commit 8f8cf48929a49c596bc38ebb2f7cd627214b4e08 Author: Lei Date: Mon Dec 2 15:42:58 2024 +0100 first commit diff --git a/README.md b/README.md new file mode 100644 index 0000000..9eeb4a2 --- /dev/null +++ b/README.md @@ -0,0 +1,100 @@ +# ActionDiffusion: An Action-aware Diffusion Model for Procedure Planning in Instructional Videos +*Lei Shi1, Paul Bürkner2, Andreas Bulling1* + +1. University of Stuttgart +2. TU Dortmund University + +IEEE/CVF Winter Conference on Applications of Computer Vision, 2025 + +Paper link: https://arxiv.org/abs/2403.08591 + +## Dataset + +Download pre-extracted features. + + +### Crosstask + +``` +cd dataset/crosstask +wget https://www.di.ens.fr/~dzhukov/crosstask/crosstask_release.zip +wget https://vision.eecs.yorku.ca/WebShare/CrossTask_s3d.zip +unzip '*.zip' +``` + +### Coin + +``` +cd dataset/coin +wget https://vision.eecs.yorku.ca/WebShare/COIN_s3d.zip +unzip COIN_s3d.zip +``` + +### NIV + +``` +cd dataset/NIV +wget https://vision.eecs.yorku.ca/WebShare/NIV_s3d.zip +unzip NIV_s3d.zip +``` + +## Train + +### Task Predicion + +Set arguments in `train_mlp.sh`. Train task prediction model for each dataset. Set `--class_dim, --action_dim, --observation_dim` accordingly. For horizon `T={3,4,5,6}`, set `--horizon, --json_path_val ,--json_path_train` accordingly. + +``` +sh train_mlp.sh +``` + +Set the checkpoint path in `temp.py` via `--checkpoint_mlp` + + +### Diffusion Model + +Set `dataset, horizon` in `train.sh` to corresponding datasets and time horizons for training. Set `mask_type` to `multi_add` to use multiple-add noise mask or `single_add` to use single-add noise mask. Set `attn` to `WithAttention` to use UNet with attention or `NoAttention` to use UNet without attention. + +To train the model, run + +``` +sh train.sh +``` + +To train the model without mask, run + +``` +sh train_no_mask.sh +``` + +## Inference + +Set `dataset, horizon` in `inference.sh` to corresponding datasets and time horizons for training. Set `checkpoint_diff` to the pre-trained model. +Set `mask_type` to `multi_add` to use multiple-add noise mask or `single_add` to use single-add noise mask. Set `attn` to `WithAttention` to use UNet with attention or `NoAttention` to use UNet without attention. + +Set `dataset, horizon` to corresponding datasets and time horizons for inference. Set `checkpoint_diff` to the path of pre-trained model. +Set `mask_type` to `multi_add` to use multiple-add noise mask or `single_add` to use single-add noise mask. Set `attn` to `WithAttention` to use UNet with attention or `NoAttention` to use UNet without attention. + +To perform inference, run + +``` +sh inference.sh +``` + +To perform inference without action mask, run + +``` +sh inference_no_mask.sh +``` + +To infer with the ditribution of the noise with action embedding, run + +``` +sh inference_dist.sh +``` + +## Acknowledgement +``` +This repository is developed based on https://github.com/MCG-NJU/PDPP/tree/main/ +``` + diff --git a/data_load_json.py b/data_load_json.py new file mode 100644 index 0000000..ba31d00 --- /dev/null +++ b/data_load_json.py @@ -0,0 +1,133 @@ +import os +import numpy as np +import torch +from torch.utils.data import Dataset +import json +from collections import namedtuple + +Batch = namedtuple('Batch', 'Observations json_id json_len') + + +class PlanningDataset(Dataset): + """ + load video and action features from dataset + """ + + def __init__(self, + root, + args=None, + is_val=False, + model=None, + ): + self.is_val = is_val + self.data_root = root + self.args = args + self.max_traj_len = args.horizon + self.vid_names = None + self.frame_cnts = None + self.images = None + self.last_vid = '' + + if args.dataset == 'crosstask': + cross_task_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_crosstask105/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + + if os.path.exists(cross_task_data_name): + with open(cross_task_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(cross_task_data_name)) + else: + assert 0 + elif args.dataset == 'coin': + coin_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_coin/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if