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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch | |
| from mmcv.runner import BaseModule | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from mmocr.models.builder import HEADS, build_loss | |
| class SDMGRHead(BaseModule): | |
| def __init__(self, | |
| num_chars=92, | |
| visual_dim=64, | |
| fusion_dim=1024, | |
| node_input=32, | |
| node_embed=256, | |
| edge_input=5, | |
| edge_embed=256, | |
| num_gnn=2, | |
| num_classes=26, | |
| loss=dict(type='SDMGRLoss'), | |
| bidirectional=False, | |
| train_cfg=None, | |
| test_cfg=None, | |
| init_cfg=dict( | |
| type='Normal', | |
| override=dict(name='edge_embed'), | |
| mean=0, | |
| std=0.01)): | |
| super().__init__(init_cfg=init_cfg) | |
| self.fusion = Block([visual_dim, node_embed], node_embed, fusion_dim) | |
| self.node_embed = nn.Embedding(num_chars, node_input, 0) | |
| hidden = node_embed // 2 if bidirectional else node_embed | |
| self.rnn = nn.LSTM( | |
| input_size=node_input, | |
| hidden_size=hidden, | |
| num_layers=1, | |
| batch_first=True, | |
| bidirectional=bidirectional) | |
| self.edge_embed = nn.Linear(edge_input, edge_embed) | |
| self.gnn_layers = nn.ModuleList( | |
| [GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)]) | |
| self.node_cls = nn.Linear(node_embed, num_classes) | |
| self.edge_cls = nn.Linear(edge_embed, 2) | |
| self.loss = build_loss(loss) | |
| def forward(self, relations, texts, x=None): | |
| node_nums, char_nums = [], [] | |
| for text in texts: | |
| node_nums.append(text.size(0)) | |
| char_nums.append((text > 0).sum(-1)) | |
| max_num = max([char_num.max() for char_num in char_nums]) | |
| all_nodes = torch.cat([ | |
| torch.cat( | |
| [text, | |
| text.new_zeros(text.size(0), max_num - text.size(1))], -1) | |
| for text in texts | |
| ]) | |
| embed_nodes = self.node_embed(all_nodes.clamp(min=0).long()) | |
| rnn_nodes, _ = self.rnn(embed_nodes) | |
| nodes = rnn_nodes.new_zeros(*rnn_nodes.shape[::2]) | |
| all_nums = torch.cat(char_nums) | |
| valid = all_nums > 0 | |
| nodes[valid] = rnn_nodes[valid].gather( | |
| 1, (all_nums[valid] - 1).unsqueeze(-1).unsqueeze(-1).expand( | |
| -1, -1, rnn_nodes.size(-1))).squeeze(1) | |
| if x is not None: | |
| nodes = self.fusion([x, nodes]) | |
| all_edges = torch.cat( | |
| [rel.view(-1, rel.size(-1)) for rel in relations]) | |
| embed_edges = self.edge_embed(all_edges.float()) | |
| embed_edges = F.normalize(embed_edges) | |
| for gnn_layer in self.gnn_layers: | |
| nodes, cat_nodes = gnn_layer(nodes, embed_edges, node_nums) | |
| node_cls, edge_cls = self.node_cls(nodes), self.edge_cls(cat_nodes) | |
| return node_cls, edge_cls | |
| class GNNLayer(nn.Module): | |
| def __init__(self, node_dim=256, edge_dim=256): | |
| super().__init__() | |
| self.in_fc = nn.Linear(node_dim * 2 + edge_dim, node_dim) | |
| self.coef_fc = nn.Linear(node_dim, 1) | |
| self.out_fc = nn.Linear(node_dim, node_dim) | |
| self.relu = nn.ReLU() | |
| def forward(self, nodes, edges, nums): | |
| start, cat_nodes = 0, [] | |
| for num in nums: | |
| sample_nodes = nodes[start:start + num] | |
| cat_nodes.append( | |
| torch.cat([ | |
