File size: 12,203 Bytes
29134bd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
class Encoder(nn.Module):
def __init__(self, d_ff=512, d_model=128, n_heads=8, dropout=.3, max_len=512):
super(Encoder, self).__init__()
#hyperparameters
self.d_ff = d_ff
self.d_model= d_model
self.n_heads = n_heads
self.dropout_rate = dropout
self.max_len = max_len
#layers
self.MultiHeadAttention = nn.MultiheadAttention(embed_dim=d_model, num_heads=n_heads, batch_first=True)
self.LayerNorm = nn.LayerNorm(normalized_shape=[max_len, d_model], eps=1e-6)
self.fc_ff = nn.Linear(d_model, d_ff)
self.relu = nn.ReLU()
self.fc_model = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, padding_mask):
residual = x
x = self.MultiHeadAttention(x,x,x, key_padding_mask=padding_mask, need_weights=False)[0] # why do they pass in x for q,k,v here?
x = self.dropout(x)
x = self.LayerNorm(x + residual)
residual = x
x = self.fc_ff(x)
x = self.relu(x)
x = self.fc_model(x)
x = self.dropout(x)
x = self.LayerNorm(x + residual)
return x
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, max_len: int = 5000):
super().__init__()
#hyperparameters
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x: torch.Tensor):
x = x + self.pe[:x.size(0)]
return x
class Encoders(nn.Module):
def __init__(self, n_vocabs, n_layers=6, d_ff=512, d_model=128, n_heads=8, dropout=.3, max_len=512):
super(Encoders, self).__init__()
#hyperparameters
self.n_vocabs = n_vocabs
self.n_layers = n_layers
self.d_ff = d_ff
self.d_model= d_model
self.n_heads = n_heads
self.dropout_rate = dropout
self.max_len = max_len
#layers
self.Embedding = nn.Embedding(num_embeddings=n_vocabs, embedding_dim=d_model, padding_idx=0)
self.PositionalEncoding = PositionalEncoding(d_model, max_len=max_len)
self.encoders = nn.ModuleList([Encoder(d_ff=d_ff, d_model=d_model, n_heads=n_heads, dropout=dropout, max_len=max_len) for _ in range(n_layers)])
def forward(self, x):
padding_mask = self.create_padding_mask(x) # (BS x max_len) -> (BS x max_len)
x = self.Embedding(x) # (BS x max_len) -> (BS x max_len x d_model)
x = self.PositionalEncoding(x)
x[padding_mask] = torch.zeros(self.d_model).to(x.device)
for i in range(self.n_layers):
x = self.encoders[i](x, padding_mask)
x[padding_mask] = torch.zeros(self.d_model).to(x.device)
return x
def create_padding_mask(self, x):
mask = torch.eq(x, torch.zeros(x.size(), device=x.device))
return mask
class Token_Pretrained_Model(nn.Module):
def __init__(self, n_vocabs, n_target_vocabs, d_ff, d_model, n_layers, n_heads, dropout, max_len):
super(Token_Pretrained_Model, self).__init__()
self.encoder = Encoders(n_vocabs=n_vocabs, d_ff=d_ff, d_model=d_model, n_layers=n_layers, n_heads=n_heads, dropout=dropout, max_len=max_len)
self.fc1_mlm = nn.Linear(d_model, d_model)
self.gelu_mlm = nn.GELU()
self.norm_mlm = nn.LayerNorm(normalized_shape=[max_len, d_model], eps=1e-6)
self.fc2_mlm = nn.Linear(d_model, n_vocabs)
self.fc1_ssp = nn.Linear(d_model, d_model)
self.gelu_ssp = nn.GELU()
self.norm_ssp = nn.LayerNorm(normalized_shape=[max_len, d_model], eps=1e-6)
self.fc2_ssp = nn.Linear(d_model, n_target_vocabs)
def forward(self, inputs_mlm, inputs_ssp):
enc_mlm = self.encoder(inputs_mlm)
output_mlm = self.fc1_mlm(enc_mlm)
