From 2f9f258ff48a1084724d9684175b311e9030efdf Mon Sep 17 00:00:00 2001 From: WenmuZhou Date: Tue, 10 Nov 2020 17:18:32 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0tps=E7=BD=91=E7=BB=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- configs/rec/rec_r34_vd_tps_bilstm_ctc.yml | 100 +++++++ ppocr/modeling/architectures/base_model.py | 11 +- ppocr/modeling/transform/__init__.py | 4 +- ppocr/modeling/transform/tps.py | 289 +++++++++++++++++++++ 4 files changed, 398 insertions(+), 6 deletions(-) create mode 100644 configs/rec/rec_r34_vd_tps_bilstm_ctc.yml create mode 100644 ppocr/modeling/transform/tps.py diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml new file mode 100644 index 00000000..269f1e41 --- /dev/null +++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml @@ -0,0 +1,100 @@ +Global: + use_gpu: true + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/r34_vd_tps_bilstm_ctc/ + save_epoch_step: 3 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [0, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words/ch/word_1.jpg + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + infer_mode: False + use_space_char: False + + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: CRNN + Transform: + name: TPS + num_fiducial: 20 + loc_lr: 0.1 + model_name: small + Backbone: + name: ResNet + layers: 34 + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 256 + Head: + name: CTCHead + fc_decay: 0 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 4 diff --git a/ppocr/modeling/architectures/base_model.py b/ppocr/modeling/architectures/base_model.py index c1119604..0c4fe650 100644 --- a/ppocr/modeling/architectures/base_model.py +++ b/ppocr/modeling/architectures/base_model.py @@ -16,13 +16,14 @@ from __future__ import division from __future__ import print_function from paddle import nn - +from ppocr.modeling.transform import build_transform from ppocr.modeling.backbones import build_backbone from ppocr.modeling.necks import build_neck from ppocr.modeling.heads import build_head __all__ = ['BaseModel'] + class BaseModel(nn.Layer): def __init__(self, config): """ @@ -31,7 +32,7 @@ class BaseModel(nn.Layer): config (dict): the super parameters for module. """ super(BaseModel, self).__init__() - + in_channels = config.get('in_channels', 3) model_type = config['model_type'] # build transfrom, @@ -50,7 +51,7 @@ class BaseModel(nn.Layer): config["Backbone"]['in_channels'] = in_channels self.backbone = build_backbone(config["Backbone"], model_type) in_channels = self.backbone.out_channels - + # build neck # for rec, neck can be cnn,rnn or reshape(None) # for det, neck can be FPN, BIFPN and so on. @@ -62,7 +63,7 @@ class BaseModel(nn.Layer): config['Neck']['in_channels'] = in_channels self.neck = build_neck(config['Neck']) in_channels = self.neck.out_channels - + # # build head, head is need for det, rec and cls config["Head"]['in_channels'] = in_channels self.head = build_head(config["Head"]) @@ -74,4 +75,4 @@ class BaseModel(nn.Layer): if self.use_neck: x = self.neck(x) x = self.head(x) - return x \ No newline at end of file + return x diff --git a/ppocr/modeling/transform/__init__.py b/ppocr/modeling/transform/__init__.py index af3b3f86..78eaeccc 100755 --- a/ppocr/modeling/transform/__init__.py +++ b/ppocr/modeling/transform/__init__.py @@ -16,7 +16,9 @@ __all__ = ['build_transform'] def build_transform(config): - support_dict = [''] + from .tps import TPS + + support_dict = ['TPS'] module_name = config.pop('name') assert module_name in support_dict, Exception( diff --git a/ppocr/modeling/transform/tps.py b/ppocr/modeling/transform/tps.py new file mode 100644 index 00000000..f5b4f60b --- /dev/null +++ b/ppocr/modeling/transform/tps.py @@ -0,0 +1,289 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn, ParamAttr +from paddle.nn import functional as F +import numpy as np + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + groups=1, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + bn_name = "bn_" + name + self.bn = nn.BatchNorm( + out_channels, + act=act, + param_attr=ParamAttr(name=bn_name + '_scale'), + bias_attr=ParamAttr(bn_name + '_offset'), + moving_mean_name=bn_name + '_mean', + moving_variance_name=bn_name + '_variance') + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class LocalizationNetwork(nn.Layer): + def __init__(self, in_channels, num_fiducial, loc_lr, model_name): + super(LocalizationNetwork, self).