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import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
class MLP(nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.soft = nn.Softmax(1)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
out = self.soft(out)
print('Original embeddings:\n', out)
return out
class Expert(nn.Module):
def __init__(self, model, output_size, verbose=True):
super().__init__()
self.verbose = verbose
self.model = model
self.output_size = output_size
def forward(self, x):
# Check if input is empty and return an empty tensor of the appropriate shape
if len(x) == 0:
return torch.empty(size=(0, self.output_size))
# Generate embeddings using the model's encode method
out = self.model.encode(x)
# Check if out is a Pandas DataFrame or list and convert to torch tensor if needed
if isinstance(out, pd.DataFrame):
out = torch.tensor(out.values, dtype=torch.float32)
elif isinstance(out, list):
out = torch.stack(out, dim=0)
# Pad the embeddings to match the desired output size
out = F.pad(out, pad=(0, self.output_size - out.shape[1], 0, 0), value=0)
# Optionally print the embeddings if verbose mode is enabled
if self.verbose:
print(f'Original embeddings:\n', out)
return out
class Net(nn.Module):
def __init__(self, smiles_embed_dim, output_dim=2, dropout=0.2):
super().__init__()
self.desc_skip_connection = True
self.fc1 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
self.dropout1 = nn.Dropout(dropout)
self.relu1 = nn.GELU()
self.fc2 = nn.Linear(smiles_embed_dim, smiles_embed_dim)
self.dropout2 = nn.Dropout(dropout)
self.relu2 = nn.GELU()
self.final = nn.Linear(smiles_embed_dim, output_dim)
def forward(self, smiles_emb):
x_out = self.fc1(smiles_emb)
x_out = self.dropout1(x_out)
x_out = self.relu1(x_out)
if self.desc_skip_connection is True:
x_out = x_out + smiles_emb
z = self.fc2(x_out)
z = self.dropout2(z)
z = self.relu2(z)
if self.desc_skip_connection is True:
z = self.final(z + x_out)
else:
z = self.final(z)
return z |