AtPatch/tab_train.py at main · SSCT-Lab/AtPatch · GitHub

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import math
import numpy as np
import toml
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from utils.dataset import CensusDataset
from utils.models import TabTransformer

def cus_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
    scale = 1.0 / math.sqrt(query.size(-1))
    attn_weights = torch.matmul(query, key.transpose(-2, -1)) * scale

    if attn_mask is not None:
        attn_weights += attn_mask

    attn_weights = torch.softmax(attn_weights, dim=-1)

    if dropout_p > 0.0:
        attn_weights = torch.nn.functional.dropout(
            attn_weights, p=dropout_p)

    output = torch.matmul(attn_weights, value)
    return output

from contextlib import contextmanager

@contextmanager
def use_cus_scaled_dot_product_attention():
    original_sdp_attention = torch.nn.functional.scaled_dot_product_attention

    try:
        torch.nn.functional.scaled_dot_product_attention = cus_scaled_dot_product_attention
        yield
    finally:
        torch.nn.functional.scaled_dot_product_attention = original_sdp_attention

def census_data():
    X = []
    Y = []
    i = 0

    with open("./dataset/census", "r") as ins:
        for line in ins:
            line = line.strip()
            line1 = line.split(',')
            if (i == 0):
                i += 1
                continue
            L = [int(i) for i in line1[:-1]]
            X.append(L)
            if int(line1[-1]) == 0:
                Y.append(0)
            else:
                Y.append(1)
    X = np.array(X, dtype=float)
    Y = np.array(Y, dtype=float)

    input_shape = (None, 13)
    nb_classes = 2

    return X, Y, input_shape, nb_classes


config = toml.load("./config/census.toml")

X, Y, input_shape, nb_classes = census_data()

X_train = X[:int(len(X) * 0.8)]
Y_train = Y[:int(len(Y) * 0.8)]
X_test = X[int(len(X) * 0.8):]
Y_test = Y[int(len(Y) * 0.8):]

train_dataset = CensusDataset(X_train, Y_train)
test_dataset = CensusDataset(X_test, Y_test)

train_loader = DataLoader(train_dataset, batch_size=config["train"]["batch_size"], shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=config["train"]["batch_size"], shuffle=False)

device = "cuda"

model = TabTransformer(**config["model"]).to(device)

criterion = nn.CrossEntropyLoss()

optimizer = optim.Adam(model.parameters(), lr=config["train"]["lr"], weight_decay=config["train"]["weight_decay"])

for epoch in range(config["train"]["num_epochs"]):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    print(f"Epoch {epoch} loss: {loss.item()}")

    model.eval()
    test_loss = 0
    correct = 0
    total = 0

    with use_cus_scaled_dot_product_attention():
        for batch_idx, (data, target) in enumerate(test_loader):
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item()
            _, predicted = output.max(1)
            total += target.size(0)
            correct += predicted.eq(target).sum().item()

    test_loss /= len(test_loader.dataset)
    test_accuracy = 100. * correct / total
    print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.2f}%")