| sample_nodes.unsqueeze(1).expand(-1, num, -1), | |
| sample_nodes.unsqueeze(0).expand(num, -1, -1) | |
| ], -1).view(num**2, -1)) | |
| start += num | |
| cat_nodes = torch.cat([torch.cat(cat_nodes), edges], -1) | |
| cat_nodes = self.relu(self.in_fc(cat_nodes)) | |
| coefs = self.coef_fc(cat_nodes) | |
| start, residuals = 0, [] | |
| for num in nums: | |
| residual = F.softmax( | |
| -torch.eye(num).to(coefs.device).unsqueeze(-1) * 1e9 + | |
| coefs[start:start + num**2].view(num, num, -1), 1) | |
| residuals.append( | |
| (residual * | |
| cat_nodes[start:start + num**2].view(num, num, -1)).sum(1)) | |
| start += num**2 | |
| nodes += self.relu(self.out_fc(torch.cat(residuals))) | |
| return nodes, cat_nodes | |
| class Block(nn.Module): | |
| def __init__(self, | |
| input_dims, | |
| output_dim, | |
| mm_dim=1600, | |
| chunks=20, | |
| rank=15, | |
| shared=False, | |
| dropout_input=0., | |
| dropout_pre_lin=0., | |
| dropout_output=0., | |
| pos_norm='before_cat'): | |
| super().__init__() | |
| self.rank = rank | |
| self.dropout_input = dropout_input | |
| self.dropout_pre_lin = dropout_pre_lin | |
| self.dropout_output = dropout_output | |
| assert (pos_norm in ['before_cat', 'after_cat']) | |
| self.pos_norm = pos_norm | |
| # Modules | |
| self.linear0 = nn.Linear(input_dims[0], mm_dim) | |
| self.linear1 = ( | |
| self.linear0 if shared else nn.Linear(input_dims[1], mm_dim)) | |
| self.merge_linears0 = nn.ModuleList() | |
| self.merge_linears1 = nn.ModuleList() | |
| self.chunks = self.chunk_sizes(mm_dim, chunks) | |
| for size in self.chunks: | |
| ml0 = nn.Linear(size, size * rank) | |
| self.merge_linears0.append(ml0) | |
| ml1 = ml0 if shared else nn.Linear(size, size * rank) | |
| self.merge_linears1.append(ml1) | |
| self.linear_out = nn.Linear(mm_dim, output_dim) | |
| def forward(self, x): | |
| x0 = self.linear0(x[0]) | |
| x1 = self.linear1(x[1]) | |
| bs = x1.size(0) | |
| if self.dropout_input > 0: | |
| x0 = F.dropout(x0, p=self.dropout_input, training=self.training) | |
| x1 = F.dropout(x1, p=self.dropout_input, training=self.training) | |
| x0_chunks = torch.split(x0, self.chunks, -1) | |
| x1_chunks = torch.split(x1, self.chunks, -1) | |
| zs = [] | |
| for x0_c, x1_c, m0, m1 in zip(x0_chunks, x1_chunks, | |
| self.merge_linears0, | |
| self.merge_linears1): | |
| m = m0(x0_c) * m1(x1_c) # bs x split_size*rank | |
| m = m.view(bs, self.rank, -1) | |
| z = torch.sum(m, 1) | |
| if self.pos_norm == 'before_cat': | |
| z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z)) | |
| z = F.normalize(z) | |
| zs.append(z) | |
| z = torch.cat(zs, 1) | |
| if self.pos_norm == 'after_cat': | |
| z = torch.sqrt(F.relu(z)) - torch.sqrt(F.relu(-z)) | |
| z = F.normalize(z) | |
| if self.dropout_pre_lin > 0: | |
| z = F.dropout(z, p=self.dropout_pre_lin, training=self.training) | |
| z = self.linear_out(z) | |
| if self.dropout_output > 0: | |
| z = F.dropout(z, p=self.dropout_output, training=self.training) | |
| return z | |
| def chunk_sizes(dim, chunks): | |
| split_size = (dim + chunks - 1) // chunks | |
| sizes_list = [split_size] * chunks | |
| sizes_list[-1] = sizes_list[-1] - (sum(sizes_list) - dim) | |
| return sizes_list | |