from torchvision import datasets
from torchvision import transforms
import torch
import torch.nn as nn
from torch.utils.data import DataLoader as dt
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.optim.lr_scheduler import LambdaLR
#define
lr=0.001
batchsize=16
warmup=5
max_epoch=50
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
root1=r'D:/学习相关/大三下/cv/fd/mouth/train'
root2=r'D:/学习相关/大三下/cv/fd/mouth/test'
transform=transforms.Compose([transforms.Grayscale(),transforms.Resize(40),transforms.RandomCrop(30),transforms.ToTensor(),transforms.Normalize([0.5],[0.5])])
trainset=datasets.ImageFolder(root1,transform=transform)
testset=datasets.ImageFolder(root2,transform=transform)
train_loader=dt(dataset=trainset,batch_size=batchsize,shuffle=True)
test_loader=dt(dataset=testset,batch_size=batchsize,shuffle=False)
class My_ResidualBlock1(nn.Module):
def __init__(self,channel):
super().__init__()
self.c1=nn.Conv2d(channel,channel,kernel_size=3,padding=1)
self.c2=nn.Conv2d(channel, channel,kernel_size=3, padding=1)
def forward(self,x):
x=nn.functional.relu(self.c1(x))
y=self.c2(x)
return nn.functional.relu(x+y)
class My_CNN_withRB_mouth(nn.Module):
def load_data(opt):
""" Load Data
Args:
opt ([type]): Argument Parser
Raises:
IOError: Cannot Load Dataset
Returns:
[type]: dataloader
"""
##
# LOAD DATA SET
if opt.dataroot == '':
opt.dataroot = './data/{}'.format(opt.dataset)
print(opt.dataset)
## CIFAR
if opt.dataset in ['cifar10']:
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_ds = CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
valid_ds = CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
train_ds, valid_ds = get_cifar_anomaly_dataset(
train_ds, valid_ds, train_ds.class_to_idx[opt.abnormal_class])
## MNIST
elif opt.dataset in ['mnist']:
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_ds = MNIST(root='./data',
train=True,
download=True,
transform=transform)
valid_ds = MNIST(root='./data',
train=False,
download=True,
transform=transform)
train_ds, valid_ds = get_mnist_anomaly_dataset(train_ds, valid_ds,
int(opt.abnormal_class))
# FOLDER
elif opt.dataset in ['csot']:
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.CenterCrop(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_ds = ImageFolder(os.path.join(opt.dataroot, 'train'), transform)
valid_ds = ImageFolder(os.path.join(opt.dataroot, 'test'), transform)
elif opt.dataset in ['yunfangbu']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_ds = ImageFolder(os.path.join(opt.dataroot, 'train'), transform)
valid_ds = ImageFolder(os.path.join(opt.dataroot, 'test'), transform)
elif opt.dataset in ['anomaly1']:
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize(opt.isize),
transforms.CenterCrop(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
])
train_ds = ImageFolder(os.path.join(opt.dataroot, 'train'), transform)
valid_ds = ImageFolder(os.path.join(opt.dataroot, 'test'), transform)
elif opt.dataset in ['anomaly10']:
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
])
train_ds = ImageFolder(os.path.join(opt.dataroot, 'train'), transform)
valid_ds = ImageFolder(os.path.join(opt.dataroot, 'test'), transform)
else:
transform = transforms.Compose([
transforms.Resize(opt.isize),
transforms.CenterCrop(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if opt.nc == 1:
print('Input is grayscale image')
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.Resize(opt.isize),
transforms.CenterCrop(opt.isize),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
])
train_ds = ImageFolder(os.path.join(opt.dataroot, 'train'), transform)
valid_ds = ImageFolder(os.path.join(opt.dataroot, 'test'), transform)
## DATALOADER
train_dl = DataLoader(dataset=train_ds,
batch_size=opt.batchsize,
shuffle=True,
drop_last=True)
valid_dl = DataLoader(dataset=valid_ds,
batch_size=opt.batchsize,
shuffle=False,
drop_last=False)
return Data(train_dl, valid_dl)
def get_transform(opt,
params=None,
grayscale=False,
method=Image.BICUBIC,
convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if 'resize' in opt.preprocess:
osize = [opt.load_size, opt.load_size]
transform_list.append(transforms.Resize(osize, method))
elif 'scale_width' in opt.preprocess:
transform_list.append(
transforms.Lambda(
lambda img: __scale_width(img, opt.load_size, method)))
if 'rotate' in opt.preprocess:
transform_list.append(transforms.RandomRotation(opt.rot_degree))
if 'crop' in opt.preprocess:
if 'center_crop' in opt.preprocess:
transform_list.append(transforms.CenterCrop(opt.crop_size))
elif params is None:
transform_list.append(transforms.RandomCrop(opt.crop_size))
else:
transform_list.append(
transforms.Lambda(lambda img: __crop(img, params['crop_pos'],
opt.crop_size)))
if opt.preprocess == 'none':
transform_list.append(
transforms.Lambda(
lambda img: __make_power_2(img, base=4, method=method)))
if not opt.no_flip:
if params is None:
transform_list.append(transforms.RandomHorizontalFlip())
elif params['flip']:
transform_list.append(
transforms.Lambda(lambda img: __flip(img, params['flip'])))
if 'color_jitter' in opt.preprocess:
# transform_list.append(transforms.ColorJitter( #TODO: parametarize
# brightness=0.25*5, #args.jitter_brightness,
# contrast=0.4*5, #args.jitter_contrast,
# saturation=0.2*5, #args.jitter_saturation,
# hue=0.05*5 #args.jitter_hue
# ))
transform_list.append(
transforms.ColorJitter( #TODO: parametarize
brightness=0.25, #args.jitter_brightness,
contrast=0.4, #args.jitter_contrast,
saturation=0.2, #args.jitter_saturation,
hue=0.05 #args.jitter_hue
))
transform_list.append(transforms.ToTensor())
transform_tensor = [transforms.ToPILImage()] + transform_list
if convert:
transform_list += [transforms.Normalize((0.5, ), (0.5, ))]
#cut or erase after normalization
if 'cutout' in opt.preprocess:
transform_list.append(Cutout(n_holes=1, length=8)) #TODO: parametarize
