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model.py
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model.py
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"""GANomaly
"""
# pylint: disable=C0301,E1101,W0622,C0103,R0902,R0915
##
from collections import OrderedDict
import os
import time
import numpy as np
from tqdm import tqdm
from torch.autograd import Variable
import torch.optim as optim
import torch.nn as nn
import torch.utils.data
import torchvision.utils as vutils
from lib.networks import NetG, NetD, weights_init
from lib.visualizer import Visualizer
from lib.loss import l2_loss
from lib.evaluate import evaluate
class BaseModel():
""" Base Model for ganomaly
"""
def __init__(self, opt, dataloader):
##
# Seed for deterministic behavior
self.seed(opt.manualseed)
# Initalize variables.
self.opt = opt
self.visualizer = Visualizer(opt)
self.dataloader = dataloader
self.trn_dir = os.path.join(self.opt.outf, self.opt.name, 'train')
self.tst_dir = os.path.join(self.opt.outf, self.opt.name, 'test')
self.device = torch.device("cuda:0" if self.opt.device != 'cpu' else "cpu")
##
def set_input(self, input:torch.Tensor):
""" Set input and ground truth
Args:
input (FloatTensor): Input data for batch i.
"""
with torch.no_grad():
self.input.resize_(input[0].size()).copy_(input[0])
self.gt.resize_(input[1].size()).copy_(input[1])
self.label.resize_(input[1].size())
# Copy the first batch as the fixed input.
if self.total_steps == self.opt.batchsize:
self.fixed_input.resize_(input[0].size()).copy_(input[0])
##
def seed(self, seed_value):
""" Seed
Arguments:
seed_value {int} -- [description]
"""
# Check if seed is default value
if seed_value == -1:
return
# Otherwise seed all functionality
import random
random.seed(seed_value)
torch.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value)
np.random.seed(seed_value)
torch.backends.cudnn.deterministic = True
##
def get_errors(self):
""" Get netD and netG errors.
Returns:
[OrderedDict]: Dictionary containing errors.
"""
errors = OrderedDict([
('err_d', self.err_d.item()),
('err_g', self.err_g.item()),
('err_g_adv', self.err_g_adv.item()),
('err_g_con', self.err_g_con.item()),
('err_g_enc', self.err_g_enc.item())])
return errors
##
def get_current_images(self):
""" Returns current images.
Returns:
[reals, fakes, fixed]
"""
reals = self.input.data
fakes = self.fake.data
fixed = self.netg(self.fixed_input)[0].data
return reals, fakes, fixed
##
def save_weights(self, epoch):
"""Save netG and netD weights for the current epoch.
Args:
epoch ([int]): Current epoch number.
"""
weight_dir = os.path.join(self.opt.outf, self.opt.name, 'train', 'weights')
if not os.path.exists(weight_dir): os.makedirs(weight_dir)
torch.save({'epoch': epoch + 1, 'state_dict': self.netg.state_dict()},
'%s/netG.pth' % (weight_dir))
torch.save({'epoch': epoch + 1, 'state_dict': self.netd.state_dict()},
'%s/netD.pth' % (weight_dir))
##
def train_one_epoch(self):
""" Train the model for one epoch.
"""
self.netg.train()
epoch_iter = 0
for data in tqdm(self.dataloader['train'], leave=False, total=len(self.dataloader['train'])):
self.total_steps += self.opt.batchsize
epoch_iter += self.opt.batchsize
self.set_input(data)
# self.optimize()
self.optimize_params()
if self.total_steps % self.opt.print_freq == 0:
errors = self.get_errors()
if self.opt.display:
counter_ratio = float(epoch_iter) / len(self.dataloader['train'].dataset)
self.visualizer.plot_current_errors(self.epoch, counter_ratio, errors)
if self.total_steps % self.opt.save_image_freq == 0:
reals, fakes, fixed = self.get_current_images()
self.visualizer.save_current_images(self.epoch, reals, fakes, fixed)
if self.opt.display:
self.visualizer.display_current_images(reals, fakes, fixed)
print(">> Training model %s. Epoch %d/%d" % (self.name, self.epoch+1, self.opt.niter))
# self.visualizer.print_current_errors(self.epoch, errors)
##
def train(self):
""" Train the model
"""
