def resize_all(output_shape):
resizer_1 = ds.resize(u.mul_shape(output_shape, 4))
resizer_2 = ds.resize(u.mul_shape(output_shape, 2))
resizer_3 = ds.resize(output_shape)
def f(x):
x = resizer_1(x)
y = resizer_2(x)
z = resizer_3(y)
return x, y, z
return f
def resize_all(output_shape):
resizer_0 = ds.resize(u.mul_shape(output_shape, 16))
resizer_1 = ds.resize(u.mul_shape(output_shape, 8))
resizer_2 = ds.resize(u.mul_shape(output_shape, 4))
resizer_3 = ds.resize(u.mul_shape(output_shape, 2))
resizer_4 = ds.resize(output_shape)
def f(x):
w = resizer_0(x)
x = resizer_1(x)
y = resizer_2(x)
z = resizer_3(y)
v = resizer_4(x)
return w, x, y, z, v
return f
def prepare(dataset, input_shape, output_shape):
resizer = ds.resize(u.mul_shape(output_shape, 32))
stream = ds.epochs(dataset.image_ids, epochs=1)
stream = ds.stream(
lambda x: (dataset._img_filenames[x], resizer(dataset.load_image(x))),
stream)
return stream
def resize_all(output_shape):
resizer_orig = ds.resize(u.mul_shape(output_shape, 32))
resizer_0 = ds.resize(u.mul_shape(output_shape, 16))
resizer_1 = ds.resize(u.mul_shape(output_shape, 8))
resizer_2 = ds.resize(u.mul_shape(output_shape, 4))
resizer_3 = ds.resize(u.mul_shape(output_shape, 2))
resizer_4 = ds.resize(output_shape)
def f(input_):
input_ = resizer_orig(input_)
w = resizer_0(input_)
x = resizer_1(w)
y = resizer_2(x)
z = resizer_3(y)
v = resizer_4(z)
return input_, w, x, y, z, v
return f
def resize_all(dataset, output_shape):
out_shape_1 = u.mul_shape(output_shape, 4)
resizer_1 = ds.resize(out_shape_1)
resizer_2 = ds.resize(u.mul_shape(output_shape, 2))
resizer_3 = ds.resize(output_shape)
def f((image_id, img)):
cat = dataset.to_categorical(image_id)
result = np.zeros(out_shape_1 + [cat.shape[2]])
for i in range(0, cat.shape[2]):
result[:, :, i] = resizer_1(cat[:, :, i])
x = resizer_1(img)
y = resizer_2(x)
z = resizer_3(y)
x = x.reshape(x.shape + (1, ))
y = y.reshape(y.shape + (1, ))
z = z.reshape(z.shape + (1, ))
return result, x, y, z
return f