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f5bb5c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | # modeling_khmerocr.py
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pad_sequence
from transformers import PreTrainedModel
from configuration_khmerocr import KhmerOCRConfig
# ==========================================
# 1. HELPER CLASSES (SequenceSE, CNN, etc.)
# ==========================================
class SequenceSE(nn.Module):
def __init__(self, channels, reduction=16):
super(SequenceSE, self).__init__()
self.fc = nn.Sequential(
nn.Conv1d(channels, channels // reduction, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv1d(channels // reduction, channels, kernel_size=1),
nn.Sigmoid()
)
def forward(self, x):
b, c, h, w = x.size()
y = torch.mean(x, dim=2).view(b, c, w)
y = self.fc(y)
y = y.view(b, c, 1, w)
return x * y
class ImprovedFeatureExtractor(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Sequential(nn.Conv2d(1, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(True))
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Sequential(nn.Conv2d(64, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU(True))
self.pool2 = nn.MaxPool2d(2, 2)
self.conv3 = nn.Sequential(nn.Conv2d(128, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU(True))
self.conv4 = nn.Sequential(nn.Conv2d(256, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU(True))
self.se3 = SequenceSE(256)
self.pool3 = nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1))
self.conv5 = nn.Sequential(nn.Conv2d(256, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU(True))
self.conv6 = nn.Sequential(nn.Conv2d(512, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU(True))
self.se4 = SequenceSE(512)
self.pool4 = nn.MaxPool2d(kernel_size=(2, 1), stride=(2, 1))
self.conv7 = nn.Conv2d(512, 512, 3, 1, 1)
self.bn7 = nn.BatchNorm2d(512)
self.relu7 = nn.ReLU(True)
self.se5 = SequenceSE(512)
self.final_pool = nn.AdaptiveAvgPool2d((2, 32))
def forward(self, x):
x = self.pool1(self.conv1(x))
x = self.pool2(self.conv2(x))
x = self.conv4(self.conv3(x))
x = self.se3(x)
x = self.pool3(x)
x = self.conv6(self.conv5(x))
x = self.se4(x)
x = self.pool4(x)
x = self.relu7(self.bn7(self.conv7(x)))
x = self.se5(x)
x = self.final_pool(x)
return x
class PatchEncoder(nn.Module):
def __init__(self, in_channels, emb_dim, k1=2, k2=1, max_patches=256):
super().__init__()
self.proj = nn.Conv2d(in_channels, emb_dim, kernel_size=(k1, k2), stride=(k1, k2))
self.pos_emb = nn.Parameter(torch.zeros(max_patches, emb_dim))
nn.init.trunc_normal_(self.pos_emb, std=0.02)
def forward(self, F):
x = self.proj(F)
B, D, Hp, Wp = x.shape
N = Hp * Wp
x = x.flatten(2).transpose(1, 2)
x = x + self.pos_emb[:N].unsqueeze(0)
return x, N
def make_encoder(emb_dim=384, nhead=8, num_layers=3, dim_feedforward=1024, dropout=0.1):
enc_layer = nn.TransformerEncoderLayer(d_model=emb_dim, nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout, activation='relu')
return nn.TransformerEncoder(enc_layer, num_layers=num_layers)
class TransformerDecoderWrapper(nn.Module):
def __init__(self, vocab_size, emb_dim, nhead=8, num_layers=3, pad_idx=0, max_len=256):
super().__init__()
self.tok_emb = nn.Embedding(vocab_size, emb_dim, padding_idx=pad_idx)
dec_layer = nn.TransformerDecoderLayer(d_model=emb_dim, nhead=nhead, dim_feedforward=emb_dim*4, dropout=0.1)
