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engine_levit.py
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engine_levit.py
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# modified from https://github.com/facebookresearch/LeViT
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from levit.losses_levit import DistillationLoss
import utils
def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler,
clip_grad: float = 0,
clip_mode: str = 'norm',
model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
set_training_mode=True):
model.train(set_training_mode)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(
window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 100
mode1 = False
mode2 = False # True
mode4 = False
CTPEP = False
if epoch < 200:
model.module.stage_wise_prune = False
model.module.set_learn_tradeoff(False)
else:
model.module.stage_wise_prune = True
model.module.set_learn_tradeoff(True)
for samples, targets in metric_logger.log_every(
data_loader, print_freq, header):
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
if True: # with torch.cuda.amp.autocast():
# outputs = model(samples)
if (mode1 or mode4):
outputs = model(samples, epoch)
loss = criterion(samples, outputs, targets) # net1distill
elif (mode2 or CTPEP):
outputs = model(samples, epoch)
loss = criterion(samples, outputs, targets)
else:
outputs = model(samples)
loss = criterion(samples, outputs, targets)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
optimizer.zero_grad()
# this attribute is added by timm on one optimizer (adahessian)
is_second_order = hasattr(
optimizer, 'is_second_order') and optimizer.is_second_order
loss_scaler(loss, optimizer, clip_grad=clip_grad, clip_mode=clip_mode,
parameters=model.parameters(), create_graph=is_second_order)
torch.cuda.synchronize()
if model_ema is not None:
model_ema.update(model)
metric_logger.update(loss=loss_value)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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()}
@torch.no_grad()
def evaluate(data_loader, model, device, epoch=None):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
mode1 = False
mode2 = False
mutual = False # True
cls = True
mode4 = False
CTPEP = False # False
for images, target in metric_logger.log_every(data_loader, 10, header):
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
with torch.cuda.amp.autocast():
if mode1:
output = model(images, epoch)
acc1, acc5 = accuracy(output[1], target, topk=(1, 5))
acc12, acc52 = accuracy(output[2], target, topk=(1, 5))
acc13, acc53 = accuracy(output[3], target, topk=(1, 5))
print("net 2 accuracy: {}, {}, net 3 accuracy: {}, {}, net 4 accuracy: {}, {}".format(acc1.item(),
acc5.item(),
acc12.item(),
acc52.item(),
acc13.item(),
acc53.item()))
output = output[0]
elif mode2:
output = model(images, epoch)
acc1, acc5 = accuracy(output[1], target, topk=(1, 5))
acc12, acc52 = accuracy(output[2], target, topk=(1, 5))
acc13, acc53 = accuracy(output[3], target, topk=(1, 5))
acc14, acc54 = accuracy(output[4], target, topk=(1, 5))
print(
"net 2 accuracy: {}, {}, net 3 accuracy: {}, {}, net 4 accuracy: {}, {}, net merge accuracy: {}, {}".format(
acc1.item(), acc5.item(), acc12.item(), acc52.item(), acc13.item(), acc53.item(), acc14.item(),
acc54.item()))
output = output[0]
elif mode4:
output = model(images, epoch)
acc1, acc5 = accuracy(output[1], target, topk=(1, 5))
acc12, acc52 = accuracy(output[2], target, topk=(1, 5))
print("net 2 accuracy: {}, {}, net 3 accuracy: {}, {}".format(acc1.item(), acc5.item(), acc12.item(),
acc52.item()))
output = output[0]
elif mutual:
output = model(images)
acc1, acc5 = accuracy(output[1], target, topk=(1, 5))
print("net depth accuracy: {}, {}".format(acc1, acc5))
output = output[0]
elif cls:
if CTPEP:
output = model(images, epoch)
else:
output = model(images)
# print("net cls accuracy: {}, {}".format(acc1, acc5))
# output = output[0]
else:
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
print(output.mean().item(), output.std().item())
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}