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engine.py
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engine.py
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import math
import sys
from typing import Iterable, Optional
from timm.utils.model import unwrap_model
import torch
import torch.nn.functional as F
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from lib import utils
import random
import time
from model.tome import apply_tome
# flops count
from fvcore.nn import FlopCountAnalysis
def test_model_latency(model, device, test_batch_size=512):
T0 = 10
T1 = 10
speed = 0
model.eval()
with torch.no_grad():
x = torch.randn(test_batch_size, 3, 224, 224).to(device)
torch.cuda.empty_cache()
torch.cuda.synchronize()
start = time.time()
while time.time() - start < T0:
model(x)
torch.cuda.synchronize()
print("*****Test model latency (images per second)*****")
timing = []
while sum(timing) < T1:
start = time.time()
model(x)
torch.cuda.synchronize()
timing.append(time.time() - start)
timing = torch.as_tensor(timing, dtype=torch.float32)
speed=512/timing.mean().item()
print("Model latency: {} imgs/s".format(speed))
return speed
# exit(0)
def count_model_flops(model, device):
model.eval()
rand_input = torch.rand((1,3,224,224)).to(device)
flops = FlopCountAnalysis(model,rand_input)
print("*****Count model FLOPS (GFLOPS)*****")
total_flops = flops.total()
print("Total FLOPS:%d, GFLOPS:%.4f"%(total_flops, float(total_flops)/1073741824))
print(flops.by_module_and_operator())
return float(total_flops)/1073741824
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
amp: bool = True, teacher_model: torch.nn.Module = None,
teach_loss: torch.nn.Module = None,
deit=False):
model.train()
criterion.train()
# set random seed
random.seed(epoch)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
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 amp:
with torch.cuda.amp.autocast():
if teacher_model:
with torch.no_grad():
teach_output = teacher_model(samples)
_, teacher_label = teach_output.topk(1, 1, True, True)
outputs = model(samples)
loss = 1/2 * criterion(outputs, targets) + 1/2 * teach_loss(outputs, teacher_label.squeeze())
else:
outputs = model(samples)
loss = criterion(outputs, targets)
else:
if not deit:
outputs = model(samples)
else:
outputs, _ = model(samples)
if teacher_model:
with torch.no_grad():
teach_output = teacher_model(samples)
_, teacher_label = teach_output.topk(1, 1, True, True)
loss = 1 / 2 * criterion(outputs, targets) + 1 / 2 * teach_loss(outputs, teacher_label.squeeze())
else:
loss = criterion(outputs, targets)
loss_value = loss.item()
grad_clip = False
if not math.isfinite(loss_value):
print("Loss is {}, clipping gradient".format(loss_value))
grad_clip = True
optimizer.zero_grad()
# this attribute is added by timm on one optimizer (adahessian)
if amp:
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order)
else:
loss.backward()
if grad_clip:
torch.nn.utils.clip_grad_norm_(model.trainable_params(), 10)
optimizer.step()
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, amp=True):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
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
if amp:
with torch.cuda.amp.autocast():
output = model(images)
loss = criterion(output, target)
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))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}