os.path.exists(coin_data_name): + with open(coin_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(coin_data_name)) + else: + assert 0 + elif args.dataset == 'NIV': + niv_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_niv/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if os.path.exists(niv_data_name): + with open(niv_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(niv_data_name)) + else: + assert 0 + else: + raise NotImplementedError( + 'Dataset {} is not implemented'.format(args.dataset)) + + self.model = model + self.prepare_data() + self.M = 3 + + def prepare_data(self): + vid_names = [] + frame_cnts = [] + for listdata in self.json_data: + vid_names.append(listdata['id']) + frame_cnts.append(listdata['instruction_len']) + self.vid_names = vid_names + self.frame_cnts = frame_cnts + + def curate_dataset(self, images, legal_range, M=2): + images_list = [] + labels_onehot_list = [] + idx_list = [] + for start_idx, end_idx, action_label in legal_range: + idx = start_idx + idx_list.append(idx) + image_start_idx = max(0, idx) + if image_start_idx + M <= len(images): + image_start = images[image_start_idx: image_start_idx + M] + else: + image_start = images[len(images) - M: len(images)] + image_start_cat = image_start[0] + for w in range(len(image_start) - 1): + image_start_cat = np.concatenate((image_start_cat, image_start[w + 1]), axis=0) + + images_list.append(image_start_cat) + labels_onehot_list.append(action_label) + + end_idx = max(2, end_idx) + image_end = images[end_idx - 2:end_idx + M - 2] + image_end_cat = image_end[0] + for w in range(len(image_end) - 1): + image_end_cat = np.concatenate((image_end_cat, image_end[w + 1]), axis=0) + images_list.append(image_end_cat) + + return images_list, labels_onehot_list, idx_list + + def sample_single(self, index): + folder_id = self.vid_names[index] + if self.args.dataset == 'crosstask': + if folder_id['vid'] != self.last_vid: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + self.last_vid = folder_id['vid'] + else: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + images, labels_matrix, idx_list = self.curate_dataset( + self.images, folder_id['legal_range'], M=self.M) + frames = torch.tensor(np.array(images)) + return frames + + def __getitem__(self, index): + frames = self.sample_single(index) + frames_t = torch.zeros(2, self.args.observation_dim) + frames_t[0, :] = frames[0, :] + frames_t[1, :] = frames[-1, :] + frames_t = frames_t.view(1, 2, -1) + + batch = Batch(frames_t, self.vid_names[index], self.frame_cnts[index]) + return batch + + def __len__(self): + return len(self.json_data) diff --git a/dataloader/data_load.py b/dataloader/data_load.py new file mode 100644 index 0000000..620806f --- /dev/null +++ b/dataloader/data_load.py @@ -0,0 +1,167 @@ +import os +import numpy as np +import torch +from torch.utils.data import Dataset +import json +from collections import namedtuple + + +Batch = namedtuple('Batch', 'Observations Actions Class') + + +class PlanningDataset(Dataset): + """ + load video and action features from dataset + """ + + def __init__(self, + root, + args=None, + is_val=False, + model=None, + ): + self.is_val = is_val + self.data_root = root + self.args = args + self.max_traj_len = args.horizon + self.vid_names = None + self.frame_cnts = None + self.images = None + self.last_vid = '' + + if args.dataset == 'crosstask': + if is_val: + cross_task_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_crosstask105/sliding_window_cross_task_data_{}_{}_new_task_id_73_with_event_class.json".format(is_val, self.max_traj_len) + else: + cross_task_data_name = args.json_path_train + # "/data1/wanghanlin/diffusion_planning/jsons_crosstask105/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + + if os.path.exists(cross_task_data_name): + with open(cross_task_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(cross_task_data_name)) + else: + assert 0 + elif args.dataset == 'coin': + if is_val: + coin_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_coin/sliding_window_cross_task_data_{}_{}_new_task_id_73_with_event_class.json".format( + # is_val, self.max_traj_len) + else: + coin_data_name = args.json_path_train + # "/data1/wanghanlin/diffusion_planning/jsons_coin/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if os.path.exists(coin_data_name): + with open(coin_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(coin_data_name)) + else: + assert 0 + elif args.dataset == 'NIV': + if is_val: + niv_data_name = args.json_path_val + # "/data1/wanghanlin/diffusion_planning/jsons_niv/sliding_window_cross_task_data_{}_{}_new_task_id_73_with_event_class.json".format( + # is_val, self.max_traj_len) + else: + niv_data_name = args.json_path_train + # "/data1/wanghanlin/diffusion_planning/jsons_niv/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if os.path.exists(niv_data_name): + with open(niv_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(niv_data_name)) + else: + assert 0 + else: + raise NotImplementedError( + 'Dataset {} is not implemented'.format(args.dataset)) + + self.model = model + self.prepare_data() + #self.M = args.horizon + self.M = 3 + + def prepare_data(self): + vid_names = [] + frame_cnts = [] + for listdata in self.json_data: + vid_names.append(listdata['id']) + frame_cnts.append(listdata['instruction_len']) + self.vid_names = vid_names + self.frame_cnts = frame_cnts + + def curate_dataset(self, images, legal_range, M=2): + images_list = [] + labels_onehot_list = [] + idx_list = [] + for start_idx, end_idx, action_label in legal_range: + idx = start_idx + idx_list.append(idx) + image_start_idx = max(0, idx) + if image_start_idx + M <= len(images): + image_start = images[image_start_idx: image_start_idx + M] + else: + image_start = images[len(images) - M: len(images)] + image_start_cat = image_start[0] + for w in range(len(image_start) - 1): + image_start_cat = np.concatenate((image_start_cat, image_start[w + 1]), axis=0) + images_list.append(image_start_cat) + labels_onehot_list.append(action_label) + + end_idx = max(2, end_idx) + image_end = images[end_idx - 2:end_idx + M - 2] + image_end_cat = image_end[0] + for w in range(len(image_end) - 1): + image_end_cat = np.concatenate((image_end_cat, image_end[w + 1]), axis=0) + images_list.append(image_end_cat) + '''end_idx = max(M-1, end_idx) + image_end = images[end_idx - (M-1):end_idx + M - (M-1)] + image_end_cat = image_end[0] + for w in range(len(image_end) - 1): + image_end_cat = np.concatenate((image_end_cat, image_end[w + 1]), axis=0) + images_list.append(image_end_cat)''' + return images_list, labels_onehot_list, idx_list + + def sample_single(self, index): + folder_id = self.vid_names[index] + + if self.is_val: + event_class = folder_id['event_class'] # was event_class + else: + task_class = folder_id['task_id'] + + if self.args.dataset == 'crosstask': + if folder_id['vid'] != self.last_vid: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + self.last_vid = folder_id['vid'] + else: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + images, labels_matrix, idx_list = self.curate_dataset( + self.images, folder_id['legal_range'], M=self.M) + frames = torch.tensor(np.array(images)) + labels_tensor = torch.tensor(labels_matrix, dtype=torch.long) + + if self.is_val: + event_class = torch.tensor(event_class, dtype=torch.long) + return frames, labels_tensor, event_class + else: + task_class = torch.tensor(task_class, dtype=torch.long) + return frames, labels_tensor, task_class + + def __getitem__(self, index): + if self.is_val: + frames, labels, event_class = self.sample_single(index) + else: + frames, labels, task = self.sample_single(index) + if self.is_val: + batch = Batch(frames, labels, event_class) + else: + batch = Batch(frames, labels, task) + return batch + + def __len__(self): + return len(self.json_data) diff --git a/dataloader/data_load_mlp.py b/dataloader/data_load_mlp.py new file mode 100644 index 0000000..d50b2d6 --- /dev/null +++ b/dataloader/data_load_mlp.py @@ -0,0 +1,456 @@ +import os +import numpy as np +import torch +from torch.utils.data import Dataset +import json +import math +from collections import namedtuple + +Batch = namedtuple('Batch', 'Observations Actions