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import os
# os.environ['CUDA_VISIBLE_DEVICES'] = "0" # in case you are using a multi GPU workstation, choose your GPU here
import tqdm
import pytorch_lightning as pl
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import pandas as pd
from datasets import load_dataset
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
#define your neural net here:
class MLP(pl.LightningModule):
def __init__(self, input_size, xcol='emb', ycol='avg_rating'):
super().__init__()
self.input_size = input_size
self.xcol = xcol
self.ycol = ycol
self.layers = nn.Sequential(
nn.Linear(self.input_size, 1024),
#nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(1024, 128),
#nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(128, 64),
#nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 16),
#nn.ReLU(),
nn.Linear(16, 1)
)
def forward(self, x):
return self.layers(x)
def training_step(self, batch, batch_idx):
x = batch[self.xcol]
y = batch[self.ycol].reshape(-1, 1)
x_hat = self.layers(x)
loss = F.mse_loss(x_hat, y)
return loss
def validation_step(self, batch, batch_idx):
x = batch[self.xcol]
y = batch[self.ycol].reshape(-1, 1)
x_hat = self.layers(x)
loss = F.mse_loss(x_hat, y)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
# load the training data
x = np.load ("/mnt/spirit/ava_x.npy")
y = np.load ("/mnt/spirit/ava_y.npy")
val_percentage = 0.05 # 5% of the trainingdata will be used for validation
train_border = int(x.shape()[0] * (1 - val_percentage) )
train_tensor_x = torch.Tensor(x[:train_border]) # transform to torch tensor
train_tensor_y = torch.Tensor(y[:train_border])
train_dataset = TensorDataset(train_tensor_x,train_tensor_y) # create your datset
train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True, num_workers=16) # create your dataloader
val_tensor_x = torch.Tensor(x[train_border:]) # transform to torch tensor
val_tensor_y = torch.Tensor(y[train_border:])
'''
print(train_tensor_x.size())
print(val_tensor_x.size())
print( val_tensor_x.dtype)
print( val_tensor_x[0].dtype)
'''
val_dataset = TensorDataset(val_tensor_x,val_tensor_y) # create your datset
val_loader = DataLoader(val_dataset, batch_size=512, num_workers=16) # create your dataloader
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MLP(768).to(device) # CLIP embedding dim is 768 for CLIP ViT L 14
optimizer = torch.optim.Adam(model.parameters())
# choose the loss you want to optimze for
criterion = nn.MSELoss()
criterion2 = nn.L1Loss()
epochs = 50
model.train()
best_loss =999
save_name = "linear_predictor_L14_MSE.pth"
for epoch in range(epochs):
losses = []
losses2 = []
for batch_num, input_data in enumerate(train_loader):
optimizer.zero_grad()
x, y = input_data
x = x.to(device).float()
y = y.to(device)
output = model(x)
loss = criterion(output, y)
loss.backward()
losses.append(loss.item())
optimizer.step()
if batch_num % 1000 == 0:
print('\tEpoch %d | Batch %d | Loss %6.2f' % (epoch, batch_num, loss.item()))
#print(y)
print('Epoch %d | Loss %6.2f' % (epoch, sum(losses)/len(losses)))
losses = []
losses2 = []
for batch_num, input_data in enumerate(val_loader):
optimizer.zero_grad()
x, y = input_data
x = x.to(device).float()
y = y.to(device)
output = model(x)
loss = criterion(output, y)
lossMAE = criterion2(output, y)
#loss.backward()
losses.append(loss.item())
losses2.append(lossMAE.item())
#optimizer.step()
if batch_num % 1000 == 0:
print('\tValidation - Epoch %d | Batch %d | MSE Loss %6.2f' % (epoch, batch_num, loss.item()))
print('\tValidation - Epoch %d | Batch %d | MAE Loss %6.2f' % (epoch, batch_num, lossMAE.item()))
#print(y)
print('Validation - Epoch %d | MSE Loss %6.2f' % (epoch, sum(losses)/len(losses)))
print('Validation - Epoch %d | MAE Loss %6.2f' % (epoch, sum(losses2)/len(losses2)))
if sum(losses)/len(losses) < best_loss:
print("Best MAE Val loss so far. Saving model")
best_loss = sum(losses)/len(losses)
print( best_loss )
torch.save(model.state_dict(), save_name )
torch.save(model.state_dict(), save_name)
print( best_loss )
print("training done")
# inferece test with dummy samples from the val set, sanity check
print( "inferece test with dummy samples from the val set, sanity check")
model.eval()
output = model(x[:5].to(device))
print(output.size())
print(output)
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