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db32e07 | 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 | import torch.nn as nn
import torch
from flare.models.encoders import MLP
from torch_geometric.nn import global_mean_pool
class SpecEncMLP_BIN(nn.Module):
def __init__(self, args, out_dim=None):
super(SpecEncMLP_BIN, self).__init__()
if not out_dim:
out_dim = args.final_embedding_dim
bin_size = int(args.max_mz / args.bin_width)
self.dropout = nn.Dropout(args.fc_dropout)
self.mz_fc1 = nn.Linear(bin_size, out_dim * 2)
self.mz_fc2 = nn.Linear(out_dim* 2, out_dim * 2)
self.mz_fc3 = nn.Linear(out_dim * 2, out_dim)
self.relu = nn.ReLU()
def forward(self, mzi_b, n_peaks=None):
h1 = self.mz_fc1(mzi_b)
h1 = self.relu(h1)
h1 = self.dropout(h1)
h1 = self.mz_fc2(h1)
h1 = self.relu(h1)
h1 = self.dropout(h1)
mz_vec = self.mz_fc3(h1)
mz_vec = self.dropout(mz_vec)
return mz_vec
class SpecMzIntTokenTransformer(nn.Module):
def __init__(self, args):
super(SpecMzIntTokenTransformer, self).__init__()
in_dim = 2
self.tokenEnc = MLP(in_dim, args.hidden_dims, dropout=args.peak_dropout)
self.returnEmb = False
if args.model in ('crossAttenContrastive', 'filipContrastive'):
self.returnEmb = True
assert(args.use_cls == False)
else:
self.specEncoder = nn.Sequential(nn.Linear(args.hidden_dims[-1], args.final_embedding_dim), nn.Dropout(args.fc_dropout))
self.use_cls = args.use_cls
if self.use_cls:
self.cls_embed = torch.nn.Embedding(1,args.hidden_dims[-1])
encoder_layer = nn.TransformerEncoderLayer(d_model=args.hidden_dims[-1], nhead=2, batch_first=True)
self.tokenTransformer = nn.TransformerEncoder(encoder_layer, num_layers=2)
def forward(self, spec, n_peaks=None):
h = self.tokenEnc(spec)
pad = (spec == -5)
pad = torch.all(pad, -1)
if self.use_cls:
cls_embed = self.cls_embed(torch.tensor(0).to(spec.device))
h = torch.concat((cls_embed.repeat(spec.shape[0], 1).unsqueeze(1), h), dim=1)
pad = torch.concat((torch.tensor(False).repeat(pad.shape[0],1).to(spec.device), pad), dim=1)
h = self.tokenTransformer(h, src_key_padding_mask=pad)
h = h[:,0,:]
else:
# mean
h = self.tokenTransformer(h, src_key_padding_mask=pad)
if self.returnEmb:
# repad h
h[pad] = -5
return h
n_peaks_indices = torch.tensor([i for i, count in enumerate(n_peaks) for _ in range(count)]).to(spec.device)
h = h[~pad].reshape(-1, h.shape[-1])
h = global_mean_pool(h, n_peaks_indices)
h = self.specEncoder(h)
return h
class SpecFormulaEncMLP(nn.Module):
def __init__(self, args, out_dim=None):
super(SpecFormulaEncMLP, self).__init__()
in_dim = len(args.element_list)
if args.add_intensities:
in_dim+=1
if args.spectra_view == "SpecFormulaMz": #mz
in_dim+=1
self.formulaEnc = MLP(in_dim, args.formula_dims, dropout=args.formula_dropout)
if not out_dim:
out_dim = args.final_embedding_dim
self.mz_fc1 = nn.Linear(args.formula_dims[-1], out_dim)
self.dropout = nn.Dropout(args.fc_dropout)
def forward(self, spec, n_peaks):
h = self.formulaEnc(spec)
h = torch.sum(h, axis=1)
h = self.mz_fc1(h)
h = self.dropout(h)
return h
class SpecFormulaTransformer(nn.Module):
def __init__(self, args, out_dim=None):
super(SpecFormulaTransformer, self).__init__()
in_dim = len(args.element_list)
if args.add_intensities: # intensity
in_dim+=1
if args.spectra_view == "SpecFormulaMz": #mz
in_dim+=1
self.returnEmb = False
if args.model in ('crossAttenContrastive', 'filipContrastive', 'filipGlobalContrastive'):
self.returnEmb = True
assert(args.use_cls == False)
self.formulaEnc = MLP(in_dim=in_dim, hidden_dims=args.formula_dims, dropout=args.formula_dropout)
self.use_cls = args.use_cls
if args.use_cls:
self.cls_embed = torch.nn.Embedding(1,args.formula_dims[-1])
encoder_layer = nn.TransformerEncoderLayer(d_model=args.formula_dims[-1], nhead=args.formula_attn_heads, batch_first=True)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=args.formula_transformer_layers)
if not self.returnEmb:
if not out_dim:
out_dim = args.final_embedding_dim
self.fc = nn.Linear(args.formula_dims[-1], out_dim)
def forward(self, spec, n_peaks=None):
h = self.formulaEnc(spec)
pad = (spec == -5)
pad = torch.all(pad, -1)
if self.use_cls:
cls_embed = self.cls_embed(torch.tensor(0).to(spec.device))
h = torch.concat((cls_embed.repeat(spec.shape[0], 1).unsqueeze(1), h), dim=1)
pad = torch.concat((torch.tensor(False).repeat(pad.shape[0],1).to(spec.device), pad), dim=1)
h = self.transformer(h, src_key_padding_mask=pad)
h = h[:,0,:]
else:
h = self.transformer(h, src_key_padding_mask=pad)
if self.returnEmb:
# repad h
h[pad] = -5
return h
h = h[~pad].reshape(-1, h.shape[-1])
indecies = torch.tensor([i for i, count in enumerate(n_peaks) for _ in range(count)]).to(h.device)
h = global_mean_pool(h, indecies)
h = self.fc(h)
return h
class SpecFormula_mz_Encoder(nn.Module):
'''
Encodes formula and mz_int
'''
def __init__(self, args):
super(SpecFormula_mz_Encoder, self).__init__()
self.formula_encoder = SpecFormulaTransformer(args, out_dim=args.final_embedding_dim//4)
self.mz_encoder = SpecEncMLP_BIN(args, out_dim=args.final_embedding_dim//4)
self.fc = nn.Sequential(nn.Linear(args.final_embedding_dim //2, args.final_embedding_dim), nn.ReLU(),
)
def forward(self, formulas, binned_mzs):
h_formula = self.formula_encoder(formulas)
h_bin = self.mz_encoder(binned_mzs)
h_spec = torch.concat((h_formula, h_bin), axis=1)
h = self.fc(h_spec)
return h
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