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test_mixup.py
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test_mixup.py
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import argparse
from trainer import VAETrainer
import torch
import pdb
import os
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from scipy.stats import entropy
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import yaml
from sklearn.decomposition import PCA
import torchvision
def get_config(config):
with open(config, 'r') as f:
return yaml.safe_load(f)
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='config.yaml', help='Configuration file')
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--gpu_id', type=str, default='0')
opt = parser.parse_args()
torch.manual_seed(opt.seed)
config = get_config(opt.config)
device = torch.device('cuda:{}'.format(opt.gpu_id) if config['cuda'] else 'cpu')
if config['task'] == 'mnist':
test_loader = torch.utils.data.DataLoader(datasets.MNIST('./data/MNIST',
train=False, transform=transforms.ToTensor()),
batch_size=config['batch_size_test'], shuffle=False, num_workers=4, drop_last=True)
config[f"vae{config['model']}"]['channels'], config[f"vae{config['model']}"]['width'], config[f"vae{config['model']}"]['height'] = 1, 28, 28
elif config['task'] == 'fmnist':
test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST('./data/FashionMNIST',
train=False, transform=transforms.ToTensor()),
batch_size=config['batch_size_test'], shuffle=False, num_workers=4, drop_last=True)
config[f"vae{config['model']}"]['channels'], config[f"vae{config['model']}"]['width'], config[f"vae{config['model']}"]['height'] = 1, 28, 28
elif config['task'] == 'cifar':
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
test_loader = torch.utils.data.DataLoader(datasets.CIFAR10(root = './data/CIFAR10', train = False,
download = True, transform = transform_test),
batch_size=config['batch_size_test'], shuffle=False, num_workers=4, drop_last=True)
config[f"vae{config['model']}"]['channels'], config[f"vae{config['model']}"]['width'], config[f"vae{config['model']}"]['height'] = 3, 32, 32
elif config['task'] == 'celeba':
transform_train = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.CenterCrop(148),
transforms.Resize(64),
transforms.ToTensor()
]
)
transform_test = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.CenterCrop(148),
transforms.Resize(64),
transforms.ToTensor()
]
)
class MyCelebA(datasets.CelebA):
"""
A work-around to address issues with pytorch's celebA dataset class.
Download and Extract
URL : https://drive.google.com/file/d/1m8-EBPgi5MRubrm6iQjafK2QMHDBMSfJ/view?usp=sharing
"""
def _check_integrity(self) -> bool:
return True
test_loader = torch.utils.data.DataLoader(MyCelebA(root = './data/CelebA', split = 'train',
download = False, transform = transform_train),
batch_size=config['batch_size_train'], shuffle=True, num_workers=4, drop_last=True)
else:
assert 0, f"Not Implemented {config['task']}"
kl_beta = np.logspace(-2, 1, 50)
results = {'score_det': [],
'score_trace': [],
'von_entr': [],
'lambda_max': [],
'S_norm': [],
'error': [],
}
output_results = config['output_results']
output_model = config['output_model']
steps = np.logspace(-1, 1, 40)
epoch = 100
robust = []
prone = []
config['kl_term'] = 1
config['output_results'] = f"/disk/scratch1/asif/workspace/RobustnessGeometryVAE/model/mixup/{config['task']}/{config['model']}kl_{config['kl_term']}"
config['output_model'] = f"/disk/scratch1/asif/workspace/RobustnessGeometryVAE/model/mixup/kl_{config['kl_term']}"
if not os.path.exists(config['output_results']):
os.makedirs(config['output_results'])
trainer = VAETrainer(config, device)
ckpt = torch.load(f"{config['output_model']}/model_{config['model']}task_{config['task']}_beta_{config['kl_term']}vae_{epoch}.pt")
trainer.vae.load_state_dict(ckpt['vae'])
trainer.optG.load_state_dict(ckpt['optG'])
for i, (data, label) in enumerate(test_loader, 0):
