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Commit c218d1c5 authored by chenzk's avatar chenzk
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v1.0

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docker/requirements.txt 0 → 100644
View file @ c218d1c5
# torch
timm==0.5.4
fvcore
onnx
wandb
docker_start.sh 0 → 100644
View file @ c218d1c5
docker run -it --shm-size=32G -v $PWD/RepViT:/home/RepViT -v /parastor/DL_DATA/HOT:/home/HOT -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name repvit c85ed27005f2 bash
# python -m torch.utils.collect_env
docker_startnv.sh 0 → 100644
View file @ c218d1c5
docker run -it --shm-size=100G -v $PWD/RepViT:/home/RepViT -v /parastor/DL_DATA/HOT:/home/HOT --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name repvit f6b99c8a0f01 bash
# python -m torch.utils.collect_env
engine.py 0 → 100644
View file @ c218d1c5
"""
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 losses import DistillationLoss
import utils
def set_bn_state(model):
for m in model.modules():
if isinstance(m, torch.nn.modules.batchnorm._BatchNorm):
m.eval()
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,
set_bn_eval=False,):
model.train(set_training_mode)
if set_bn_eval:
set_bn_state(model)
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
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)
with torch.cuda.amp.autocast():
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):
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
with torch.cuda.amp.autocast():
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()}
eval.sh 0 → 100644
View file @ c218d1c5
python main.py --eval --model repvit_m1_1 --resume pretrain/repvit_m1_1_distill_300e.pth --data-path ~/imagenet
\ No newline at end of file
export_coreml.py 0 → 100644
View file @ c218d1c5
import torch
from timm import create_model
import model
import utils
import torch
import torchvision
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--model', default='repvit_m1_1', type=str)
parser.add_argument('--resolution', default=224, type=int)
parser.add_argument('--ckpt', default=None, type=str)
if __name__ == "__main__":
# Load a pre-trained version of MobileNetV2
args = parser.parse_args()
model = create_model(args.model, distillation=True)
# model = create_model(args.model, distillation=False)
# print(torch.load(args.ckpt)['model'])
if args.ckpt:
model.load_state_dict(torch.load(args.ckpt)['model'])
utils.replace_batchnorm(model)
model.eval()
# Trace the model with random data.
resolution = args.resolution
example_input = torch.rand(1, 3, resolution, resolution)
traced_model = torch.jit.trace(model, example_input)
torch.onnx.export(traced_model, example_input, "model.onnx", verbose=True, input_names=["input"], output_names=["output"])
out = traced_model(example_input)
'''
import coremltools as ct
# Using image_input in the inputs parameter:
# Convert to Core ML neural network using the Unified Conversion API.
model = ct.convert(
traced_model,
inputs=[ct.ImageType(shape=example_input.shape)]
)
# Save the converted model.
model.save(f"coreml/{args.model}_{resolution}.mlmodel")
'''
exportonnx.sh 0 → 100644
View file @ c218d1c5
python export_coreml.py --ckpt checkpoints/repvit_m0_9/2024_05_22_11_20_59/checkpoint_0.pth --model repvit_m0_9
flops.py 0 → 100644
View file @ c218d1c5
import torch
import time
from timm import create_model
import model
import utils
from fvcore.nn import FlopCountAnalysis
T0 = 5
T1 = 10
for n, batch_size, resolution in [
('repvit_m0_9', 1024, 224),
]:
inputs = torch.randn(1, 3, resolution,
resolution)
model = create_model(n, num_classes=1000)
utils.replace_batchnorm(model)
n_parameters = sum(p.numel()
for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters / 1e6)
flops = FlopCountAnalysis(model, inputs)
print("flops: ", flops.total() / 1e9)
\ No newline at end of file
logs/repvit_m0_9_distill_300e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m0_9_distill_450e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_0_distill_300e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_0_distill_450e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_1_distill_300e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_1_distill_450e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_5_distill_300e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m1_5_distill_450e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m2_3_distill_300e.txt 0 → 100644
View file @ c218d1c5
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logs/repvit_m2_3_distill_450e.txt 0 → 100644
View file @ c218d1c5
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