transform_tensor.append(Cutout(n_holes=1,
length=8)) #TODO: parametarize
if 'erasing' in opt.preprocess:
transform_list.append(
RandomErasing(p=0.5, sl=0.02, sh=0.3, r1=0.3,
r2=1 / 0.3)) #TODO: parametarize
transform_tensor.append(
RandomErasing(p=0.5, sl=0.02, sh=0.3, r1=0.3,
r2=1 / 0.3)) #TODO: parametarize
try:
if opt.phase == "train" and "multi_transforms" in opt.dataset_mode:
print("return multi transforms, phase: ", opt.phase)
non_transform = []
if grayscale:
non_transform.append(transforms.Grayscale(1))
non_transform.extend([
transforms.Lambda(
lambda img: __make_power_2(img, base=4, method=method)),
transforms.Resize(
opt.crop_size, method
), #TODO: adjust size to crop size because it's same if crop is specified
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
])
return transforms.Compose(non_transform), transforms.Compose(
transform_list), transforms.Compose(transform_tensor)
except:
pass
return transforms.Compose(transform_list)
part = np.asarray(np_probs[i * (N // splits): (i+1) * (N // splits)], dtype=np.float32)
kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
kl = np.mean(np.sum(kl, 1))
scores.append(np.exp(kl))
return np.mean(scores), np.std(scores)
return np.mean(split_scores), np.std(split_scores)
# Make dataloader for the to-be-classified images
# Get the names of the images
filenames = os.listdir(test_path)
# Transform the images
transform = tf.Compose([
tf.Grayscale(),
tf.Resize([224, 224]),
tf.ToTensor()
])
test_data = torchvision.datasets.ImageFolder(test_path, transform=transform)
test_loader = DataLoader(dataset=test_data, num_workers=threads, batch_size=1, shuffle=False)
# Setup the loader index, hardcoded since we only use the pretrained model
if task == 'initials':
model = '/model_snapshots/initials.pt'
class_dictionary = ['M', 'Q', 'C', 'N', 'P', 'O', 'D', 'T', 'H', 'S', 'V', 'B', 'A', 'W', 'I', 'E', 'L', 'F', 'R', 'Z', 'G', 'Y', 'X', 'K', '']
class_count = 25
loader_index = 1
elif task == 'countries':
class mnist_three_component(nn.Module):
NOISE_RGB_AMOUNT = 15 # 15
BLUR = 2 # 2
DIM = 64
F_DIM = 64 # 64
_transformations = Group([
tf.Grayscale(num_output_channels=3),
tf.Resize((DIM, DIM)),
tf.ToTensor(),
# mytransforms.NormalizeGray
])
_train = Group([
tf.Grayscale(num_output_channels=1),
# mytransforms.Add1DNoise(config.BORDER_COLOR_GRAY, NOISE_RGB_AMOUNT),
# mytransforms.Gaussian_Blur(BLUR),
])
_test = []
transformations = {
'train': tf.Compose(_train + _transformations),
'test': tf.Compose(_test + _transformations)
}
def __init__(self):
F_DIM = self.F_DIM
super().__init__()
self.feats = nn.Sequential(
nn.Conv2d(3, 32, 3, 1, 1),
nn.MaxPool2d(2, 2),
nn.ReLU(True),
nn.BatchNorm2d(32),
nn.Conv2d(32, 64, 3, 1, 1),
nn.ReLU(True),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 5, 1, 1),
nn.MaxPool2d(2, 2),
nn.ReLU(True),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, 5, 1, 1),
nn.ReLU(True),
nn.BatchNorm2d(64),
)
self.classifier = nn.Sequential(nn.Linear(32448, F_DIM, bias=False),
nn.BatchNorm1d(F_DIM),
nn.ReLU(inplace=True), nn.Dropout(0.5),
nn.Linear(F_DIM, F_DIM, bias=False),
nn.BatchNorm1d(F_DIM),
nn.ReLU(inplace=True), nn.Dropout(0.5),
nn.Linear(F_DIM, 2))
def forward(self, components):
n, z, e = components
nout, zout, eout = self.feats(n), self.feats(z), self.feats(e)
out = torch.cat((nout, zout, eout), 1)
#print(out)
out = out.view(out.size(0), -1)
out = self.classifier(out)
return out
)
parser.add_argument(
"--save-file",
default="data\prepared_data.pkl",
help="File path with data to check",
)
parser.add_argument(
"--show-example",
default=False,
help="Either to show an example of the transformation or not",
)
args = parser.parse_args()
data = read_data(args.file)
states, actions = map(np.array, zip(*data))
dataset = CarV0GymDataset(args.file, classification=True)
if args.show_example:
selected_state = states[int(len(states) / 2)]
selected_state = transforms.ToPILImage()(selected_state)
selected_state.show(title="Original Image")
selected_state = transforms.Grayscale(1)(selected_state)
selected_state.show(title="Grayscale")
selected_state = transforms.Pad((12, 12, 12, 0))(selected_state)
selected_state.show(title="Padding")
selected_state = transforms.CenterCrop(84)(selected_state)
selected_state.show(title="CenterCrop")
selected_state = transforms.ToTensor()(selected_state)
selected_state = transforms.Normalize((0,), (1,))(selected_state)
selected_state = transforms.ToPILImage()(selected_state)
selected_state.show(title="Final Normalized")
def main(args):
### config
global noise_multiplier
dataset = args.dataset
num_discriminators = args.num_discriminators
noise_multiplier = args.noise_multiplier
z_dim = args.z_dim
model_dim = args.model_dim
batchsize = args.batchsize
L_gp = args.L_gp
L_epsilon = args.L_epsilon
critic_iters = args.critic_iters
latent_type = args.latent_type
load_dir = args.load_dir
save_dir = args.save_dir
if_dp = (args.dp > 0.)
gen_arch = args.gen_arch
num_gpus = args.num_gpus
### CUDA
use_cuda = torch.cuda.is_available()
devices = [torch.device("cuda:%d" % i if use_cuda else "cpu") for i in range(num_gpus)]
device0 = devices[0]
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
### Random seed
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
### Fix noise for visualization
if latent_type == 'normal':
fix_noise = torch.randn(10, z_dim)
elif latent_type == 'bernoulli':
p = 0.5
bernoulli = torch.distributions.Bernoulli(torch.tensor([p]))
fix_noise = bernoulli.sample((10, z_dim)).view(10, z_dim)
else:
raise NotImplementedError
### Set up models
print('gen_arch:' + gen_arch)
netG = GeneratorDCGAN(z_dim=z_dim, model_dim=model_dim, num_classes=10)
netGS = copy.deepcopy(netG)
netD_list = []
for i in range(num_discriminators):
netD = DiscriminatorDCGAN()
netD_list.append(netD)
### Load pre-trained discriminators
print("load pre-training...")
