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eval_baseline.py
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eval_baseline.py
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import pytorch_lightning as pl
import torch
import torchvision
import wandb
import timm
import src.adversarial
import src.baseline
# MODEL_PATH = "/home/pedro/school/disertation/adversarial/pretrained_cifar/c3t1ovq6_resnet18_epoch=39_val_accuracy_top1=0.95.ckpt"
# DATASETS_ROOT = "/raid/data/pedro/datasets"
DATASETS_ROOT = "/home/pedro/imagenet_val"
EPOCHS = 1
DEVICE = 2
IMAGENET_ITER_STEP = 1 # 100 original
BATCH_SIZE = 512 # different for efficientnet!
cfg = list(
# [
# {
# "advnn": {
# "cls": src.baseline.Blur,
# "kwargs": {
# "kernel_size": sigma * 4
# - 1, # in contradiction with paper, but kernel size must be odd
# "sigma": sigma,
# },
# },
# "bboxnn": {
# "cls": timm.create_model,
# "kwargs": {"model_name": model, "pretrained": True},
# },
# "simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
# }
# for sigma in range(2, 10)
# ]
# + [
# [
# {
# "advnn": {
# "cls": src.baseline.Noise,
# # "kwargs": {"sigma": sigma.round(decimals=2).item()},
# "kwargs": {"sigma": sigma},
# },
# "bboxnn": {
# "cls": timm.create_model,
# "kwargs": {'model_name': model, 'pretrained': True},
# },
# "simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
# }
# # for sigma in range(0, 100, 10)
# # for sigma in torch.arange(0, 110 / 255, 10 / 255)
# for sigma in [80]
# ]
# + [
# # [
# {
# "advnn": {
# "cls": src.baseline.Contrast,
# "kwargs": {"factor": factor.round(decimals=2).item()},
# },
# "bboxnn": {
# "cls": timm.create_model,
# "kwargs": {'model_name': model, 'pretrained': True},
# },
# "simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
# }
# # for factor in range(0,1.1,0.1)
# for factor in torch.arange(0.1, 2.1, 0.1)
# ]
# +[
# [
# {
# "advnn": {
# "cls": src.baseline.JPEG,
# "kwargs": {"q": q},
# },
# "bboxnn": {
# "cls": timm.create_model,
# "kwargs": {'model_name': model, 'pretrained': True},
# },
# "simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
# }
# # for factor in range(0,1.1,0.1)
# for q in list(range(2, 22, 2)) + [100]
# ]
# + [
# # # [
# {
# "advnn": {
# "cls": src.baseline.Rescale,
# "kwargs": {"factor": factor},
# },
# "bboxnn": {
# "cls": timm.create_model,
# "kwargs": {'model_name': model, 'pretrained': True},
# },
# "simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
# }
# # for factor in range(0,1.1,0.1)
# for factor in [1/100, 5/100, 1/10, 1/5, 3/10, 1/2, 4/5, 1]
# ]
# + [
[
{
"advnn": {
"cls": src.baseline.Grid,
"kwargs": {"factor": factor, "gridsize": gridsize},
},
"bboxnn": {
"cls": timm.create_model,
"kwargs": {'model_name': model, 'pretrained': True},
},
"simnn": {"cls": src.adversarial.SimulationNN, "kwargs": {}},
}
# for factor in range(0,1.1,0.1)
# for gridsize, factor in [(2, x) for x in torch.arange(0.5, 1.5, 0.2)] # Lastest
for gridsize, factor in [(2, x) for x in [1.0, 1.5]] # Lastest
# + [(4, x) for x in torch.arange(0.5, 1.5, 0.2)]
# + [(8, x) for x in torch.arange(0.5, 1.5, 0.2)]
# + [(16, x) for x in torch.arange(0.5, 1.5, 0.2)]
]
for model in [
# "resnet50.a1_in1k",
# "resnet50.fb_ssl_yfcc100m_ft_in1k",
# "resnet50.fb_swsl_ig1b_ft_in1k",
# # compare with timm's effnet
# 'efficientnet_b0',
# 'efficientnet_b1',
# 'efficientnet_b2',
# 'efficientnet_b3',
# 'efficientnet_b4',
#standard experiment mode,
'resnet18d',
'resnet26d',
'resnet34d',
'resnet101d',
'resnet152d',
'resnet200d',
# 'vit_huge_patch14_224_in21k', # NO PRETRAINED WEIGH',
'vit_large_patch16_224',
'vit_base_patch16_224',
