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ce9b7f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | import torch
from typing import Iterable, Optional
from timm.utils import accuracy, ModelEmaV2, dispatch_clip_grad
import time
from torch_cluster import radius_graph
import torch_geometric
ModelEma = ModelEmaV2
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
norm_factor: list,
target: int,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int,
model_ema: Optional[ModelEma] = None,
amp_autocast=None,
loss_scaler=None,
clip_grad=None,
print_freq: int = 100,
logger=None):
model.train()
criterion.train()
loss_metric = AverageMeter()
mae_metric = AverageMeter()
start_time = time.perf_counter()
task_mean = norm_factor[0] #model.task_mean
task_std = norm_factor[1] #model.task_std
#atomref = dataset.atomref()
#if atomref is None:
# atomref = torch.zeros(100, 1)
#atomref = atomref.to(device)
for step, data in enumerate(data_loader):
data = data.to(device)
#data.edge_d_index = radius_graph(data.pos, r=10.0, batch=data.batch, loop=True)
#data.edge_d_attr = data.edge_attr
with amp_autocast():
pred = model(f_in=data.x, pos=data.pos, batch=data.batch,
node_atom=data.z,
edge_d_index=data.edge_d_index, edge_d_attr=data.edge_d_attr)
pred = pred.squeeze()
#loss = (pred - data.y[:, target])
#loss = loss.pow(2).mean()
#atomref_value = atomref(data.z)
loss = criterion(pred, (data.y[:, target] - task_mean) / task_std)
optimizer.zero_grad()
if loss_scaler is not None:
loss_scaler(loss, optimizer, parameters=model.parameters())
else:
loss.backward()
if clip_grad is not None:
dispatch_clip_grad(model.parameters(),
value=clip_grad, mode='norm')
optimizer.step()
#err = (pred.detach() * task_std + task_mean) - data.y[:, target]
#err_list += [err.cpu()]
loss_metric.update(loss.item(), n=pred.shape[0])
err = pred.detach() * task_std + task_mean - data.y[:, target]
mae_metric.update(torch.mean(torch.abs(err)).item(), n=pred.shape[0])
if model_ema is not None:
model_ema.update(model)
torch.cuda.synchronize()
# logging
if step % print_freq == 0 or step == len(data_loader) - 1: #time.perf_counter() - wall_print > 15:
w = time.perf_counter() - start_time
e = (step + 1) / len(data_loader)
info_str = 'Epoch: [{epoch}][{step}/{length}] \t loss: {loss:.5f}, MAE: {mae:.5f}, time/step={time_per_step:.0f}ms, '.format(
epoch=epoch, step=step, length=len(data_loader),
mae=mae_metric.avg,
loss=loss_metric.avg,
time_per_step=(1e3 * w / e / len(data_loader))
)
info_str += 'lr={:.2e}'.format(optimizer.param_groups[0]["lr"])
logger.info(info_str)
return mae_metric.avg
def evaluate(model, norm_factor, target, data_loader, device, amp_autocast=None,
print_freq=100, logger=None):
model.eval()
loss_metric = AverageMeter()
mae_metric = AverageMeter()
criterion = torch.nn.L1Loss()
criterion.eval()
task_mean = norm_factor[0] #model.task_mean
task_std = norm_factor[1] #model.task_std
with torch.no_grad():
for data in data_loader:
data = data.to(device)
#data.edge_d_index = radius_graph(data.pos, r=10.0, batch=data.batch, loop=True)
#data.edge_d_attr = data.edge_attr
with amp_autocast():
pred = model(f_in=data.x, pos=data.pos, batch=data.batch,
node_atom=data.z,
edge_d_index=data.edge_d_index, edge_d_attr=data.edge_d_attr)
pred = pred.squeeze()
loss = criterion(pred, (data.y[:, target] - task_mean) / task_std)
loss_metric.update(loss.item(), n=pred.shape[0])
err = pred.detach() * task_std + task_mean - data.y[:, target]
mae_metric.update(torch.mean(torch.abs(err)).item(), n=pred.shape[0])
return mae_metric.avg, loss_metric.avg
def compute_stats(data_loader, max_radius, logger, print_freq=1000):
'''
Compute mean of numbers of nodes and edges
'''
log_str = '\nCalculating statistics with '
log_str = log_str + 'max_radius={}\n'.format(max_radius)
logger.info(log_str)
avg_node = AverageMeter()
avg_edge = AverageMeter()
avg_degree = AverageMeter()
for step, data in enumerate(data_loader):
pos = data.pos
batch = data.batch
edge_src, edge_dst = radius_graph(pos, r=max_radius, batch=batch,
max_num_neighbors=1000)
batch_size = float(batch.max() + 1)
num_nodes = pos.shape[0]
num_edges = edge_src.shape[0]
num_degree = torch_geometric.utils.degree(edge_src, num_nodes)
num_degree = torch.sum(num_degree)
avg_node.update(num_nodes / batch_size, batch_size)
avg_edge.update(num_edges / batch_size, batch_size)
avg_degree.update(num_degree / (num_nodes), num_nodes)
if step % print_freq == 0 or step == (len(data_loader) - 1):
log_str = '[{}/{}]\tavg node: {}, '.format(step, len(data_loader), avg_node.avg)
log_str += 'avg edge: {}, '.format(avg_edge.avg)
log_str += 'avg degree: {}, '.format(avg_degree.avg)
logger.info(log_str) |