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from torch import nn |
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import pdb |
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import torch |
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import numpy as np |
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def to_var(x): |
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if torch.cuda.is_available(): |
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x = x.cuda() |
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return x |
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class MultiHeadAttentionSequence(nn.Module): |
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def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): |
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super().__init__() |
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self.n_head = n_head |
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self.d_model = d_model |
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self.d_k = d_k |
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self.d_v = d_v |
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self.W_Q = nn.Linear(d_model, n_head*d_k) |
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self.W_K = nn.Linear(d_model, n_head*d_k) |
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self.W_V = nn.Linear(d_model, n_head*d_v) |
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self.W_O = nn.Linear(n_head*d_v, d_model) |
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self.layer_norm = nn.LayerNorm(d_model) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, q, k, v): |
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batch, len_q, _ = q.size() |
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batch, len_k, _ = k.size() |
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batch, len_v, _ = v.size() |
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Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k]) |
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K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k]) |
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V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v]) |
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Q = Q.transpose(1, 2) |
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K = K.transpose(1, 2).transpose(2, 3) |
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V = V.transpose(1, 2) |
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attention = torch.matmul(Q, K) |
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attention = attention / np.sqrt(self.d_k) |
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attention = F.softmax(attention, dim=-1) |
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output = torch.matmul(attention, V) |
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output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head]) |
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output = self.W_O(output) |
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output = self.dropout(output) |
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output = self.layer_norm(output + q) |
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return output, attention |
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class MultiHeadAttentionReciprocal(nn.Module): |
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def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): |
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super().__init__() |
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self.n_head = n_head |
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self.d_model = d_model |
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self.d_k = d_k |
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self.d_v = d_v |
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self.W_Q = nn.Linear(d_model, n_head*d_k) |
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self.W_K = nn.Linear(d_model, n_head*d_k) |
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self.W_V = nn.Linear(d_model, n_head*d_v) |
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self.W_O = nn.Linear(n_head*d_v, d_model) |
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self.W_V_2 = nn.Linear(d_model, n_head*d_v) |
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self.W_O_2 = nn.Linear(n_head*d_v, d_model) |
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self.layer_norm = nn.LayerNorm(d_model) |
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self.dropout = nn.Dropout(dropout) |
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self.layer_norm_2 = nn.LayerNorm(d_model) |
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self.dropout_2 = nn.Dropout(dropout) |
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def forward(self, q, k, v, v_2): |
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batch, len_q, _ = q.size() |
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batch, len_k, _ = k.size() |
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batch, len_v, _ = v.size() |
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batch, len_v_2, _ = v_2.size() |
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Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k]) |
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K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k]) |
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V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v]) |
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V_2 = self.W_V_2(v_2).view([batch, len_v_2, self.n_head, self.d_v]) |
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Q = Q.transpose(1, 2) |
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K = K.transpose(1, 2).transpose(2, 3) |
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V = V.transpose(1, 2) |
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V_2 = V_2.transpose(1,2) |
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attention = torch.matmul(Q, K) |
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attention = attention /np.sqrt(self.d_k) |
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attention_2 = attention.transpose(-2, -1) |
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attention = F.softmax(attention, dim=-1) |
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attention_2 = F.softmax(attention_2, dim=-1) |
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output = torch.matmul(attention, V) |
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output_2 = torch.matmul(attention_2, V_2) |
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output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head]) |
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output_2 = output_2.transpose(1, 2).reshape([batch, len_k, self.d_v*self.n_head]) |
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output = self.W_O(output) |
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output_2 = self.W_O_2(output_2) |
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output = self.dropout(output) |
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output = self.layer_norm(output + q) |
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output_2 = self.dropout(output_2) |
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output_2 = self.layer_norm(output_2 + k) |
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return output, output_2, attention, attention_2 |
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class FFN(nn.Module): |
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def __init__(self, d_in, d_hid, dropout=0.1): |
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super().__init__() |
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self.layer_1 = nn.Conv1d(d_in, d_hid,1) |
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self.layer_2 = nn.Conv1d(d_hid, d_in,1) |
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self.relu = nn.ReLU() |
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self.layer_norm = nn.LayerNorm(d_in) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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residual = x |
