50 train with samples

alodataset/base_dataset.py

@@ -133,12 +133,11 @@ def __init__(
         super(BaseDataset, self).__init__(**kwargs)
         self.name = name
         self.sample = sample
-        if not self.sample:
-            self.items = []
-            self.dataset_dir = self.get_dataset_dir()
+        self.dataset_dir = self.get_dataset_dir()
         if self.sample:
             self.items = self.download_sample()
-            self.dataset_dir = os.path.join(self.vb_folder, "samples")
+        else:
+            self.items = []
         self.transform_fn = transform_fn
         self.ignore_errors = ignore_errors
         self.print_errors = print_errors
@@ -212,6 +211,9 @@ def get_dataset_dir(self) -> str:
         """Look for dataset_dir based on the given name. To work properly a alodataset_config.json
         file must be save into /home/USER/.aloception/alodataset_config.json
         """
+        if self.sample:
+            return os.path.join(self.vb_folder, "samples")
+
         streaming_dt_config = os.path.join(self.vb_folder, "alodataset_config.json")
         if not os.path.exists(streaming_dt_config):
             self.set_dataset_dir(None)
@@ -247,19 +249,19 @@ def set_dataset_dir(self, dataset_dir: str):
 
         if dataset_dir is None:
             dataset_dir = _user_prompt(
-                f"{self.name} does not exist in config file."
+                f"{self.name} does not exist in config file. "
                 + "Do you want to download and use a sample?: (Y)es or (N)o: "
             )
-            if dataset_dir.lower() in ["y", "yes"]:
+            if dataset_dir.lower() in ["y", "yes"]:  # Download sample and change root directory
                 self.sample = True
-                return
+                return os.path.join(self.vb_folder, "samples")
             dataset_dir = _user_prompt(f"Please write a new root directory for {self.name} dataset: ")
             dataset_dir = os.path.expanduser(dataset_dir)
 
         # Save the config
         if not os.path.exists(dataset_dir):
             dataset_dir = _user_prompt(
-                f"[WARNING] {dataset_dir} path does not exists for dataset: {self.name}."
+                f"[WARNING] {dataset_dir} path does not exists for dataset: {self.name}. "
                 + "Please write a new directory:"
             )
             dataset_dir = os.path.expanduser(dataset_dir)

alodataset/coco_detection_dataset.py

@@ -82,7 +82,8 @@ def __init__(
         if "sample" not in kwargs:
             kwargs["sample"] = False
 
-        if not kwargs["sample"]:
+        self.sample = kwargs["sample"]
+        if not self.sample:
             assert img_folder is not None, "When sample = False, img_folder must be given."
             assert ann_file is not None, "When sample = False, ann_file must be given."
 
@@ -91,8 +92,8 @@ def __init__(
             img_folder = os.path.join(dataset_dir, img_folder)
             ann_file = os.path.join(dataset_dir, ann_file)
             stuff_ann_file = None if stuff_ann_file is None else os.path.join(dataset_dir, stuff_ann_file)
+            kwargs["sample"] = self.sample
 
-        self.sample = kwargs["sample"]
         super(CocoDetectionDataset, self).__init__(name=name, root=img_folder, annFile=ann_file, **kwargs)
         if self.sample:
             return

alonet/deformable_detr/train_on_coco.py

@@ -15,9 +15,6 @@ def get_arg_parser():
     parser = ArgumentParser(conflict_handler="resolve")
     parser = alonet.common.add_argparse_args(parser)  # Common alonet parser
     parser = CocoDetection2Detr.add_argparse_args(parser)  # Coco detection parser
-    parser.add_argument(
-        "--use_sample", action="store_true", help="Download a sample for train process (Default: %(default)s)"
-    )
     parser = LitDeformableDetr.add_argparse_args(parser)  # LitDeformableDetr training parser
     # parser = pl.Trainer.add_argparse_args(parser) # Pytorch lightning Parser
     return parser