if load_dir is not None:
for netD_id in range(num_discriminators):
print('Load NetD ', str(netD_id))
network_path = os.path.join(load_dir, 'netD_%d' % netD_id, 'netD.pth')
netD = netD_list[netD_id]
netD.load_state_dict(torch.load(network_path))
netG = netG.to(device0)
for netD_id, netD in enumerate(netD_list):
device = devices[get_device_id(netD_id, num_discriminators, num_gpus)]
netD.to(device)
### Set up optimizers
optimizerD_list = []
for i in range(num_discriminators):
netD = netD_list[i]
optimizerD = optim.Adam(netD.parameters(), lr=1e-4, betas=(0.5, 0.99))
optimizerD_list.append(optimizerD)
optimizerG = optim.Adam(netG.parameters(), lr=1e-4, betas=(0.5, 0.99))
### Data loaders
if dataset == 'mnist' or dataset == 'fashionmnist':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.ToTensor(),
#transforms.Grayscale(),
])
elif dataset == 'cifar_100' or dataset == 'cifar_10':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.Grayscale(),
transforms.ToTensor(),
])
if dataset == 'mnist':
dataloader = datasets.MNIST
trainset = dataloader(root=os.path.join(DATA_ROOT, 'MNIST'), train=True, download=True,
transform=transform_train)
IMG_DIM = 784
NUM_CLASSES = 10
elif dataset == 'fashionmnist':
dataloader = datasets.FashionMNIST
trainset = dataloader(root=os.path.join(DATA_ROOT, 'FashionMNIST'), train=True, download=True,
transform=transform_train)
elif dataset == 'cifar_100':
dataloader = datasets.CIFAR100
trainset = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR100'), train=True, download=True,
transform=transform_train)
IMG_DIM = 3072
NUM_CLASSES = 100
elif dataset == 'cifar_10':
IMG_DIM = 784
NUM_CLASSES = 10
dataloader = datasets.CIFAR10
trainset = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR10'), train=True, download=True,
transform=transform_train)
else:
raise NotImplementedError
print('creat indices file')
indices_full = np.arange(len(trainset))
np.random.shuffle(indices_full)
#indices_full.dump(os.path.join(save_dir, 'indices.npy'))
trainset_size = int(len(trainset) / num_discriminators)
print('Size of the dataset: ', trainset_size)
input_pipelines = []
for i in range(num_discriminators):
start = i * trainset_size
end = (i + 1) * trainset_size
indices = indices_full[start:end]
trainloader = DataLoader(trainset, batch_size=args.batchsize, drop_last=False,
num_workers=args.num_workers, sampler=SubsetRandomSampler(indices))
#input_data = inf_train_gen(trainloader)
input_pipelines.append(trainloader)
### not add noise by hook which is middle between G/D
'''
if if_dp:
### Register hook
global dynamic_hook_function
for netD in netD_list:
netD.conv1.register_backward_hook(master_hook_adder)
'''
prg_bar = tqdm(range(args.iterations+1))
for iters in prg_bar:
#########################
### Update D network
#########################
netD_id = np.random.randint(num_discriminators, size=1)[0]
device = devices[get_device_id(netD_id, num_discriminators, num_gpus)]
netD = netD_list[netD_id]
optimizerD = optimizerD_list[netD_id]
input_data = input_pipelines[netD_id]
for p in netD.parameters():
p.requires_grad = True
### Register hook for add noise to D
if if_dp:
for parameter in netD.parameters():
if parameter.requires_grad:
parameter.register_hook(
lambda grad: grad + (1 / batchsize) * noise_multiplier * torch.randn(grad.shape) * SENSITIVITY
#lambda grad: grad
)
for iter_d in range(critic_iters):
real_data, real_y = next(iter(input_data))
real_data = real_data.view(-1, IMG_DIM)
real_data = real_data.to(device)
real_y = real_y.to(device)
real_data_v = autograd.Variable(real_data)
### train with real
dynamic_hook_function = dummy_hook
netD.zero_grad()
D_real_score = netD(real_data_v, real_y)
D_real = -D_real_score.mean()
### train with fake
batchsize = real_data.shape[0]
if latent_type == 'normal':
noise = torch.randn(batchsize, z_dim).to(device0)
elif latent_type == 'bernoulli':
noise = bernoulli.sample((batchsize, z_dim)).view(batchsize, z_dim).to(device0)
else:
raise NotImplementedError
noisev = autograd.Variable(noise)
fake = autograd.Variable(netG(noisev, real_y.to(device0)).data)
inputv = fake.to(device)
D_fake = netD(inputv, real_y.to(device))
D_fake = D_fake.mean()
### train with gradient penalty
gradient_penalty = netD.calc_gradient_penalty(real_data_v.data, fake.data, real_y, L_gp, device)
D_cost = D_fake + D_real + gradient_penalty
### train with epsilon penalty
logit_cost = L_epsilon * torch.pow(D_real_score, 2).mean()
D_cost += logit_cost
### update
D_cost.backward()
Wasserstein_D = -D_real - D_fake
optimizerD.step()
del real_data, real_y, fake, noise, inputv, D_real, D_fake, logit_cost, gradient_penalty
torch.cuda.empty_cache()
############################
# Update G network
###########################
if if_dp:
### Sanitize the gradients passed to the Generator
dynamic_hook_function = dp_conv_hook
else:
### Only modify the gradient norm, without adding noise
dynamic_hook_function = modify_gradnorm_conv_hook
for p in netD.parameters():
p.requires_grad = False
netG.zero_grad()
### train with sanitized discriminator output
if latent_type == 'normal':
noise = torch.randn(batchsize, z_dim).to(device0)
elif latent_type == 'bernoulli':
noise = bernoulli.sample((batchsize, z_dim)).view(batchsize, z_dim).to(device0)
else:
raise NotImplementedError
label = torch.randint(0, NUM_CLASSES, [batchsize]).to(device0)
noisev = autograd.Variable(noise)
fake = netG(noisev, label)
#summary(netG, input_data=[noisev,label])
fake = fake.to(device)
label = label.to(device)
G = netD(fake, label)
G = - G.mean()
### update
G.backward()
G_cost = G
optimizerG.step()
### update the exponential moving average
exp_mov_avg(netGS, netG, alpha=0.999, global_step=iters)
############################
### Results visualization
############################
prg_bar.set_description('iter:{}, G_cost:{:.2f}, D_cost:{:.2f}, Wasserstein:{:.2f}'.format(iters, G_cost.cpu().data,
D_cost.cpu().data,
Wasserstein_D.cpu().data
))
if iters % args.vis_step == 0:
if dataset == 'mnist':
generate_image_mnist(iters, netGS, fix_noise, save_dir, device0)
elif dataset == 'cifar_100':
generate_image_cifar100(iters, netGS, fix_noise, save_dir, device0)
elif dataset == 'cifar_10':
generate_image_mnist(iters, netGS, fix_noise, save_dir, device0)
if iters % args.save_step == 0:
### save model
torch.save(netGS.state_dict(), os.path.join(save_dir, 'netGS_%d.pth' % iters))
del label, fake, noisev, noise, G, G_cost, D_cost
torch.cuda.empty_cache()
if ((iters+1) % 500 == 0):
classify_training(netGS, dataset, iters+1)
### save model
torch.save(netG, os.path.join(save_dir, 'netG.pth'))
torch.save(netGS, os.path.join(save_dir, 'netGS.pth'))
self.classes.index(x.split("/")[-2]) for x in self.files
]
def __len__(self):
return len(self.files)
def __getitem__(self, idx):
im = Image.open(self.files[idx])
if self.transform is not None:
im = self.transform(im)
label = self.labels[idx]
return im, label
if __name__ == "__main__":
pv_transforms = transforms.Compose([
transforms.Grayscale(1),
transforms.Resize((64, 64)),
transforms.ToTensor()
])
dataset = PlantVillageDataset("data/mini-plantvillage",
transform=pv_transforms)
img, label = dataset[0]
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=100,
shuffle=True)
print(len(data_loader))
def create_datasets(data_path, input_size, rgb=False):
"""
This function creates and returns a training data loader and a
testing/validation data loader. The dataloader should also perform some
pre-processing steps on each of the datasets. Most of this function is
implemented for you, you will only need to add a few additional lines.
A data loader in pyTorch is a class inherited from the
torch.utils.data.Dataset abstract class. In this project we will use the
ImageFolder data loader. See
http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder for
details. Although you don't need to for this project, you can also create your
own custom data loader by inheriting from the abstract class and implementing
the __len__() and __getitem__() methods as described in
http://pytorch.org/tutorials/beginner/data_loading_tutorial.html
As mentioned, the data loader should perform any necessary pre-processing
steps on the data (images) and targets (labels). In pyTorch, this is done
with 'transforms', which can be composed (chained together) as shown in
http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#transforms.
While that example implements its own transforms, for this project the
built-in transforms in torchvision.transforms should suffice. See
http://pytorch.org/docs/master/torchvision/transforms.html for the list of
available built-in transforms.
Args:
- data_path: (string) Path to the directory that contains the 'test' and
'train' data directories.
- input_size: (w, h) Size of input image. The images will be resized to
this size.
- rgb: (boolean) Flag indicating if input images are RGB or grayscale. If
False, images will be converted to grayscale.
Returns:
- train_dataloader: Dataloader for the training dataset.
- test_dataloader: Dataloader for the testing/validation dataset.
