Spaces:
Runtime error
Runtime error
| from extractor import BasicEncoder | |
| from position_encoding import build_position_encoding | |
| from unet import U_Net_mini | |
| import argparse | |
| import numpy as np | |
| import torch | |
| from torch import nn, Tensor | |
| import torch.nn.functional as F | |
| import copy | |
| from typing import Optional | |
| class attnLayer(nn.Module): | |
| def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| self.multihead_attn_list = nn.ModuleList( | |
| [copy.deepcopy(nn.MultiheadAttention(d_model, nhead, dropout=dropout)) for i in range(2)]) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2_list = nn.ModuleList([copy.deepcopy(nn.LayerNorm(d_model)) for i in range(2)]) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2_list = nn.ModuleList([copy.deepcopy(nn.Dropout(dropout)) for i in range(2)]) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, tgt, memory_list, tgt_mask=None, memory_mask=None, | |
| tgt_key_padding_mask=None, memory_key_padding_mask=None, | |
| pos=None, memory_pos=None): | |
| q = k = self.with_pos_embed(tgt, pos) | |
| tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt = self.norm1(tgt) | |
| for memory, multihead_attn, norm2, dropout2, m_pos in zip(memory_list, self.multihead_attn_list, | |
| self.norm2_list, self.dropout2_list, memory_pos): | |
| tgt2 = multihead_attn(query=self.with_pos_embed(tgt, pos), | |
| key=self.with_pos_embed(memory, m_pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + dropout2(tgt2) | |
| tgt = norm2(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| tgt = self.norm3(tgt) | |
| return tgt | |
| def forward_pre(self, tgt, memory, tgt_mask=None, memory_mask=None, | |
| tgt_key_padding_mask=None, memory_key_padding_mask=None, | |
| pos=None, memory_pos=None): | |
| tgt2 = self.norm1(tgt) | |
| q = k = self.with_pos_embed(tgt2, pos) | |
| tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt2 = self.norm2(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, pos), | |
| key=self.with_pos_embed(memory, memory_pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt2 = self.norm3(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| return tgt | |
| def forward(self, tgt, memory_list, tgt_mask=None, memory_mask=None, | |
| tgt_key_padding_mask=None, memory_key_padding_mask=None, | |
| pos=None, memory_pos=None): | |
| if self.normalize_before: | |
| return self.forward_pre(tgt, memory_list, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, memory_pos) | |
| return self.forward_post(tgt, memory_list, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, memory_pos) | |
| def _get_clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | |
| def _get_activation_fn(activation): | |
| """Return an activation function given a string""" | |
| if activation == "relu": | |
| return F.relu | |
| if activation == "gelu": | |
| return F.gelu | |
| if activation == "glu": | |
| return F.glu | |
| raise RuntimeError(F"activation should be relu/gelu, not {activation}.") | |
| class TransDecoder(nn.Module): | |
| def __init__(self, num_attn_layers, hidden_dim=128): | |
| super(TransDecoder, self).__init__() | |
| attn_layer = attnLayer(hidden_dim) | |
| self.layers = _get_clones(attn_layer, num_attn_layers) | |
| self.position_embedding = build_position_encoding(hidden_dim) | |
| def forward(self, imgf, query_embed): | |
| pos = self.position_embedding( | |
| torch.ones(imgf.shape[0], imgf.shape[2], imgf.shape[3]).bool()) # torch.Size([1, 128, 36, 36]) | |
| bs, c, h, w = imgf.shape | |
| imgf = imgf.flatten(2).permute(2, 0, 1) # torch.Size([1296, 1, 256]) | |
| query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) | |
| pos = pos.flatten(2).permute(2, 0, 1) # torch.Size([1296, 1, 256]) | |
| for layer in self.layers: | |
| query_embed = layer(query_embed, [imgf], pos=pos, memory_pos=[pos, pos]) | |
| query_embed = query_embed.permute(1, 2, 0).reshape(bs, c, h, w) | |
| return query_embed | |
| class TransEncoder(nn.Module): | |
| def __init__(self, num_attn_layers, hidden_dim=128): | |
| super(TransEncoder, self).__init__() | |
| attn_layer = attnLayer(hidden_dim) | |
| self.layers = _get_clones(attn_layer, num_attn_layers) | |
