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random_restrictedImgNet_dists.py
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random_restrictedImgNet_dists.py
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from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import numpy as np
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
import torch
from tqdm import tqdm
import joblib
n_classes = 9
trn_ds = ImageFolder("./data/RestrictedImgNet/train",
transform=transforms.Compose([
transforms.Resize(72),
transforms.CenterCrop(64),
transforms.ToTensor(),
]))
tst_ds = ImageFolder("./data/RestrictedImgNet/val",
transform=transforms.Compose([
transforms.Resize(72),
transforms.CenterCrop(64),
transforms.ToTensor(),
]))
tst_dists = torch.ones((len(tst_ds), n_classes)).float()
batch_size = 256
############# for randomized labeling experiment
np.random.seed(0)
trn_ds.targets = np.random.choice(np.arange(9), size=len(trn_ds.targets))
for i in range(len(trn_ds.imgs)):
trn_ds.imgs[i] = (trn_ds.imgs[i][0], trn_ds.targets[i])
tst_ds.targets = np.random.choice(np.arange(9), size=len(tst_ds.targets))
for i in range(len(tst_ds.imgs)):
tst_ds.imgs[i] = (tst_ds.imgs[i][0], tst_ds.targets[i])
import ipdb; ipdb.set_trace()
############
trn_loader = torch.utils.data.DataLoader(trn_ds, batch_size=batch_size, shuffle=False, num_workers=16)
tst_loader = torch.utils.data.DataLoader(tst_ds, batch_size=batch_size, shuffle=False, num_workers=16)
for x, y in tqdm(trn_loader):
y = y.numpy()
x = x.flatten(1).cuda()
for i, (xi, yi) in enumerate(tst_loader):
yi = yi.numpy()
xi = xi.flatten(1).cuda()
cov = torch.norm(x.repeat((len(xi), 1)) - xi.repeat_interleave(len(x), dim=0), p=np.inf, dim=1)
cov = cov.view(len(xi), len(x)).cpu()
#cov = pairwise_distances(xi, x, metric='minkowski', n_jobs=16, p=np.inf)
for j in range(n_classes):
ty = (y == j)
if ty.sum() >= 1:
min_dist = cov[:, ty].min(axis=1)[0]
tst_dists[i*batch_size: (i+1)*batch_size, j] = np.minimum(
tst_dists[i*batch_size: (i+1)*batch_size, j],
min_dist
)
#joblib.dump(tst_dists.numpy(), "./restricted_tst_linf.pkl")
joblib.dump(tst_dists.numpy(), "./rand_restricted_tst_linf.pkl")
trn_dists = torch.ones((len(trn_ds), n_classes)).float()
batch_size = 256
############## for randomized labeling experiment
np.random.seed(0)
trn_ds.targets = np.random.choice(np.arange(9), size=len(trn_ds.targets))
for i in range(len(trn_ds.imgs)):
trn_ds.imgs[i] = (trn_ds.imgs[i][0], trn_ds.targets[i])
#############
trn_loader = torch.utils.data.DataLoader(trn_ds, batch_size=batch_size, shuffle=False, num_workers=16)
tst_loader = torch.utils.data.DataLoader(trn_ds, batch_size=batch_size, shuffle=False, num_workers=16)
for x, y in tqdm(trn_loader):
y = y.numpy()
x = x.flatten(1).cuda()
for i, (xi, yi) in enumerate(tst_loader):
yi = yi.numpy()
xi = xi.flatten(1).cuda()
cov = torch.norm(x.repeat((len(xi), 1)) - xi.repeat_interleave(len(x), dim=0), p=np.inf, dim=1)
cov = cov.view(len(xi), len(x)).cpu()
for j in range(n_classes):
ty = (y == j)
if ty.sum() >= 1:
min_dist = cov[:, ty].min(axis=1)[0]
trn_dists[i*batch_size: (i+1)*batch_size, j] = np.minimum(
trn_dists[i*batch_size: (i+1)*batch_size, j],
min_dist
)
import ipdb; ipdb.set_trace()
#joblib.dump(trn_dists.numpy(), "./restricted_trn_linf.pkl")
joblib.dump(trn_dists.numpy(), "./rand_restricted_trn_linf.pkl")