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test.py
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import sys
import os
import warnings
import utils
import torch
from nn_modules import LandmarkExtractor, FaceXZooProjector
from config import patch_config_types
from torchvision import transforms
from PIL import Image
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import label_binarize
import matplotlib
from pathlib import Path
import pickle
import seaborn as sns
import pandas as pd
matplotlib.use('Agg')
global device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Evaluator:
def __init__(self, config, best_patch) -> None:
super().__init__()
self.config = config
self.best_patch = best_patch
face_landmark_detector = utils.get_landmark_detector(self.config, device)
self.location_extractor = LandmarkExtractor(device, face_landmark_detector, self.config.img_size).to(device)
self.fxz_projector = FaceXZooProjector(device, self.config.img_size, self.config.patch_size).to(device)
self.transform = transforms.Compose([transforms.Resize(self.config.patch_size), transforms.ToTensor()])
self.embedders = utils.load_embedder(self.config.test_embedder_names, device=device)
emb_loaders, self.test_loaders = utils.get_test_loaders(self.config, self.config.test_celeb_lab.keys())
self.target_embedding_w_mask, self.target_embedding_wo_mask = {}, {}
for dataset_name, loader in emb_loaders.items():
self.target_embedding_w_mask[dataset_name] = utils.get_person_embedding(self.config, loader, self.config.test_celeb_lab_mapper[dataset_name], self.location_extractor,
self.fxz_projector, self.embedders, device, include_others=True)
self.target_embedding_wo_mask[dataset_name] = utils.get_person_embedding(self.config, loader, self.config.test_celeb_lab_mapper[dataset_name], self.location_extractor,
self.fxz_projector, self.embedders, device, include_others=False)
self.random_mask_t = utils.load_mask(self.config, self.config.random_mask_path, device)
self.blue_mask_t = utils.load_mask(self.config, self.config.blue_mask_path, device)
self.face1_mask_t = utils.load_mask(self.config, self.config.face1_mask_path, device)
self.face3_mask_t = utils.load_mask(self.config, self.config.face3_mask_path, device)
self.mask_names = ['Clean', 'Adv', 'Random', 'Blue', 'Face1', 'Face3']
Path(self.config.current_dir).mkdir(parents=True, exist_ok=True)
utils.save_class_to_file(self.config, self.config.current_dir)
def test(self):
self.calc_overall_similarity()
for dataset_name in self.test_loaders.keys():
similarities_target_with_mask_by_person = self.get_final_similarity_from_disk('with_mask', dataset_name=dataset_name, by_person=True)
similarities_target_without_mask_by_person = self.get_final_similarity_from_disk('without_mask', dataset_name=dataset_name, by_person=True)
self.calc_similarity_statistics(similarities_target_with_mask_by_person, target_type='with', dataset_name=dataset_name, by_person=True)
self.calc_similarity_statistics(similarities_target_without_mask_by_person, target_type='without', dataset_name=dataset_name, by_person=True)
self.plot_sim_box(similarities_target_with_mask_by_person, target_type='with', dataset_name=dataset_name, by_person=True)
self.plot_sim_box(similarities_target_without_mask_by_person, target_type='without', dataset_name=dataset_name, by_person=True)
@torch.no_grad()
def calc_overall_similarity(self):
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
adv_patch = self.best_patch.to(device)
for dataset_name, loader in self.test_loaders.items():
df_with_mask = pd.DataFrame(columns=['y_true', 'y_pred'])
df_without_mask = pd.DataFrame(columns=['y_true', 'y_pred'])
for img_batch, img_names, cls_id in tqdm(loader):
img_batch = img_batch.to(device)
cls_id = cls_id.to(device).type(torch.int32)
# Apply different types of masks
img_batch_applied = self.apply_all_masks(img_batch, adv_patch)
# Get embedding
all_embeddings = self.get_all_embeddings(img_batch, img_batch_applied)
