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45461c9 | 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 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import sys
import torch
import os
import util.misc as misc
import util.lr_sched as lr_sched
import numpy as np
def train_one_epoch(
model,
data_loader,
optimizer,
device,
epoch: int,
loss_scaler,
log_writer=None,
args=None,
):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", misc.SmoothedValue(window_size=1, fmt="{value:.6f}"))
header = "Epoch: [{}]".format(epoch)
print_freq = 20
loss_cal = torch.nn.MSELoss()
optimizer.zero_grad()
if log_writer is not None:
print("log_dir: {}".format(log_writer.log_dir))
last_norm = 0.0
for data_iter_step, (img, gt, dataidx) in enumerate(
metric_logger.log_every(data_loader, print_freq, header)
):
# we use a per iteration (instead of per epoch) lr scheduler
img, gt = img.to(device, non_blocking=True), gt.to(device, non_blocking=True)
lr_sched.adjust_learning_rate(
optimizer, data_iter_step / len(data_loader) + epoch, args
)
logit = model(img)
loss = loss_cal(logit, gt)
loss_value = loss.item()
if not math.isfinite(loss_value):
print(
"nan",
torch.isnan(logit).any(),
torch.isnan(img).any(),
dataidx,
last_norm,
)
print(
"inf",
torch.isinf(logit).any(),
torch.isinf(img).any(),
dataidx,
last_norm,
)
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
optimizer.zero_grad()
loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
# last_norm = loss_scaler(loss, optimizer, parameters=model.parameters())
# optimizer.zero_grad()
# torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
lr = optimizer.param_groups[0]["lr"]
metric_logger.update(lr=lr)
loss_value_reduce = misc.all_reduce_mean(loss_value)
if log_writer is not None:
"""We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar("train_loss", loss_value_reduce, epoch_1000x)
log_writer.add_scalar("lr", lr, epoch_1000x)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
def validation(model, data_loader_val, device, epoch, args):
model.eval()
loss_cal = torch.nn.MSELoss()
with torch.no_grad():
loss_summary = []
for idx, (img, gt, _) in enumerate(data_loader_val):
img, gt = img.to(device), gt.to(device)
loss = loss_cal(model(img), gt)
loss_summary.append(loss.detach().cpu().numpy())
print(
"epoch: {}/{}, iter: {}/{}".format(
epoch, args.epochs, idx, len(data_loader_val)
)
+ " loss:"
+ str(loss_summary[-1].flatten()[0])
)
avg_loss = np.mean(loss_summary)
print("Averaged stats:", str(avg_loss))
return avg_loss
def test(model, test_loader, args):
filepath_best = os.path.join(args.output_dir, "best.pth.tar")
model.load_state_dict(torch.load(filepath_best)["model"], weights_only=False)
model.eval()
log_stats = {}
pred, gts = [], []
with torch.no_grad():
for idx, (img, gt, _) in enumerate(test_loader):
img, gt = img.to(args.device), gt.to(args.device)
pred.append(model(img))
gts.append(gt)
pred = torch.cat(pred, 0)
gts = torch.cat(gts, 0)
pred = pred * 500000 + 70000
gts = gts * 500000 + 70000
mse = torch.nn.MSELoss()(pred, gts)
mae = torch.nn.L1Loss()(pred, gts)
print("MSE", mse.item(), "MAE", mae.item())
log_stats = {"MSE": mse.item(), "MAE": mae.item()}
return log_stats
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