# modified from https://github.com/zyddnys/manga-image-translator/blob/main/ocr/model_48px_ctc.py import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import math import einops from typing import List, Tuple, Optional from utils.textblock import TextBlock class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x, offset = 0): x = x + self.pe[:, offset: offset + x.size(1), :] return x class CustomTransformerEncoderLayer(nn.Module): r"""TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application. Args: d_model: the number of expected features in the input (required). nhead: the number of heads in the multiheadattention models (required). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of intermediate layer, relu or gelu (default=relu). layer_norm_eps: the eps value in layer normalization components (default=1e-5). batch_first: If ``True``, then the input and output tensors are provided as (batch, seq, feature). Default: ``False``. norm_first: if ``True``, layer norm is done prior to attention and feedforward operations, respectivaly. Otherwise it's done after. Default: ``False`` (after). Examples:: >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) >>> src = torch.rand(10, 32, 512) >>> out = encoder_layer(src) Alternatively, when ``batch_first`` is ``True``: >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) >>> src = torch.rand(32, 10, 512) >>> out = encoder_layer(src) """ __constants__ = ['batch_first', 'norm_first'] def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="gelu", layer_norm_eps=1e-5, batch_first=False, norm_first=False, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super(CustomTransformerEncoderLayer, self).__init__() self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, **factory_kwargs) # Implementation of Feedforward model self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) self.norm_first = norm_first self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.pe = PositionalEncoding(d_model, max_len = 3072) self.activation = F.gelu def __setstate__(self, state): if 'activation' not in state: state['activation'] = F.relu super(CustomTransformerEncoderLayer, self).__setstate__(state) def forward(self, src: torch.Tensor, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, is_causal = None) -> torch.Tensor: r"""Pass the input through the encoder layer. Args: src: the sequence to the encoder layer (required). src_mask: the mask for the src sequence (optional). src_key_padding_mask: the mask for the src keys per batch (optional). Shape: see the docs in Transformer class. """ # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf x = src if self.norm_first: x = x + self._sa_block(self.norm1(x), src_mask, src_key_padding_mask) x = x + self._ff_block(self.norm2(x)) else: x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask)) x = self.norm2(x + self._ff_block(x)) return x # self-attention block def _sa_block(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor], key_padding_mask: Optional[torch.Tensor]) -> torch.Tensor: x = self.self_attn(self.pe(x), self.pe(x), x, # no PE for value attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=False)[0] return self.dropout1(x) # feed forward block def _ff_block(self, x: torch.Tensor) -> torch.Tensor: x = self.linear2(self.dropout(self.activation(self.linear1(x)))) return self.dropout2(x) class ResNet(nn.Module): def __init__(self, input_channel, output_channel, block, layers): super(ResNet, self).__init__() self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] self.inplanes = int(output_channel / 8) self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 8), kernel_size=3, stride=1, padding=1, bias=False) self.bn0_1 = nn.BatchNorm2d(int(output_channel / 8)) self.conv0_2 = nn.Conv2d(int(output_channel / 8), self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) self.maxpool1 = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0]) self.bn1 = nn.BatchNorm2d(self.output_channel_block[0]) self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[ 0], kernel_size=3, stride=1, padding=1, bias=False) self.maxpool2 = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1) self.bn2 = nn.BatchNorm2d(self.output_channel_block[1]) self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[ 1], kernel_size=3, stride=1, padding=1, bias=False) self.maxpool3 = nn.AvgPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1)) self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1) self.bn3 = nn.BatchNorm2d(self.output_channel_block[2]) self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[ 