self.decoder = nn.TransformerDecoder(dec_layer, num_layers=num_layers)
self.pos_emb = nn.Parameter(torch.zeros(max_len, emb_dim))
nn.init.trunc_normal_(self.pos_emb, std=0.1)
self.out_proj = nn.Linear(emb_dim, vocab_size)
self.pad_idx = pad_idx
def generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def forward(self, tgt_tokens, memory, memory_key_padding_mask):
B, T = tgt_tokens.size()
device = tgt_tokens.device
tok = self.tok_emb(tgt_tokens)
pos = self.pos_emb[:T,:].unsqueeze(0).expand(B,-1,-1)
tgt = (tok + pos).transpose(0,1)
tgt_key_padding_mask = (tgt_tokens == self.pad_idx)
if memory_key_padding_mask is not None:
memory_key_padding_mask = memory_key_padding_mask.bool()
tgt_mask = self.generate_square_subsequent_mask(T).to(device)
mem = memory.transpose(0,1)
dec_out = self.decoder(tgt, mem, tgt_mask=tgt_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask)
return self.out_proj(dec_out.transpose(0,1))
# ==========================================
# 2. MAIN MODEL WRAPPER
# ==========================================
class KhmerOCR(PreTrainedModel):
config_class = KhmerOCRConfig
def __init__(self, config):
super().__init__(config)
self.vocab_size = config.vocab_size
self.pad_idx = config.pad_idx
self.emb_dim = config.emb_dim
self.cnn = ImprovedFeatureExtractor()
self.patch = PatchEncoder(512, emb_dim=self.emb_dim, k1=2, k2=1)
self.enc = make_encoder(emb_dim=self.emb_dim, nhead=config.nhead, num_layers=config.num_encoder_layers)
self.global_pos = nn.Parameter(torch.zeros(config.max_global_len, self.emb_dim))
nn.init.trunc_normal_(self.global_pos, std=0.02)
self.context_bilstm = nn.LSTM(
input_size=self.emb_dim,
hidden_size=self.emb_dim // 2,
num_layers=1,
batch_first=True,
bidirectional=True
)
self.dec = TransformerDecoderWrapper(self.vocab_size, emb_dim=self.emb_dim, nhead=config.nhead,
num_layers=config.num_decoder_layers, pad_idx=self.pad_idx)
def forward(self, chunk_lists, tgt_tokens=None):
# 1. Flatten
chunk_sizes = [len(c) for c in chunk_lists]
flat_input_list = [chunk for img_chunks in chunk_lists for chunk in img_chunks]
flat_input = torch.stack(flat_input_list)
# 2. Pipeline
f = self.cnn(flat_input)
p, _ = self.patch(f)
p = p.transpose(0, 1).contiguous()
enc_out = self.enc(p)
enc_out = enc_out.transpose(0, 1)
# 3. Merge
batch_encoded_list = []
cursor = 0
feature_dim = enc_out.size(-1)
for size in chunk_sizes:
img_chunks = enc_out[cursor : cursor + size]
merged_seq = img_chunks.reshape(-1, feature_dim)
batch_encoded_list.append(merged_seq)
cursor += size
# 4. Pad & Global Pos
memory = pad_sequence(batch_encoded_list, batch_first=True, padding_value=0.0)
B, T, _ = memory.shape
limit = min(T, self.global_pos.size(0))
pos_emb = self.global_pos[:limit, :].unsqueeze(0)
if T > self.global_pos.size(0):
memory = memory[:, :limit, :] + pos_emb
T = limit
else:
memory = memory + pos_emb
# 5. BiLSTM
self.context_bilstm.flatten_parameters()
memory, _ = self.context_bilstm(memory)
# If inference (no targets), return memory for search
if tgt_tokens is None:
return memory
# 6. Decoder
memory_key_padding_mask = torch.ones((B, T), dtype=torch.bool, device=memory.device)
for i, seq in enumerate(batch_encoded_list):
valid_len = min(seq.shape[0], T)
memory_key_padding_mask[i, :valid_len] = False
logits = self.dec(tgt_tokens, memory, memory_key_padding_mask)
return logits |