microsoft
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ORBIT-Dataset
зеркало из https://github.com/microsoft/ORBIT-Dataset.git
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added option to filter train and test differently

This commit is contained in:
Daniela Massiceti 2022-12-20 02:16:00 +00:00
Родитель a5393bc73c
Коммит 9a0c19de93
4 изменённых файлов: 15 добавлений и 16 удалений
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6
data/dataloaders.py
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@ -27,7 +27,7 @@ class DataLoader():
dataset_info['frame_size'],
dataset_info['frame_norm_method'],
dataset_info['annotations_to_load'],
dataset_info['filter_by_annotations'],
dataset_info['train_filter_by_annotations'],
dataset_info['num_train_tasks'],
with_cluster_labels=dataset_info['with_cluster_labels'],
with_caps=dataset_info['with_train_shot_caps'],
@ -46,7 +46,7 @@ class DataLoader():
dataset_info['frame_size'],
dataset_info['frame_norm_method'],
dataset_info['annotations_to_load'],
dataset_info['filter_by_annotations'],
dataset_info['test_filter_by_annotations'],
dataset_info['num_val_tasks'],
test_mode=True,
logfile=dataset_info['logfile'])
@ -64,7 +64,7 @@ class DataLoader():
dataset_info['frame_size'],
dataset_info['frame_norm_method'],
dataset_info['annotations_to_load'],
dataset_info['filter_by_annotations'],
dataset_info['test_filter_by_annotations'],
dataset_info['num_test_tasks'],
test_mode=True,
logfile=dataset_info['logfile'])

8
multi-step-learner.py
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@ -63,14 +63,12 @@ class Learner:
random.seed(self.args.seed)
torch.manual_seed(self.args.seed)
device_id = 'cpu'
self.map_location = 'cpu'
if torch.cuda.is_available() and self.args.gpu >= 0:
cudnn.enabled = True
cudnn.benchmark = False
cudnn.deterministic = True
device_id = 'cuda:' + str(self.args.gpu)
torch.cuda.manual_seed_all(self.args.seed)
self.map_location = lambda storage, loc: storage.cuda()
self.device = torch.device(device_id)
self.ops_counter = OpsCounter(count_backward=True)
@ -101,7 +99,7 @@ class Learner:
'frame_size': self.args.frame_size,
'frame_norm_method': self.args.frame_norm_method,
'annotations_to_load': self.args.annotations_to_load,
'filter_by_annotations': [self.args.filter_context, self.args.filter_target],
'test_filter_by_annotations': [self.args.test_filter_context, self.args.test_filter_target],
'logfile': self.logfile
}
@ -137,7 +135,7 @@ class Learner:
self.model = self.init_model()
if path and os.path.exists(path): # if path exists, load from disk
self.model.load_state_dict(torch.load(path, map_location=self.map_location), strict=False)
self.model.load_state_dict(torch.load(path), strict=False)
else:
print_and_log(self.logfile, 'warning: saved model path could not be found; using original param initialisation.')
path = self.checkpoint_dir
@ -190,7 +188,7 @@ class Learner:
if (step+1) % self.args.num_test_tasks == 0:
self.test_evaluator.set_current_user(task_dict["task_id"])
_,_,_,current_video_stats = self.test_evaluator.get_mean_stats(current_user=True)
print_and_log(self.logfile, f'{self.args.test_set} user {task_dict["task_id"]} ({self.test_evaluator.current_user+1}/{len(self.test_queue)}) stats: {stats_to_str(current_video_stats)} avg. #context clips/task: {np.mean(num_context_clips_per_task):.0f} avg. #target clips/task: {np.mean(num_target_clips_per_task):.0f}')
print_and_log(self.logfile, f'{self.args.test_set} user {task_dict["task_id"]} ({self.test_evaluator.current_user+1}/{len(self.test_queue)}) stats: {stats_to_str(current_video_stats)} avg # context clips/task: {np.mean(num_context_clips_per_task):.0f} avg # target clips/task: {np.mean(num_target_clips_per_task):.0f}')
if (step+1) < num_test_tasks:
num_context_clips_per_task, num_target_clips_per_task = [], []
self.test_evaluator.next_user()

