def downsample(img, scale, border='reflect'):
"""Bicubical downsample via **CONV2D**. Using PIL's kernel.
Args:
img: a tf tensor of 2/3/4-D.
scale: n or 1/n. `n` must be integer >= 2.
border: padding mode. Recommend to 'REFLECT'.
"""
device = img.device
kernel, s = weights_downsample(scale)
if s == 1:
return img # bypass
kernel = kernel.astype('float32')
kernel = torch.from_numpy(kernel)
p1 = int(s * 3 / 2)
p2 = 4 * s - int(s * 3 / 2)
img, shape = _push_shape_4d(img)
img_ex = F.pad(img, [p1, p2, p1, p2], mode=border)
c = img_ex.shape[1]
assert c is not None, "img must define channel number"
c = int(c)
filters = torch.reshape(torch.eye(c, c), [c, c, 1, 1]) * kernel
img_s = F.conv2d(img_ex, filters.to(device), stride=s)
img_s = _pop_shape(img_s, shape)
return img_s
def downsample(img, scale, border='REFLECT'):
"""Bicubical downsample via **CONV2D**. Using PIL's kernel.
Args:
img: a tf tensor of 2/3/4-D.
scale: n or 1/n. `n` must be integer >= 2.
border: padding mode. Recommend to 'REFLECT'.
"""
kernel, s = weights_downsample(scale)
if s == 1:
return img # bypass
kernel = tf.convert_to_tensor(kernel, dtype='float32')
p1 = int(s * 3 / 2)
p2 = 4 * s - int(s * 3 / 2)
img, shape = _push_shape_4d(img)
img_ex = tf.pad(img, [[0, 0], [p1, p2], [p1, p2], [0, 0]], border)
c = img_ex.shape[-1]
assert c is not None, "img must define channel number"
c = int(c)
filters = tf.reshape(tf.eye(c, c), [c, c, 1, 1]) * kernel
filters = tf.transpose(filters, [2, 3, 0, 1])
img_s = tf.nn.conv2d(img_ex, filters, [1, s, s, 1], 'VALID')
img_s = _pop_shape(img_s, shape)
return img_s