"""
train_data_path = osp.join(data_path, 'train')
test_data_path = osp.join(data_path, 'test')
# Below variables are provided for your convenience. You may or may not need
# all of them.
train_mean, train_std = utils.get_mean_std(train_data_path, input_size,
rgb)
test_mean, test_std = utils.get_mean_std(test_data_path, input_size, rgb)
""" TRAIN DATA TRANSFORMS """
train_data_tforms = []
train_data_tforms.append(transforms.Resize(size=max(input_size)))
train_data_tforms.append(transforms.CenterCrop(size=input_size))
if not rgb:
train_data_tforms.append(transforms.Grayscale())
#######################################################################
# TODO: YOUR CODE HERE #
#######################################################################
# TODO Add a transformation to you train_data_tforms that left-right mirrors
# the image randomly. Which transformation should you add?
# pass
train_data_tforms.append(transforms.RandomHorizontalFlip(p=0.5))
# train_data_tforms.append(transforms.RandomAffine(degrees=(-30, 30)))
# Do not move the position of the below line (leave it between the left-right
# mirroring and normalization transformations.
train_data_tforms.append(transforms.ToTensor())
# TODO Add a transformation to your train_data_tforms that normalizes the
# tensor by subtracting mean and dividing by std. You may use train_mean,
# test_mean, train_std, or test_std values that are already calculated for
# you. Which mean and std should you use to normalize the data?
# pass
train_data_tforms.append(transforms.Normalize(train_mean, train_std))
#######################################################################
# END OF YOUR CODE #
#######################################################################
train_data_tforms = transforms.Compose(train_data_tforms)
""" TEST/VALIDATION DATA TRANSFORMS """
test_data_tforms = []
test_data_tforms.append(transforms.Resize(size=max(input_size)))
test_data_tforms.append(transforms.CenterCrop(size=input_size))
if not rgb:
test_data_tforms.append(transforms.Grayscale())
test_data_tforms.append(transforms.ToTensor())
#######################################################################
# TODO: YOUR CODE HERE #
#######################################################################
# TODO Add a transformation to your test_data_tforms that normalizes the
# tensor by subtracting mean and dividing by std. You may use train_mean,
# test_mean, train_std, or test_std values that are already calculated for
# you. Which mean and std should you use to normalize the data?
# pass
test_data_tforms.append(transforms.Normalize(test_mean, test_std))
#######################################################################
# END OF YOUR CODE #
#######################################################################
test_data_tforms = transforms.Compose(test_data_tforms)
""" DATASET LOADERS """
# Creating dataset loaders using the transformations specified above.
train_dset = datasets.ImageFolder(root=osp.join(data_path, 'train'),
transform=train_data_tforms)
test_dset = datasets.ImageFolder(root=osp.join(data_path, 'test'),
transform=test_data_tforms)
return train_dset, test_dset
def train_model(device,
model_name,
classification_model,
colorization_model,
dataloaders,
criterion,
optimizer,
save_dir=None,
save_all_epochs=False,
num_epochs=25):
'''
model: The NN to train
dataloaders: A dictionary containing at least the keys
'train','val' that maps to Pytorch data loaders for the dataset
criterion: The Loss function
optimizer: The algorithm to update weights
(Variations on gradient descent)
num_epochs: How many epochs to train for
save_dir: Where to save the best model weights that are found,
as they are found. Will save to save_dir/weights_best.pt
Using None will not write anything to disk
save_all_epochs: Whether to save weights for ALL epochs, not just the best
validation error epoch. Will save to save_dir/weights_e{#}.pt
'''
since = time.time()
val_acc_history = []
best_model_wts = copy.deepcopy(colorization_model.state_dict())
best_loss = float('inf')
train_loss = []
val_loss = []
start_time = time.perf_counter()
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
colorization_model.train() # Set model to training mode
else:
colorization_model.eval() # Set model to evaluate mode
running_loss = 0.0
# Iterate over data.
# TQDM has nice progress bars
num_iter = 0
for inputs, _ in tqdm(dataloaders[phase]):
if num_iter > MAX_ITER:
break
num_iter += 1
inputs = inputs.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
# Get model outputs and calculate loss
outputs = forwards(inputs, classification_model,
colorization_model)
loss = post_processing(inputs, outputs, criterion)
# torch.max outputs the maximum value, and its index
# Since the input is batched, we take the max along axis 1
# (the meaningful outputs)
_, preds = torch.max(outputs, 1)
# backprop + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
#running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloaders[phase].dataset)
#epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
print("epoch_loss", epoch_loss)
#val_loss = evaluate(classification_model,colorization_model, dataloaders['val'], criterion, is_labelled = True, generate_labels = generate_validation_labels, k = 5)
if phase == "train":
train_loss.append(epoch_loss)
plt.clf()
plt.plot(train_loss)
plt.xlabel("Epoch")
plt.ylabel("Train Loss")
plt.title(model_name + " Accuracy")
plt.savefig("plots/" + model_name + '/' + 'TrainAccuracy.png')
plt.clf()
pass
# deep copy the model
if phase == 'val' and epoch_loss < best_loss:
best_loss = epoch_loss
best_model_wts = copy.deepcopy(colorization_model.state_dict())
torch.save(best_model_wts,
os.path.join(save_dir, model_name + '_best.pt'))
if phase == 'val':
val_loss.append(epoch_loss)
plt.clf()
plt.plot(val_loss)
plt.xlabel("Epoch")
plt.ylabel("Val Loss")
plt.title(model_name + " Accuracy")
plt.savefig("plots/" + model_name + '/' + 'ValAccuracy.png')
plt.clf()
pass
if save_all_epochs:
torch.save(colorization_model.state_dict(),
os.path.join(save_dir, f'weights_{epoch}.pt'))
print()
for inputs, _ in tqdm(dataloaders['example']):
gray = transforms.Grayscale(num_output_channels=3)(inputs)
outputs = forwards(inputs, classification_model,
colorization_model)
inputs = np.swapaxes(inputs, 1, 3)
inputs = np.swapaxes(inputs, 1, 2)
outputs = np.swapaxes(outputs, 1, 3)
outputs = np.swapaxes(outputs, 1, 2)
gray = np.swapaxes(gray, 1, 3)
gray = np.swapaxes(gray, 1, 2)
f, ax = plt.subplots(inputs.shape[0], 3)
ax[0][0].set_title("Original")
ax[0][1].set_title("Grayscale")
ax[0][2].set_title("Recolorized")
for i in range(inputs.shape[0]):
# use the created array to output your multiple images. In this case I have stacked 4 images vertically
ax[i][0].imshow(inputs[i])
remove_axis(ax[i][0])
remove_axis(ax[i][1])
remove_axis(ax[i][2])
#ax[i][1].imshow(outputs[i].detach().numpy())
ax[i][1].imshow(gray[i])
lab = color.rgb2lab(inputs[i])
full_output = np.stack([
lab[:, :, 0], outputs[i, :, :, 0].detach().numpy(),
outputs[i, :, :, 1].detach().numpy()
],
axis=2)
ax[i][2].imshow(color.lab2rgb(full_output))
plt.savefig('outputs/' + save_file + '/figure epoch number ' +
str(epoch) + '.png')
break
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_loss))
# save and load best model weights
torch.save(best_model_wts, os.path.join(save_dir, model_name + '_best.pt'))
colorization_model.load_state_dict(best_model_wts)
return colorization_model, val_acc_history
def __init__(self,
training,
p_data,
dmap_seq_paths,
poses,
intrin_path,
img_size=[1248, 380],
digitize=False,
d_candi=None,
d_candi_up=None,
resize_dmap=None,
if_process=True,
crop_w=384,
velodyne_depth=True,
pytorch_scaling=False):
'''
inputs:
traning - if for training or not
p_data - a pykitti.raw object, returned by get_paths()
dmap_seq_paths - list of dmaps, returned by get_paths()
poses - list of posesc
d_candi - the candidate depths
digitize (Optional; False) - if digitize the depth map using d_candi
resize_dmap - the scale for re-scaling the dmap the GT dmap size would be self.img_size * resize_dmap
if_process - if do post process
crop_w - the cropped image width. We will crop the central region with wdith crop_w
'''
assert len(p_data) == len(dmap_seq_paths[0]) == len(poses)
assert len(p_data) == len(dmap_seq_paths[1]) == len(poses)
if crop_w is not None:
assert (img_size[0] - crop_w) % 2 == 0 and crop_w % 4 == 0
assert resize_dmap is not None
self.pytorch_scaling = pytorch_scaling
self.velodyne_depth = velodyne_depth
self.p_data = p_data
self.dmap_seq_paths = dmap_seq_paths
self.poses = poses
self.training = training
self.intrin_path = intrin_path # reserved
self.to_gray = tfv_transform.Compose(
[tfv_transform.Grayscale(),
tfv_transform.ToTensor()])
self.d_candi = d_candi
self.digitize = digitize
self.crop_w = crop_w
if digitize:
self.label_min = 0
self.label_max = len(d_candi) - 1
# usample in the d dimension #
self.dup4_candi = d_candi_up
self.dup4_label_min = 0
self.dup4_label_max = len(self.dup4_candi) - 1
##
self.resize_dmap = resize_dmap
self.img_size = img_size # the raw input image size (used for resizing the input images)
self.if_preprocess = if_process
if crop_w is not None:
self.crop_amt = [self.img_size[0] / self.crop_w, 1.]