11
single-step-learner.py
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@ -64,14 +64,12 @@ class Learner:
random.seed(self.args.seed)
torch.manual_seed(self.args.seed)
device_id='cpu'
self.map_location='cpu'
if torch.cuda.is_available() and self.args.gpu>=0:
cudnn.enabled = True
cudnn.benchmark = False
cudnn.deterministic = True
device_id = 'cuda:' + str(self.args.gpu)
torch.cuda.manual_seed_all(self.args.seed)
self.map_location=lambda storage, loc: storage.cuda()
self.device = torch.device(device_id)
self.ops_counter = OpsCounter()
@ -112,7 +110,8 @@ class Learner:
'frame_size': self.args.frame_size,
'frame_norm_method': self.args.frame_norm_method,
'annotations_to_load': self.args.annotations_to_load,
'filter_by_annotations': [self.args.filter_context, self.args.filter_target],
'train_filter_by_annotations': [self.args.train_filter_context, self.args.train_filter_target],
'test_filter_by_annotations': [self.args.test_filter_context, self.args.test_filter_target],
'logfile': self.logfile
}
@ -277,7 +276,7 @@ class Learner:
if (step+1) % self.args.num_val_tasks == 0:
self.validation_evaluator.set_current_user(task_dict["task_id"])
_,_,_,current_video_stats = self.validation_evaluator.get_mean_stats(current_user=True)
print_and_log(self.logfile, f'validation user {task_dict["task_id"]} ({self.validation_evaluator.current_user+1}/{len(self.validation_queue)}) stats: {stats_to_str(current_video_stats)} avg. #context clips/task: {np.mean(num_context_clips_per_task):.0f} avg. #target clips/task: {np.mean(num_target_clips_per_task):.0f}')
print_and_log(self.logfile, f'validation user {task_dict["task_id"]} ({self.validation_evaluator.current_user+1}/{len(self.validation_queue)}) stats: {stats_to_str(current_video_stats)} avg # context clips/task: {np.mean(num_context_clips_per_task):.0f} avg # target clips/task: {np.mean(num_target_clips_per_task):.0f}')
if (step+1) < num_val_tasks:
num_context_clips_per_task, num_target_clips_per_task = [], []
self.validation_evaluator.next_user()
@ -302,7 +301,7 @@ class Learner:
self.init_model()
if path and os.path.exists(path): #if path exists
self.model.load_state_dict(torch.load(path, map_location=self.map_location))
self.model.load_state_dict(torch.load(path))
else:
print_and_log(self.logfile, 'warning: saved model path could not be found; using pretrained initialisation.')
path = self.checkpoint_dir
@ -344,7 +343,7 @@ class Learner:
if (step+1) % self.args.num_test_tasks == 0:
self.test_evaluator.set_current_user(task_dict["task_id"])
_,_,_,current_video_stats = self.test_evaluator.get_mean_stats(current_user=True)
print_and_log(self.logfile, f'{self.args.test_set} user {task_dict["task_id"]} ({self.test_evaluator.current_user+1}/{len(self.test_queue)}) stats: {stats_to_str(current_video_stats)} avg. #context clips/task: {np.mean(num_context_clips_per_task):.0f} avg. #target clips/task: {np.mean(num_target_clips_per_task):.0f}')
print_and_log(self.logfile, f'{self.args.test_set} user {task_dict["task_id"]} ({self.test_evaluator.current_user+1}/{len(self.test_queue)}) stats: {stats_to_str(current_video_stats)} avg # context clips/task: {np.mean(num_context_clips_per_task):.0f} avg # target clips/task: {np.mean(num_target_clips_per_task):.0f}')
if (step+1) < num_test_tasks:
num_context_clips_per_task, num_target_clips_per_task = [], []
self.test_evaluator.next_user()

6
utils/args.py
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@ -174,8 +174,10 @@ def parse_args(learner='default'):
help="Momentum for SGD optimizer during personalization (default: 0.0).")
args = parser.parse_args()
args.filter_context = expand_issues(args.filter_context)
args.filter_target = expand_issues(args.filter_target)
args.train_filter_context = expand_issues(args.train_filter_context)
args.train_filter_target = expand_issues(args.train_filter_target)
args.test_filter_context = expand_issues(args.test_filter_context)
args.test_filter_target = expand_issues(args.test_filter_target)
if args.feature_extractor == 'efficientnet_b0':
args.frame_norm_method = 'imagenet'
elif args.feature_extractor in ['efficientnet_v2_s', 'vit_s_32', 'vit_b_32']: