BIDI_EXTRACTION_AI/BiddingKG/dl/table_head/models/model_torch.py

import torch.nn as nn
import torch


class TableHeadModel(nn.Module):
    def __init__(self):
        super(TableHeadModel, self).__init__()
        self.char_num = 20
        self.char_embed = 60
        self.char_embed_expand = 128

        self.dense0 = nn.Linear(self.char_embed, self.char_embed_expand)

        self.dense3 = nn.Linear(self.char_num * self.char_embed_expand, 64)
        self.dense4 = nn.Linear(64, 1)

        self.sigmoid = nn.Sigmoid()

        self.ln_dnn_2 = nn.LayerNorm([64])

        self.device = torch.device("cpu")

        self.relu = nn.LeakyReLU()
        self.dropout = nn.Dropout(0.3)

        self.cnn1d_0 = nn.Conv1d(self.char_embed_expand,
                                 self.char_embed_expand,
                                 (3,), padding=self.get_padding(3))
        self.cnn1d_1 = nn.Conv1d(self.char_embed_expand,
                                 self.char_embed_expand,
                                 (3,), padding=self.get_padding(3))

        self.cnn3d_0 = nn.Conv3d(self.char_embed_expand, self.char_embed_expand,
                                 (3, 3, 3), padding=self.get_padding(3))
        self.cnn3d_1 = nn.Conv3d(self.char_embed_expand, self.char_embed_expand,
                                 (3, 3, 3), padding=self.get_padding(3))

    def get_padding(self, kernel_size, stride=1):
        return (kernel_size - 1) // 2 * stride

    def forward(self, x):
        batch, row, col, char_num, char_embed = x.shape

        # cnn 1d
        cnn1d_x = torch.squeeze(x, 0)
        cnn1d_x = cnn1d_x.view([row*col, char_num, char_embed])

        cnn1d_x = self.dense0(cnn1d_x)

        cnn1d_x = torch.permute(cnn1d_x, [0, 2, 1])
        cnn1d_x = self.cnn1d_0(cnn1d_x)
        cnn1d_x = self.relu(cnn1d_x)
        cnn1d_x = self.dropout(cnn1d_x)
        cnn1d_x = self.cnn1d_1(cnn1d_x)
        cnn1d_x = self.relu(cnn1d_x)
        cnn1d_x = self.dropout(cnn1d_x)

        cnn1d_x = torch.permute(cnn1d_x, [0, 2, 1])
        cnn1d_x = cnn1d_x.contiguous().view(row, col, char_num, self.char_embed_expand)
        cnn1d_x = torch.unsqueeze(cnn1d_x, 0)
        # print(cnn1d_x.shape)

        # cnn 3d
        cnn3d_x = torch.permute(cnn1d_x, [0, 4, 3, 1, 2])
        cnn3d_x = self.cnn3d_0(cnn3d_x)
        cnn3d_x = self.relu(cnn3d_x)
        cnn3d_x = self.dropout(cnn3d_x)
        cnn3d_x = self.cnn3d_1(cnn3d_x)
        cnn3d_x = self.relu(cnn3d_x)
        cnn3d_x = self.dropout(cnn3d_x)

        cnn3d_x = torch.squeeze(cnn3d_x, 0)
        cnn3d_x = torch.permute(cnn3d_x, [2, 3, 1, 0])
        cnn3d_x = cnn3d_x.contiguous().view(row, col, char_num * self.char_embed_expand)

        # dnn
        x = self.dense3(cnn3d_x)
        x = self.ln_dnn_2(x)
        x = self.relu(x)
        x = self.dense4(x)
        x = self.sigmoid(x)
        x = torch.squeeze(x, -1)
        return x


class TableHeadModel2(nn.Module):
    def __init__(self):
        super(TableHeadModel2, self).__init__()
        self.char_num = 20
        self.char_embed = 60
        self.char_embed_expand = 128

        self.dense0 = nn.Linear(self.char_embed, self.char_embed_expand)

        self.dense3 = nn.Linear(self.char_num * self.char_embed_expand, 64)
        self.dense4 = nn.Linear(64, 1)

        self.sigmoid = nn.Sigmoid()

        self.ln_dnn_2 = nn.LayerNorm([64])

        self.device = torch.device("cpu")

        self.relu = nn.LeakyReLU()
        self.dropout = nn.Dropout(0.6)