2], kernel_size=3, stride=1, padding=1, bias=False) self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1) self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3]) self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ 3], kernel_size=3, stride=(2, 1), padding=(1, 1), bias=False) self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3]) self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ 3], kernel_size=3, stride=1, padding=0, bias=False) self.bn4_3 = nn.BatchNorm2d(self.output_channel_block[3]) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.BatchNorm2d(self.inplanes), nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv0_1(x) x = self.bn0_1(x) x = F.relu(x) x = self.conv0_2(x) x = self.maxpool1(x) x = self.layer1(x) x = self.bn1(x) x = F.relu(x) x = self.conv1(x) x = self.maxpool2(x) x = self.layer2(x) x = self.bn2(x) x = F.relu(x) x = self.conv2(x) x = self.maxpool3(x) x = self.layer3(x) x = self.bn3(x) x = F.relu(x) x = self.conv3(x) x = self.layer4(x) x = self.bn4_1(x) x = F.relu(x) x = self.conv4_1(x) x = self.bn4_2(x) x = F.relu(x) x = self.conv4_2(x) x = self.bn4_3(x) return x class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.bn1 = nn.BatchNorm2d(inplanes) self.conv1 = self._conv3x3(inplanes, planes) self.bn2 = nn.BatchNorm2d(planes) self.conv2 = self._conv3x3(planes, planes) self.downsample = downsample self.stride = stride def _conv3x3(self, in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) def forward(self, x): residual = x out = self.bn1(x) out = F.relu(out) out = self.conv1(out) out = self.bn2(out) out = F.relu(out) out = self.conv2(out) if self.downsample is not None: residual = self.downsample(residual) return out + residual def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class ResNet_FeatureExtractor(nn.Module): """ FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) """ def __init__(self, input_channel, output_channel=128): super(ResNet_FeatureExtractor, self).__init__() self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [4, 6, 8, 6, 3]) def forward(self, input): return self.ConvNet(input) class OCR(nn.Module) : def __init__(self, dictionary, max_len): super(OCR, self).__init__() self.max_len = max_len self.dictionary = dictionary self.dict_size = len(dictionary) self.backbone = ResNet_FeatureExtractor(3, 320) enc = CustomTransformerEncoderLayer(320, 8, 320 * 4, dropout=0.05,batch_first=True,norm_first=True) self.encoders = nn.TransformerEncoder(enc, 3) self.char_pred_norm = nn.Sequential(nn.LayerNorm(320), nn.Dropout(0.1), nn.GELU()) self.char_pred = nn.Linear(320, self.dict_size) self.color_pred1 = nn.Sequential(nn.Linear(320, 6)) def forward(self, img: torch.FloatTensor ) : feats = self.backbone(img).squeeze(2) feats = self.encoders(feats.permute(0, 2, 1)) pred_char_logits = self.char_pred(self.char_pred_norm(feats)) pred_color_values = self.color_pred1(feats) return pred_char_logits, pred_color_values def decode(self, img: torch.Tensor, img_widths: List[int], blank, verbose = False) -> List[List[Tuple[str, float, int, int, int, int, int, int]]] : N, C, H, W = img.shape assert H == 48 and C == 3 feats = self.backbone(img).squeeze(2) feats = self.encoders(feats.permute(0, 2, 1)) pred_char_logits = self.char_pred(self.char_pred_norm(feats)) pred_color_values = self.color_pred1(feats) return self.decode_ctc_top1(pred_char_logits, pred_color_values, blank, verbose = verbose) def decode_ctc_top1(self, pred_char_logits, pred_color_values, blank, verbose = False) -> List[List[Tuple[str, float, int, int, int, int, int, int]]] : pred_chars: List[List[Tuple[str, float, int, int, int, int, int, int]]] = [] for _ in range(pred_char_logits.size(0)) : pred_chars.append([]) logprobs = pred_char_logits.log_softmax(2) _, preds_index = logprobs.max(2) preds_index = preds_index.cpu() pred_color_values = pred_color_values.cpu().clamp_(0, 1) for b in range(pred_char_logits.size(0)) : if verbose : print('------------------------------') last_ch = blank for t in range(pred_char_logits.size(1)) : pred_ch = preds_index[b, t] if pred_ch != last_ch and pred_ch != blank : lp = logprobs[b, t, pred_ch].item() if verbose : if lp < math.log(0.9) : top5 = torch.topk(logprobs[b, t], 5) top5_idx = top5.indices top5_val = top5.values r = '' for i in range(5) : r += f'{self.dictionary[top5_idx[i]]}: {math.exp(top5_val[i])}, ' print(r) else : print(f'{self.dictionary[pred_ch]}: {math.exp(lp)}') pred_chars[b].append(( pred_ch, lp, pred_color_values[b, t][0].item(), pred_color_values[b, t][1].item(), pred_color_values[b, t][2].item(), pred_color_values[b, t][3].item(), pred_color_values[b, t][4].item(), pred_color_values[b, t][5].item() )) last_ch = pred_ch return pred_chars def eval_ocr(self, input_lengths, target_lengths, pred_char_logits, pred_color_values, gt_char_index, gt_color_values, blank, blank1) : correct_char = 0 total_char = 0 color_diff = 0 color_diff_dom = 0 _, preds_index = pred_char_logits.max(2) pred_chars = torch.zeros_like(gt_char_index).cpu() for b in range(pred_char_logits.size(0)) : last_ch = blank i = 0 for t in range(input_lengths[b]) : pred_ch = preds_index[b, t] if pred_ch != last_ch and pred_ch != blank : total_char += 1 if gt_char_index[b, i] == pred_ch : correct_char += 1 if pred_ch != blank1 : color_diff += ((pred_color_values[b, t] - gt_color_values[b, i]).abs().mean() * 255.0).item() color_diff_dom += 1 pred_chars[b, i] = pred_ch i += 1 if i >= gt_color_values.size(1) or i >= gt_char_index.size(1) : break last_ch = pred_ch return correct_char / (total_char + 1), color_diff / (color_diff_dom + 1), pred_chars def chunks(lst, n): """Yield successive n-sized chunks from lst.""" for i in range(0, len(lst), n): yield lst[i:i + n] class AvgMeter() : def __init__(self) : self.reset() def reset(self) : self.sum = 0 self.count = 0 def __call__(self, val = None) : if val is not None : self.sum += val self.count += 1 if self.count > 0 : return self.sum / self.count else : return 0 class OCR48pxCTC: def __init__(self, model_path: str, device='cpu'): with open('data/alphabet-all-v5.txt', 'r', encoding = 'utf-8') as fp : dictionary = [s[:-1] for s in fp.readlines()] self.device = device self.text_height = 48 self.maxwidth = 8100 model = OCR(dictionary, 768) sd = torch.load(model_path, map_location = 'cpu') del sd['encoders.layers.0.pe.pe'] del sd['encoders.layers.1.pe.pe'] del sd['encoders.layers.2.pe.pe'] model.load_state_dict(sd['model'] if 'model' in sd else sd, strict=False) model.eval() if self.device != 'cpu' : model = model.to(self.device) self.net = model def to(self, device: str) -> None: self.net.to(device) self.device = device @torch.no_grad() def __call__(self, textblk_lst: List[TextBlock], regions: List[np.ndarray], textblk_lst_indices: List, chunk_size = 16) -> None: perm = range(len(regions)) chunck_idx = 0 for indices in chunks(perm, chunk_size) : N = len(indices) widths = [regions[i].shape[1] for i in indices] # max_width = 4 * (max(widths) + 7) // 4 max_width = (4 * (max(widths) + 7) // 4) + 128 region = np.zeros((N, self.text_height, max_width, 3), dtype = np.uint8) for i, idx in enumerate(indices) : W = regions[idx].shape[1] region[i, :, : W, :] = regions[idx] images = (torch.from_numpy(region).float() - 127.5) / 127.5 images = einops.rearrange(images, 'N H W C -> N C H W') if self.device != 'cpu': images = images.to(self.device) with torch.inference_mode() : texts = self.net.decode(images, widths, 0) for i, single_line in enumerate(texts) : if not single_line : continue textblk = textblk_lst[textblk_lst_indices[i+chunck_idx]] cur_texts = [] total_fr = AvgMeter() total_fg = AvgMeter() total_fb = AvgMeter() total_br = AvgMeter() total_bg = AvgMeter() total_bb = AvgMeter() total_logprob = AvgMeter() for (chid, logprob, fr, fg, fb, br, bg, bb) in single_line : ch = self.net.dictionary[chid] if ch == '' : ch = ' ' cur_texts.append(ch) total_logprob(logprob) if ch != ' ' : total_fr(int(fr * 255)) total_fg(int(fg * 255)) total_fb(int(fb * 255)) total_br(int(br * 255)) total_bg(int(bg * 255)) total_bb(int(bb * 255)) prob = np.exp(total_logprob()) if prob < 0.3 : continue textblk.text.append(''.join(cur_texts)) textblk.update_font_colors( [int(total_fr()), int(total_fg()), int(total_fb())], [int(total_br()), int(total_bg()), int(total_bb())] ) chunck_idx += N # def test2() : # with open('alphabet-all-v5.txt', 'r') as fp : # dictionary = [s[:-1] for s in fp.readlines()] # img = torch.randn(4, 3, 48, 1536) # idx = torch.zeros(4, 32).long() # mask = torch.zeros(4, 32).bool() # model = OCR(dictionary, 1024) # pred_char_logits, pred_color_values = model(img) # print(pred_char_logits.shape, pred_color_values.shape) # def test_inference() : # with torch.no_grad() : # with open('../SynthText/alphabet-all-v3.txt', 'r') as fp : # dictionary = [s[:-1] for s in fp.readlines()] # img = torch.zeros(1, 3, 32, 128) # model = OCR(dictionary, 32) # m = torch.load("ocr_ar_v2-3-test.ckpt", map_location='cpu') # model.load_state_dict(m['model']) # model.eval() # (char_probs, _, _, _, _, _, _, _), _ = model.infer_beam(img, max_seq_length = 20) # _, pred_chars_index = char_probs.max(2) # pred_chars_index = pred_chars_index.squeeze_(0) # seq = [] # for chid in pred_chars_index : # ch = dictionary[chid] # if ch == '' : # ch == ' ' # seq.append(ch) # print(''.join(seq))