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import torch
import torch.nn as nn
from data import _STD_ENERGY, _STD_FORCE_SCALE
from torch_scatter import scatter
from tqdm import tqdm
class ForceRMSELoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, target, batch):
return scatter((pred - target).pow(2).sum(dim=-1), batch, reduce="mean", dim=0, dim_size=batch.max().item() + 1).sqrt().mean()
def train(model, device, train_loader, optimizer, criterion_energy, criterion_force, energy_weight=1.0, force_weight=1.0, clip_gradients=False, grad_clip_norm=1.0):
model.train()
total_energy_loss = 0.
total_force_loss = 0.
progress_bar = tqdm(train_loader, desc='Training', bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
for batch in progress_bar:
optimizer.zero_grad()
data = batch.to(device, non_blocking=True)
energies, forces, mask = model(data)
energy_loss = criterion_energy(energies, data.y)
force_loss = criterion_force(forces, data.y_force[mask], data.batch[mask])
loss = energy_weight * energy_loss + force_weight * force_loss
total_energy_loss += energy_loss.item()
total_force_loss += force_loss.item()
loss.backward()
if clip_gradients:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=grad_clip_norm)
optimizer.step()
progress_bar.set_description(
f"Training - Energy Loss: {energy_loss * _STD_ENERGY:.5f}, "
f"Force Loss: {force_loss * _STD_FORCE_SCALE:.5f}")
average_energy_loss = total_energy_loss / len(train_loader)
average_force_loss = total_force_loss / len(train_loader)
return average_energy_loss, average_force_loss
def evaluate(model, device, loader, criterion_energy, criterion_force):
model.eval()
total_energy_loss = 0.
total_force_loss = 0.
progress_bar = tqdm(loader, desc='Evaluating', bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
for batch in progress_bar:
data = batch.to(device, non_blocking=True)
energies, forces, mask = model(data)
energy_loss = criterion_energy(energies, data.y)
force_loss = criterion_force(forces, data.y_force[mask], data.batch[mask])
total_energy_loss += energy_loss.item()
total_force_loss += force_loss.item()
progress_bar.set_description(
f"Evaluation - Energy Loss: {energy_loss * _STD_ENERGY:.5f}, Force Loss: {force_loss * _STD_FORCE_SCALE:.5f}")
average_energy_loss = total_energy_loss / len(loader)
average_force_loss = total_force_loss / len(loader)
return average_energy_loss, average_force_loss |