map_location = lambda storage, loc: storage
optimizer_generator = Adam(netG.parameters(), 2e-4, betas=(0.5, 0.999))
optimizer_discriminator = Adam(netD.parameters(), 2e-4, betas=(0.5, 0.999))
criterion = BCELoss()
true_labels = Variable(t.ones(CONFIG["BATCH_SIZE"]))
fake_labels = Variable(t.zeros(CONFIG["BATCH_SIZE"]))
fix_noises = Variable(t.randn(CONFIG["BATCH_SIZE"], CONFIG["NOISE_DIM"], 1, 1))
noises = Variable(t.randn(CONFIG["BATCH_SIZE"], CONFIG["NOISE_DIM"], 1, 1))
if CONFIG["GPU_NUMS"] > 0:
netD.cuda()
netG.cuda()
criterion.cuda()
true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda()
fix_noises, noises = fix_noises.cuda(), noises.cuda()
proBar = ProgressBar(CONFIG["EPOCHS"], len(dataLoader),
"D Loss:%.3f;G Loss:%.3f")
for epoch in range(1, CONFIG["EPOCHS"] + 1):
if epoch % 30 == 0:
optimizer_discriminator.param_groups[0]['lr'] /= 10
optimizer_generator.param_groups[0]['lr'] /= 10
for ii, (img, _) in enumerate(dataLoader):
real_img = Variable(img.cuda() if CONFIG["GPU_NUMS"] > 1 else img)
if ii % 1 == 0:
# 训练判别器
NetG = Generator()
NetD = Discriminator()
BCE_LOSS = BCELoss()
G_optimizer = Adam(NetG.parameters(),
lr=CONFIG["LEARNING_RATE"],
betas=(0.5, 0.999))
D_optimizer = Adam(NetD.parameters(),
lr=CONFIG["LEARNING_RATE"],
betas=(0.5, 0.999))
if CONFIG["GPU_NUMS"] > 0:
NetG = NetG.cuda()
NetD = NetD.cuda()
BCE_LOSS = BCE_LOSS.cuda()
transform = Compose([
Resize((CONFIG["IMAGE_SIZE"], CONFIG["IMAGE_SIZE"])),
ToTensor(),
Normalize(mean=[0.5] * 3, std=[0.5] * 3)
])
train_loader = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(
root=CONFIG["DATA_PATH"], transform=transform),
batch_size=CONFIG["BATCH_SIZE"],
shuffle=True)
def one_hot(target):
y = torch.zeros(target.size()[0], 10)
class trainDeepPix(object):
r'''
Object to train DeepPix Network
@params::
`model`:: Initialized DeepPix Model
`lr`:: Learning Rate for Adam Optimizer
`weight_decay` :: L2 Regularization parameter
'''
def __init__(self, model, lr, weight_decay):
super(trainDeepPix, self).__init__()
self.model = model
self.lossC = BCELoss()
self.lossS = BCELoss()
if CUDA:
self.model = self.model.cuda()
self.lossC = self.lossC.cuda()
self.lossS = self.lossS.cuda()
self.optimizer = Adam(self.model.parameters(),
lr=lr,
weight_decay=weight_decay)
@staticmethod
def _convertYLabel(y):
returnY = torch.ones((y.shape[0], 196)).type(torch.FloatTensor)
for i in range(y.shape[0]):
returnY[i] = returnY[i] * y[i]
return returnY.cuda() if CUDA else returnY
@staticmethod
def _calcAccuracy(yPred, yTrue):
yPred = yPred.view(-1)
yTrue = yTrue.view(-1)
if CUDA:
yPred = (yPred > 0.5).type(torch.cuda.LongTensor)
else:
yPred = (yPred > 0.5).type(torch.LongTensor)
return torch.sum((yTrue == yPred).type(torch.LongTensor),
dim=0) / float(yTrue.shape[0])
def train(self,
ImgList,
LabelList,
mtcnn,
batch_size=32,
epochs=50,
detectFace=True):
r'''
Utility to train DeepPix Model,
@params::
`ImgList`:: List of Image Paths
`LabelList`:: List of Labels correspoding to images
[should be 0 or 1]
'''
ImgArray = []
widgets = [
f"Cropping faces: " if detectFace else f"Preprocessing Data: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=len(ImgList),
widgets=widgets).start()
__ctr = 0
for img in ImgList:
res = cropFace(mtcnn, img, detectFace=detectFace)
