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def to_tensor(x,channel=None,single_sample=True):
if single_sample:
x = x[np.newaxis]
if channel is not None and channel >= 0:
channel += 1
if channel is None:
x, channel = np.expand_dims(x,-1), -1
return move_channel_for_backend(x,channel)
assert X.shape[0] == Y.shape[0]
assert 0 < n_images <= X.shape[0]
assert 0 <= validation_split < 1
X, Y = X[:n_images], Y[:n_images]
channel = axes_dict(axes)['C']
if validation_split > 0:
n_val = int(round(n_images * validation_split))
n_train = n_images - n_val
assert 0 < n_val and 0 < n_train
X_t, Y_t = X[-n_val:], Y[-n_val:]
X, Y = X[:n_train], Y[:n_train]
assert X.shape[0] == n_train and X_t.shape[0] == n_val
X_t = move_channel_for_backend(X_t,channel=channel)
Y_t = move_channel_for_backend(Y_t,channel=channel)
X = move_channel_for_backend(X,channel=channel)
Y = move_channel_for_backend(Y,channel=channel)
axes = axes.replace('C','') # remove channel
if backend_channels_last():
axes = axes+'C'
else:
axes = axes[:1]+'C'+axes[1:]
data_val = (X_t,Y_t) if validation_split > 0 else None
if verbose:
ax = axes_dict(axes)
n_train, n_val = len(X), len(X_t) if validation_split>0 else 0
image_size = tuple( X.shape[ax[a]] for a in axes if a in 'TZYX' )
assert 0 <= validation_split < 1
X, Y = X[:n_images], Y[:n_images]
channel = axes_dict(axes)['C']
if validation_split > 0:
n_val = int(round(n_images * validation_split))
n_train = n_images - n_val
assert 0 < n_val and 0 < n_train
X_t, Y_t = X[-n_val:], Y[-n_val:]
X, Y = X[:n_train], Y[:n_train]
assert X.shape[0] == n_train and X_t.shape[0] == n_val
X_t = move_channel_for_backend(X_t,channel=channel)
Y_t = move_channel_for_backend(Y_t,channel=channel)
X = move_channel_for_backend(X,channel=channel)
Y = move_channel_for_backend(Y,channel=channel)
axes = axes.replace('C','') # remove channel
if backend_channels_last():
axes = axes+'C'
else:
axes = axes[:1]+'C'+axes[1:]
data_val = (X_t,Y_t) if validation_split > 0 else None
if verbose:
ax = axes_dict(axes)
n_train, n_val = len(X), len(X_t) if validation_split>0 else 0
image_size = tuple( X.shape[ax[a]] for a in axes if a in 'TZYX' )
n_dim = len(image_size)
n_channel_in, n_channel_out = X.shape[ax['C']], Y.shape[ax['C']]
X, Y = X[:n_images], Y[:n_images]
channel = axes_dict(axes)['C']
if validation_split > 0:
n_val = int(round(n_images * validation_split))
n_train = n_images - n_val
assert 0 < n_val and 0 < n_train
X_t, Y_t = X[-n_val:], Y[-n_val:]
X, Y = X[:n_train], Y[:n_train]
assert X.shape[0] == n_train and X_t.shape[0] == n_val
X_t = move_channel_for_backend(X_t,channel=channel)
Y_t = move_channel_for_backend(Y_t,channel=channel)
X = move_channel_for_backend(X,channel=channel)
Y = move_channel_for_backend(Y,channel=channel)
axes = axes.replace('C','') # remove channel
if backend_channels_last():
axes = axes+'C'
else:
axes = axes[:1]+'C'+axes[1:]
data_val = (X_t,Y_t) if validation_split > 0 else None
if verbose:
ax = axes_dict(axes)
n_train, n_val = len(X), len(X_t) if validation_split>0 else 0
image_size = tuple( X.shape[ax[a]] for a in axes if a in 'TZYX' )
n_dim = len(image_size)
n_channel_in, n_channel_out = X.shape[ax['C']], Y.shape[ax['C']]
n_images = X.shape[0]
assert X.shape[0] == Y.shape[0]
assert 0 < n_images <= X.shape[0]
assert 0 <= validation_split < 1
X, Y = X[:n_images], Y[:n_images]
channel = axes_dict(axes)['C']
if validation_split > 0:
n_val = int(round(n_images * validation_split))
n_train = n_images - n_val
assert 0 < n_val and 0 < n_train
X_t, Y_t = X[-n_val:], Y[-n_val:]
X, Y = X[:n_train], Y[:n_train]
assert X.shape[0] == n_train and X_t.shape[0] == n_val
X_t = move_channel_for_backend(X_t,channel=channel)
Y_t = move_channel_for_backend(Y_t,channel=channel)
X = move_channel_for_backend(X,channel=channel)
Y = move_channel_for_backend(Y,channel=channel)
axes = axes.replace('C','') # remove channel
if backend_channels_last():
axes = axes+'C'
else:
axes = axes[:1]+'C'+axes[1:]
data_val = (X_t,Y_t) if validation_split > 0 else None
if verbose:
ax = axes_dict(axes)
n_train, n_val = len(X), len(X_t) if validation_split>0 else 0