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tsne_new.py
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tsne_new.py
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import argparse
import os
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import numpy as np
from models import API_Net
from datasets import RandomDataset_test
from utils import accuracy_test, AverageMeter
from pathlib import Path
import pandas as pd
from sklearn.manifold import TSNE
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import tikzplotlib
from matplotlib.pyplot import figure
from tqdm import tqdm
np.set_printoptions(suppress=True,
formatter={'float_kind': '{:0.4f}'.format})
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--exp_name', default=None, type=str,
help='name of experiment')
parser.add_argument('--data', metavar='DIR', default='',
help='path to dataset')
parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=1, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=100, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--evaluate-freq', default=10, type=int,
help='the evaluation frequence')
parser.add_argument('--resume', default='./checkpoint.pth.tar', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--model_load_path', default='./save', type=str,
help='model path you want to test')
parser.add_argument('--output_path', default='./output/test.txt', type=str,
help='test result output path')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--n_classes', default=2, type=int,
help='the number of classes')
parser.add_argument('--n_classes_total', default=5, type=int,
help='the overall number of classes')
parser.add_argument('--n_samples', default=8, type=int,
help='the number of samples per class')
parser.add_argument('--test_list', default='data_list/trycode.txt', type=str,
help='test list')
parser.add_argument('--model_name', default='res101', type=str)
parser.add_argument('--dist_type', default='euclidean', type=str)
parser.add_argument('--image_loader', default='default_loader', type=str)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
def main(args):
model_path = args.model_load_path
result_write_path = args.output_path
if os.path.exists(result_write_path):
os.remove(result_write_path)
if not os.path.exists(Path(result_write_path).parent):
os.makedirs(Path(result_write_path).parent)
test_list = args.test_list
batch_size = args.batch_size
n_classes_total = args.n_classes_total
model_name = args.model_name
dist_type = args.dist_type
image_loader = args.image_loader
# create model
model = API_Net(num_classes=n_classes_total, model_name=model_name)
model = model.to(device)
model.conv = nn.DataParallel(model.conv)
if os.path.isfile(args.resume):
print('loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print('loaded checkpoint {}(epoch {})'.format(args.resume, checkpoint['epoch']))
else:
print('no checkpoint found at {}'.format(args.resume))
transform_3 = transforms.Compose([
transforms.Resize([512, 512]),
transforms.RandomCrop([448, 448]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)
)])
transform_6 = transforms.Compose([
transforms.ToTensor(),
transforms.Resize([512, 512]),
transforms.RandomCrop([448, 448]),
transforms.RandomHorizontalFlip(),
transforms.Normalize(
mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225)
)])
transform_9 = transforms.Compose([
transforms.ToTensor(),
transforms.Resize([512, 512]),
transforms.RandomCrop([448, 448]),
transforms.RandomHorizontalFlip(),
transforms.Normalize(
mean=(0.485, 0.456, 0.406, 0.485, 0.456, 0.406, 0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225, 0.229, 0.224, 0.225, 0.229, 0.224, 0.225)
)])
if image_loader == 'nine_channels' or image_loader == 'temporal_9':
transform_picked = transform_9
elif image_loader == 'rgb_hsv' or image_loader == 'rgb_lab' or image_loader == 'rgb_ycbcr':
transform_picked = transform_6
else:
transform_picked = transform_3
test_dataset = RandomDataset_test(val_list=test_list,
loader=image_loader,
transform=transform_picked
)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
features, labels = test(test_loader, model, batch_size, dist_type, image_loader, result_write_path) # shape: [449,1048]
print(features.shape)
print(labels.shape)
np.save(f'plots/features_3209785_1.npy', features)
np.save(f'plots/labels_3209785_1.npy', labels)
# tsne = TSNE(n_components=2, random_state=0)
# projections = tsne.fit_transform(features)
#
# sne_plot = pd.DataFrame()
# sne_plot["comp-1"] = projections[:, 0]
# sne_plot["comp-2"] = projections[:, 1]
#
# figure(figsize=(10, 10), dpi=100)
#
# ax = sns.scatterplot(x="comp-1", y="comp-2", hue=labels.tolist(),
# data=sne_plot, s=30, legend='full', style=labels.tolist(),
# palette=sns.color_palette("husl", 5))
#
# ax.set_title("T-SNE projection", fontsize=10)
# # plt.setp(ax.get_legend().get_texts(), fontsize='1500')
# ax.legend(markerscale=1)
# plt.xticks(fontsize=10)
# plt.yticks(fontsize=10)
# plt.legend(labels=['original', 'DF', 'F2F', 'FS', 'NT', 'DFDC_Real', 'DFDC_fake', 'Celeb_real', 'Youtube_real',
# 'Celeb-synthesis', 'Deeper_Fake', 'Deeper_Real', 'Faceshifter', 'DeepFakeDetection'])
# plt.tight_layout()
# plt.show()
# plt.savefig('plots/test.png')
# tikzplotlib.save('plots/test.tex')
def test(test_loader, model, bs, dist_type, image_loader, output_file='output-predictions.txt'):
batch_time = AverageMeter()
top1 = AverageMeter()
# switch to evaluate mode
model.eval()
features = pd.DataFrame()
labels = pd.Series(dtype=int)
with torch.no_grad():
for i, (input, target, image_name) in tqdm(enumerate(test_loader), total=len(test_loader)):
input_val = input.to(device)
# target_val = target.to(device)
# compute output
feature = model(input_val, targets=None, flag='tsne', dist_type=dist_type, loader=image_loader)
feature = feature.cpu()
feature = feature.view(1, -1)
df_f = pd.DataFrame(feature)
features = pd.concat([features, df_f], ignore_index=True)
df_l = pd.Series(target.view(-1))
labels = pd.concat([labels, df_l], ignore_index=True)
return features, labels
if __name__ == '__main__':
args = parser.parse_args()
main(args)