if self.crop_amt[0] != 2: raise Exception('Check this')
# initialization about the camera intrsinsics, which is the same for all data #
if crop_w is None:
left_cam_intrinsics = self.get_cam_intrinsics(None, "left")
right_cam_intrinsics = self.get_cam_intrinsics(None, "right")
else:
width_ = int(crop_w * self.resize_dmap)
height_ = int(float(self.img_size[1] * self.resize_dmap))
img_size_ = np.array([width_, height_], dtype=int)
left_cam_intrinsics = self.get_cam_intrinsics(
img_size=img_size_, mode="left", crop_amt=self.crop_amt)
right_cam_intrinsics = self.get_cam_intrinsics(
img_size=img_size_, mode="right", crop_amt=self.crop_amt)
self.left_cam_intrinsics = left_cam_intrinsics
self.right_cam_intrinsics = right_cam_intrinsics
def forwards(inputs, classification_model, colorization_model):
gray = transforms.Grayscale(num_output_channels=3)(inputs)
mid_level_features = classification_model(gray)
return colorization_model(mid_level_features)
def get_dataset(dataset_name):
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = T.Compose(
[T.Resize(256),
T.CenterCrop(224),
T.ToTensor(), normalize])
grey_transform = T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Grayscale(3),
T.ToTensor(), normalize
])
data_dir = 'data'
if not osp.exists(data_dir): os.makedirs(data_dir)
if dataset_name == 'mnist':
return datasets.MNIST("./data/mnist",
train=True,
transform=grey_transform,
target_transform=None,
download=True)
if dataset_name == 'fashionmnist':
return datasets.FashionMNIST("./data/fashionmnist",
train=True,
transform=grey_transform,
target_transform=None,
download=True)
if dataset_name == 'svhn':
return datasets.SVHN("./data/svhn",
split='train',
transform=transform,
target_transform=None,
download=True)
if dataset_name == 'cifar':
return datasets.CIFAR10("./data/cifar",
train=True,
transform=transform,
target_transform=None,
download=True)
if dataset_name == 'cifar_watermarked':
return datasets.ImageFolder("./data/CIFAR_data/watermarked_ds",
transform=transform)
if dataset_name == 'pubfig':
return PUBFIG83(root='./data/pubfig_data/pubfig83-aligned',
train=True,
num_perclass=108,
transform=transform)
if dataset_name == 'pubfig_watermarked':
return PUBFIG83(root='./data/pubfig_data/watermarked_ds',
train=True,
num_perclass=108,
transform=transform)
if dataset_name == 'tinyimagenet':
return datasets.ImageFolder("./data/tiny-imagenet-200/train",
transform=transform)
if dataset_name == 'test_custom_mnist':
return create_custom_mnist_ds("./data/mnistForData/test.npz")
if dataset_name == 'train_custom_mnist':
return create_custom_mnist_ds("./data/mnistForData/train.npz")
if dataset_name == 'prediction_custom_mnist':
return create_custom_mnist_ds("./data/mnistForData/prediction.npz")
if dataset_name == 'train_usps_ds':
return create_usps_ds('./data/usps/usps.h5')
if dataset_name == 'test_usps_ds':
return create_usps_ds('./data/usps/usps.h5', train=False)
raise NotImplementedError(f'dataset = {dataset_name} not implemented')
def __init__(self, test=None):
rgb_transform = transforms.Compose(
[transforms.Resize((384, 128), interpolation=3)])
gray_transform = transforms.Compose([
transforms.Resize((384, 128), interpolation=3),
transforms.Grayscale(3)
])
test_transform = transforms.Compose([
transforms.Resize((384, 128), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
process_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
RandomErasing(probability=0.5, mean=[0.0, 0.0, 0.0])
])
woEr_process_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.trainset = Market1501(data_path=opt.data_path,
dtype="train",
rgb_trans=rgb_transform,
gray_trans=gray_transform,
process_trans=process_transform)
self.trainset_woEr = Market1501(data_path=opt.data_path,
dtype="train",
rgb_trans=rgb_transform,
gray_trans=gray_transform,
process_trans=woEr_process_transform)
if test is None:
self.testset = Market1501(data_path=opt.data_path,
dtype="test",
test_trans=test_transform)
else:
self.testset = Market1501(data_path=opt.data_path + '/' +
str(test),
dtype="test",
test_trans=test_transform)
self.queryset = Market1501(data_path=opt.data_path,
dtype="query",
test_trans=test_transform)
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomSampler(self.trainset,
batch_id=opt.batchid,
batch_image=opt.batchimage),
batch_size=opt.batchid * opt.batchimage,
num_workers=0,
pin_memory=True) # 8
self.train_loader_woEr = dataloader.DataLoader(
self.trainset_woEr,
sampler=RandomSampler(self.trainset_woEr,
batch_id=opt.batchid,
batch_image=opt.batchimage),
batch_size=opt.batchid * opt.batchimage,
num_workers=0,
pin_memory=True) # 8
self.test_loader = dataloader.DataLoader(self.testset,
batch_size=opt.batchtest,
num_workers=0,
pin_memory=True) # 8
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=opt.batchtest,
num_workers=0,
pin_memory=True) # 8
import torch
import torch.utils.data as Data
import torch.nn as nn
import torchvision
import numpy as np
import torchvision.transforms as transforms
import scipy.io as sio
test_data = torchvision.datasets.ImageFolder('./Testing_set', transform=transforms.Compose([
transforms.Grayscale(num_output_channels=1),transforms.ToTensor()]))
test_loader=Data.DataLoader(dataset=test_data, batch_size=1, shuffle=False)
# NN network
class Autoencoder1(nn.Module):
def __init__(self):
super(Autoencoder1, self).__init__()
self.encoder = nn.Sequential( # two layers encoder
nn.Linear(74*74, 8000),
nn.ReLU(True), # ReLU, Tanh, etc.