__init__() + self.F = num_fiducial + F = num_fiducial + if model_name == "large": + num_filters_list = [64, 128, 256, 512] + fc_dim = 256 + else: + num_filters_list = [16, 32, 64, 128] + fc_dim = 64 + + self.block_list = [] + for fno in range(0, len(num_filters_list)): + num_filters = num_filters_list[fno] + name = "loc_conv%d" % fno + conv = self.add_sublayer( + name, + ConvBNLayer( + in_channels=in_channels, + out_channels=num_filters, + kernel_size=3, + act='relu', + name=name)) + self.block_list.append(conv) + if fno == len(num_filters_list) - 1: + pool = nn.AdaptiveAvgPool2D(1) + else: + pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) + in_channels = num_filters + self.block_list.append(pool) + name = "loc_fc1" + self.fc1 = nn.Linear( + in_channels, + fc_dim, + weight_attr=ParamAttr( + learning_rate=loc_lr, name=name + "_w"), + bias_attr=ParamAttr(name=name + '.b_0'), + name=name) + + # Init fc2 in LocalizationNetwork + initial_bias = self.get_initial_fiducials() + initial_bias = initial_bias.reshape(-1) + name = "loc_fc2" + param_attr = ParamAttr( + learning_rate=loc_lr, + initializer=paddle.fluid.initializer.NumpyArrayInitializer( + np.zeros([fc_dim, F * 2])), + name=name + "_w") + bias_attr = ParamAttr( + learning_rate=loc_lr, + initializer=paddle.fluid.initializer.NumpyArrayInitializer( + initial_bias), + name=name + "_b") + self.fc2 = nn.Linear( + fc_dim, + F * 2, + weight_attr=param_attr, + bias_attr=bias_attr, + name=name) + self.out_channels = F * 2 + + def forward(self, x): + """ + Estimating parameters of geometric transformation + Args: + image: input + Return: + batch_C_prime: the matrix of the geometric transformation + """ + B = x.shape[0] + i = 0 + for block in self.block_list: + x = block(x) + x = x.reshape([B, -1]) + x = self.fc1(x) + + x = F.relu(x) + x = self.fc2(x) + x = x.reshape(shape=[-1, self.F, 2]) + return x + + def get_initial_fiducials(self): + """ see RARE paper Fig. 6 (a) """ + F = self.F + ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2)) + ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2)) + ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=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) + initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) + return initial_bias + + +class GridGenerator(nn.Layer): + def __init__(self, in_channels, num_fiducial): + super(GridGenerator, self).__init__() + self.eps = 1e-6 + self.F = num_fiducial + + name = "ex_fc" + initializer = nn.initializer.Constant(value=0.0) + param_attr = ParamAttr( + learning_rate=0.0, initializer=initializer, name=name + "_w") + bias_attr = ParamAttr( + learning_rate=0.0, initializer=initializer, name=name + "_b") + self.fc = nn.Linear( + in_channels, + 6, + weight_attr=param_attr, + bias_attr=bias_attr, + name=name) + + def forward(self, batch_C_prime, I_r_size): + """ + Generate the grid for the grid_sampler. + Args: + batch_C_prime: the matrix of the geometric transformation + I_r_size: the shape of the input image + 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') + + 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) + + batch_C_ex_part_tensor.stop_gradient = True + + batch_C_prime_with_zeros = paddle.concat( + [batch_C_prime, batch_C_ex_part_tensor], axis=1) + batch_T = paddle.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros) + batch_P_prime = paddle.matmul(P_hat_tensor, batch_T) + return batch_P_prime + + def build_C(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) + 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 + # 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) + # n (= self.I_r_width x self.I_r_height) x 2 + return P.reshape([-1, 2]) + + def build_inv_delta_C(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 + 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 + [ + 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 + ], + axis=0) + inv_delta_C = np.linalg.inv(delta_C) + return inv_delta_C # F+3 x F+3 + + def build_P_hat(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_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: 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) + 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]) + 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 + + +class TPS(nn.Layer): + def __init__(self, in_channels, num_fiducial, loc_lr, model_name): + super(TPS, self).__init__() + self.loc_net = LocalizationNetwork(in_channels, num_fiducial, loc_lr, + model_name) + self.grid_generator = GridGenerator(self.loc_net.out_channels, + num_fiducial) + self.out_channels = in_channels + + 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 = batch_P_prime.reshape( + [-1, image.shape[2], image.shape[3], 2]) + batch_I_r = F.grid_sample(x=image, grid=batch_P_prime) + return batch_I_r