'vit_small_patch16_224',
'vit_tiny_patch16_224',
'tf_efficientnet_b0',
'tf_efficientnet_b1',
'tf_efficientnet_b2',
'tf_efficientnet_b3',
'tf_efficientnet_b4',
'tf_efficientnet_b5',
'tf_efficientnet_b6',
'tf_efficientnet_b7',
'swin_tiny_patch4_window7_224',
'swin_small_patch4_window7_224',
'swin_base_patch4_window7_224',
'swin_large_patch4_window7_224',
'convnext_atto',
'convnext_base',
'convnext_femto',
'convnext_large',
'convnext_nano',
'convnext_pico',
'convnext_small',
'convnext_tiny',
'convnext_xlarge',
'mobilenetv3_small_050',
'mobilenetv3_small_075',
'mobilenetv3_small_100',
]
)
def kwargs2str(kwargs):
return f"{','.join([f'{k}={v}' for k,v in kwargs.items()])}"
def main():
m = timm.create_model('resnet18', pretrained=True)
# format check
architectures = ["resnet", "efficientnet", "convnext", "mobilenetv3", "swin", "vit"]
all_architectures_known = []
for x in cfg:
for c in x:
all_architectures_known.append(
any([a in c["bboxnn"]["kwargs"]["model_name"] for a in architectures])
)
assert all(all_architectures_known)
# computation
for x in cfg:
for c in x:
model_family = [
x for x in architectures if x in c["bboxnn"]["kwargs"]["model_name"]
][0]
name = "ball"
advnn_kwargs = kwargs2str(c["advnn"]["kwargs"])
bboxnn_kwargs = kwargs2str(c["bboxnn"]["kwargs"])
name = f"{name}_advnn:{c['advnn']['cls'].__name__}({advnn_kwargs})_bboxnn:{c['bboxnn']['cls'].__name__}({bboxnn_kwargs})"
wandb_exp = wandb.init(
reinit=True,
project="adversarial",
entity="petr_petr",
name=name,
group="advgen_baseline2",
tags=["whole_imagenet", model_family, c["advnn"]["cls"].__name__, "TODO"]
)
blackbox_classifier = c["bboxnn"]["cls"](**c["bboxnn"]["kwargs"])
blackbox_classifier.eval()
try:
blackbox_input_size = blackbox_classifier.pretrained_cfg[
"test_input_size"
][-1]
except KeyError:
blackbox_input_size = blackbox_classifier.pretrained_cfg["input_size"][
-1
]
adversarial_nn = c["advnn"]["cls"](**c["advnn"]["kwargs"])
simulation_nn = c["simnn"]["cls"](**c["simnn"]["kwargs"])
test_imagenet = torchvision.datasets.ImageNet(
root="/raid/data/imagenet/2012/images",
split="val",
transform=src.adversarial.Transform(
resize_size=blackbox_input_size,
crop_size=blackbox_input_size,
interpolation=torchvision.transforms.functional.InterpolationMode(
blackbox_classifier.pretrained_cfg["interpolation"]
),
),
)
imagenet_subset_indices = list(
range(0, len(test_imagenet), IMAGENET_ITER_STEP)
)
imagenet_sampler = src.adversarial.ImagenetSampler(imagenet_subset_indices)
eval_loader = torch.utils.data.DataLoader(
test_imagenet,
batch_size=BATCH_SIZE if not 'efficientnet' in name else 96,
shuffle=False,
num_workers=32,
drop_last=True,
sampler=imagenet_sampler,
pin_memory=True,
)
wandb_logger = pl.loggers.WandbLogger(experiment=wandb_exp)
trainer = pl.Trainer(
max_epochs=EPOCHS,
precision="16",
accelerator="gpu",
devices=[DEVICE],
logger=wandb_logger,
callbacks=[
pl.callbacks.LearningRateMonitor(logging_interval="step"),
],
)
trainable_params = sum(
p.numel() for p in blackbox_classifier.parameters() if p.requires_grad
)
adversarial_transform = c["advnn"]["cls"].__name__
wandb.log(
{
"params": trainable_params,
"input_size": blackbox_input_size,
"base_architecture": model_family,
"adv_transform": adversarial_transform,
**c["advnn"]["kwargs"],
**c["bboxnn"]["kwargs"],
"imagenet_iter_step": IMAGENET_ITER_STEP,
}
)
trainee = src.adversarial.Trainee(
blackbox_classifier,
adversarial_nn,
simulation_nn,
EPOCHS,
mean=blackbox_classifier.pretrained_cfg["mean"],
std=blackbox_classifier.pretrained_cfg["std"],
)
trainer.validate(trainee, eval_loader)
if __name__ == "__main__":
main()