nn.Linear(8000, 1000),
nn.ReLU(True), # input is in (0,1), so select this one
)
self.decoder = nn.Sequential( # two layers decoder
nn.Linear(1000, 8000),
nn.ReLU(True),
nn.Linear(8000, 74*74),
nn.Sigmoid()
)
def forward(self, x):
def load_data(image_size, category_filter, train_test_split, is_gray=False):
if is_gray:
train_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(30, translate=(0.3, 0.3), scale=(1.0, 1.5)),
ImageNetPolicy(),
transforms.Grayscale(3),
transforms.ToTensor()
])
test_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.Grayscale(3),
transforms.ToTensor()
])
else:
train_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.RandomHorizontalFlip(),
transforms.RandomAffine(30, translate=(0.3, 0.3), scale=(1.0, 1.5)),
ImageNetPolicy(),
transforms.ToTensor()
])
test_transform = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor()
])
root_dir = '/train-data/inaturalist-2019/'
train_test_set = InaturalistDataset(
root_dir + 'train2019.json',
root_dir,
train_transform,
category_filter=category_filter
)
val_set = InaturalistDataset(
root_dir + 'val2019.json',
root_dir,
test_transform,
category_filter=category_filter
)
if isinstance(train_test_split, float):
train_size = int(len(train_test_set) * train_test_split)
test_size = len(train_test_set) - train_size
train_set, test_set = torch.utils.data.random_split(train_test_set, [train_size, test_size])
elif isinstance(train_test_split, dict):
train_indices, test_indices = train_test_set.sample(train_test_split)
train_set = torch.utils.data.Subset(train_test_set, train_indices)
test_set = torch.utils.data.Subset(train_test_set, test_indices)
test_set.__getattribute__('dataset').__setattr__('transform', test_transform)
print(f'train: {len(train_set)}, val: {len(val_set)}, test: {len(test_set)}')
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True, num_workers=32)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True, num_workers=32)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=True, num_workers=32)
return (train_loader, val_loader, test_loader)
def main():
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
wali = create_WALI().to(device)
optimizerEG = Adam(list(wali.get_encoder_parameters()) +
list(wali.get_generator_parameters()),
lr=LEARNING_RATE,
betas=(BETA1, BETA2))
optimizerC = Adam(wali.get_critic_parameters(),
lr=LEARNING_RATE,
betas=(BETA1, BETA2))
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.Grayscale(1),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))
])
mold_img = ImageFolder('data/folder/train', transform)
loader = data.DataLoader(mold_img, BATCH_SIZE, shuffle=True)
noise = torch.randn(64, NLAT, 1, 1, device=device)
EG_losses, C_losses = [], []
curr_iter = C_iter = EG_iter = 0
C_update, EG_update = True, False
print('Training starts...')
while curr_iter < ITER:
for batch_idx, (x, _) in enumerate(loader, 1):
x = x.to(device)
if curr_iter == 0:
init_x = x
curr_iter += 1
z = torch.randn(x.size(0), NLAT, 1, 1).to(device)
C_loss, EG_loss = wali(x, z, lamb=LAMBDA)
if C_update:
optimizerC.zero_grad()
C_loss.backward()
C_losses.append(C_loss.item())
optimizerC.step()
C_iter += 1
if C_iter == C_ITERS:
C_iter = 0
C_update, EG_update = False, True
continue
if EG_update:
optimizerEG.zero_grad()
EG_loss.backward()
EG_losses.append(EG_loss.item())
optimizerEG.step()
EG_iter += 1
if EG_iter == EG_ITERS:
EG_iter = 0
C_update, EG_update = True, False
curr_iter += 1
else:
continue
# print training statistics
if curr_iter % 100 == 0:
print('[%d/%d]\tW-distance: %.4f\tC-loss: %.4f' %
(curr_iter, ITER, EG_loss.item(), C_loss.item()))
# plot reconstructed images and samples
wali.eval()
real_x, rect_x = init_x[:32], wali.reconstruct(
init_x[:32]).detach_()
rect_imgs = torch.cat(
(real_x.unsqueeze(1), rect_x.unsqueeze(1)), dim=1)
rect_imgs = rect_imgs.view(64, NUM_CHANNELS, IMAGE_SIZE,
IMAGE_SIZE).cpu()
genr_imgs = wali.generate(noise).detach_().cpu()
utils.save_image(rect_imgs * 0.5 + 0.5,
'Runs/fsize/rect%d.png' % curr_iter)
utils.save_image(genr_imgs * 0.5 + 0.5,
'Runs/fsize/genr%d.png' % curr_iter)
wali.train()
# save model
if curr_iter % (ITER // 10) == 0:
torch.save(wali.state_dict(), 'Runs/fsize/%d.ckpt' % curr_iter)
def load_dataset(dataset, train_size, valid_size, test_size):
"""Load the dataset passed in argument with the corresponding sizes for the training, validation and testing set."""
if dataset == 'mnist_012':
root = './data/mnist'
num_classes = 3
trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.ToTensor(), transforms.Normalize(mean=MNIST_MEAN, std=MNIST_STD)])
train_valid_set = datasets.MNIST(root=root, train=True, transform=trans)
test_set = datasets.MNIST(root=root, train=False, transform=trans)
train_valid_set = MNIST_bis(dataset=train_valid_set, size=train_size+valid_size, digits_to_keep=[0,1,2])
test_set = MNIST_bis(dataset=test_set, size=test_size, digits_to_keep=[0,1,2])
train_sampler, valid_sampler = train_valid_split(dataset=train_valid_set, train_size=train_size)
train_loader = DataLoader(dataset=train_valid_set, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=4, pin_memory=True, drop_last=True)
valid_loader = DataLoader(dataset=train_valid_set, batch_size=BATCH_SIZE, sampler=valid_sampler, num_workers=4, pin_memory=True, drop_last=True)
test_loader = DataLoader(dataset=test_set, batch_size=BATCH_SIZE, num_workers=4, pin_memory=True, drop_last=True)
elif dataset == 'mnist_rot':
root = './data/mnist'
num_classes = 9
train_trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize((26,26)), transforms.ToTensor(), transforms.Normalize(mean=MNIST_MEAN, std=MNIST_STD)])
test_trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize((26,26)), transforms.RandomRotation((0,360)), transforms.ToTensor(), transforms.Normalize(mean=MNIST_MEAN, std=MNIST_STD)])
train_valid_set = datasets.MNIST(root=root, train=True, transform=train_trans)
test_set = datasets.MNIST(root=root, train=False, transform=test_trans)
train_valid_set_bis = MNIST_bis(dataset=train_valid_set, size=train_size+valid_size, digits_to_keep=[0,1,2,3,4,5,6,7,8])
test_set = MNIST_bis(dataset=test_set, size=test_size, digits_to_keep=[0,1,2,3,4,5,6,7,8])
train_sampler, valid_sampler = train_valid_split(dataset=train_valid_set_bis, train_size=train_size)
train_loader = DataLoader(dataset=train_valid_set_bis, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=4, pin_memory=True, drop_last=True)
valid_loader = DataLoader(dataset=train_valid_set_bis, batch_size=BATCH_SIZE, sampler=valid_sampler, num_workers=4, pin_memory=True, drop_last=True)
test_loader = DataLoader(dataset=test_set, batch_size=BATCH_SIZE, num_workers=4, pin_memory=True, drop_last=True)
elif dataset == 'mnist_trans':
root = './data/mnist'
num_classes = 9
train_trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize((26,26)), transforms.ToTensor(), transforms.Normalize(mean=MNIST_MEAN, std=MNIST_STD)])
