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Replace np.float and np.row_stack across 2 files (removed in NumPy 1.24 / NumPy 2.0) #99

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Replace np.float and np.row_stack across 2 files (removed in NumPy 1.24 / NumPy 2.0) #99

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20 changes: 10 additions & 10 deletions cords/utils/data/datasets/SL/builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -466,30 +466,30 @@ def create_imbalance(x_trn, y_trn, x_val, y_val, x_tst, y_tst, num_cls, ratio, s
subset_idxs = rng.choice(np.where(y_trn == i)[0], size=min_samples, replace=False)
else:
subset_idxs = np.where(y_trn == i)[0]
x_trn_new = np.row_stack((x_trn_new, x_trn[subset_idxs]))
y_trn_new = np.row_stack((y_trn_new, y_trn[subset_idxs].reshape(-1, 1)))
x_trn_new = np.vstack((x_trn_new, x_trn[subset_idxs]))
y_trn_new = np.vstack((y_trn_new, y_trn[subset_idxs].reshape(-1, 1)))
max_samples = int(np.max(val_samples_per_class))
for i in range(num_cls):
y_class = np.where(y_val == i)[0]
if i == 0:
subset_ids = rng.choice(y_class, size=max_samples - y_class.shape[0], replace=True)
x_val_new = np.row_stack((x_val, x_val[subset_ids]))
y_val_new = np.row_stack((y_val.reshape(-1, 1), y_val[subset_ids].reshape(-1, 1)))
x_val_new = np.vstack((x_val, x_val[subset_ids]))
y_val_new = np.vstack((y_val.reshape(-1, 1), y_val[subset_ids].reshape(-1, 1)))
else:
subset_ids = rng.choice(y_class, size=max_samples - y_class.shape[0], replace=True)
x_val_new = np.row_stack((x_val, x_val_new, x_val[subset_ids]))
y_val_new = np.row_stack((y_val.reshape(-1, 1), y_val_new, y_val[subset_ids].reshape(-1, 1)))
x_val_new = np.vstack((x_val, x_val_new, x_val[subset_ids]))
y_val_new = np.vstack((y_val.reshape(-1, 1), y_val_new, y_val[subset_ids].reshape(-1, 1)))
max_samples = int(np.max(tst_samples_per_class))
for i in range(num_cls):
y_class = np.where(y_tst == i)[0]
if i == 0:
subset_ids = rng.choice(y_class, size=max_samples - y_class.shape[0], replace=True)
x_tst_new = np.row_stack((x_tst, x_tst[subset_ids]))
y_tst_new = np.row_stack((y_tst.reshape(-1, 1), y_tst[subset_ids].reshape(-1, 1)))
x_tst_new = np.vstack((x_tst, x_tst[subset_ids]))
y_tst_new = np.vstack((y_tst.reshape(-1, 1), y_tst[subset_ids].reshape(-1, 1)))
else:
subset_ids = rng.choice(y_class, size=max_samples - y_class.shape[0], replace=True)
x_tst_new = np.row_stack((x_tst, x_tst_new, x_tst[subset_ids]))
y_tst_new = np.row_stack((y_tst.reshape(-1, 1), y_tst_new, y_tst[subset_ids].reshape(-1, 1)))
x_tst_new = np.vstack((x_tst, x_tst_new, x_tst[subset_ids]))
y_tst_new = np.vstack((y_tst.reshape(-1, 1), y_tst_new, y_tst[subset_ids].reshape(-1, 1)))

return x_trn_new, y_trn_new.reshape(-1), x_val_new, y_val_new.reshape(-1), x_tst_new, y_tst_new.reshape(-1)

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2 changes: 1 addition & 1 deletion cords/utils/data/datasets/SSL/augmentation/augmentation_pool.py
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Expand Up @@ -253,7 +253,7 @@ def __repr__(self):
"""
def numpy_batch_gcn(images, multiplier=55, eps=1e-10):
# global contrast normalization
images = images.astype(np.float)
images = images.astype(float)
images -= images.mean(axis=(1,2,3), keepdims=True)
per_image_norm = np.sqrt(np.square(images).sum((1,2,3), keepdims=True))
per_image_norm[per_image_norm < eps] = 1
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