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Source code for mmocr.models.textrecog.preprocessor.tps_preprocessor
# Modified from https://github.com/clovaai/deep-text-recognition-benchmark
#
# Licensed under the Apache License, Version 2.0 (the "License");s
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmocr.models.builder import PREPROCESSOR
from .base_preprocessor import BasePreprocessor
[docs]@PREPROCESSOR.register_module()
class TPSPreprocessor(BasePreprocessor):
"""Rectification Network of RARE, namely TPS based STN in
https://arxiv.org/pdf/1603.03915.pdf.
Args:
num_fiducial (int): Number of fiducial points of TPS-STN.
img_size (tuple(int, int)): Size :math:`(H, W)` of the input image.
rectified_img_size (tuple(int, int)): Size :math:`(H_r, W_r)` of
the rectified image.
num_img_channel (int): Number of channels of the input image.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self,
num_fiducial=20,
img_size=(32, 100),
rectified_img_size=(32, 100),
num_img_channel=1,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
assert isinstance(num_fiducial, int)
assert num_fiducial > 0
assert isinstance(img_size, tuple)
assert isinstance(rectified_img_size, tuple)
assert isinstance(num_img_channel, int)
self.num_fiducial = num_fiducial
self.img_size = img_size
self.rectified_img_size = rectified_img_size
self.num_img_channel = num_img_channel
self.LocalizationNetwork = LocalizationNetwork(self.num_fiducial,
self.num_img_channel)
self.GridGenerator = GridGenerator(self.num_fiducial,
self.rectified_img_size)
[docs] def forward(self, batch_img):
"""
Args:
batch_img (Tensor): Images to be rectified with size
:math:`(N, C, H, W)`.
Returns:
Tensor: Rectified image with size :math:`(N, C, H_r, W_r)`.
"""
batch_C_prime = self.LocalizationNetwork(
batch_img) # batch_size x K x 2
build_P_prime = self.GridGenerator.build_P_prime(
batch_C_prime, batch_img.device
) # batch_size x n (= rectified_img_width x rectified_img_height) x 2
build_P_prime_reshape = build_P_prime.reshape([
build_P_prime.size(0), self.rectified_img_size[0],
self.rectified_img_size[1], 2
])
batch_rectified_img = F.grid_sample(
batch_img,
build_P_prime_reshape,
padding_mode='border',
align_corners=True)
return batch_rectified_img
class LocalizationNetwork(nn.Module):
"""Localization Network of RARE, which predicts C' (K x 2) from input
(img_width x img_height)
Args:
num_fiducial (int): Number of fiducial points of TPS-STN.
num_img_channel (int): Number of channels of the input image.
"""
def __init__(self, num_fiducial, num_img_channel):
super().__init__()
self.num_fiducial = num_fiducial
self.num_img_channel = num_img_channel
self.conv = nn.Sequential(
nn.Conv2d(
in_channels=self.num_img_channel,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(2, 2), # batch_size x 64 x img_height/2 x img_width/2
nn.Conv2d(64, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.MaxPool2d(2, 2), # batch_size x 128 x img_h/4 x img_w/4
nn.Conv2d(128, 256, 3, 1, 1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.MaxPool2d(2, 2), # batch_size x 256 x img_h/8 x img_w/8
nn.Conv2d(256, 512, 3, 1, 1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.AdaptiveAvgPool2d(1) # batch_size x 512
)
self.localization_fc1 = nn.Sequential(
nn.Linear(512, 256), nn.ReLU(True))
self.localization_fc2 = nn.Linear(256, self.num_fiducial * 2)
# Init fc2 in LocalizationNetwork
self.localization_fc2.weight.data.fill_(0)
ctrl_pts_x = np.linspace(-1.0, 1.0, int(num_fiducial / 2))
ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(num_fiducial / 2))
ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(num_fiducial / 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)
initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
self.localization_fc2.bias.data = torch.from_numpy(
initial_bias).float().view(-1)
def forward(self, batch_img):
"""
Args:
batch_img (Tensor): Batch input image of shape
:math:`(N, C, H, W)`.
Returns:
Tensor: Predicted coordinates of fiducial points for input batch.
The shape is :math:`(N, F, 2)` where :math:`F` is ``num_fiducial``.
"""
batch_size = batch_img.size(0)
features = self.conv(batch_img).view(batch_size, -1)
batch_C_prime = self.localization_fc2(
self.localization_fc1(features)).view(batch_size,
self.num_fiducial, 2)
return batch_C_prime
class GridGenerator(nn.Module):
"""Grid Generator of RARE, which produces P_prime by multiplying T with P.
Args:
num_fiducial (int): Number of fiducial points of TPS-STN.
rectified_img_size (tuple(int, int)):
Size :math:`(H_r, W_r)` of the rectified image.
