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jblitzar
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github-python-corpus

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model.load_state_dict(update_state_dict(torch.load(model_path), idx_start=7))
model.eval()
print(f"=> loaded wrn-70-16-dropout checkpoint")
else:
raise NotImplementedError(f'unknown {classifier_name}')
wrapper_resnet = model
elif 'celebahq' in classifier_name:
attribute = classifier_name.split('__')[-1] # `celebahq__Smiling`
ckpt_path = f'pretrained/celebahq/{attribute}/net_best.pth'
from classifiers.attribute_classifier import ClassifierWrapper
model = ClassifierWrapper(attribute, ckpt_path=ckpt_path)
wrapper_resnet = model
else:
raise NotImplementedError(f'unknown {classifier_name}')
return wrapper_resnet
def load_data(args, adv_batch_size):
if 'imagenet' in args.domain:
val_dir = './dataset/imagenet_lmdb/val' # using imagenet lmdb data
val_transform = data.get_transform(args.domain, 'imval', base_size=224)
val_data = data.imagenet_lmdb_dataset_sub(val_dir, transform=val_transform,
num_sub=args.num_sub, data_seed=args.data_seed)
n_samples = len(val_data)
val_loader = DataLoader(val_data, batch_size=n_samples, shuffle=False, pin_memory=True, num_workers=4)
x_val, y_val = next(iter(val_loader))
elif 'cifar10' in args.domain:
data_dir = './dataset'
transform = transforms.Compose([transforms.ToTensor()])
val_data = data.cifar10_dataset_sub(data_dir, transform=transform,
num_sub=args.num_sub, data_seed=args.data_seed)
n_samples = len(val_data)
val_loader = DataLoader(val_data, batch_size=n_samples, shuffle=False, pin_memory=True, num_workers=4)
x_val, y_val = next(iter(val_loader))
elif 'celebahq' in args.domain:
data_dir = './dataset/celebahq'
attribute = args.classifier_name.split('__')[-1] # `celebahq__Smiling`
val_transform = data.get_transform('celebahq', 'imval')
clean_dset = data.get_dataset('celebahq', 'val', attribute, root=data_dir, transform=val_transform,
fraction=2, data_seed=args.data_seed) # data_seed randomizes here
loader = DataLoader(clean_dset, batch_size=adv_batch_size, shuffle=False,
pin_memory=True, num_workers=4)
x_val, y_val = next(iter(loader)) # [0, 1], 256x256
else:
raise NotImplementedError(f'Unknown domain: {args.domain}!')
print(f'x_val shape: {x_val.shape}')
x_val, y_val = x_val.contiguous().requires_grad_(True), y_val.contiguous()
print(f'x (min, max): ({x_val.min()}, {x_val.max()})')
return x_val, y_val
# <FILESEP>
#!/usr/bin/env python3
# This file is covered by the LICENSE file in the root of this project.
import argparse
import os
import yaml
import numpy as np
# possible splits
splits = ["train", "valid", "test"]
if __name__ == '__main__':
parser = argparse.ArgumentParser("./remap_semantic_labels.py")
parser.add_argument(
'--dataset', '-d',
type=str,
required=False,
default=None,
help='Dataset dir. WARNING: This file remaps the labels in place, so the original labels will be lost. Cannot be used together with -predictions- flag.'
)
parser.add_argument(
'--predictions', '-p',
type=str,
required=False,
default=None,
help='Prediction dir. WARNING: This file remaps the predictions in place, so the original predictions will be lost. Cannot be used together with -dataset- flag.'
)
parser.add_argument(
'--split', '-s',
type=str,
required=False,
default="valid",
help='Split to evaluate on. One of ' +
str(splits) + '. Defaults to %(default)s',
)
parser.add_argument(
'--datacfg', '-dc',
type=str,
required=False,
default="config/semantic-kitti.yaml",
help='Dataset config file. Defaults to %(default)s',
)
parser.add_argument(