alonet/detr/coco_data_modules.py

@@ -19,7 +19,6 @@ def __init__(
         train_ann: str = "annotations/instances_train2017.json",
         val_folder: str = "val2017",
         val_ann: str = "annotations/instances_val2017.json",
-        sample: bool = False,
         **kwargs
     ):
         """LightningDataModule to use coco dataset in Detr models
@@ -58,6 +57,7 @@ def __init__(
         Arguments entered by the user (kwargs) will replace those stored in args attribute
         """
         # Update class attributes with args and kwargs inputs
+
         super().__init__()
         alonet.common.pl_helpers.params_update(self, args, kwargs)
 
@@ -71,17 +71,16 @@ def __init__(
             # Split=Split.TRAIN if not self.train_on_val else Split.VAL,
             classes=classes,
             name=name,
-            sample=sample,
         )
         self.val_loader_kwargs = dict(
             img_folder=val_folder,
             ann_file=val_ann,
             # split=Split.VAL,
             classes=classes,
             name=name,
-            sample=sample,
         )
         self.args = args
+        self.val_check()  # Check val loader and set some previous parameters
 
     @staticmethod
     def add_argparse_args(parent_parser):
@@ -104,18 +103,11 @@ def add_argparse_args(parent_parser):
             nargs="+",
             help="If no augmentation (--no_augmentation) is used, --size can be used to resize all the frame.",
         )
-        # parser.add_argument("--classes", type=str, default=None, nargs="+", help="List to classes to be filtered in dataset. (%(default)s by default)")
+        parser.add_argument(
+            "--sample", action="store_true", help="Download a sample for train/val process (Default: %(default)s)"
+        )
         return parent_parser
 
-    @property
-    def CATEGORIES(self):
-        if not hasattr(self, "coco_train"):
-            self.setup()
-        if not hasattr(self, "coco_train"):
-            return None
-        else:
-            return self.coco_train.CATEGORIES if hasattr(self.coco_train, "CATEGORIES") else None
-
     def train_transform(self, frame, same_on_sequence: bool = True, same_on_frames: bool = False):
         if self.no_augmentation:
             if self.size[0] is not None and self.size[1] is not None:
@@ -158,15 +150,27 @@ def val_transform(
 
         return frame.norm_resnet()
 
+    def val_check(self):
+        # Instance a default loader to set attributes
+        self.coco_val = alodataset.CocoDetectionDataset(
+            transform_fn=self.val_transform, sample=self.sample, **self.val_loader_kwargs,
+        )
+        self.sample = self.coco_val.sample or self.sample  # Update sample if user prompt is given
+        self.CATEGORIES = self.coco_val.CATEGORIES if hasattr(self.coco_val, "CATEGORIES") else None
+
     def setup(self, stage: Optional[str] = None) -> None:
         if stage == "fit" or stage is None:
             # Setup train/val loaders
             self.coco_train = alodataset.CocoDetectionDataset(
-                transform_fn=self.train_transform, **self.train_loader_kwargs
+                transform_fn=self.train_transform, sample=self.sample, **self.train_loader_kwargs
+            )
+            self.coco_val = alodataset.CocoDetectionDataset(
+                transform_fn=self.val_transform, sample=self.sample, **self.val_loader_kwargs
             )
-            self.coco_val = alodataset.CocoDetectionDataset(transform_fn=self.val_transform, **self.val_loader_kwargs)
 
     def train_dataloader(self):
+        """Train dataloader"""
+        # Init training loader
         if not hasattr(self, "coco_train"):
             self.setup()
         return self.coco_train.train_loader(batch_size=self.batch_size, num_workers=self.num_workers)

alonet/detr/train_on_coco.py

@@ -9,9 +9,6 @@ def get_arg_parser():
     parser = ArgumentParser(conflict_handler="resolve")
     parser = alonet.common.add_argparse_args(parser)  # Common alonet parser
     parser = CocoDetection2Detr.add_argparse_args(parser)  # Coco detection parser
-    parser.add_argument(
-        "--use_sample", action="store_true", help="Download a sample for train process (Default: %(default)s)"
-    )
     parser = LitDetr.add_argparse_args(parser)  # LitDetr training parser
     # parser = pl.Trainer.add_argparse_args(parser) # Pytorch lightning Parser
     return parser
@@ -24,7 +21,7 @@ def main():
 