if res is not None:
ImgArray.append(res.unsqueeze(0))
else:
del LabelList[__ctr]
pbar.update(__ctr)
__ctr += 1
pbar.finish()
for epoch in range(epochs):
widgets = [
f"Epoch {epoch+1}/{epochs} ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=np.arange(
0, len(ImgArray), batch_size).shape[0],
widgets=widgets).start()
__ctr = 0
batch_loss = []
batch_accClass = []
batch_accSeg = []
for item in np.arange(0, len(ImgArray), batch_size):
trainX = torch.cat(ImgArray[item:item + batch_size], dim=0)
trainY = torch.tensor(LabelList[item:item + batch_size]).type(
torch.FloatTensor)
if CUDA:
trainX = trainX.cuda()
trainY = trainY.cuda()
self.model.train()
self.optimizer.zero_grad()
classPred, segPred = self.model(trainX)
segPred = segPred.view(trainX.shape[0], -1)
train_loss = self.lossC(
classPred.squeeze(), trainY) + self.lossC(
segPred.squeeze(), self._convertYLabel(trainY))
train_loss.backward()
self.optimizer.step()
classAcc = self._calcAccuracy(classPred, trainY)
SegAcc = self._calcAccuracy(segPred,
self._convertYLabel(trainY))
batch_loss.append(train_loss.item())
batch_accClass.append(classAcc.item())
batch_accSeg.append(SegAcc.item())
pbar.update(__ctr)
__ctr += 1
pbar.finish()
print(
f'Summary -> train_loss:: {mean(batch_loss)}, class_acc:: {mean(batch_accClass)}, seg_acc:: {mean(batch_accSeg)}'
)
def predict(self,
ImgList,
mtcnn,
batch_size=16,
thresh=0.5,
testLabel=None,
detectFace=True):
r'''
Utility to predict `spoof/bonafide` viz `0/1` given list
of test image Path
@params::
`ImgList`:: Test Image Path List
`mtcnn`:: Face Cropping Module
`batch size`:: Batch Size for testing
`thresh`:: Threshold to classify an image as spoof or bonafide
'''
self.model.eval()
ImgArray = []
widgets = [
f"Cropping faces: " if detectFace else f"Preprocessing Data: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=len(ImgList),
widgets=widgets).start()
__ctr = 0
for img in ImgList:
res = cropFace(mtcnn, img, detectFace=detectFace)
if res is not None:
ImgArray.append(res.unsqueeze(0))
else:
if testLabel is not None:
del testLabel[__ctr]
pbar.update(__ctr)
__ctr += 1
# ImgArray = torch.cat(ImgArray, dim=0)
pbar.finish()
returnY = np.zeros((len(ImgArray)), dtype="uint8")
widgets = [
f"Predicting ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=np.arange(
0, len(ImgArray), batch_size).shape[0],
widgets=widgets).start()
__ctr = 0
for item in np.arange(0, len(ImgArray), batch_size):
_, segPred = self.model(
torch.cat(ImgArray[item:item + batch_size], dim=0).cuda() if
CUDA else torch.cat(ImgArray[item:item + batch_size], dim=0))
segPred = segPred.view(segPred.shape[0], -1)
segPred = torch.mean(segPred, dim=1)
segPred = (segPred > thresh).type(
torch.LongTensor).cpu().detach().numpy()
returnY[item:item + batch_size] = segPred
pbar.update(__ctr)
__ctr += 1
pbar.finish()
return returnY
def saveModel(self, path):
r'''
Saves current model state to the path given
'''
torch.save(self.model.state_dict(), path)
print("[INFO] Model Saved")
def loadModel(self, path):
r'''
Loads model state from the path given
and maps to available/given device
'''
self.model.load_state_dict(
torch.load(path,
map_location=DEVICE if CUDA else torch.device("cpu")))
print("[INFO] Model Loaded..")
return output
NetG = Generator()
NetD = Discriminator()
optimizerD = Adam(NetD.parameters(),lr=CONFIG["LEARNING_RATE"],betas=(CONFIG["BETA1"],0.999))
optimizerG = Adam(NetG.parameters(),lr=CONFIG["LEARNING_RATE"],betas=(CONFIG["BETA1"],0.999))
criterion = BCELoss()
fix_noise = Variable(torch.FloatTensor(CONFIG["BATCH_SIZE"],CONFIG["NOISE_DIM"],1,1).normal_(0,1))
if CONFIG["GPU_NUMS"] > 0:
NetD = NetD.cuda()
NetG = NetG.cuda()
fix_noise = fix_noise.cuda()
criterion.cuda() # it's a good habit
transform=Compose([
ToTensor()
])
dataset = MNISTDataSetForPytorch(root=CONFIG["DATA_PATH"],train=True, transform=transform)
dataloader=torch.utils.data.DataLoader(dataset,CONFIG["BATCH_SIZE"],shuffle = True)
bar = ProgressBar(CONFIG["EPOCHS"], len(dataloader), "D Loss:%.3f;G Loss:%.3f")
for epoch in range(1, CONFIG["EPOCHS"] + 1):
if epoch % 30 == 0:
optimizerD.param_groups[0]['lr'] /= 10
optimizerG.param_groups[0]['lr'] /= 10
for ii, data in enumerate(dataloader,0):
'''
Net_G = Generator()
Net_D = Discriminator()
Net_G.normal_weight_init(mean=0.0, std=0.02)
Net_D.normal_weight_init(mean=0.0, std=0.02)
Net_G = DataParallel(Net_G)
Net_D = DataParallel(Net_D)
BCE_loss = BCELoss()
L1_loss = L1Loss()
if GPU_NUMS > 1:
Net_D.cuda()
Net_G.cuda()
BCE_loss = BCE_loss.cuda()
L1_loss = L1_loss.cuda()
G_optimizer = Adam(Net_G.parameters(), lr=0.0002, betas=(0.5, 0.999))
D_optimizer = Adam(Net_D.parameters(), lr=0.0002, betas=(0.5, 0.999))
'''
读入数据
'''
if not os.path.exists("output"):
os.mkdir("output")
train_set = DataSetFromFolderForPicTransfer(
os.path.join("/data/facades_fixed", "train"))
test_set = DataSetFromFolderForPicTransfer(
os.path.join("/data/facades_fixed", "test"))
train_data_loader = DataLoader(dataset=train_set,
num_workers=2,
class YOLOLayer(Module):
"""Detection layer"""
def __init__(self, anchors, num_classes, img_dim):
super(YOLOLayer, self).__init__()
self.anchors = anchors
self.num_anchors = len(anchors)
self.num_classes = num_classes
self.bbox_attrs = 5 + num_classes
self.img_dim = img_dim
self.ignore_thres = 0.5
self.lambda_coord = 1
self.mse_loss = MSELoss()
self.bce_loss = BCELoss()
def forward(self, x, targets=None):
bs = x.size(0)
g_dim = x.size(2)
stride = self.img_dim / g_dim
# Tensors for cuda support
FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
prediction = x.view(bs, self.num_anchors, self.bbox_attrs, g_dim, g_dim).permute(0, 1, 3, 4, 2).contiguous()
# Get outputs
x = torch.sigmoid(prediction[..., 0]) # Center x
y = torch.sigmoid(prediction[..., 1]) # Center y
w = prediction[..., 2] # Width
h = prediction[..., 3] # Height
conf = torch.sigmoid(prediction[..., 4]) # Conf
pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred.
