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trans numpy to paddle

This commit is contained in:
WenmuZhou 2020-12-14 18:23:47 +08:00
parent 7b53596ce6
commit e6878b2113
1 changed files with 56 additions and 48 deletions
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104
ppocr/modeling/transforms/tps.py
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@ -128,7 +128,7 @@ class LocalizationNetwork(nn.Layer):
i = 0
for block in self.block_list:
x = block(x)
x = x.reshape([B, -1])
x = x.squeeze(axis=2).squeeze(axis=2)
x = self.fc1(x)
x = F.relu(x)
@ -176,14 +176,15 @@ class GridGenerator(nn.Layer):
Return:
batch_P_prime: the grid for the grid_sampler
"""
C = self.build_C()
P = self.build_P(I_r_size)
inv_delta_C = self.build_inv_delta_C(C).astype('float32')
P_hat = self.build_P_hat(C, P).astype('float32')
C = self.build_C_paddle()
P = self.build_P_paddle(I_r_size)
inv_delta_C_tensor = self.build_inv_delta_C_paddle(C).astype('float32')
# inv_delta_C_tensor = paddle.zeros((23,23)).astype('float32')
P_hat_tensor = self.build_P_hat_paddle(
C, paddle.to_tensor(P)).astype('float32')
inv_delta_C_tensor = paddle.to_tensor(inv_delta_C)
inv_delta_C_tensor.stop_gradient = True
P_hat_tensor = paddle.to_tensor(P_hat)
P_hat_tensor.stop_gradient = True
batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
@ -196,71 +197,80 @@ class GridGenerator(nn.Layer):
batch_P_prime = paddle.matmul(P_hat_tensor, batch_T)
return batch_P_prime
def build_C(self):
def build_C_paddle(self):
""" Return coordinates of fiducial points in I_r; C """
F = self.F
ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
ctrl_pts_y_top = -1 * np.ones(int(F / 2))
ctrl_pts_y_bottom = np.ones(int(F / 2))
ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
C = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
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_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)
return C # F x 2
def build_P(self, I_r_size):
I_r_width, I_r_height = I_r_size
I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + 1.0) \
/ I_r_width # self.I_r_width
I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + 1.0) \
/ I_r_height # self.I_r_height
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
# P: self.I_r_width x self.I_r_height x 2
P = np.stack(np.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
P = paddle.stack(paddle.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
P = paddle.transpose(P, perm=[1, 0, 2])
# n (= self.I_r_width x self.I_r_height) x 2
return P.reshape([-1, 2])
def build_inv_delta_C(self, C):
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_C = np.zeros((F, F), dtype=float) # F x F
hat_C = paddle.zeros((F, F), dtype='float32') # F x F
for i in range(0, F):
for j in range(i, F):
r = np.linalg.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
np.fill_diagonal(hat_C, 1)
hat_C = (hat_C**2) * np.log(hat_C)
# print(C.shape, hat_C.shape)
delta_C = np.concatenate( # F+3 x F+3
if i == j:
hat_C[i, j] = 1
else:
r = paddle.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3
[
np.concatenate(
[np.ones((F, 1)), C, hat_C], axis=1), # F x F+3
np.concatenate(
[np.zeros((2, 3)), np.transpose(C)], axis=1), # 2 x F+3
np.concatenate(
[np.zeros((1, 3)), np.ones((1, F))], axis=1) # 1 x F+3
paddle.concat(
[paddle.ones((F, 1)), C, hat_C], axis=1), # F x F+3
paddle.concat(
[paddle.zeros((2, 3)), paddle.transpose(
C, perm=[1, 0])],
axis=1), # 2 x F+3
paddle.concat(
[paddle.zeros((1, 3)), paddle.ones((1, F))],
axis=1) # 1 x F+3
],
axis=0)
inv_delta_C = np.linalg.inv(delta_C)
inv_delta_C = paddle.inverse(delta_C)
return inv_delta_C # F+3 x F+3
def build_P_hat(self, C, P):
def build_P_hat_paddle(self, C, P):
F = self.F
eps = self.eps
n = P.shape[0] # n (= self.I_r_width x self.I_r_height)
# P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1))
C_tile = np.expand_dims(C, axis=0) # 1 x F x 2
P_tile = paddle.tile(paddle.unsqueeze(P, axis=1), (1, F, 1))
C_tile = paddle.unsqueeze(C, axis=0) # 1 x F x 2
P_diff = P_tile - C_tile # n x F x 2
# rbf_norm: n x F
rbf_norm = np.linalg.norm(P_diff, ord=2, axis=2, keepdims=False)
rbf_norm = paddle.norm(P_diff, p=2, axis=2, keepdim=False)
# rbf: n x F
rbf = np.multiply(np.square(rbf_norm), np.log(rbf_norm + eps))
P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1)
rbf = paddle.multiply(
paddle.square(rbf_norm), paddle.log(rbf_norm + eps))
P_hat = paddle.concat([paddle.ones((n, 1)), P, rbf], axis=1)
return P_hat # n x F+3
def get_expand_tensor(self, batch_C_prime):
B = batch_C_prime.shape[0]
batch_C_prime = batch_C_prime.reshape([B, -1])
B, H, C = batch_C_prime.shape
batch_C_prime = batch_C_prime.reshape([B, H * C])
batch_C_ex_part_tensor = self.fc(batch_C_prime)
batch_C_ex_part_tensor = batch_C_ex_part_tensor.reshape([-1, 3, 2])
return batch_C_ex_part_tensor
@ -277,10 +287,8 @@ class TPS(nn.Layer):
def forward(self, image):
image.stop_gradient = False
I_r_size = [image.shape[3], image.shape[2]]
batch_C_prime = self.loc_net(image)
batch_P_prime = self.grid_generator(batch_C_prime, I_r_size)
batch_P_prime = self.grid_generator(batch_C_prime, image.shape[2:])
batch_P_prime = batch_P_prime.reshape(
[-1, image.shape[2], image.shape[3], 2])
batch_I_r = F.grid_sample(x=image, grid=batch_P_prime)