output_mlm = self.gelu_mlm(output_mlm)
output_mlm = self.norm_mlm(output_mlm)
output_mlm = self.fc2_mlm(output_mlm)
output_mlm = F.log_softmax(output_mlm, dim=1)
enc_ssp = self.encoder(inputs_ssp)
output_ssp = self.fc1_ssp(enc_ssp)
output_ssp = self.gelu_ssp(output_ssp)
output_ssp = self.norm_ssp(output_ssp)
output_ssp = self.fc2_ssp(output_ssp)
output_ssp = F.log_softmax(output_ssp, dim=1)
return output_mlm, output_ssp
class Convolution_Block(nn.Module):
def __init__(self, kernel_size):
super(Convolution_Block, self).__init__()
self.conv1 = nn.Conv2d(kernel_size, kernel_size, (4, 4), padding='same')
self.batchnorm1 = nn.BatchNorm2d(kernel_size)
self.conv2 = nn.Conv2d(kernel_size, kernel_size, (4, 4), padding='same')
self.batchnorm2 = nn.BatchNorm2d(kernel_size)
self.gelu = nn.GELU()
def forward(self, inputs):
output = self.conv1(inputs)
output = self.batchnorm1(output)
output = self.gelu(output)
output = self.conv2(output)
output = self.batchnorm2(output)
output = self.gelu(output)
output = output + inputs
return output
class Downsized_Convolution_Block(nn.Module):
def __init__(self, input_kernel_size, output_kernel_size):
super(Downsized_Convolution_Block, self).__init__()
self.conv1 = nn.Conv2d(input_kernel_size, output_kernel_size, (4, 4), padding='same')
self.batchnorm1 = nn.BatchNorm2d(output_kernel_size)
self.conv2 = nn.Conv2d(output_kernel_size, output_kernel_size, (4, 4), padding='same')
self.batchnorm2 = nn.BatchNorm2d(output_kernel_size)
self.maxpool = nn. MaxPool2d((2, 2))
self.gelu = nn.GELU()
def forward(self, inputs):
output = self.maxpool(inputs)
output = self.conv1(output)
output = self.batchnorm1(output)
output = self.gelu(output)
output = self.conv2(output)
output = self.batchnorm2(output)
output = self.gelu(output)
return output
class AptaTrans(nn.Module):
def __init__(self, apta_encoder, prot_encoder, n_apta_vocabs, n_prot_vocabs, dropout, apta_max_len, prot_max_len):
super(AptaTrans, self).__init__()
#hyperparameters
self.n_apta_vocabs = n_apta_vocabs
self.n_prot_vocabs = n_prot_vocabs
self.apta_max_len = apta_max_len
self.prot_max_len = prot_max_len
self.dropout = dropout
self.apta_encoder = apta_encoder
self.prot_encoder = prot_encoder
self.c = 85
self.kernel_size = self.c
self.batchnorm_fm = nn.BatchNorm2d(1)
self.conv = nn.Conv2d(1, self.kernel_size, (4, 4))
self.batchnorm = nn.BatchNorm2d(self.kernel_size)
self.gelu = nn.GELU()
self.maxpool = nn.MaxPool2d((2,2))
self.conv64_1 = Convolution_Block(self.c)
self.conv64_2 = Convolution_Block(self.c)
self.conv64_3 = Convolution_Block(self.c)
self.dconv128 = Downsized_Convolution_Block(self.c, 2*self.c)
self.conv128_1 = Convolution_Block(2*self.c)
self.conv128_2 = Convolution_Block(2*self.c)
self.dconv256 = Downsized_Convolution_Block(2*self.c, 4*self.c)
self.conv256_1 = Convolution_Block(4*self.c)
self.conv256_2 = Convolution_Block(4*self.c)
# self.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.flatten = nn.Flatten()
# self.fc = nn.Linear(256 * 34 * 108, 128) #128, 136, 432 #68 * 216 * 256 # 256 * 34 * 108 #256, 68, 255
self.fc = nn.Linear(2416040, 128) # TODO: clean this up | redefine due to combination of ESM2 and AptaTrans encoder outputs
self.fc_reshape = nn.Linear(1280, 128) # Needed to reshape ESM-2 encodings to match AptaTrans encodings
