From 9331e0c498e855eb38434528da3dd804c9bd2faf Mon Sep 17 00:00:00 2001 From: Laurens Hoogenboom Date: Wed, 9 Oct 2024 14:40:57 +0200 Subject: [PATCH] adds dataloader and optimiser --- data/datasplits/datasplit.npz | Bin 2802964 -> 2802964 bytes dataloading.py | 50 +++++++++++++++++++ datasplitting.py | 5 ++ optimiser.py | 9 ++++ out/images/histogram_labels_train_val_00.png | Bin 68026 -> 68293 bytes 5 files changed, 64 insertions(+) create mode 100644 optimiser.py diff --git a/data/datasplits/datasplit.npz b/data/datasplits/datasplit.npz index 817fa9e061dc3d0e9f657f8933ec28e7719a74bd..fb629bdf1f40eaa4bd60bcbdb7a3533b9e26aae1 100644 GIT binary patch literal 2802964 zcmbT7hhLBH-}fuB_sobwNoFz=%E*>2LS$u+%!urfy=TfOD~Uuy0~y(}63GaWC}iut zuIu^w{sZ^*c-)WY_w_!G^Ei*=y^iyf-`%TQzWn73_&;-lKCK#D@_AUm;D7&LVS{o8 zGp3IiH@U&&sdEhtO8uYle<%L$;Q#*L=AQj~^crMnFw0!zxW1i_z7OM zfpvDnj8oA18O*VS4bH+tdT($)ba~n5yhm?~gW=WTH0{?#^No|S7uEX17o+VGVS{SW z*%Y3zg%g^?z=E*6JKV4w_8Jd=?Sm)V!P7CYkuw}y7ADINT_2-6;7)@khtT!zK_mI~ 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tt.nn.functional.one_hot(self.labels.long(), num_classes=10) # (Batch, 10) + + def __len__(self): + return len(self.images) + + def __getitem__(self, index): # get image-label pair + return dict(image=self.images[index], label=self.labels[index]) + +if __name__ == "__main__": + + here = Path(__file__).resolve().parent + path_splits = here/"data"/"datasplits" + path_processed = here/"data"/"processed" + + # split contains test data + train data + train data-->(5 * (train_##,val_##) overlapping) + split = np.load(path_splits/"datasplit.npz") + + # load processed images and labels + images = np.load(path_processed/"images.npy") + labels = np.load(path_processed/"labels.npy") + + # use indices from split to create data for splits + split_images_data = images[split["train_00"]] + split_labels_data = labels[split["train_00"]] + + # create Dataset for specific split + dataset_train = Dataset(split_images_data,split_labels_data) + + # pass to dataset loader + loader_train = tt.utils.data.DataLoader(dataset_train, batch_size=5, shuffle=True) + + for data in loader_train: + image = data["image"] + label = data["label"] + print(f"Image shape: {image.shape}, Label shape: {label.shape}") + print(f"Image min: {image.min()}, Image max: {image.max()}") + sys.exit() # Exit after first iteration \ No newline at end of file diff --git a/datasplitting.py b/datasplitting.py index 8266b26..162ca7d 100644 --- a/datasplitting.py +++ b/datasplitting.py @@ -15,6 +15,7 @@ images = np.load(path_images) labels = np.load(path_labels) + # Create index lists for splitting data split = dict() idx_train, idx_test, _, _ = train_test_split( range(len(labels)), @@ -22,14 +23,18 @@ test_size=0.2, stratify=labels, ) + + # Indices in images & labels of training and test data split["train"] = idx_train split["test"] = idx_test + # Use indexes with training data to create 5-fold split of training data into 5 new train+val sets (new sets overlap in data) skf_train_val = StratifiedKFold(n_splits=5, shuffle=True) for i, (idx_train, idx_va) in enumerate(skf_train_val.split(split["train"], labels[split["train"]])): split[f"train_{i:02d}"] = idx_train split[f"val_{i:02d}"] = idx_va + # save test data, train data and 5 splits of the train data (train_## + val_##) np.savez(path_split/"datasplit.npz", **split) """Print datasplit information""" diff --git a/optimiser.py b/optimiser.py new file mode 100644 index 0000000..3ac5330 --- /dev/null +++ b/optimiser.py @@ -0,0 +1,9 @@ +import torch.optim + + +class Solver(torch.optim): + def __init__(lr): + self.optimizer = super().AdamW( + model.parameters(), + lr=configuration["lr"], + ) \ No newline at end of file diff --git a/out/images/histogram_labels_train_val_00.png b/out/images/histogram_labels_train_val_00.png index bac32289676b16117305ff2f8d9d151e6d6b5795..22d903222f261e04ba5ef1bbfb39ab8f6de0b8f8 100644 GIT binary patch literal 68293 zcmeFZ`9Ia$`#!GIX*f+zrxBq}BSmG%%;{7_hK$LWBAI70cbYVzL}n7Unaty+k|9Fo 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