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pick_indices_afhq.py
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pick_indices_afhq.py
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import pickle
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, models, transforms
import torch.backends.cudnn as cudnn
import numpy as np
import matplotlib.pyplot as plt
import time
import os
from tqdm import tqdm
from configs.paths_config import (
DATASET_PATHS,
MODEL_PATHS,
HYBRID_MODEL_PATHS,
HYBRID_CONFIG,
)
from datasets.data_utils import get_dataset
import torchvision.transforms as tfs
import random
random.seed(0)
def shuffle_helper(lst):
random.shuffle(lst)
return lst
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # device object
train_transform = tfs.Compose(
[
transforms.Resize((256, 256)),
# transforms.RandomHorizontalFlip(), # data augmentation
tfs.ToTensor(),
tfs.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),
]
)
test_transform = tfs.Compose(
[
transforms.Resize((256, 256)),
tfs.ToTensor(),
tfs.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),
]
)
data_dir = "data/afhq"
train_dataset = datasets.ImageFolder(os.path.join(data_dir, "train"), train_transform)
test_dataset = datasets.ImageFolder(os.path.join(data_dir, "val"), test_transform)
print("Train dataset size:", len(train_dataset))
print("Test dataset size:", len(test_dataset))
print(train_dataset.class_to_idx)
print(test_dataset.class_to_idx)
print("Train dataset size:", len(train_dataset))
print("Test dataset size:", len(test_dataset))
source_index_train = shuffle_helper(np.arange(len(train_dataset)))
target_index_train = shuffle_helper(np.arange(len(train_dataset)))
source_index_test = shuffle_helper(np.arange(len(test_dataset)))
target_index_test = shuffle_helper(np.arange(len(test_dataset)))
saved_indices = {
"source_index_train": [],
"target_index_train": [],
"source_index_test": [],
"target_index_test": [],
}
class_names = train_dataset.classes
# print('Class names:', class_names)
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(
num_features, len(train_dataset.classes)
) # multi-class classification (num_of_class == 307)
model = model.to(device)
# quit()
if True: # device == 'cuda':
model = torch.nn.DataParallel(model)
cudnn.benchmark = True
save_path = "afhq_resnet18.pth"
model.module.load_state_dict(torch.load(save_path, map_location="cuda"))
"""train dataset"""
model.eval()
with torch.no_grad():
for i in tqdm(range(0, len(source_index_train))):
# if len(saved_indices["target_index_train"]) > 500:
# break
source_inputs, source_labels = train_dataset[source_index_train[i]]
target_inputs, target_labels = train_dataset[target_index_train[i]]
source_inputs, target_inputs = source_inputs.unsqueeze(0).to(
device
), target_inputs.unsqueeze(0).to(device)
source_outputs, target_outputs = (
model(source_inputs).max(dim=1)[1].item(),
model(target_inputs).max(dim=1)[1].item(),
)
if (
source_outputs == source_labels
and target_outputs == target_labels
and source_outputs != target_outputs
and source_labels != target_labels
and source_labels == train_dataset.class_to_idx["dog"]
):
saved_indices["source_index_train"].append(source_index_train[i])
saved_indices["target_index_train"].append(target_index_train[i])
else:
pass
"""test dataset"""
model.eval()
with torch.no_grad():
for i in tqdm(range(0, len(source_index_test))):
# if len(saved_indices["target_index_test"]) > 500:
# break
source_inputs, source_labels = test_dataset[source_index_test[i]]
target_inputs, target_labels = test_dataset[target_index_test[i]]
source_inputs, target_inputs = source_inputs.unsqueeze(0).to(
device
), target_inputs.unsqueeze(0).to(device)
source_outputs, target_outputs = (
model(source_inputs).max(dim=1)[1].item(),
model(target_inputs).max(dim=1)[1].item(),
)
if (
source_outputs == source_labels
and target_outputs == target_labels
and source_outputs != target_outputs
and source_labels != target_labels
and source_labels == test_dataset.class_to_idx["dog"]
):
saved_indices["source_index_test"].append(source_index_test[i])
saved_indices["target_index_test"].append(target_index_test[i])
else:
pass
for k, v in saved_indices.items():
print(len(v))
with open("saved_indices/saved_indices_afhq_dog.pkl", "wb") as f:
pickle.dump(saved_indices, f)