"""
def __init__(self, num_fiducial, rectified_img_size):
"""Generate P_hat and inv_delta_C for later."""
super().__init__()
self.eps = 1e-6
self.rectified_img_height = rectified_img_size[0]
self.rectified_img_width = rectified_img_size[1]
self.num_fiducial = num_fiducial
self.C = self._build_C(self.num_fiducial) # num_fiducial x 2
self.P = self._build_P(self.rectified_img_width,
self.rectified_img_height)
# for multi-gpu, you need register buffer
self.register_buffer(
'inv_delta_C',
torch.tensor(self._build_inv_delta_C(
self.num_fiducial,
self.C)).float()) # num_fiducial+3 x num_fiducial+3
self.register_buffer('P_hat',
torch.tensor(
self._build_P_hat(
self.num_fiducial, self.C,
self.P)).float()) # n x num_fiducial+3
# for fine-tuning with different image width,
# you may use below instead of self.register_buffer
# self.inv_delta_C = torch.tensor(
# self._build_inv_delta_C(
# self.num_fiducial,
# self.C)).float().cuda() # num_fiducial+3 x num_fiducial+3
# self.P_hat = torch.tensor(
# self._build_P_hat(self.num_fiducial, self.C,
# self.P)).float().cuda() # n x num_fiducial+3
def _build_C(self, num_fiducial):
"""Return coordinates of fiducial points in rectified_img; C."""
ctrl_pts_x = np.linspace(-1.0, 1.0, int(num_fiducial / 2))
ctrl_pts_y_top = -1 * np.ones(int(num_fiducial / 2))
ctrl_pts_y_bottom = np.ones(int(num_fiducial / 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)
return C # num_fiducial x 2
def _build_inv_delta_C(self, num_fiducial, C):
"""Return inv_delta_C which is needed to calculate T."""
hat_C = np.zeros((num_fiducial, num_fiducial), dtype=float)
for i in range(0, num_fiducial):
for j in range(i, num_fiducial):
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( # num_fiducial+3 x num_fiducial+3
[
np.concatenate([np.ones((num_fiducial, 1)), C, hat_C],
axis=1), # num_fiducial x num_fiducial+3
np.concatenate([np.zeros(
(2, 3)), np.transpose(C)], axis=1), # 2 x num_fiducial+3
np.concatenate([np.zeros(
(1, 3)), np.ones((1, num_fiducial))],
axis=1) # 1 x num_fiducial+3
],
axis=0)
inv_delta_C = np.linalg.inv(delta_C)
return inv_delta_C # num_fiducial+3 x num_fiducial+3
def _build_P(self, rectified_img_width, rectified_img_height):
rectified_img_grid_x = (
np.arange(-rectified_img_width, rectified_img_width, 2) +
1.0) / rectified_img_width # self.rectified_img_width
rectified_img_grid_y = (
np.arange(-rectified_img_height, rectified_img_height, 2) +
1.0) / rectified_img_height # self.rectified_img_height
P = np.stack( # self.rectified_img_w x self.rectified_img_h x 2
np.meshgrid(rectified_img_grid_x, rectified_img_grid_y),
axis=2)
return P.reshape([
-1, 2
]) # n (= self.rectified_img_width x self.rectified_img_height) x 2
def _build_P_hat(self, num_fiducial, C, P):
n = P.shape[
0] # n (= self.rectified_img_width x self.rectified_img_height)
P_tile = np.tile(np.expand_dims(P, axis=1),
(1, num_fiducial,
1)) # n x 2 -> n x 1 x 2 -> n x num_fiducial x 2
C_tile = np.expand_dims(C, axis=0) # 1 x num_fiducial x 2
P_diff = P_tile - C_tile # n x num_fiducial x 2
rbf_norm = np.linalg.norm(
P_diff, ord=2, axis=2, keepdims=False) # n x num_fiducial
rbf = np.multiply(np.square(rbf_norm),
np.log(rbf_norm + self.eps)) # n x num_fiducial
P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1)
return P_hat # n x num_fiducial+3
def build_P_prime(self, batch_C_prime, device='cuda'):
"""Generate Grid from batch_C_prime [batch_size x num_fiducial x 2]"""
batch_size = batch_C_prime.size(0)
batch_inv_delta_C = self.inv_delta_C.repeat(batch_size, 1, 1)
batch_P_hat = self.P_hat.repeat(batch_size, 1, 1)
batch_C_prime_with_zeros = torch.cat(
(batch_C_prime, torch.zeros(batch_size, 3, 2).float().to(device)),
dim=1) # batch_size x num_fiducial+3 x 2
batch_T = torch.bmm(
batch_inv_delta_C,
batch_C_prime_with_zeros) # batch_size x num_fiducial+3 x 2
batch_P_prime = torch.bmm(batch_P_hat, batch_T) # batch_size x n x 2
return batch_P_prime # batch_size x n x 2