From fb2b7f5447f97a6f6dbd635d189a14aa8ef14e84 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Wed, 28 Sep 2022 10:32:09 +0800 Subject: [PATCH] add paddle2onnx support for tablerec-rare --- configs/table/table_mv3.yml | 1 - ppocr/modeling/heads/table_att_head.py | 59 +++++++++++--------------- 2 files changed, 24 insertions(+), 36 deletions(-) diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 9355a236..9d286f41 100755 --- a/configs/table/table_mv3.yml +++ b/configs/table/table_mv3.yml @@ -43,7 +43,6 @@ Architecture: Head: name: TableAttentionHead hidden_size: 256 - loc_type: 2 max_text_length: *max_text_length loc_reg_num: &loc_reg_num 4 diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index d3c86e22..50910c5b 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/ppocr/modeling/heads/table_att_head.py @@ -16,6 +16,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import math import paddle import paddle.nn as nn from paddle import ParamAttr @@ -42,7 +43,6 @@ class TableAttentionHead(nn.Layer): def __init__(self, in_channels, hidden_size, - loc_type, in_max_len=488, max_text_length=800, out_channels=30, @@ -57,20 +57,16 @@ class TableAttentionHead(nn.Layer): self.structure_attention_cell = AttentionGRUCell( self.input_size, hidden_size, self.out_channels, use_gru=False) self.structure_generator = nn.Linear(hidden_size, self.out_channels) - self.loc_type = loc_type self.in_max_len = in_max_len - if self.loc_type == 1: - self.loc_generator = nn.Linear(hidden_size, 4) + if self.in_max_len == 640: + self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1) + elif self.in_max_len == 800: + self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) else: - if self.in_max_len == 640: - self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1) - elif self.in_max_len == 800: - self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) - else: - self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) - self.loc_generator = nn.Linear(self.input_size + hidden_size, - loc_reg_num) + self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) + self.loc_generator = nn.Linear(self.input_size + hidden_size, + loc_reg_num) def _char_to_onehot(self, input_char, onehot_dim): input_ont_hot = F.one_hot(input_char, onehot_dim) @@ -80,16 +76,13 @@ class TableAttentionHead(nn.Layer): # if and else branch are both needed when you want to assign a variable # if you modify the var in just one branch, then the modification will not work. fea = inputs[-1] - if len(fea.shape) == 3: - pass - else: - last_shape = int(np.prod(fea.shape[2:])) # gry added - fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape]) - fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels) + last_shape = int(np.prod(fea.shape[2:])) # gry added + fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape]) + fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels) batch_size = fea.shape[0] hidden = paddle.zeros((batch_size, self.hidden_size)) - output_hiddens = [] + output_hiddens = paddle.zeros((batch_size, self.max_text_length + 1, self.hidden_size)) if self.training and targets is not None: structure = targets[0] for i in range(self.max_text_length + 1): @@ -97,7 +90,8 @@ class TableAttentionHead(nn.Layer): structure[:, i], onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) - output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) + output_hiddens[:, i, :] = outputs + # output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) output = paddle.concat(output_hiddens, axis=1) structure_probs = self.structure_generator(output) if self.loc_type == 1: @@ -118,30 +112,25 @@ class TableAttentionHead(nn.Layer): outputs = None alpha = None max_text_length = paddle.to_tensor(self.max_text_length) - i = 0 - while i < max_text_length + 1: + for i in range(max_text_length + 1): elem_onehots = self._char_to_onehot( temp_elem, onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) - output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) + output_hiddens[:, i, :] = outputs + # output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) structure_probs_step = self.structure_generator(outputs) temp_elem = structure_probs_step.argmax(axis=1, dtype="int32") - i += 1 - output = paddle.concat(output_hiddens, axis=1) + output = output_hiddens structure_probs = self.structure_generator(output) structure_probs = F.softmax(structure_probs) - if self.loc_type == 1: - loc_preds = self.loc_generator(output) - loc_preds = F.sigmoid(loc_preds) - else: - loc_fea = fea.transpose([0, 2, 1]) - loc_fea = self.loc_fea_trans(loc_fea) - loc_fea = loc_fea.transpose([0, 2, 1]) - loc_concat = paddle.concat([output, loc_fea], axis=2) - loc_preds = self.loc_generator(loc_concat) - loc_preds = F.sigmoid(loc_preds) + loc_fea = fea.transpose([0, 2, 1]) + loc_fea = self.loc_fea_trans(loc_fea) + loc_fea = loc_fea.transpose([0, 2, 1]) + loc_concat = paddle.concat([output, loc_fea], axis=2) + loc_preds = self.loc_generator(loc_concat) + loc_preds = F.sigmoid(loc_preds) return {'structure_probs': structure_probs, 'loc_preds': loc_preds} -- GitLab