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output = self.layer_1(x.transpose(1, 2)) |
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output = self.relu(output) |
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output = self.layer_2(output) |
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output = self.dropout(output) |
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output = self.layer_norm(output.transpose(1, 2)+residual) |
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return output |
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class ConvLayer(nn.Module): |
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def __init__(self, in_channels, out_channels, kernel_size, padding, dilation): |
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super(ConvLayer, self).__init__() |
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self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation) |
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self.relu = nn.ReLU() |
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def forward(self, x): |
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out = self.conv(x) |
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out = self.relu(out) |
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return out |
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class DilatedCNN(nn.Module): |
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def __init__(self, d_model, d_hidden): |
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super(DilatedCNN, self).__init__() |
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self.first_ = nn.ModuleList() |
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self.second_ = nn.ModuleList() |
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self.third_ = nn.ModuleList() |
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dilation_tuple = (1, 2, 3) |
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dim_in_tuple = (d_model, d_hidden, d_hidden) |
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dim_out_tuple = (d_hidden, d_hidden, d_hidden) |
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for i, dilation_rate in enumerate(dilation_tuple): |
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self.first_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=3, padding=dilation_rate, |
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dilation=dilation_rate)) |
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for i, dilation_rate in enumerate(dilation_tuple): |
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self.second_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=5, padding=2*dilation_rate, |
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dilation=dilation_rate)) |
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for i, dilation_rate in enumerate(dilation_tuple): |
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self.third_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=7, padding=3*dilation_rate, |
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dilation=dilation_rate)) |
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def forward(self, protein_seq_enc): |
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protein_seq_enc = protein_seq_enc.transpose(1, 2) |
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first_embedding = protein_seq_enc |
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second_embedding = protein_seq_enc |
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third_embedding = protein_seq_enc |
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for i in range(len(self.first_)): |
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first_embedding = self.first_[i](first_embedding) |
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for i in range(len(self.second_)): |
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second_embedding = self.second_[i](second_embedding) |
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for i in range(len(self.third_)): |
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third_embedding = self.third_[i](third_embedding) |
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protein_seq_enc = first_embedding + second_embedding + third_embedding |
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return protein_seq_enc.transpose(1, 2) |
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class ReciprocalLayerwithCNN(nn.Module): |
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def __init__(self, d_model, d_inner, d_hidden, n_head, d_k, d_v): |
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super().__init__() |
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self.cnn = DilatedCNN(d_model, d_hidden) |
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self.sequence_attention_layer = MultiHeadAttentionSequence(n_head, d_hidden, d_k, d_v) |
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self.protein_attention_layer = MultiHeadAttentionSequence(n_head, d_hidden, d_k, d_v) |
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self.reciprocal_attention_layer = MultiHeadAttentionReciprocal(n_head, d_hidden, d_k, d_v) |
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self.ffn_seq = FFN(d_hidden, d_inner) |
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self.ffn_protein = FFN(d_hidden, d_inner) |
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def forward(self, sequence_enc, protein_seq_enc): |
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protein_seq_enc = self.cnn(protein_seq_enc) |
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prot_enc, prot_attention = self.protein_attention_layer(protein_seq_enc, protein_seq_enc, protein_seq_enc) |
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seq_enc, sequence_attention = self.sequence_attention_layer(sequence_enc, sequence_enc, sequence_enc) |
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prot_enc, seq_enc, prot_seq_attention, seq_prot_attention = self.reciprocal_attention_layer(prot_enc, seq_enc, seq_enc, prot_enc) |
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prot_enc = self.ffn_protein(prot_enc) |
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seq_enc = self.ffn_seq(seq_enc) |
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return prot_enc, seq_enc, prot_attention, sequence_attention, prot_seq_attention, seq_prot_attention |
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class ReciprocalLayer(nn.Module): |
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def __init__(self, d_model, d_inner, n_head, d_k, d_v): |
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super().__init__() |
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self.sequence_attention_layer = MultiHeadAttentionSequence(n_head, d_model, d_k, d_v) |
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self.protein_attention_layer = MultiHeadAttentionSequence(n_head, d_model, d_k, d_v) |
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self.reciprocal_attention_layer = MultiHeadAttentionReciprocal(n_head, d_model, d_k, d_v) |
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self.ffn_seq = FFN(d_model, d_inner) |
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self.ffn_protein = FFN(d_model, d_inner) |
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def forward(self, sequence_enc, protein_seq_enc): |
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prot_enc, prot_attention = self.protein_attention_layer(protein_seq_enc, protein_seq_enc, protein_seq_enc) |
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seq_enc, sequence_attention = self.sequence_attention_layer(sequence_enc, sequence_enc, sequence_enc) |
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prot_enc, seq_enc, prot_seq_attention, seq_prot_attention = self.reciprocal_attention_layer(prot_enc, seq_enc, seq_enc, prot_enc) |
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prot_enc = self.ffn_protein(prot_enc) |
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seq_enc = self.ffn_seq(seq_enc) |
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return prot_enc, seq_enc, prot_attention, sequence_attention, prot_seq_attention, seq_prot_attention |