test_trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize((26,26)), RandomTranslation(horizontal=6, vertical=6), transforms.ToTensor(), transforms.Normalize(mean=MNIST_MEAN, std=MNIST_STD)])
train_valid_set = datasets.MNIST(root=root, train=True, transform=train_trans)
test_set = datasets.MNIST(root=root, train=False, transform=test_trans)
train_valid_set_bis = MNIST_bis(dataset=train_valid_set, size=train_size+valid_size, digits_to_keep=[0,1,2,3,4,5,6,7,8])
test_set = MNIST_bis(dataset=test_set, size=test_size, digits_to_keep=[0,1,2,3,4,5,6,7,8])
train_sampler, valid_sampler = train_valid_split(dataset=train_valid_set_bis, train_size=train_size)
train_loader = DataLoader(dataset=train_valid_set_bis, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=4, pin_memory=True, drop_last=True)
valid_loader = DataLoader(dataset=train_valid_set_bis, batch_size=BATCH_SIZE, sampler=valid_sampler, num_workers=4, pin_memory=True, drop_last=True)
test_loader = DataLoader(dataset=test_set, batch_size=BATCH_SIZE, num_workers=4, pin_memory=True, drop_last=True)
elif dataset == 'eth80':
root = './data/eth80'
num_classes = 8
trans = transforms.Compose([transforms.Grayscale(num_output_channels=1), transforms.Resize((50,50)), transforms.ToTensor(), transforms.Normalize(mean=ETH80_MEAN, std=ETH80_STD)])
complete_set = datasets.ImageFolder(root=root, transform=trans)
class_names = complete_set.classes
train_sampler, valid_sampler, test_sampler = train_valid_test_split(dataset=complete_set, train_size=train_size, valid_size=valid_size)
train_loader = DataLoader(dataset=complete_set, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=4, pin_memory=True, drop_last=True)
valid_loader = DataLoader(dataset=complete_set, batch_size=BATCH_SIZE, sampler=valid_sampler, num_workers=4, pin_memory=True, drop_last=True)
test_loader = DataLoader(dataset=complete_set, batch_size=BATCH_SIZE, sampler=test_sampler, num_workers=4, pin_memory=True, drop_last=True)
else:
raise ValueError('Specified dataset does not exist.')
logger.debug('Class frequency train loader: {} validation loader: {} test loader: {}'.format(
count_class_freq(train_loader, num_classes),count_class_freq(valid_loader, num_classes), count_class_freq(test_loader, num_classes))
)
logging.info('Loaded {} dataset with the split {}-{}-{} for the [train]-[valid]-[test] setup.'.format(dataset, len(train_loader)*BATCH_SIZE, len(valid_loader)*BATCH_SIZE, len(test_loader)*BATCH_SIZE))
return train_loader, valid_loader, test_loader, get_dim(train_loader)
def classify_training(netGS, dataset, iter):
### Data loaders
if dataset == 'mnist' or dataset == 'fashionmnist':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.ToTensor(),
#transforms.Grayscale(),
])
elif dataset == 'cifar_100' or dataset == 'cifar_10':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.Grayscale(),
transforms.ToTensor(),
])
if dataset == 'mnist':
dataloader = datasets.MNIST
test_set = dataloader(root=os.path.join(DATA_ROOT, 'MNIST'), train=False, download=True,
transform=transform_train)
IMG_DIM = 784
NUM_CLASSES = 10
elif dataset == 'fashionmnist':
dataloader = datasets.FashionMNIST
test_set = dataloader(root=os.path.join(DATA_ROOT, 'FashionMNIST'), train=False, download=True,
transform=transform_train)
elif dataset == 'cifar_100':
dataloader = datasets.CIFAR100
test_set = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR100'), train=False, download=True,
transform=transform_train)
IMG_DIM = 3072
NUM_CLASSES = 100
elif dataset == 'cifar_10':
IMG_DIM = 784
NUM_CLASSES = 10
dataloader = datasets.CIFAR10
test_set = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR10'), train=False, download=True,
transform=transform_train)
else:
raise NotImplementedError
test_loader = DataLoader(test_set, batch_size=1000, shuffle=False)
netGS.eval()
for i in tqdm(range(25)):
gen_labels = Variable(LongTensor(np.random.randint(0, 10, 2000)))
noise = Variable(FloatTensor(np.random.normal(0, 1, (2000, args.z_dim))))
synthesized = netGS(noise, gen_labels)
if (i == 0):
new_data = synthesized.cpu().detach()
new_label = gen_labels.cpu().detach()
else:
new_data = torch.cat((new_data, synthesized.cpu().detach()), 0)
new_label = torch.cat((new_label, gen_labels.cpu().detach()), 0)
new_data = torch.clamp(new_data, min=0., max=1.)
C = Classifier().cuda()
C.train()
opt_C = torch.optim.Adam(C.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()
gen_set = TensorDataset(new_data.cuda(), new_label.cuda())
gen_loader = DataLoader(gen_set, batch_size=32, shuffle=True)
prg_bar = tqdm(range(50))
for epoch in prg_bar:
train_acc = 0.0
train_loss = 0.0
for i, (data, label) in enumerate(gen_loader):
pred = C(data)
loss = criterion(pred, label)
opt_C.zero_grad()
loss.backward()
opt_C.step()
train_acc += np.sum(np.argmax(pred.cpu().data.numpy(), axis=1) == label.cpu().numpy())
train_loss += loss.item()
prg_bar.set_description(f'acc: {train_acc/gen_set.__len__():.3f} loss: {train_loss/gen_set.__len__():.4f}')
test_acc = 0.0
C.eval()
for i, (data, label) in enumerate(test_loader):
data = Variable(data.type(FloatTensor))
label = Variable(label.type(LongTensor))
pred = C(data)
test_acc += np.sum(np.argmax(pred.cpu().data.numpy(), axis=1) == label.cpu().numpy())
test_acc /= test_set.__len__()
test_acc *= 100
print(f'the final result of test accuracy = {test_acc/100:.3f}')
for i in range(9):
if (test_acc > acc_milestone[i] and acc_passed[i] == False):
acc_passed[i] = True
torch.save(netGS, os.path.join(args.checkpoint, f'diff_acc/{acc_milestone[i]}.pth'))
with open(os.path.join(args.checkpoint, f'diff_acc/result.txt'), 'a') as f:
f.write(f"thres:{acc_milestone[i]}% iter:{iter}, acc:{test_acc:.1f}%\n")
if (iter in iter_milestone):
torch.save(netGS, os.path.join(args.checkpoint, f'diff_iter/{iter}.pth'))
with open(os.path.join(args.checkpoint, f'diff_iter/result.txt'), 'a') as f:
f.write(f"iter:{iter}, acc:{test_acc:.1f}%\n")
del C, new_data, new_label, gen_set, gen_loader
torch.cuda.empty_cache()
output = self.discriminator_block_1(inputs)
output = self.discriminator_block_2(output)
output = self.discriminator_block_3(output)
output = self.discriminator_block_4(output)
output = self.discriminator_block_5(output)
output = self.discriminator_block_6(output)
output = self.last_layer(output)
return output
# Configure dataset loader
loading_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
gray_scale_transform = transforms.Compose(
[transforms.ToPILImage(), transforms.Grayscale(num_output_channels=1), transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])])
# Initialize generator and discriminator
generator = Generator('G', training=training)
discriminator = Discriminator('D', training=training)
if cuda:
generator.cuda(gpu_id)
discriminator.cuda(gpu_id)
if training:
# Loss function
bce_loss_with_logits = torch.nn.BCEWithLogitsLoss()
if cuda:
bce_loss_with_logits.cuda(gpu_id)