| self.position_embedding = build_position_encoding(hidden_dim) | |
| def forward(self, imgf): | |
| pos = self.position_embedding( | |
| torch.ones(imgf.shape[0], imgf.shape[2], imgf.shape[3]).bool()) # torch.Size([1, 128, 36, 36]) | |
| bs, c, h, w = imgf.shape | |
| imgf = imgf.flatten(2).permute(2, 0, 1) | |
| pos = pos.flatten(2).permute(2, 0, 1) | |
| for layer in self.layers: | |
| imgf = layer(imgf, [imgf], pos=pos, memory_pos=[pos, pos]) | |
| imgf = imgf.permute(1, 2, 0).reshape(bs, c, h, w) | |
| return imgf | |
| class FlowHead(nn.Module): | |
| def __init__(self, input_dim=128, hidden_dim=256, out_cha=2): | |
| super(FlowHead, self).__init__() | |
| self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) | |
| self.conv2 = nn.Conv2d(hidden_dim, out_cha, 3, padding=1) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| return self.conv2(self.relu(self.conv1(x))) | |
| class UpdateBlock(nn.Module): | |
| def __init__(self, hidden_dim=128): | |
| super(UpdateBlock, self).__init__() | |
| self.flow_head = FlowHead(hidden_dim, hidden_dim=256) | |
| self.mask = nn.Sequential( | |
| nn.Conv2d(hidden_dim, 256, 3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(256, 64 * 9, 1, padding=0)) | |
| def forward(self, imgf, coords1): | |
| mask = .25 * self.mask(imgf) # scale mask to balence gradients | |
| dflow = self.flow_head(imgf) | |
| coords1 = coords1 + dflow | |
| return mask, coords1 | |
| def coords_grid(batch, ht, wd): | |
| coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) | |
| coords = torch.stack(coords[::-1], dim=0).float() | |
| return coords[None].repeat(batch, 1, 1, 1) | |
| def upflow8(flow, mode='bilinear'): | |
| new_size = (8 * flow.shape[2], 8 * flow.shape[3]) | |
| return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) | |
| class Up_block(nn.Module): | |
| def __init__(self, hidden_dim=128, out_cha=3): | |
| super(Up_block, self).__init__() | |
| self.flow_head = FlowHead(hidden_dim, hidden_dim=256, out_cha=out_cha) | |
| self.acf = nn.Hardtanh(0, 1) | |
| def forward(self, x): | |
| x = self.flow_head(x) | |
| x = upflow8(x) | |
| x = self.acf(x) | |
| return x | |
| class DocGeoNet(nn.Module): | |
| def __init__(self): | |
| super(DocGeoNet, self).__init__() | |
| self.hidden_dim = hdim = 128 | |
| self.imcnn = BasicEncoder(input_dim=3, output_dim=hdim, norm_fn='instance') | |
| # uv | |
| self.wc_encoder = TransEncoder(4, hidden_dim=hdim) | |
| # uv tail | |
| self.Up_block_wc = nn.Sequential(TransEncoder(2, hidden_dim=hdim), | |
| Up_block(self.hidden_dim)) | |
| # text | |
| self.text_encoder = U_Net_mini(3, 1) | |
| self.textcnn = nn.Conv2d(128, 64, 3, 2, 1) # BasicEncoder(input_dim=32, output_dim=64, norm_fn='instance') | |
| # 6 | |
| self.bm_encoder = TransEncoder(6, hidden_dim=hdim + 64) | |
| # bm tail | |
| self.update_block = UpdateBlock(self.hidden_dim + 64) | |
| def initialize_flow(self, img): | |
| N, C, H, W = img.shape | |
| coodslar = coords_grid(N, H, W).to(img.device) | |
| coords0 = coords_grid(N, H // 8, W // 8).to(img.device) | |
| coords1 = coords_grid(N, H // 8, W // 8).to(img.device) | |
| return coodslar, coords0, coords1 | |
| def upsample_flow(self, flow, mask): | |
| N, _, H, W = flow.shape | |
| mask = mask.view(N, 1, 9, 8, 8, H, W) | |
| mask = torch.softmax(mask, dim=2) | |
| up_flow = F.unfold(8 * flow, [3, 3], padding=1) | |
| up_flow = up_flow.view(N, 2, 9, 1, 1, H, W) | |
| up_flow = torch.sum(mask * up_flow, dim=2) | |
| up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) | |
| return up_flow.reshape(N, 2, 8 * H, 8 * W) | |
| def forward(self, image1): | |
| # wc | |
| imfmap = self.imcnn(image1) | |
| imfmap = torch.relu(imfmap) | |
| wcfea = self.wc_encoder(imfmap) | |
| wc_pred = self.Up_block_wc(wcfea) | |
| # text | |
| d4, text_pred = self.text_encoder(image1) | |
| textfea = self.textcnn(d4) | |
| fmap = torch.cat((wcfea, textfea), 1) | |
| # bm encoder | |
| fmap = self.bm_encoder(fmap) | |
| # upsample | |
| coodslar, coords0, coords1 = self.initialize_flow(image1) | |
| coords1 = coords1.detach() | |
| mask, coords1 = self.update_block(fmap, coords1) | |
| flow_up = self.upsample_flow(coords1 - coords0, mask) | |
| bm_up = coodslar + flow_up | |
| return wc_pred, text_pred, bm_up | |