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
from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver


ex = Experiment("426206", interactive=False, save_git_info=False)
ex.observers.append(FileStorageObserver('my_runs'))
ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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)

# 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()
#python3 dlgssdpytorch.py with lr=0.01 n_epochs=10

@ex.config
def my_config():    
    lr = 1e-3
    n_epochs = 100

@ex.capture
def train(lr, n_epochs, _run):
    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)
       
    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):

        _run.log_scalar("Epoch",  str(epoch))
        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)

        _run.log_scalar("MSE", str(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')

@ex.automain
def my_main(lr, n_epochs, _run):
    train(lr, n_epochs, _run)    

ex.run()
ex.add_artifact('model.pt')