# Calculate offsets for each grid
grid_x = torch.linspace(0, g_dim-1, g_dim).repeat(g_dim,1).repeat(bs*self.num_anchors, 1, 1).view(x.shape).type(FloatTensor)
grid_y = torch.linspace(0, g_dim-1, g_dim).repeat(g_dim,1).t().repeat(bs*self.num_anchors, 1, 1).view(y.shape).type(FloatTensor)
scaled_anchors = [(a_w / stride, a_h / stride) for a_w, a_h in self.anchors]
anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, g_dim*g_dim).view(w.shape)
anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, g_dim*g_dim).view(h.shape)
# Add offset and scale with anchors
pred_boxes = FloatTensor(prediction[..., :4].shape)
pred_boxes[..., 0] = x.data + grid_x
pred_boxes[..., 1] = y.data + grid_y
pred_boxes[..., 2] = torch.exp(w.data) * anchor_w
pred_boxes[..., 3] = torch.exp(h.data) * anchor_h
# Training
if targets is not None:
if x.is_cuda:
self.mse_loss = self.mse_loss.cuda()
self.bce_loss = self.bce_loss.cuda()
nGT, nCorrect, mask, conf_mask, tx, ty, tw, th, tconf, tcls = build_targets(pred_boxes.cpu().data,
targets.cpu().data,
scaled_anchors,
self.num_anchors,
self.num_classes,
g_dim,
self.ignore_thres,
self.img_dim)
nProposals = int((conf > 0.25).sum().item())
recall = float(nCorrect / nGT) if nGT else 1
# Handle masks
mask = Variable(mask.type(FloatTensor))
cls_mask = Variable(mask.unsqueeze(-1).repeat(1, 1, 1, 1, self.num_classes).type(FloatTensor))
conf_mask = Variable(conf_mask.type(FloatTensor))
# Handle target variables
tx = Variable(tx.type(FloatTensor), requires_grad=False)
ty = Variable(ty.type(FloatTensor), requires_grad=False)
tw = Variable(tw.type(FloatTensor), requires_grad=False)
th = Variable(th.type(FloatTensor), requires_grad=False)
tconf = Variable(tconf.type(FloatTensor), requires_grad=False)
tcls = Variable(tcls.type(FloatTensor), requires_grad=False)
# Mask outputs to ignore non-existing objects
loss_x = self.lambda_coord * self.bce_loss(x * mask, tx * mask)
loss_y = self.lambda_coord * self.bce_loss(y * mask, ty * mask)
loss_w = self.lambda_coord * self.mse_loss(w * mask, tw * mask) / 2
loss_h = self.lambda_coord * self.mse_loss(h * mask, th * mask) / 2
loss_conf = self.bce_loss(conf * conf_mask, tconf * conf_mask)
loss_cls = self.bce_loss(pred_cls * cls_mask, tcls * cls_mask)
loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls
return loss, loss_x.item(), loss_y.item(), loss_w.item(), loss_h.item(), loss_conf.item(), loss_cls.item(), recall
else:
# If not in training phase return predictions
output = torch.cat((pred_boxes.view(bs, -1, 4) * stride, conf.view(bs, -1, 1), pred_cls.view(bs, -1, self.num_classes)), -1)
return output.data
def main_train():
discriminator = PatchGANDiscriminator()
generator = Generator()
learning_rate = .005
updateable_params = filter(lambda p: p.requires_grad,
discriminator.parameters())
discriminator_optimizer = optim.Adam(updateable_params, lr=learning_rate)
updateable_params = filter(lambda p: p.requires_grad,
generator.parameters())
generator_optimizer = optim.Adam(updateable_params, lr=learning_rate)
criterion = BCELoss().cuda()
if use_gpu:
discriminator = discriminator.cuda()
generator = generator.cuda()
criterion = criterion.cuda()
train_loader, gan_loader = load_loaders()
for i in range(1, 10):
print(f"GAN EPOCH: {i}")
# Train discriminator on color images
train(discriminator,
None,
discriminator_optimizer,
criterion,
train_loader,
use_gpu,
epochs=1,
total_iter=0,
epoch_start=0)
# Train discriminator on fakes
train(discriminator,
generator,
generator_optimizer,
criterion,
gan_loader,
use_gpu,
epochs=10,
total_iter=0,
epoch_start=0)
real_score, fake_score = score_it(discriminator, generator)
print("REAL SCORE:", real_score)
print("FAKE SCORE:", fake_score)
print("These should all be 1")
debug_dat_model(discriminator, None, train_loader)
print("These should all be 0")
debug_dat_model(discriminator, generator, gan_loader)
now_time = time.strftime("%H-%M-%S")
os.makedirs("./pickles", exist_ok=True)
disc_file = f"./pickles/discriminator-{now_time}.p"
gen_file = f"./pickles/generator-{now_time}.p"
print("Saving to: ")
print(disc_file)
print(gen_file)
torch.save(discriminator, disc_file)
torch.save(generator, gen_file)