     # Init the Detr model with the dataset
     detr = LitDetr(args)
-    coco_loader = CocoDetection2Detr(args, sample=args.use_sample)
+    coco_loader = CocoDetection2Detr(args)
 
     detr.run_train(data_loader=coco_loader, args=args, project="detr", expe_name="detr_50")
 

alonet/raft/data_modules/chairs2raft.py

@@ -13,12 +13,13 @@ def __init__(self, args):
     def train_dataloader(self):
         split = Split.VAL if self.train_on_val else Split.TRAIN
         dataset = FlyingChairs2Dataset(split=split, transform_fn=self.train_transform, sample=self.sample)
+        self.sample = self.sample or dataset.sample
         sampler = SequentialSampler if self.sequential else RandomSampler
         return dataset.train_loader(batch_size=self.batch_size, num_workers=self.num_workers, sampler=sampler)
 
     def val_dataloader(self):
         dataset = FlyingChairs2Dataset(split=Split.VAL, transform_fn=self.val_transform, sample=self.sample)
-
+        self.sample = self.sample or dataset.sample
         return dataset.train_loader(batch_size=1, num_workers=self.num_workers, sampler=SequentialSampler)
 
 

alonet/raft/data_modules/data2raft.py

@@ -25,6 +25,9 @@ def add_argparse_args(parent_parser):
         parser.add_argument("--num_workers", type=int, default=8, help="num_workers to use on the dataset")
         parser.add_argument("--limit_val_batches", type=_int_or_float_type, default=100)
         parser.add_argument("--sequential_sampler", action="store_true", help="sample data sequentially (no shuffle)")
+        parser.add_argument(
+            "--sample", action="store_true", help="Download a sample for train/val process (Default: %(default)s)"
+        )
         return parent_parser
 
     def train_transform(self, frame):

alonet/raft/train_on_chairs.py

@@ -9,9 +9,6 @@ def get_args_parser():
     parser = argparse.ArgumentParser(conflict_handler="resolve")
     parser = alonet.common.add_argparse_args(parser, add_pl_args=True)
     parser = Chairs2RAFT.add_argparse_args(parser)
-    parser.add_argument(
-        "--use_sample", action="store_true", help="Download a sample for train process (Default: %(default)s)"
-    )
     parser = LitRAFT.add_argparse_args(parser)
     return parser
 

unittest/test_train.py

@@ -18,7 +18,7 @@ def get_argparse_defaults(parser):
 detr_args["weights"] = "detr-r50"
 detr_args["train_on_val"] = True
 detr_args["fast_dev_run"] = True
-detr_args["use_sample"] = True
+detr_args["sample"] = True
 
 
 @mock.patch("argparse.ArgumentParser.parse_args", return_value=argparse.Namespace(**detr_args))
@@ -38,7 +38,7 @@ def test_detr(mock_args):
 def_detr_args["model_name"] = "deformable-detr-r50"
 def_detr_args["train_on_val"] = True
 def_detr_args["fast_dev_run"] = True
-def_detr_args["use_sample"] = True
+def_detr_args["sample"] = True
 
 
 @mock.patch("argparse.ArgumentParser.parse_args", return_value=argparse.Namespace(**def_detr_args))
@@ -54,7 +54,7 @@ def test_deformable_detr(mock_args):
 raft_args["weights"] = "raft-things"
 raft_args["train_on_val"] = True
 raft_args["fast_dev_run"] = True
-raft_args["use_sample"] = True
+raft_args["sample"] = True
 
 
 @mock.patch("argparse.ArgumentParser.parse_args", return_value=argparse.Namespace(**raft_args))