提交 30279833 编写于 作者: W WenmuZhou

tps 前反向对齐

上级 c3566221
......@@ -203,9 +203,9 @@ class GridGenerator(nn.Layer):
def build_C_paddle(self):
""" Return coordinates of fiducial points in I_r; C """
F = self.F
ctrl_pts_x = paddle.linspace(-1.0, 1.0, int(F / 2))
ctrl_pts_y_top = -1 * paddle.ones([int(F / 2)])
ctrl_pts_y_bottom = paddle.ones([int(F / 2)])
ctrl_pts_x = paddle.linspace(-1.0, 1.0, int(F / 2), dtype='float64')
ctrl_pts_y_top = -1 * paddle.ones([int(F / 2)], dtype='float64')
ctrl_pts_y_bottom = paddle.ones([int(F / 2)], dtype='float64')
ctrl_pts_top = paddle.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = paddle.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
C = paddle.concat([ctrl_pts_top, ctrl_pts_bottom], axis=0)
......@@ -213,12 +213,16 @@ class GridGenerator(nn.Layer):
def build_P_paddle(self, I_r_size):
I_r_height, I_r_width = I_r_size
I_r_grid_x = (
paddle.arange(-I_r_width, I_r_width, 2).astype('float32') + 1.0
) / I_r_width # self.I_r_width
I_r_grid_y = (
paddle.arange(-I_r_height, I_r_height, 2).astype('float32') + 1.0
) / I_r_height # self.I_r_height
I_r_grid_x = paddle.divide(
paddle.arange(
-I_r_width, I_r_width, 2, dtype='float64') + 1.0,
paddle.to_tensor(
I_r_width, dtype='float64')) # / 2.2128224363981985e-08
I_r_grid_y = paddle.divide(
paddle.arange(
-I_r_height, I_r_height, 2, dtype='float64') + 1.0,
paddle.to_tensor(
I_r_height, dtype='float64')) # self.I_r_height
# P: self.I_r_width x self.I_r_height x 2
P = paddle.stack(paddle.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
P = paddle.transpose(P, perm=[1, 0, 2])
......@@ -228,7 +232,7 @@ class GridGenerator(nn.Layer):
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_C = paddle.zeros((F, F), dtype='float32') # F x F
hat_C = paddle.zeros((F, F), dtype='float64') # F x F
for i in range(0, F):
for j in range(i, F):
if i == j:
......@@ -241,13 +245,21 @@ class GridGenerator(nn.Layer):
delta_C = paddle.concat( # F+3 x F+3
[
paddle.concat(
[paddle.ones((F, 1)), C, hat_C], axis=1), # F x F+3
[paddle.ones(
(F, 1), dtype='float64'), C, hat_C], axis=1), # F x F+3
paddle.concat(
[paddle.zeros((2, 3)), paddle.transpose(
C, perm=[1, 0])],
[
paddle.zeros(
(2, 3), dtype='float64'), paddle.transpose(
C, perm=[1, 0])
],
axis=1), # 2 x F+3
paddle.concat(
[paddle.zeros((1, 3)), paddle.ones((1, F))],
[
paddle.zeros(
(1, 3), dtype='float64'), paddle.ones(
(1, F), dtype='float64')
],
axis=1) # 1 x F+3
],
axis=0)
......@@ -268,7 +280,9 @@ class GridGenerator(nn.Layer):
# rbf: n x F
rbf = paddle.multiply(
paddle.square(rbf_norm), paddle.log(rbf_norm + eps))
P_hat = paddle.concat([paddle.ones((n, 1)), P, rbf], axis=1)
P_hat = paddle.concat(
[paddle.ones(
(n, 1), dtype='float64'), P, rbf], axis=1)
return P_hat # n x F+3
def get_expand_tensor(self, batch_C_prime):
......@@ -296,3 +310,34 @@ class TPS(nn.Layer):
[-1, image.shape[2], image.shape[3], 2])
batch_I_r = F.grid_sample(x=image, grid=batch_P_prime)
return batch_I_r
if __name__ == '__main__':
import paddle
from ppocr.utils.save_load import load_dygraph_pretrain
from ppocr.utils.logging import get_logger
np.random.seed(0)
img = np.random.random((1, 3, 32, 100)).astype(np.float32)
x = paddle.to_tensor(img)
batch_P_prime = np.load(
'/Users/zhoujun20/Desktop/code/static/PaddleOCR/output/tps1.npy')
batch_P_prime = paddle.to_tensor(batch_P_prime)
model = TPS(in_channels=3, num_fiducial=20, loc_lr=0.1, model_name='small')
load_dygraph_pretrain(
model,
get_logger(),
'/Users/zhoujun20/Desktop/code/static/PaddleOCR/output/tps',
load_static_weights=True)
model.eval()
y = model(x)
print(y.shape)
static = np.load(
'/Users/zhoujun20/Desktop/code/static/PaddleOCR/output/tps.npy')
print(static.dtype, y.numpy().dtype, static.shape, y.numpy().shape)
diff = y.numpy() - static
print(diff.max())
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