Class') + + +def get_vids_from_json(path): + task_vids = {} + with open(path, 'r') as f: + json_data = json.load(f) + + for i in json_data: + task = i['task'] + vid = i['vid'] + if task not in task_vids: + task_vids[task] = [] + task_vids[task].append(vid) + return task_vids + + +def get_vids(path): + task_vids = {} + with open(path, 'r') as f: + for line in f: + task, vid, url = line.strip().split(',') + if task not in task_vids: + task_vids[task] = [] + task_vids[task].append(vid) + return task_vids + + +def read_task_info(path): + titles = {} + urls = {} + n_steps = {} + steps = {} + with open(path, 'r') as f: + idx = f.readline() + while idx != '': + idx = idx.strip() + titles[idx] = f.readline().strip() + urls[idx] = f.readline().strip() + n_steps[idx] = int(f.readline().strip()) + steps[idx] = f.readline().strip().split(',') + next(f) + idx = f.readline() + return {'title': titles, 'url': urls, 'n_steps': n_steps, 'steps': steps} + + +class PlanningDataset(Dataset): + def __init__(self, + root, + args=None, + is_val=False, + model=None, + crosstask_use_feature_how=True, + ): + self.is_val = is_val + self.data_root = root + self.args = args + self.max_traj_len = args.horizon + self.vid_names = None + self.frame_cnts = None + self.images = None + self.last_vid = '' + self.crosstask_use_feature_how = crosstask_use_feature_how + + if args.dataset == 'crosstask': + """ + . + └── crosstask + ├── crosstask_features + └── crosstask_release + ├── tasks_primary.txt + ├── videos.csv or json + └── videos_val.csv or json + """ + val_csv_path = os.path.join( + root, 'dataset', 'crosstask', 'crosstask_release', 'test_list.json') # 'videos_val.csv') + video_csv_path = os.path.join( + root, 'dataset', 'crosstask', 'crosstask_release', 'train_list.json') # 'videos.csv') + + if crosstask_use_feature_how: + self.features_path = os.path.join(root, 'dataset', 'crosstask', 'processed_data') + else: + self.features_path = os.path.join(root, 'dataset', 'crosstask', 'crosstask_features') + + self.constraints_path = os.path.join( + root, 'dataset', 'crosstask', 'crosstask_release', 'annotations') + + self.action_one_hot = np.load( + os.path.join(root, 'dataset', 'crosstask', 'crosstask_release', 'actions_one_hot.npy'), + allow_pickle=True).item() + + self.task_class = { + '23521': 0, + '59684': 1, + '71781': 2, + '113766': 3, + '105222': 4, + '94276': 5, + '53193': 6, + '105253': 7, + '44047': 8, + '76400': 9, + '16815': 10, + '95603': 11, + '109972': 12, + '44789': 13, + '40567': 14, + '77721': 15, + '87706': 16, + '91515': 17 + } + + # cross_task_data_name = "/data1/wanghanlin/diffusion_planning/jsons_crosstask105/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + + if is_val: + cross_task_data_name = args.json_path_val + else: + cross_task_data_name = args.json_path_train + + if os.path.exists(cross_task_data_name): + with open(cross_task_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(cross_task_data_name)) + else: + file_type = val_csv_path.split('.')[-1] + if file_type == 'json': + all_task_vids = get_vids_from_json(video_csv_path) + val_vids = get_vids_from_json(val_csv_path) + else: + all_task_vids = get_vids(video_csv_path) + val_vids = get_vids(val_csv_path) + + if is_val: + task_vids = val_vids + else: + task_vids = {task: [vid for vid in vids if task not in val_vids or vid not in val_vids[task]] for + task, vids in + all_task_vids.items()} + + primary_info = read_task_info(os.path.join( + root, 'dataset', 'crosstask', 'crosstask_release', 'tasks_primary.txt')) + + self.n_steps = primary_info['n_steps'] + all_tasks = set(self.n_steps.keys()) + + task_vids = {task: vids for task, + vids in task_vids.items() if task in all_tasks} + + all_vids = [] + for task, vids in task_vids.items(): + all_vids.extend([(task, vid) for vid in vids]) + json_data = [] + for idx in range(len(all_vids)): + task, vid = all_vids[idx] + if self.crosstask_use_feature_how: + video_path = os.path.join( + self.features_path, str(task) + '_' + str(vid) + '.npy') + else: + video_path = os.path.join( + self.features_path, str(vid) + '.npy') + legal_range = self.process_single(task, vid) + if