        # self.cnn1d_0 = nn.Conv1d(self.char_embed_expand,
        #                          self.char_embed_expand,
        #                          (3,), padding=self.get_padding(3))
        # self.cnn1d_1 = nn.Conv1d(self.char_embed_expand,
        #                          self.char_embed_expand,
        #                          (3,), padding=self.get_padding(3))

        encoder_layer1 = nn.TransformerEncoderLayer(d_model=self.char_embed_expand, nhead=2,
                                                    dim_feedforward=128, batch_first=True)
        self.transformer1 = nn.TransformerEncoder(encoder_layer1, 2)
        self.ln_encoder_0 = nn.LayerNorm([self.char_embed_expand])

        self.cnn3d_0 = nn.Conv3d(self.char_embed_expand, self.char_embed_expand,
                                 (3, 3, 3), padding=self.get_padding(3))
        self.cnn3d_1 = nn.Conv3d(self.char_embed_expand, self.char_embed_expand,
                                 (3, 3, 3), padding=self.get_padding(3))
        # self.cnn3d_2 = nn.Conv3d(self.char_embed, self.char_embed,
        #                          (3, 3, 3), padding=self.get_padding(3))

    def get_padding(self, kernel_size, stride=1):
        return (kernel_size - 1) // 2 * stride

    def forward(self, x):
        batch, row, col, char_num, char_embed = x.shape

        # Embedding
        x = torch.squeeze(x, 0)
        x = x.view([row*col, char_num, char_embed])
        x = self.dense0(x)

        # transformer
        box_attention = self.transformer1(x)
        box_attention = self.ln_encoder_0(box_attention)
        box_attention = torch.permute(box_attention, [0, 2, 1])
        box_attention = box_attention.contiguous().view(row, col, char_num, self.char_embed_expand)
        box_attention = torch.unsqueeze(box_attention, 0)

        # cnn1d_x = torch.permute(cnn1d_x, [0, 2, 1])
        # cnn1d_x = self.cnn1d_0(cnn1d_x)
        # cnn1d_x = self.relu(cnn1d_x)
        # cnn1d_x = self.dropout(cnn1d_x)
        # cnn1d_x = self.cnn1d_1(cnn1d_x)
        # cnn1d_x = self.relu(cnn1d_x)
        # cnn1d_x = self.dropout(cnn1d_x)
        #
        # cnn1d_x = torch.permute(cnn1d_x, [0, 2, 1])
        # cnn1d_x = cnn1d_x.contiguous().view(row, col, char_num, self.char_embed_expand)
        # cnn1d_x = torch.unsqueeze(cnn1d_x, 0)
        # print(cnn1d_x.shape)

        # cnn 3d
        cnn3d_x = torch.permute(box_attention, [0, 4, 3, 1, 2])
        cnn3d_x = self.cnn3d_0(cnn3d_x)
        cnn3d_x = self.relu(cnn3d_x)
        cnn3d_x = self.dropout(cnn3d_x)
        cnn3d_x = self.cnn3d_1(cnn3d_x)
        cnn3d_x = self.relu(cnn3d_x)
        cnn3d_x = self.dropout(cnn3d_x)

        cnn3d_x = torch.squeeze(cnn3d_x, 0)
        cnn3d_x = torch.permute(cnn3d_x, [2, 3, 1, 0])
        cnn3d_x = cnn3d_x.contiguous().view(row, col, char_num * self.char_embed_expand)

        # dnn
        x = self.dense3(cnn3d_x)
        x = self.ln_dnn_2(x)
        x = self.relu(x)
        x = self.dense4(x)
        x = self.sigmoid(x)
        x = torch.squeeze(x, -1)
        return x