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test_resnet.py
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test_resnet.py
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import copy
import os
import pickle
import torch
import torch.nn as nn
import numpy as np
from matplotlib import pyplot as plt
from library import inputs
from Utils.checkpoints import save_context, Logger
from Utils import flags
from Utils import config
import Torture
from library import evaluation, eval_inception_score
FLAGS = flags.FLAGS
KEY_ARGUMENTS = config.load_config(FLAGS.config_file)
FLAGS.subfolder = "Test"
old_c = FLAGS.old_model_c
old_gan = FLAGS.old_model_gan
FLAGS.old_model_c = "loaded"
FLAGS.old_model_gan = "loaded"
text_logger, MODELS_FOLDER, SUMMARIES_FOLDER = save_context(__file__, KEY_ARGUMENTS)
torch.manual_seed(1234)
torch.cuda.manual_seed(1235)
np.random.seed(1236)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
FLAGS.device = device
# model = FLAGS.old_model_c
# dirname = os.path.dirname(model)
# basename = os.path.basename(model)
# config_path = os.path.join(dirname, "..", "source", "configs_dict.pkl")
# summary_path = os.path.join(dirname, "..", "summary")
# with open(config_path, "rb") as f:
# new_dict = pickle.load(f)
# FLAGS.set_dict(new_dict)
itr = inputs.get_data_iter(batch_size=FLAGS.bs_c, subset=1000)
itr_u = inputs.get_data_iter(batch_size=FLAGS.bs_c)
itr_test = inputs.get_data_iter(batch_size=FLAGS.bs_c, train=False)
# itr_t = inputs.get_data_iter_twice(subset=1000)
# itr_ut = inputs.get_data_iter_twice()
netG, optim_G = inputs.get_generator_optimizer()
netD, optim_D = inputs.get_discriminator_optimizer()
netC, optim_c = inputs.get_classifier_optimizer()
netC_T, _ = inputs.get_classifier_optimizer()
netG, netD, netC = netG.to(device), netD.to(device), netC.to(device)
netG = nn.DataParallel(netG)
netD = nn.DataParallel(netD)
netC = nn.DataParallel(netC)
netC_T = nn.DataParallel(netC_T)
checkpoint_io_gan = Torture.utils.checkpoint.CheckpointIO(checkpoint_dir=MODELS_FOLDER)
checkpoint_io_gan.register_modules(netG=netG, netD=netD)
checkpoint_io_c = Torture.utils.checkpoint.CheckpointIO(checkpoint_dir=MODELS_FOLDER)
checkpoint_io_c.register_modules(netC=netC, netC_T=netC_T)
checkpoint_io_gan.load_file(FLAGS.old_model_gan)
# checkpoint_io_c.load_file(old_c)
logger = Logger(log_dir=SUMMARIES_FOLDER)
# total, correct, _ = evaluation.test_classifier(netC_T)
# print(total, correct, (100 * (correct / total)))
# with torch.no_grad():
# netG.eval()
# data, label = itr.__next__()
# sample_z = torch.randn(FLAGS.bs_g, FLAGS.g_z_dim).to(device)
# tlabel = label[: FLAGS.bs_g // 10]
# tlabel = torch.cat([tlabel for _ in range(10)], 0)
# x_fake = netG(sample_z, tlabel)
# logger.add_imgs(x_fake, "imgtest", nrow=FLAGS.bs_g // 10)
# # # # Inception score
with torch.no_grad():
netG.eval()
img_list = []
for _ in range(100):
sample_z = torch.randn(FLAGS.bs_g, FLAGS.g_z_dim).to(device)
data, label = itr.__next__()
x_fake = netG(sample_z.to(device), label.to(device))
img_list.append(x_fake.data.cpu().numpy() * 0.5 + 0.5)
img_list = np.concatenate(img_list, axis=0)
img_list = np.transpose(img_list, [0, 2, 3, 1])
print(img_list.shape)
print(eval_inception_score.get_inception_score(img_list))
with torch.no_grad():
netG.eval()
confidence_sum = 0
total, correct = 0, 0
softmax = nn.Softmax(dim=1)
conf_cor_list, conf_wor_list = [], []
for _ in range(100):
sample_z = torch.randn(FLAGS.bs_g, FLAGS.g_z_dim).to(device)
data, label = itr_test.__next__()
label = label.to(device)
# x_fake = netG(sample_z.to(device), label.to(device))
x_fake = data.to(device)
outputs = netC_T(x_fake)
confidence, predicted = torch.max(softmax(outputs).data, 1)
confidence_sum += confidence.sum()
total += label.size(0)
correct += (predicted == label).sum().item()
confidence_cor = confidence[predicted == label]
confidence_wor = confidence[predicted != label]
conf_cor_list.append(confidence_cor.data.cpu().numpy())
conf_wor_list.append(confidence_wor.data.cpu().numpy())
print(total, correct, (100 * (correct / total)), confidence_sum / total)
conf_cor_list = np.concatenate(conf_cor_list)
conf_wor_list = np.concatenate(conf_wor_list)
print(conf_cor_list.shape)
bins = []
for i in range(10):
bins.append(0.9 + 0.01 * i)
figure = plt.figure()
plt.hist([conf_cor_list, conf_wor_list], bins, label=["c", "w"])
plt.legend()
figure.savefig("confidence.pdf")