##
# TRAIN
self.total_steps = 0
best_auc = 0
# Train for niter epochs.
print(">> Training model %s." % self.name)
for self.epoch in range(self.opt.iter, self.opt.niter):
# Train for one epoch
self.train_one_epoch()
res = self.test()
if res['AUC'] > best_auc:
best_auc = res['AUC']
self.save_weights(self.epoch)
self.visualizer.print_current_performance(res, best_auc)
print(">> Training model %s.[Done]" % self.name)
##
def test(self):
""" Test GANomaly model.
Args:
dataloader ([type]): Dataloader for the test set
Raises:
IOError: Model weights not found.
"""
with torch.no_grad():
# Load the weights of netg and netd.
if self.opt.load_weights:
path = "./output/{}/{}/train/weights/netG.pth".format(self.name.lower(), self.opt.dataset)
pretrained_dict = torch.load(path)['state_dict']
try:
self.netg.load_state_dict(pretrained_dict)
except IOError:
raise IOError("netG weights not found")
print(' Loaded weights.')
self.opt.phase = 'test'
# Create big error tensor for the test set.
self.an_scores = torch.zeros(size=(len(self.dataloader['test'].dataset),), dtype=torch.float32, device=self.device)
self.gt_labels = torch.zeros(size=(len(self.dataloader['test'].dataset),), dtype=torch.long, device=self.device)
self.latent_i = torch.zeros(size=(len(self.dataloader['test'].dataset), self.opt.nz), dtype=torch.float32, device=self.device)
self.latent_o = torch.zeros(size=(len(self.dataloader['test'].dataset), self.opt.nz), dtype=torch.float32, device=self.device)
# print(" Testing model %s." % self.name)
self.times = []
self.total_steps = 0
epoch_iter = 0
for i, data in enumerate(self.dataloader['test'], 0):
self.total_steps += self.opt.batchsize
epoch_iter += self.opt.batchsize
time_i = time.time()
self.set_input(data)
self.fake, latent_i, latent_o = self.netg(self.input)
error = torch.mean(torch.pow((latent_i-latent_o), 2), dim=1)
time_o = time.time()
self.an_scores[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = error.reshape(error.size(0))
self.gt_labels[i*self.opt.batchsize : i*self.opt.batchsize+error.size(0)] = self.gt.reshape(error.size(0))
self.latent_i [i*self.opt.batchsize : i*self.opt.batchsize+error.size(0), :] = latent_i.reshape(error.size(0), self.opt.nz)
self.latent_o [i*self.opt.batchsize : i*self.opt.batchsize+error.size(0), :] = latent_o.reshape(error.size(0), self.opt.nz)
self.times.append(time_o - time_i)
# Save test images.
if self.opt.save_test_images:
dst = os.path.join(self.opt.outf, self.opt.name, 'test', 'images')
if not os.path.isdir(dst):
os.makedirs(dst)
real, fake, _ = self.get_current_images()
vutils.save_image(real, '%s/real_%03d.eps' % (dst, i+1), normalize=True)
vutils.save_image(fake, '%s/fake_%03d.eps' % (dst, i+1), normalize=True)
