97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import numpy as np
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import torch
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from DataLoader import get_image_loader
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from Net import ImageNN
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# 01.05.22 -- 0.5h
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from netio import save_model, load_model
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def get_train_device():
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f'Device to train net: {device}')
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return torch.device(device)
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def train_model():
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# Set a known random seed for reproducibility
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np.random.seed(0)
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torch.manual_seed(0)
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device = get_train_device()
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# Load datasets
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train_loader, test_loader = get_image_loader("training/")
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nn = ImageNN(n_in_channels=3) # todo pass size ason.
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nn.train() # init with train mode
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nn.to(device) # send net to device available
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optimizer = torch.optim.AdamW(nn.parameters(), lr=0.1, weight_decay=1e-5) # todo adjust parameters and lr
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loss_function = torch.nn.MSELoss()
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n_epochs = 10 # todo epcchs here
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# todo look wtf is that
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nn.double()
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train_sample_size = len(train_loader)
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losses = []
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best_eval_loss = np.inf
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for epoch in range(n_epochs):
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print(f"Epoch {epoch}/{n_epochs}\n")
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i = 0
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for input_tensor, target_tensor in train_loader:
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input_tensor.to(device)
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target_tensor.to(device)
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output = nn(input_tensor) # get model output (forward pass)
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loss = loss_function(output, target_tensor) # compute loss given model output and true target
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loss.backward() # compute gradients (backward pass)
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optimizer.step() # perform gradient descent update step
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optimizer.zero_grad() # reset gradients
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losses.append(loss.item())
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print(
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f'\rTraining epoch {epoch} [{i}/{train_sample_size * train_loader.batch_size}] (curr loss: {loss.item():.3})',
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end='')
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i += train_loader.batch_size
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# eval model every 3000th sample
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if i % 15 == 0:
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print(f"\nEvaluating model")
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eval_loss = eval_model(nn, test_loader, loss_function, device)
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print(f"Evalution loss={eval_loss}")
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if eval_loss < best_eval_loss:
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best_eval_loss = eval_loss
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save_model(nn)
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# switch net to eval mode
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print(eval_model(nn, test_loader, loss_function, device=device))
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# func to evaluate our trained model
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def eval_model(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader, loss_fn, device: torch.device):
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# switch to eval mode
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model.eval()
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loss = .0
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# disable gradient calculations
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with torch.no_grad():
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i = 0
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for input, target in dataloader:
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input.to(device)
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target.to(device)
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out = model(input)
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loss += loss_fn(out, target).item()
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print(f'\rEval prog[{i}/{len(dataloader) * dataloader.batch_size}]', end='')
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i += dataloader.batch_size
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print()
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loss /= len(dataloader)
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model.train()
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return loss
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def apply_model():
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model = load_model()
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pass
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