self.calc_all_similarity(all_embeddings, img_names, cls_id, 'with_mask', dataset_name)
self.calc_all_similarity(all_embeddings, img_names, cls_id, 'without_mask', dataset_name)
df_with_mask = df_with_mask.append(self.calc_preds(cls_id, all_embeddings, target_type='with_mask', dataset_name=dataset_name))
df_without_mask = df_without_mask.append(self.calc_preds(cls_id, all_embeddings, target_type='without_mask', dataset_name=dataset_name))
Path(os.path.join(self.config.current_dir, 'saved_preds', dataset_name)).mkdir(parents=True, exist_ok=True)
df_with_mask.to_csv(os.path.join(self.config.current_dir, 'saved_preds', dataset_name, 'preds_with_mask.csv'), index=False)
df_without_mask.to_csv(os.path.join(self.config.current_dir, 'saved_preds', dataset_name, 'preds_without_mask.csv'), index=False)
def plot_sim_box(self, similarities, target_type, dataset_name, by_person=False):
Path(os.path.join(self.config.current_dir, 'final_results', 'sim-boxes', dataset_name, target_type)).mkdir(parents=True, exist_ok=True)
for emb_name in self.config.test_embedder_names:
sim_df = pd.DataFrame()
for i in range(len(similarities[emb_name])):
sim_df[self.mask_names[i]] = similarities[emb_name][i]
sorted_index = sim_df.mean().sort_values(ascending=False).index
sim_df_sorted = sim_df[sorted_index]
sns.boxplot(data=sim_df_sorted).set_title('Similarities for Different Masks')
plt.xlabel('Mask Type')
plt.ylabel('Similarity')
avg_type = 'person' if by_person else 'image'
plt.savefig(os.path.join(self.config.current_dir, 'final_results', 'sim-boxes', dataset_name, target_type, avg_type + '_' + emb_name + '.png'))
plt.close()
def write_similarities_to_disk(self, sims, img_names, cls_ids, sim_type, emb_name, dataset_name):
Path(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name)).mkdir(parents=True, exist_ok=True)
for i, lab in self.config.test_celeb_lab_mapper[dataset_name].items():
Path(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name, lab)).mkdir(parents=True, exist_ok=True)
for similarity, mask_name in zip(sims, self.mask_names):
sim = similarity[cls_ids.cpu().numpy() == i].tolist()
sim = {img_name: s for img_name, s in zip(img_names, sim)}
with open(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name, lab, sim_type + '_' + mask_name + '.pickle'), 'ab') as f:
pickle.dump(sim, f)
for similarity, mask_name in zip(sims, self.mask_names):
sim = {img_name: s for img_name, s in zip(img_names, similarity.tolist())}
with open(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name, sim_type + '_' + mask_name + '.pickle'), 'ab') as f:
pickle.dump(sim, f)
def apply_all_masks(self, img_batch, adv_patch):
img_batch_applied_adv = utils.apply_mask(self.location_extractor,
self.fxz_projector, img_batch, adv_patch)
img_batch_applied_random = utils.apply_mask(self.location_extractor,
self.fxz_projector, img_batch,
self.random_mask_t)
img_batch_applied_blue = utils.apply_mask(self.location_extractor,
self.fxz_projector, img_batch,
self.blue_mask_t[:, :3],
self.blue_mask_t[:, 3], is_3d=True)
img_batch_applied_face1 = utils.apply_mask(self.location_extractor,
self.fxz_projector, img_batch,
self.face1_mask_t[:, :3],
self.face1_mask_t[:, 3], is_3d=True)
img_batch_applied_face3 = utils.apply_mask(self.location_extractor,
self.fxz_projector, img_batch,
self.face3_mask_t[:, :3],
self.face3_mask_t[:, 3], is_3d=True)
return img_batch_applied_adv, img_batch_applied_random, img_batch_applied_blue, img_batch_applied_face1, img_batch_applied_face3
def get_all_embeddings(self, img_batch, img_batch_applied_masks):
batch_embs = {}
for emb_name, emb_model in self.embedders.items():
batch_embs[emb_name] = [emb_model(img_batch.to(device)).cpu().numpy()]
for img_batch_applied_mask in img_batch_applied_masks:
batch_embs[emb_name].append(emb_model(img_batch_applied_mask.to(device)).cpu().numpy())
return batch_embs
def calc_all_similarity(self, all_embeddings, img_names, cls_id, target_type, dataset_name):
for emb_name in self.config.test_embedder_names:
target = self.target_embedding_w_mask[dataset_name][emb_name] if target_type == 'with_mask' else self.target_embedding_wo_mask[dataset_name][emb_name]
target_embedding = torch.index_select(target, index=cls_id, dim=0).cpu().numpy().squeeze(-2)
sims = []
for emb in all_embeddings[emb_name]:
sims.append(np.diag(cosine_similarity(emb, target_embedding)))
self.write_similarities_to_disk(sims, img_names, cls_id, sim_type=target_type, emb_name=emb_name, dataset_name=dataset_name)
def get_final_similarity_from_disk(self, sim_type, dataset_name, by_person=False):
sims = {}
for emb_name in self.config.test_embedder_names:
sims[emb_name] = []
for i, mask_name in enumerate(self.mask_names):
if not by_person:
with open(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name, sim_type + '_' + mask_name + '.pickle'), 'rb') as f:
sims[emb_name].append([])
while True:
try:
data = pickle.load(f).values()
sims[emb_name][i].extend(list(data))
except EOFError:
break
else:
sims[emb_name].append([])
for lab in self.config.test_celeb_lab[dataset_name]:
with open(os.path.join(self.config.current_dir, 'saved_similarities', dataset_name, emb_name, lab, sim_type + '_' + mask_name + '.pickle'), 'rb') as f:
person_sims = []
while True:
try:
data = pickle.load(f).values()
person_sims.extend(list(data))
except EOFError:
break
person_avg_sim = sum(person_sims) / len(person_sims)
sims[emb_name][i].append(person_avg_sim)
return sims
def calc_preds(self, cls_id, all_embeddings, target_type, dataset_name):
df = pd.DataFrame(columns=['emb_name', 'mask_name', 'y_true', 'y_pred'])
class_labels = list(range(0, len(self.config.test_celeb_lab_mapper[dataset_name])))
y_true = label_binarize(cls_id.cpu().numpy(), classes=class_labels)
y_true = [lab.tolist() for lab in y_true]
for emb_name in self.config.test_embedder_names:
target_embedding = self.target_embedding_w_mask[dataset_name][emb_name] \
if target_type == 'with_mask' else self.target_embedding_wo_mask[dataset_name][emb_name]
target_embedding = target_embedding.cpu().numpy().squeeze(-2)
for i, mask_name in enumerate(self.mask_names):
emb = all_embeddings[emb_name][i]
cos_sim = cosine_similarity(emb, target_embedding)
y_pred = [lab.tolist() for lab in cos_sim]
new_rows = pd.DataFrame({
'emb_name': [emb_name] * len(y_true),
'mask_name': [mask_name] * len(y_true),
'y_true': y_true,
'y_pred': y_pred
})
df = df.append(new_rows, ignore_index=True)
return df
def calc_similarity_statistics(self, sim_dict, target_type, dataset_name, by_person=False):
df_mean = pd.DataFrame(columns=['emb_name'] + self.mask_names)
df_std = pd.DataFrame(columns=['emb_name'] + self.mask_names)
for emb_name, sim_values in sim_dict.items():
sim_values = np.array([np.array(lst) for lst in sim_values])
sim_mean = np.round(sim_values.mean(axis=1), decimals=3)
sim_std = np.round(sim_values.std(axis=1), decimals=3)
df_mean = df_mean.append(pd.Series([emb_name] + sim_mean.tolist(), index=df_mean.columns), ignore_index=True)
df_std = df_std.append(pd.Series([emb_name] + sim_std.tolist(), index=df_std.columns), ignore_index=True)
avg_type = 'person' if by_person else 'image'
Path(os.path.join(self.config.current_dir, 'final_results', 'stats', 'similarity', dataset_name, target_type)).mkdir(parents=True, exist_ok=True)
df_mean.to_csv(os.path.join(self.config.current_dir, 'final_results', 'stats', 'similarity', dataset_name, target_type, 'mean_df' + '_' + avg_type + '.csv'), index=False)
df_std.to_csv(os.path.join(self.config.current_dir, 'final_results', 'stats', 'similarity', dataset_name, target_type, 'std_df' + '_' + avg_type + '.csv'), index=False)
def main():
mode = 'universal'
config = patch_config_types[mode]()
adv_mask = Image.open('../data/masks/final_patch-arccosmag.png').convert('RGB')
adv_mask_t = transforms.ToTensor()(adv_mask).unsqueeze(0)
print('Starting test...', flush=True)
evaluator = Evaluator(config, adv_mask_t)
evaluator.test()
print('Finished test...', flush=True)
if __name__ == '__main__':
main()