# Measure inference time.
self.times = np.array(self.times)
self.times = np.mean(self.times[:100] * 1000)
# Scale error vector between [0, 1]
self.an_scores = (self.an_scores - torch.min(self.an_scores)) / (torch.max(self.an_scores) - torch.min(self.an_scores))
# auc, eer = roc(self.gt_labels, self.an_scores)
auc = evaluate(self.gt_labels, self.an_scores, metric=self.opt.metric)
performance = OrderedDict([('Avg Run Time (ms/batch)', self.times), ('AUC', auc)])
if self.opt.display_id > 0 and self.opt.phase == 'test':
counter_ratio = float(epoch_iter) / len(self.dataloader['test'].dataset)
self.visualizer.plot_performance(self.epoch, counter_ratio, performance)
return performance
##
class Ganomaly(BaseModel):
"""GANomaly Class
"""
@property
def name(self): return 'Ganomaly'
def __init__(self, opt, dataloader):
super(Ganomaly, self).__init__(opt, dataloader)
# -- Misc attributes
self.epoch = 0
self.times = []
self.total_steps = 0
##
# Create and initialize networks.
self.netg = NetG(self.opt).to(self.device)
self.netd = NetD(self.opt).to(self.device)
self.netg.apply(weights_init)
self.netd.apply(weights_init)
##
if self.opt.resume != '':
print("\nLoading pre-trained networks.")
self.opt.iter = torch.load(os.path.join(self.opt.resume, 'netG.pth'))['epoch']
self.netg.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netG.pth'))['state_dict'])
self.netd.load_state_dict(torch.load(os.path.join(self.opt.resume, 'netD.pth'))['state_dict'])
print("\tDone.\n")
self.l_adv = l2_loss
self.l_con = nn.L1Loss()
self.l_enc = l2_loss
self.l_bce = nn.BCELoss()
##
# Initialize input tensors.
self.input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize, self.opt.isize), dtype=torch.float32, device=self.device)
self.label = torch.empty(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.gt = torch.empty(size=(opt.batchsize,), dtype=torch.long, device=self.device)
self.fixed_input = torch.empty(size=(self.opt.batchsize, 3, self.opt.isize, self.opt.isize), dtype=torch.float32, device=self.device)
self.real_label = torch.ones (size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
self.fake_label = torch.zeros(size=(self.opt.batchsize,), dtype=torch.float32, device=self.device)
##
# Setup optimizer
if self.opt.isTrain:
self.netg.train()
self.netd.train()
self.optimizer_d = optim.Adam(self.netd.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
self.optimizer_g = optim.Adam(self.netg.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, 0.999))
##
def forward_g(self):
""" Forward propagate through netG
"""
self.fake, self.latent_i, self.latent_o = self.netg(self.input)
##
def forward_d(self):
""" Forward propagate through netD
"""
self.pred_real, self.feat_real = self.netd(self.input)
self.pred_fake, self.feat_fake = self.netd(self.fake.detach())
##
def backward_g(self):
""" Backpropagate through netG
"""
self.err_g_adv = self.l_adv(self.netd(self.input)[1], self.netd(self.fake)[1])
self.err_g_con = self.l_con(self.fake, self.input)
self.err_g_enc = self.l_enc(self.latent_o, self.latent_i)
self.err_g = self.err_g_adv * self.opt.w_adv + \
self.err_g_con * self.opt.w_con + \
self.err_g_enc * self.opt.w_enc
self.err_g.backward(retain_graph=True)
##
def backward_d(self):
""" Backpropagate through netD
"""
# Real - Fake Loss
self.err_d_real = self.l_bce(self.pred_real, self.real_label)
self.err_d_fake = self.l_bce(self.pred_fake, self.fake_label)
# NetD Loss & Backward-Pass
self.err_d = (self.err_d_real + self.err_d_fake) * 0.5
self.err_d.backward()
##
def reinit_d(self):
""" Re-initialize the weights of netD
"""
self.netd.apply(weights_init)
print(' Reloading net d')
def optimize_params(self):
""" Forwardpass, Loss Computation and Backwardpass.
"""
# Forward-pass
self.forward_g()
self.forward_d()
# Backward-pass
# netg
self.optimizer_g.zero_grad()
self.backward_g()
self.optimizer_g.step()
# netd
self.optimizer_d.zero_grad()
self.backward_d()
self.optimizer_d.step()
if self.err_d.item() < 1e-5: self.reinit_d()