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datasets.py
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import os
import json
from collections import namedtuple
import numpy as np
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.datasets.utils import list_dir
from torchvision.datasets.folder import make_dataset
from torchvision.datasets.vision import VisionDataset
from brainscore_vision.model_helpers.activations.temporal.inputs.video import Video
ListData = namedtuple('ListData', ['id', 'label', 'path'])
def video_from_imgs(imgs, transform):
# Shape is (T, H, W, C)
imgs = np.array(imgs)
# Shape is (T, H, W, C)
data = torch.from_numpy(imgs).float()
# Need shape (T, C, H, W) https://discuss.pytorch.org/t/can-transforms-compose-handle-a-batch-of-images/4850/5
data = data.permute(0, 3, 1, 2)
data = transform(data)
# Need shape (C, T, H, W) https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html
data = data.permute(1, 0, 2, 3)
return data
class SmthSmthV2Dataset(Dataset):
def __init__(self, root, json_file_input, json_file_labels, is_val, label_start=0, fps=12, duration=2000,
size=(224, 224), is_test=False):
self.json_file_input = json_file_input
self.json_file_labels = json_file_labels
self.root = root
self.fps = fps
self.duration = duration
self.size = size
self.is_val = is_val
self.is_test = is_test
self.label_start = label_start
self.classes = self.read_json_labels()
self.classes_dict = self.get_two_way_dict(self.classes)
self.json_data = self.read_json_input()
self.transform = transforms.Compose([
transforms.CenterCrop(self.size),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def read_json_labels(self):
classes = []
with open(self.json_file_labels, 'rb') as json_file:
json_reader = json.load(json_file)
for elem in json_reader:
classes.append(elem)
return sorted(classes)
def get_two_way_dict(self, classes):
classes_dict = {}
for i, item in enumerate(classes):
classes_dict[item] = i
classes_dict[i] = item
return classes_dict
def read_json_input(self):
json_data = []
if not self.is_test:
with open(self.json_file_input, 'rb') as json_file:
json_reader = json.load(json_file)
for elem in json_reader:
label = self.clean_template(elem['template'])
if label not in self.classes:
raise ValueError(f'Label {label} not found in classes')
item = ListData(elem['id'], label, os.path.join(self.root, elem['id'] + '.webm'))
json_data.append(item)
else:
with open(self.json_file_input, 'rb') as json_file:
json_reader = json.load(json_file)
for elem in json_reader:
item = ListData(elem['id'], "Dummy label", os.path.join(self.root, elem['id'] + '.webm'))
json_data.append(item)
return json_data
def clean_template(self, template):
return template.replace('[', '').replace(']', '')
def __getitem__(self, index):
item = self.json_data[index]
video = Video.from_path(item.path)
video = video.set_fps(self.fps)
video = video.set_window(0, self.duration)
imgs = video.to_frames()
label = item.label
label = self.classes_dict[label] + self.label_start
data = video_from_imgs(imgs, self.transform)
return data, label
def __len__(self):
return len(self.json_data)
class ImageNetVid(Dataset):
def __init__(self, root, label_start=0, fps=12, duration=2000, size=(224, 224), split='train'):
self.root = os.path.join(root, split)
self.fps = fps
self.duration = duration
self.size = size
self.image_paths = []
self.labels = []
classes = sorted(os.listdir(self.root))
self.class_to_idx = {class_name: idx+label_start for idx, class_name in enumerate(classes)}
for class_name in classes:
class_dir = os.path.join(self.root, class_name)
if not os.path.isdir(class_dir):
continue
for fname in os.listdir(class_dir):
if fname.lower().endswith(('png', 'jpg', 'jpeg')):
self.image_paths.append(os.path.join(class_dir, fname))
self.labels.append(self.class_to_idx[class_name])
self.transform = transforms.Compose([
transforms.CenterCrop(self.size),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def __getitem__(self, idx):
img_path = self.image_paths[idx]
label = self.labels[idx]
video = Video.from_img_path(img_path, self.duration, self.fps, size=self.size)
imgs = video.to_frames()
data = video_from_imgs(imgs, self.transform)
return data, label
def __len__(self):
return len(self.image_paths)
class AFD101(VisionDataset):
def __init__(self, root, annotation_path, label_start=0, fold=1, fps=12, duration=2000, size=(224, 224), train=True):
super(AFD101, self).__init__(root)
if not 1 <= fold <= 3:
raise ValueError("fold should be between 1 and 3, got {}".format(fold))
extensions = ('avi',)
self.fold = fold
self.fps = fps
self.duration = duration
self.size = size
self.train = train
classes = list(sorted(list_dir(root)))
self.class_to_idx = {classes[i]: i+label_start for i in range(len(classes))}
self.idx_to_class = {v:k for k,v in self.class_to_idx.items()}
self.samples = make_dataset(self.root, self.class_to_idx, extensions, is_valid_file=None)
self.classes = classes
video_list = [x[0] for x in self.samples]
self.indices = self._select_fold(video_list, annotation_path, fold, train)
self.video_clips = [video_list[i] for i in self.indices]
self.num_classes = len(classes)
self.transform = transforms.Compose([
transforms.CenterCrop(self.size),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def _select_fold(self, video_list, annotation_path, fold, train):
name = "train" if train else "test"
name = "{}list{:02d}.txt".format(name, fold)
f = os.path.join(annotation_path, name)
selected_files = []
with open(f, "r") as fid:
data = fid.readlines()
data = [x.strip().split(" ") for x in data]
data = [x[0] for x in data]
selected_files.extend(data)
selected_files = set(selected_files)
indices = [i for i in range(len(video_list)) if video_list[i][len(self.root) + 1:] in selected_files]
return indices
def __len__(self):
return len(self.video_clips)
def __getitem__(self, idx):
video_path, label = self.samples[self.indices[idx]]
video = Video.from_path(video_path)
video = video.set_fps(self.fps)
video = video.set_window(0, self.duration)
imgs = video.to_frames()
data = video_from_imgs(imgs, self.transform)
return data, label