Spaces:
Running
Running
File size: 12,205 Bytes
29f689c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 |
import torch
import torch.nn as nn
from openrec.modeling.decoders.nrtr_decoder import PositionalEncoding, TransformerBlock
class Transformer_Encoder(nn.Module):
def __init__(
self,
n_layers=3,
n_head=8,
d_model=512,
d_inner=2048,
dropout=0.1,
n_position=256,
):
super(Transformer_Encoder, self).__init__()
self.pe = PositionalEncoding(dropout=dropout,
dim=d_model,
max_len=n_position)
self.layer_stack = nn.ModuleList([
TransformerBlock(d_model, n_head, d_inner) for _ in range(n_layers)
])
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, enc_output, src_mask):
enc_output = self.pe(enc_output) # position embeding
for enc_layer in self.layer_stack:
enc_output = enc_layer(enc_output, self_mask=src_mask)
enc_output = self.layer_norm(enc_output)
return enc_output
class PP_layer(nn.Module):
def __init__(self, n_dim=512, N_max_character=25, n_position=256):
super(PP_layer, self).__init__()
self.character_len = N_max_character
self.f0_embedding = nn.Embedding(N_max_character, n_dim)
self.w0 = nn.Linear(N_max_character, n_position)
self.wv = nn.Linear(n_dim, n_dim)
self.we = nn.Linear(n_dim, N_max_character)
self.active = nn.Tanh()
self.softmax = nn.Softmax(dim=2)
def forward(self, enc_output):
reading_order = torch.arange(self.character_len,
dtype=torch.long,
device=enc_output.device)
reading_order = reading_order.unsqueeze(0).expand(
enc_output.shape[0], -1) # (S,) -> (B, S)
reading_order = self.f0_embedding(reading_order) # b,25,512
# calculate attention
t = self.w0(reading_order.transpose(1, 2)) # b,512,256
t = self.active(t.transpose(1, 2) + self.wv(enc_output)) # b,256,512
t = self.we(t) # b,256,25
t = self.softmax(t.transpose(1, 2)) # b,25,256
g_output = torch.bmm(t, enc_output) # b,25,512
return g_output
class Prediction(nn.Module):
def __init__(
self,
n_dim=512,
n_class=37,
N_max_character=25,
n_position=256,
):
super(Prediction, self).__init__()
self.pp = PP_layer(n_dim=n_dim,
N_max_character=N_max_character,
n_position=n_position)
self.pp_share = PP_layer(n_dim=n_dim,
N_max_character=N_max_character,
n_position=n_position)
self.w_vrm = nn.Linear(n_dim, n_class) # output layer
self.w_share = nn.Linear(n_dim, n_class) # output layer
self.nclass = n_class
def forward(self, cnn_feature, f_res, f_sub, is_Train=False, use_mlm=True):
if is_Train:
if not use_mlm:
g_output = self.pp(cnn_feature) # b,25,512
g_output = self.w_vrm(g_output)
f_res = 0
f_sub = 0
return g_output, f_res, f_sub
g_output = self.pp(cnn_feature) # b,25,512
f_res = self.pp_share(f_res)
f_sub = self.pp_share(f_sub)
g_output = self.w_vrm(g_output)
f_res = self.w_share(f_res)
f_sub = self.w_share(f_sub)
return g_output, f_res, f_sub
else:
g_output = self.pp(cnn_feature) # b,25,512
g_output = self.w_vrm(g_output)
return g_output
class MLM(nn.Module):
"""Architecture of MLM."""
def __init__(
self,
n_dim=512,
n_position=256,
n_head=8,
dim_feedforward=2048,
max_text_length=25,
):
super(MLM, self).__init__()
self.MLM_SequenceModeling_mask = Transformer_Encoder(
n_layers=2,
n_head=n_head,
d_model=n_dim,
d_inner=dim_feedforward,
n_position=n_position,
)
self.MLM_SequenceModeling_WCL = Transformer_Encoder(
n_layers=1,
n_head=n_head,
d_model=n_dim,
d_inner=dim_feedforward,
n_position=n_position,
)
self.pos_embedding = nn.Embedding(max_text_length, n_dim)
self.w0_linear = nn.Linear(1, n_position)
self.wv = nn.Linear(n_dim, n_dim)
self.active = nn.Tanh()
self.we = nn.Linear(n_dim, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, input, label_pos):
# transformer unit for generating mask_c
feature_v_seq = self.MLM_SequenceModeling_mask(input, src_mask=None)
# position embedding layer
pos_emb = self.pos_embedding(label_pos.long())
pos_emb = self.w0_linear(torch.unsqueeze(pos_emb,
dim=2)).transpose(1, 2)
# fusion position embedding with features V & generate mask_c
att_map_sub = self.active(pos_emb + self.wv(feature_v_seq))
att_map_sub = self.we(att_map_sub) # b,256,1
att_map_sub = self.sigmoid(att_map_sub.transpose(1, 2)) # b,1,256
# WCL
# generate inputs for WCL
f_res = input * (1 - att_map_sub.transpose(1, 2)
