94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
import os
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import sys
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import torch
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import torch.optim as optim
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from data.dataset import Dataset
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from utils.utils import Accumulator
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from models.depth_estim import DepthEstimation
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def train_step(batch):
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### Training
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model.train() # train mode is on by default after construction
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# 1. Forward pass
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y_pred = model(batch['mesh_posed'])
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# 2. Calculate loss
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loss = loss_fn(y_pred, batch['mesh_posed'])
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# 3. Zero grad optimizer
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optimizer.zero_grad()
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# 4. Loss backward
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loss.backward()
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# 5. Step the optimizer
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optimizer.step()
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return { 'train/loss': loss.detach().cpu().numpy() }
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def valid_step(batch):
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### Testing
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model.eval() # put the model in evaluation mode for testing (inference)
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# 1. Forward pass
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with torch.inference_mode():
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pred = model(batch['mesh_posed'])
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# 2. Calculate the loss
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loss = loss_fn(pred, batch['mesh_posed'])
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return { 'valid/loss': loss.detach().cpu().numpy() }
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def batch_to_device(batch, device):
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filter = {'mesh_posed'}
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for key in batch:
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if key in filter:
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batch[key] = batch[key].to(device)
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if __name__ == '__main__':
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device = "cuda" if torch.cuda.is_available() else "cpu"
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train_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_all.txt')
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valid_dataset = Dataset('dataset/CLOTH3D/train', 'dataset/CLOTH3D2RGB/train', 'frames_all.txt')
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train_dataloader = DataLoader(train_dataset, batch_size=4, drop_last=True, num_workers=2)
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valid_dataloader = DataLoader(valid_dataset, batch_size=4, drop_last=True, num_workers=2)
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train_accumulator = Accumulator()
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valid_accumulator = Accumulator()
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model = DepthEstimation()
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loss_fn = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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iteration = 0
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while True:
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for batch in train_dataloader:
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train_loss = train_step(batch)
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train_accumulator.update(train_loss)
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if iteration % 1000 == 0:
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print(f"Iteration: {iteration} | Train loss: {train_accumulator.get()['train/loss']}")
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train_accumulator.reset()
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iteration += 1
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for batch in valid_dataloader:
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valid_loss = valid_step(batch)
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valid_accumulator.update(valid_loss)
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print(f"Iteration: {iteration} | Valid loss: {valid_accumulator.get()['valid/loss']}")
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valid_accumulator.reset()
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pass |