(-1,2,25,3).transpose(3,0,2,1) # t,m,v,c--->c,t,v,m
disps = get_displacements(feature)
rel_coords = get_relative_coordinates(feature)
feature = np.concatenate([disps, rel_coords], axis=0)
feature = feature.transpose(3,0,2,1).reshape(12, 25, -1) # c,t,v,m--->mc,v,t
# M, C, V, T = feature.shape[3], feature.shape[0], feature.shape[2], feature.shape[1]
# feature = feature.transpose(3, 0, 2, 1).reshape(M * C, V, T)
elif re.match(self.babel, self.dataset):
disps = get_displacements(feature)
rel_coords = get_relative_coordinates(feature)
feature = np.concatenate([disps, rel_coords], axis=0)
M, C, V, T = feature.shape[3], feature.shape[0], feature.shape[2], feature.shape[1]
feature = feature.transpose(3, 0, 2, 1).reshape(M * C, V, T)
elif (self.dataset == 'LARA'):
disps = get_displacements(feature)
rel_coords = get_relative_coordinates(feature)
feature = np.concatenate([disps, rel_coords], axis=0)
feature = feature.transpose(3,0,2,1).reshape(12, 19, -1) # c,t,v,m--->mc,v,t
if self.transform is not None:
feature, label, boundary = self.transform([feature, label, boundary])
sample = {
"feature": feature,
"label": label,
"feature_path": feature_path,
"boundary": boundary,
}
return sample
def collate_fn(sample: List[Dict[str, Any]]) -> Dict[str, Any]:
max_length = max([s["feature"].shape[2] for s in sample])
feat_list = []
label_list = []
path_list = []
boundary_list = []
length_list = []
for s in sample:
feature = s["feature"]
label = s["label"]
boundary = s["boundary"]
feature_path = s["feature_path"]
_, _, t = feature.shape
pad_t = max_length - t
length_list.append(t)
if pad_t > 0:
feature = F.pad(
feature, (0, pad_t), mode='constant', value=0.)
label = F.pad(label, (0, pad_t), mode='constant', value=255)
boundary = F.pad(boundary, (0, pad_t), mode='constant', value=0.)
# reshape boundary (T) => (1, T)
boundary = boundary.unsqueeze(0)
feat_list.append(feature)
label_list.append(label)
path_list.append(feature_path)
boundary_list.append(boundary)
# merge features from tuple of 2D tensor to 3D tensor
features = torch.stack(feat_list, dim=0) #（N，C，V，T）
# merge labels from tuple of 1D tensor to 2D tensor
labels = torch.stack(label_list, dim=0) #（N，T）
# merge labels from tuple of 2D tensor to 3D tensor
# shape (N, 1, T)
boundaries = torch.stack(boundary_list, dim=0) # (N, 1, T)
# generate masks which shows valid length for each video (N, 1, T)
masks = [
[[1 if i < length else 0 for i in range(max_length)]] for length in length_list
]
masks = torch.tensor(masks, dtype=torch.bool)
return {
"feature": features,
"label": labels,
"boundary": boundaries,
"feature_path": path_list,
"mask": masks,
}
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