data = data.to(device)
error_eigen, entr, maxeigen = trainer.sample_eigen(data, config['output_results'], i)
results['von_entr'].append(entr)
results['lambda_max'].append(maxeigen)
results['error'].append(error_eigen)
if i==5:
break
results['von_entr'] = torch.stack(results['von_entr'], 0).numpy().flatten()
results['lambda_max'] = torch.stack(results['lambda_max'], 0).numpy().flatten()
#results['S_norm'][kl] = torch.stack(results['S_norm'][kl], 0).numpy()
results['error'] = torch.stack(results['error'], 0).numpy()
nm_eigen = 5
fig, ax = plt.subplots(figsize=(w, h), dpi=150)
for k in range(nm_eigen):
ax.plot(steps, results['error'][:,k], label=fr"$\lambda_{k+1}$")
box = ax.get_position()
ax.set_position([box.x0, box.y0+box.height*0.1, box.width, box.height*0.9])
ax.legend(loc='upper center', shadow=True, ncol=5, bbox_to_anchor=(0.5, -0.12), fancybox=True)
ax.set_xlabel('Step Size')
ax.set_ylabel('MSE')
ax.set_title(fr"$\beta={kl:.4f}$")
plt.savefig(f"{config['output_results']}/mixup_{config['mixup']}error_avg_batch_{kl}_{task}.png")
plt.cla()
plt.close()
results_mixup = {'score_det': [],
'score_trace': [],
'von_entr': [],
'lambda_max': [],
'S_norm': [],
'error': [],
}
config['mixup'] = False
config['kl_term'] = 1
config['output_results'] = f"/disk/scratch1/asif/workspace/RobustnessGeometryVAE/model/mixup/{config['task']}/{config['model']}kl_{config['kl_term']}"
config['output_model'] = f"/disk/scratch1/asif/workspace/RobustnessGeometryVAE/model/mixup/kl_{config['kl_term']}"
trainer = VAETrainer(config, device)
ckpt = torch.load(f"{config['output_model']}/model_{config['model']}task_{config['task']}_beta_{config['kl_term']}vae_{epoch}.pt")
trainer.vae.load_state_dict(ckpt['vae'])
trainer.optG.load_state_dict(ckpt['optG'])
for i, (data, label) in enumerate(test_loader, 0):
data = data.to(device)
error_eigen, entr, maxeigen = trainer.sample_eigen(data, config['output_results'], i)
results_mixup['von_entr'].append(entr)
results_mixup['lambda_max'].append(maxeigen)
results_mixup['error'].append(error_eigen)
if i==5:
break
results_mixup['von_entr'] = torch.stack(results['von_entr'], 0).numpy().flatten()
results_mixup['lambda_max'] = torch.stack(results['lambda_max'], 0).numpy().flatten()
#results['S_norm'][kl] = torch.stack(results['S_norm'][kl], 0).numpy()
results_mixup['error'] = torch.stack(results['error'], 0).numpy()
nm_eigen = 5
fig, ax = plt.subplots(figsize=(w, h), dpi=150)
for k in range(nm_eigen):
ax.plot(steps, results['error'][:,k], label=fr"$\lambda_{k+1}$")
box = ax.get_position()
ax.set_position([box.x0, box.y0+box.height*0.1, box.width, box.height*0.9])
ax.legend(loc='upper center', shadow=True, ncol=5, bbox_to_anchor=(0.5, -0.12), fancybox=True)
ax.set_xlabel('Step Size')
ax.set_ylabel('MSE')
ax.set_title(fr"$\beta={kl:.4f}$")
plt.savefig(f"{config['output_results']}/mixup_{config['mixup']}error_avg_batch_{kl}_{task}.png")
plt.cla()
plt.close()
plt.figure(figsize=(w,h))
plt.hist(results_mixup['lambda_max'], 80, alpha=0.75, density=True, label="mixup")
plt.hist(results['lambda_max'], 80, alpha=0.75, label="no-mixup")
plt.legend(loc='upper center', shadow=True, ncol=5, bbox_to_anchor=(0.5, -0.15), fancybox=True)
plt.xlabel(r'$\rho (G)$ (Spectral Radius)')
plt.ylabel('Frequency')
plt.title(f"{config['task']}")
plt.xlim(0, 10)
plt.savefig(f"{config['output_results']}/histogram_lambda_max_{kl}_{task}.png")
plt.cla()
plt.close()
plt.figure(figsize=(w,h))
plt.hist(results_mixup['lambda_max'], 80, alpha=0.75, density=True, label="mixup")
plt.hist(results['von_entr'], 80, alpha=0.75, density=True, label="no-mixup")
plt.xlabel(r'$\mathbf{S}$ (Von Neumann Entropy)')
plt.ylabel('Frequency')
plt.xlim(0, 10)
plt.title(f"{config['task']}")
plt.savefig(f"{output_results}/histogram_Entropy_{kl}_{task}.png")
plt.cla()
plt.close()
np.savez(f"{output_results}/{config['task']}/{config['model']}mixup_{config['mixup']}.npz", results['von_entr'][kl], results['lambda_max'][kl], results['error'][kl])