) # second path with remaining string
f_sub = input * (att_map_sub.transpose(1, 2)
) # first path with occluded character
# transformer units in WCL
f_res = self.MLM_SequenceModeling_WCL(f_res, src_mask=None)
f_sub = self.MLM_SequenceModeling_WCL(f_sub, src_mask=None)
return f_res, f_sub, att_map_sub
class MLM_VRM(nn.Module):
def __init__(
self,
n_layers=3,
n_position=256,
n_dim=512,
n_head=8,
dim_feedforward=2048,
max_text_length=25,
nclass=37,
):
super(MLM_VRM, self).__init__()
self.MLM = MLM(
n_dim=n_dim,
n_position=n_position,
n_head=n_head,
dim_feedforward=dim_feedforward,
max_text_length=max_text_length,
)
self.SequenceModeling = Transformer_Encoder(
n_layers=n_layers,
n_head=n_head,
d_model=n_dim,
d_inner=dim_feedforward,
n_position=n_position,
)
self.Prediction = Prediction(
n_dim=n_dim,
n_position=n_position,
N_max_character=max_text_length + 1,
n_class=nclass,
) # N_max_character = 1 eos + 25 characters
self.nclass = nclass
self.max_text_length = max_text_length
def forward(self, input, label_pos, training_step, is_Train=False):
nT = self.max_text_length
b, c, h, w = input.shape
input = input.reshape(b, c, -1)
input = input.transpose(1, 2)
if is_Train:
if training_step == 'LF_1':
f_res = 0
f_sub = 0
input = self.SequenceModeling(input, src_mask=None)
text_pre, text_rem, text_mas = self.Prediction(input,
f_res,
f_sub,
is_Train=True,
use_mlm=False)
return text_pre, text_pre, text_pre
elif training_step == 'LF_2':
# MLM
f_res, f_sub, mask_c = self.MLM(input, label_pos)
input = self.SequenceModeling(input, src_mask=None)
text_pre, text_rem, text_mas = self.Prediction(input,
f_res,
f_sub,
is_Train=True)
return text_pre, text_rem, text_mas
elif training_step == 'LA':
# MLM
f_res, f_sub, mask_c = self.MLM(input, label_pos)
# use the mask_c (1 for occluded character and 0 for remaining characters) to occlude input
# ratio controls the occluded number in a batch
ratio = 2
character_mask = torch.zeros_like(mask_c)
character_mask[0:b // ratio, :, :] = mask_c[0:b // ratio, :, :]
input = input * (1 - character_mask.transpose(1, 2))
# VRM
# transformer unit for VRM
input = self.SequenceModeling(input, src_mask=None)
# prediction layer for MLM and VSR
text_pre, text_rem, text_mas = self.Prediction(input,
f_res,
f_sub,
is_Train=True)
return text_pre, text_rem, text_mas
else: # VRM is only used in the testing stage
f_res = 0
f_sub = 0
contextual_feature = self.SequenceModeling(input, src_mask=None)
C = self.Prediction(contextual_feature,
f_res,
f_sub,
is_Train=False,
use_mlm=False)
C = C.transpose(1, 0) # (25, b, 38))
out_res = torch.zeros(nT, b, self.nclass).type_as(input.data)
out_length = torch.zeros(b).type_as(input.data)
now_step = 0
while 0 in out_length and now_step < nT:
tmp_result = C[now_step, :, :]
out_res[now_step] = tmp_result
tmp_result = tmp_result.topk(1)[1].squeeze(dim=1)
for j in range(b):
if out_length[j] == 0 and tmp_result[j] == 0:
out_length[j] = now_step + 1
now_step += 1
for j in range(0, b):
if int(out_length[j]) == 0:
out_length[j] = nT
start = 0
output = torch.zeros(int(out_length.sum()),
self.nclass).type_as(input.data)
for i in range(0, b):
cur_length = int(out_length[i])
output[start:start + cur_length] = out_res[0:cur_length, i, :]
start += cur_length
return output, out_length
class VisionLANDecoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
n_head=None,
training_step='LA',
n_layers=3,
n_position=256,
max_text_length=25,
):
super(VisionLANDecoder, self).__init__()
self.training_step = training_step
n_dim = in_channels
dim_feedforward = n_dim * 4
n_head = n_head if n_head is not None else n_dim // 32
self.MLM_VRM = MLM_VRM(
n_layers=n_layers,
n_position=n_position,
n_dim=n_dim,
n_head=n_head,
dim_feedforward=dim_feedforward,
max_text_length=max_text_length,
nclass=out_channels + 1,
)
def forward(self, x, data=None):
# MLM + VRM
if self.training:
label_pos = data[-2]
text_pre, text_rem, text_mas = self.MLM_VRM(x,
label_pos,
self.training_step,
is_Train=True)
return text_pre, text_rem, text_mas
else:
output, out_length = self.MLM_VRM(x,
None,
self.training_step,
is_Train=False)
return output, out_length
|