ium_426206/dlgssdpytorch.py

import torch
import numpy as np

import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, TensorDataset, DataLoader
import argparse

parser = argparse.ArgumentParser(description='Program do uczenia modelu')
parser.add_argument('-l', '--lr', type=float, default=1e-3, help="Współczynik uczenia (lr)", required=False)
parser.add_argument('-e', '--epochs', type=int, default=100, help="Liczba epok", required=False)
args = parser.parse_args()

lr = args.lr
n_epochs = args.epochs

train_dataset = torch.load('train_dataset.pt')
#val_dataset = torch.load('val_dataset.pt')

train_loader = DataLoader(dataset=train_dataset)
#val_loader = DataLoader(dataset=val_dataset)

class LayerLinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        # Instead of our custom parameters, we use a Linear layer with single input and single output
        self.linear = nn.Linear(1, 1)
                
    def forward(self, x):
        # Now it only takes a call to the layer to make predictions
        return self.linear(x)
    
model = LayerLinearRegression()
# Checks model's parameters
#print(model.state_dict())   

loss_fn = nn.MSELoss(reduction='mean')
optimizer = optim.SGD(model.parameters(), lr=lr)

def make_train_step(model, loss_fn, optimizer):
    # Builds function that performs a step in the train loop
    def train_step(x, y):
        # Sets model to TRAIN mode
        model.train()
        # Makes predictions
        yhat = model(x)
        # Computes loss
        loss = loss_fn(y, yhat)
        # Computes gradients
        loss.backward()
        # Updates parameters and zeroes gradients
        optimizer.step()
        optimizer.zero_grad()
        # Returns the loss
        return loss.item()
    
    # Returns the function that will be called inside the train loop
    return train_step

# Creates the train_step function for our model, loss function and optimizer
train_step = make_train_step(model, loss_fn, optimizer)
training_losses = []
validation_losses = []
#print(model.state_dict())   
# For each epoch...
for epoch in range(n_epochs):
    losses = []
    # Uses loader to fetch one mini-batch for training
    for x_batch, y_batch in train_loader:
        # NOW, sends the mini-batch data to the device
        # so it matches location of the MODEL
        # x_batch = x_batch.to(device)
        # y_batch = y_batch.to(device)
        # One stpe of training
        loss = train_step(x_batch, y_batch)
        losses.append(loss)
    training_loss = np.mean(losses)
    training_losses.append(training_loss)
        
    # After finishing training steps for all mini-batches,
    # it is time for evaluation!
    # Ewaluacja jest już tutaj nie potrzebna bo odbywa sie w evaluation.py. Można jednak włączyć podgląd ewaluacji dla poszczególnych epok.    
    # # We tell PyTorch to NOT use autograd...
    # # Do you remember why?
    # with torch.no_grad():
    #     val_losses = []
    #     # Uses loader to fetch one mini-batch for validation
    #     for x_val, y_val in val_loader:
    #         # Again, sends data to same device as model
    #         # x_val = x_val.to(device)
    #         # y_val = y_val.to(device)
            
    #         model.eval()
    #         # Makes predictions
    #         yhat = model(x_val)
    #         # Computes validation loss
    #         val_loss = loss_fn(y_val, yhat)
    #         val_losses.append(val_loss.item())
    #     validation_loss = np.mean(val_losses)
    #     validation_losses.append(validation_loss)

    # print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
    print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")

torch.save({
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
                'loss': lr,
            }, 'model.pt')