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