not legal_range: + continue + + temp_len = len(legal_range) + temp = [] + while temp_len < self.max_traj_len: + temp.append(legal_range[0]) + temp_len += 1 + + temp.extend(legal_range) + legal_range = temp + + for i in range(len(legal_range) - self.max_traj_len + 1): + legal_range_current = legal_range[i:i + self.max_traj_len] + json_data.append({'id': {'vid': vid, 'task': task, 'feature': video_path, + 'legal_range': legal_range_current, 'task_id': self.task_class[task]}, + 'instruction_len': self.n_steps[task]}) + + self.json_data = json_data + with open(cross_task_data_name, 'w') as f: + json.dump(json_data, f) + + elif args.dataset == 'coin': + coin_path = os.path.join(root, 'dataset/coin', 'full_npy/') + val_csv_path = os.path.join( + root, 'dataset/coin', 'coin_test_30.json') + video_csv_path = os.path.join( + root, 'dataset/coin', 'coin_train_70.json') + + # coin_data_name = "/data1/wanghanlin/diffusion_planning/jsons_coin/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if is_val: + coin_data_name = args.json_path_val + else: + coin_data_name = args.json_path_train + + if os.path.exists(coin_data_name): + with open(coin_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(coin_data_name)) + else: + json_data = [] + num = 0 + if is_val: + with open(val_csv_path, 'r') as f: + coin_data = json.load(f) + else: + with open(video_csv_path, 'r') as f: + coin_data = json.load(f) + for i in coin_data: + for (k, v) in i.items(): + file_name = v['class'] + '_' + str(v['recipe_type']) + '_' + k + '.npy' + file_path = coin_path + file_name + images_ = np.load(file_path, allow_pickle=True) + images = images_['frames_features'] + legal_range = [] + + last_action = v['annotation'][-1]['segment'][1] + last_action = math.ceil(last_action) + if last_action > len(images): + print(k, last_action, len(images)) + num += 1 + continue + + for annotation in v['annotation']: + action_label = int(annotation['id']) - 1 + start_idx, end_idx = annotation['segment'] + start_idx = math.floor(start_idx) + end_idx = math.ceil(end_idx) + + if end_idx < images.shape[0]: + legal_range.append((start_idx, end_idx, action_label)) + else: + legal_range.append((start_idx, images.shape[0] - 1, action_label)) + + temp_len = len(legal_range) + temp = [] + while temp_len < self.max_traj_len: + temp.append(legal_range[0]) + temp_len += 1 + + temp.extend(legal_range) + legal_range = temp + + for i in range(len(legal_range) - self.max_traj_len + 1): + legal_range_current = legal_range[i:i + self.max_traj_len] + json_data.append({'id': {'vid': k, 'feature': file_path, + 'legal_range': legal_range_current, 'task_id': v['recipe_type']}, + 'instruction_len': 0}) + print(num) + self.json_data = json_data + with open(coin_data_name, 'w') as f: + json.dump(json_data, f) + + elif args.dataset == 'NIV': + val_csv_path = os.path.join( + root, 'dataset/NIV', 'test30_new.json') + video_csv_path = os.path.join( + root, 'dataset/NIV', 'train70_new.json') + + # niv_data_name = "/data1/wanghanlin/diffusion_planning/jsons_niv/sliding_window_cross_task_data_{}_{}_new_task_id_73.json".format( + # is_val, self.max_traj_len) + if is_val: + niv_data_name = args.json_path_val + else: + niv_data_name = args.json_path_train + + + if os.path.exists(niv_data_name): + with open(niv_data_name, 'r') as f: + self.json_data = json.load(f) + print('Loaded {}'.format(niv_data_name)) + else: + json_data = [] + if is_val: + with open(val_csv_path, 'r') as f: + niv_data = json.load(f) + else: + with open(video_csv_path, 'r') as f: + niv_data = json.load(f) + for d in niv_data: + legal_range = [] + path = os.path.join( root, 'dataset/NIV', 'processed_data' , d['feature']) + info = np.load(path, allow_pickle=True) + num_steps = int(info['num_steps']) + assert num_steps == len(info['steps_ids']) + assert info['num_steps'] == len(info['steps_starts']) + assert info['num_steps'] == len(info['steps_ends']) + starts = info['steps_starts'] + ends = info['steps_ends'] + action_labels = info['steps_ids'] + images = info['frames_features'] + + for i in range(num_steps): + action_label = int(action_labels[i]) + start_idx = math.floor(float(starts[i])) + end_idx = math.ceil(float(ends[i])) + + if end_idx < images.shape[0]: + legal_range.append((start_idx, end_idx, action_label)) + else: + legal_range.append((start_idx, images.shape[0] - 1, action_label)) + + temp_len = len(legal_range) + temp = [] + while temp_len < self.max_traj_len: + temp.append(legal_range[0]) + temp_len += 1 + + temp.extend(legal_range) + legal_range = temp + + for i in range(len(legal_range) - self.max_traj_len + 1): + legal_range_current = legal_range[i:i + self.max_traj_len] + json_data.append({'id': {'feature': path, + 'legal_range': legal_range_current, 'task_id': d['task_id']}, + 'instruction_len': 0}) + self.json_data = json_data + with open(niv_data_name, 'w') as f: + json.dump(json_data, f) + print(len(json_data)) + else: + raise NotImplementedError( + 'Dataset {} is not implemented'.format(args.dataset)) + + self.model = model + self.prepare_data() + self.M = 3 + + def process_single(self, task, vid): + if self.crosstask_use_feature_how: + if not os.path.exists(os.path.join(self.features_path, str(task) + '_' + str(vid) + '.npy')): + return False + images_ = np.load(os.path.join(self.features_path, str(task) + '_' + str(vid) + '.npy'), allow_pickle=True) + images = images_['frames_features'] + else: + if not os.path.exists(os.path.join(self.features_path, vid + '.npy')): + return False + images = np.load(os.path.join(self.features_path, vid + '.npy')) + + cnst_path = os.path.join( + self.constraints_path, task + '_' + vid + '.csv') + legal_range = self.read_assignment(task, cnst_path) + legal_range_ret = [] + for (start_idx, end_idx, action_label) in legal_range: + if not start_idx < images.shape[0]: + print(task, vid, end_idx, images.shape[0]) + return False + if end_idx < images.shape[0]: + legal_range_ret.append((start_idx, end_idx, action_label)) + else: + legal_range_ret.append((start_idx, images.shape[0] - 1, action_label)) + + return legal_range_ret + + def read_assignment(self, task_id, path): + legal_range = [] + with open(path, 'r') as f: + for line in f: + step, start, end = line.strip().split(',') + start = int(math.floor(float(start))) + end = int(math.ceil(float(end))) + action_label_ind = self.action_one_hot[task_id + '_' + step] + legal_range.append((start, end, action_label_ind)) + + return legal_range + + def prepare_data(self): + vid_names = [] + frame_cnts = [] + for listdata in self.json_data: + vid_names.append(listdata['id']) + frame_cnts.append(listdata['instruction_len']) + self.vid_names = vid_names + self.frame_cnts = frame_cnts + + def curate_dataset(self, images, legal_range, M=2): + images_list = [] + labels_onehot_list = [] + idx_list = [] + for start_idx, end_idx, action_label in legal_range: + idx = start_idx + idx_list.append(idx) + image_start_idx = max(0, idx) + if image_start_idx + M <= len(images): + image_start = images[image_start_idx: image_start_idx + M] + else: + image_start = images[len(images) - M: len(images)] + image_start_cat = image_start[0] + for w in range(len(image_start) - 1): + image_start_cat = np.concatenate((image_start_cat, image_start[w + 1]), axis=0) + images_list.append(image_start_cat) + labels_onehot_list.append(action_label) + + end_idx = max(2, end_idx) + image_end = images[end_idx - 2:end_idx + M - 2] + image_end_cat = image_end[0] + for w in range(len(image_end) - 1): + image_end_cat = np.concatenate((image_end_cat, image_end[w + 1]), axis=0) + images_list.append(image_end_cat) + return images_list, labels_onehot_list, idx_list + + def sample_single(self, index): + folder_id = self.vid_names[index] + if self.is_val: + event_class = folder_id['task_id'] + else: + task_class = folder_id['task_id'] + + if self.args.dataset == 'crosstask': + if folder_id['vid'] != self.last_vid: + if self.crosstask_use_feature_how: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + self.last_vid = folder_id['vid'] + else: + self.images = np.load(os.path.join(self.features_path, folder_id['vid'] + '.npy')) + else: + images_ = np.load(folder_id['feature'], allow_pickle=True) + self.images = images_['frames_features'] + + images, labels_matrix, idx_list = self.curate_dataset( + self.images, folder_id['legal_range'], M=self.M) + frames = torch.tensor(np.array(images)) + labels_tensor = torch.tensor(labels_matrix, dtype=torch.long) + + if self.is_val: + event_class = torch.tensor(event_class, dtype=torch.long) + return frames, labels_tensor, event_class + else: + task_class = torch.tensor(task_class, dtype=torch.long) + return frames, labels_tensor, task_class + + def __getitem__(self, index): + if self.is_val: + frames, labels, event_class = self.sample_single(index) + else: + frames, labels, task = self.sample_single(index) + if self.is_val: + batch = Batch(frames, labels, event_class) + else: + batch = Batch(frames, labels, task) + return batch + + def __len__(self): + return len(self.json_data) diff --git a/dataset/NIV/NIV_mlp_T3.json b/dataset/NIV/NIV_mlp_T3.json new file mode 100644 index 0000000..71932b8 --- /dev/null +++ b/dataset/NIV/NIV_mlp_T3.json @@ -0,0 +1 @@ +[{"id": {"feature": "dataset/NIV/processed_data/changing_tire_0001.npy", "legal_range": [[8, 26, 9], [32, 40, 0], [44, 48, 1]], "task_id": 0, "event_class": 0}, "instruction_len": 0}, {"id": {"feature": "dataset/NIV/processed_data/changing_tire_0001.npy", "legal_range": [[32, 40, 0], [44, 48, 1], [59, 66, 2]], "task_id": 0, "event_class": 0}, "instruction_len": 0}, {"id": {"feature": "dataset/NIV/processed_data/changing_tire_0001.npy", "legal_range": [[44, 48, 1], [59, 66, 2], [66, 68, 3]], "task_id": 0, "event_class": 0}, "instruction_len": 0}, {"id": {"feature": "dataset/NIV/processed_data/changing_tire_0001.npy", "legal_range": [[59, 66, 2], [66, 68, 3], [94, 96, 4]], "task_id": 0, 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diff --git a/dataset/crosstask/crosstask_release/annotations/109972_24cTWScIYlc.csv b/dataset/crosstask/crosstask_release/annotations/109972_24cTWScIYlc.csv new file mode 100644 index 0000000..825d788 --- /dev/null +++ b/dataset/crosstask/crosstask_release/annotations/109972_24cTWScIYlc.csv @@ -0,0 +1,4 @@ +1,24.89,30.29 +2,31.89,37.12 +3,73.28,77.89 +5,78.91,84.47 diff --git a/dataset/crosstask/crosstask_release/annotations/109972_2Lb_HjJWrJQ.csv b/dataset/crosstask/crosstask_release/annotations/109972_2Lb_HjJWrJQ.csv new file mode 100644 index 0000000..be37804 --- /dev/null +++ b/dataset/crosstask/crosstask_release/annotations/109972_2Lb_HjJWrJQ.csv @@ -0,0 +1,4 @@ +3,32.68,32.98 +4,33.74,46.36 +5,56.84,99.73 +4,62.45,85.67 diff --git a/dataset/crosstask/crosstask_release/annotations/109972_2Vd4i71klEw.csv b/dataset/crosstask/crosstask_release/annotations/109972_2Vd4i71klEw.csv new file mode 100644 index 0000000..034853c --- /dev/null +++ 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--git a/dataset/crosstask/crosstask_release/annotations/109972_5mHafjBjTP8.csv b/dataset/crosstask/crosstask_release/annotations/109972_5mHafjBjTP8.csv new file mode 100644 index 0000000..c802568 --- /dev/null +++ b/dataset/crosstask/crosstask_release/annotations/109972_5mHafjBjTP8.csv @@ -0,0 +1,4 @@ +2,12.9,24.13 +3,36.44,42.63 +5,42.88,55.41 +5,83.25,91.22 diff --git a/dataset/crosstask/crosstask_release/annotations/109972_644CPtcNQMA.csv b/dataset/crosstask/crosstask_release/annotations/109972_644CPtcNQMA.csv new file mode 100644 index 0000000..920a148 --- /dev/null +++ b/dataset/crosstask/crosstask_release/annotations/109972_644CPtcNQMA.csv @@ -0,0 +1,3 @@ +2,48.6,51.0 +3,53.43,56.43 +5,65.36,78.36 diff --git a/dataset/crosstask/crosstask_release/annotations/109972_68nFo4ao324.csv b/dataset/crosstask/crosstask_release/annotations/109972_68nFo4ao324.csv new file mode 100644 index 0000000..81fe895 --- /dev/null +++ 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file mode 100644 index 0000000..e19fcd3 --- /dev/null +++ b/dataset/crosstask/crosstask_release/readme.txt @@ -0,0 +1,12 @@ +tasks_primary.txt -- list of 18 primary tasks in the following format: +Task ID +Task name +URL of corresponding WikiHow page +Number of steps +Ordered list of comma-separated steps of the task + +tasks_primary.txt -- list of the related tasks in the same format. + +videos.csv -- list of videos in format ,, + +annotations/_