self.fc1 = nn.Linear(128, 1)
def forward(self, apta, esm_prot):
apta = self.apta_encoder(apta) # output: (BS X #apt_toks x apt_embed_dim), encoder outputs (BS x MLM & sec. structure feature embeddings)
prot = self.prot_encoder(esm_prot, repr_layers=[33], return_contacts=False)['representations'][33]
# FF layer that converts embedding dim to match Aptatrans embedding dim
prot = self.fc_reshape(prot)
prot = torch.transpose(prot, 1, 2) # (BS x embed_dim x #toks)
output = torch.bmm(apta, prot) # (BS x #apt_toks x apt_embed_dim) x (BS x apt_embed_dim x #prot_toks) = (BS x #apt_toks x #prot_toks)
output = torch.unsqueeze(output, 1)
output = self.batchnorm_fm(output)
output = self.conv(output)
output = self.batchnorm(output)
output = self.gelu(output)
output = self.conv64_1(output)
output = self.conv64_2(output)
output = self.conv64_3(output)
output = self.dconv128(output)
output = self.conv128_1(output)
output = self.conv128_2(output)
output = self.dconv256(output)
output = self.conv256_1(output)
output = self.conv256_2(output)
output = self.maxpool(output)
# print(output.shape)
output = self.flatten(output)
output = self.fc(output)
output = self.gelu(output)
output = self.fc1(output)
output = torch.sigmoid(output)
return output
def generate_interaction_map(self, apta, prot):
with torch.no_grad():
apta = self.apta_encoder(apta)
prot = self.prot_encoder(prot)
prot = torch.transpose(prot, 1, 2)
interaction_map = torch.bmm(apta, prot)
interaction_map = torch.unsqueeze(interaction_map, 1)
interaction_map = self.batchnorm_fm(interaction_map)
return interaction_map
def conv_block_proba(self, interaction_map):
with torch.no_grad():
output = torch.tensor(interaction_map).float().to('cuda:0')
output = torch.unsqueeze(output, 1)
output = torch.mean(output, 4)
output = self.conv(output)
output = self.batchnorm(output)
output = self.gelu(output)
output = self.conv64_1(output)
output = self.conv64_2(output)
output = self.conv64_3(output)
output = self.dconv128(output)
output = self.conv128_1(output)
output = self.conv128_2(output)
output = self.dconv256(output)
output = self.conv256_1(output)
output = self.conv256_2(output)
output = self.maxpool(output)
output = self.flatten(output)
output = self.fc(output)
output = self.gelu(output)
output = self.fc1(output)
output = torch.sigmoid(output)
output = np.array([[1 - o[0], o[0]]for o in output.clone().detach().cpu().numpy()])
return output
def find_opt_threshold(target, pred):
result = 0
best = 0
for i in range(0, 1000):
pred_threshold = np.where(pred > i/1000, 1, 0)
now = f1_score(target, pred_threshold)
if now > best:
result = i/1000
best = now
return result
def argument_seqset(seqset):
arg_seqset = []
for s, ss in seqset:
arg_seqset.append([s, ss])
arg_seqset.append([s[::-1], ss[::-1]])
return arg_seqset
def augment_apis(apta, prot, ys):
aug_apta = []
aug_prot = []
aug_y = []
for a, p, y in zip(apta, prot, ys):
aug_apta.append(a)
aug_prot.append(p)
aug_y.append(y)
aug_apta.append(a[::-1])
aug_prot.append(p)
aug_y.append(y)
aug_apta.append(a)
aug_prot.append(p[::-1])
aug_y.append(y)
aug_apta.append(a[::-1])
aug_prot.append(p[::-1])
aug_y.append(y)
return np.array(aug_apta), np.array(aug_prot), np.array(aug_y)
|