assert (len(sys.argv) > 1)
save_path = sys.argv[1]
n_epochs = 1500
log_interval = 100
use_cuda = torch.cuda.is_available()
if use_cuda:
torch.cuda.empty_cache()
#torch.cuda.set_device(1)
preprocess_train = transforms.Compose([
# transforms.RandomRotation(30),
# transforms.RandomResizedCrop(28),
# transforms.RandomHorizontalFlip(),
transforms.Resize(224),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor()
])
preprocess_test = transforms.Compose([
transforms.Resize(224),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
])
trainset = torchvision.datasets.FashionMNIST(root="fashion",
train=True,
download=True,
transform=preprocess_train)
testset = torchvision.datasets.FashionMNIST(root="fashion",
train=False,
def train_model(model, output_dir):
class IndexedDataset(datasets.ImageFolder):
def __getitem__(self, index):
data, target = super(IndexedDataset, self).__getitem__(index)
return data, target, index
# Load data
train_trans = transforms.Compose([
transforms.Grayscale(1),
transforms.Resize([image_width, image_width]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.8], std=[0.2])
])
trainset = IndexedDataset(train_dir, transform=train_trans)
classes = trainset.classes
train_loader = DataLoader(dataset=trainset,
batch_size=batch_size,
shuffle=True)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
loss_list = []
acc_list = []
# Visualization object
visu = Visualization(
classes
) # , VIS_DATA, VIS_TARGET, VIS_PRED, test_vis_data, TEST_TARGET, TEST_PRED, VIS_ACC, TEST_ACC
for epoch in range(num_epochs):
# Per batch
total_totals = 0
total_correct = 0
epoch_indices = []
epoch_vis_data = []
epoch_vis_target = []
epoch_vis_predicted = []
for i, (images, labels, indices) in enumerate(train_loader):
# print(f"indices = {indices.numpy()}")
# Batch i
# Run the forward pass
outputs, train_vis_data = model(images)
epoch_indices.extend(indices)
epoch_vis_data.extend(train_vis_data)
epoch_vis_target.extend(labels.numpy())
loss = criterion(outputs, labels)
loss_list.append(loss.item())
# Backprop and perform Adam optimisation
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Track the accuracy
total = labels.size(0)
total_totals += total
_, predicted = torch.max(outputs.data, 1)
epoch_vis_predicted.extend(predicted.numpy())
correct = (predicted == labels).sum().item()
total_correct += correct
accuracy = correct / total
acc_list.append(accuracy)
if (i + 1) % 20 == 0:
print(
'Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Accuracy: {:.2f}%'
.format(epoch + 1, num_epochs, i + 1, total_step,
loss.item(), (accuracy) * 100))
visu.add_data(epoch_indices, epoch_vis_data, epoch_vis_target,
epoch_vis_predicted)
# Add (0,0) point for the test accuracy graph
if epoch == 0:
_, _, _, test_acc, _ = test_model(model)
visu.TEST_ACC.append((epoch + 1, test_acc))
visu.VIS_ACC.append((epoch + 1, total_correct / total_totals))
if (epoch + 1) % show_after_epochs == 0:
visu.TEST_DATA, visu.TEST_TARGET, visu.TEST_PRED, test_acc, images_prediction = test_model(
model)
visu.TEST_ACC.append((epoch + 1, test_acc))
# print(images_prediction)
for image, pred, label in images_prediction:
# print(image)
# print(pred)
visu.add_class_colour(image, pred, label)
# visu.make_vis(output_dir, epoch+1)
visu.make_label_vis(output_dir, epoch + 1)
visu.clear_after_epoch()
return classes, train_loader, loss_list, acc_list
def __init__(self):
self.config = GlobalConfig()
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
print("torch.distributed.is_available: " +
str(torch.distributed.is_available()))
print("Device Count: {0}".format(torch.cuda.device_count()))
# Creates training set
train_transform = transforms.Compose([
transforms.Resize(self.config.Width),
transforms.RandomCrop(self.config.Width),
transforms.ToTensor(),
transforms.Normalize(mean=self.config.mean, std=self.config.std)
])
self.train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(self.config.TRAIN_PATH, train_transform),
batch_size=self.config.train_batch_size,
num_workers=4,
pin_memory=True,
shuffle=True,
drop_last=True)
# Creates water set
train_water_transform = transforms.Compose([
transforms.Resize(self.config.Water_Width),
transforms.RandomCrop(self.config.Water_Width),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize(mean=self.config.mean[0],
std=self.config.std[0])
])
self.train_water_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(self.config.TRAIN_PATH,
train_water_transform),
batch_size=self.config.train_batch_size,
num_workers=4,
pin_memory=True,
shuffle=True,
drop_last=True)
# Creates test set
test_transform = transforms.Compose([
transforms.Resize(self.config.Width),
transforms.RandomCrop(self.config.Width),
transforms.ToTensor(),
transforms.Normalize(mean=self.config.mean, std=self.config.std)
])
self.test_loader = torch.utils.data.DataLoader(datasets.ImageFolder(
self.config.TEST_PATH, test_transform),
batch_size=1,
num_workers=4,
pin_memory=True,
shuffle=True,
drop_last=True)
# Creates water test set
test_water_transform = transforms.Compose([
transforms.Resize(self.config.Water_Width),
transforms.RandomCrop(self.config.Water_Width),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Normalize(mean=self.config.mean[0],
std=self.config.std[0])
])
self.test_water_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(self.config.TEST_PATH, test_water_transform),
batch_size=1,
num_workers=4,
pin_memory=True,
shuffle=True,
drop_last=True)
# self.train_dataset = MyDataset(root=self.config.TRAIN_PATH,filename=self.config.TAG_PATH,mean=self.config.mean,std=self.config.std)
# self.another_dataset = MyDataset(root=self.config.TRAIN_PATH, filename=self.config.TAG_PATH, grayscale=True, size=64,mean=self.config.mean,std=self.config.std)
# print(len(self.train_dataset))
# self.train_loader = data.DataLoader(dataset=self.train_dataset, batch_size=self.config.train_batch_size,
# shuffle=True, num_workers=4)
# self.another_loader = data.DataLoader(dataset=self.another_dataset, batch_size=self.config.train_batch_size,
# shuffle=True, num_workers=4)
self.net = LinJingZhiNet(config=self.config)
self.train_cover, self.train_water = None, None
self.test_cover, self.test_water = None, None
self.pseudo = np.random.choice(
[0, 1], (self.config.message_length,
self.config.message_length * self.config.message_length))
pt_transforms.Pad((0, 1, 0, 1)),
pt_transforms.Lambda(lambda x: my_transform_1(x)),
# pt_transforms.RandomApply(p=0.85,transforms=[pt_transforms.Resize((20,20))]),
# pt_transforms.RandomChoice([pt_transforms.ToTensor()]),
# pt_transforms.ToPILImage(),
# pt_transforms.RandomOrder([pt_transforms.Resize(20)]),
# pt_transforms.RandomCrop((20, 20)),
# pt_transforms.RandomHorizontalFlip(p=0),
# pt_transforms.RandomVerticalFlip(p=0),
# pt_transforms.RandomResizedCrop(100, (1,1), (2,2)),
# pt_transforms.RandomSizedCrop(100, (1,1), (2,2)),
# pt_transforms.ColorJitter(brightness = 2)
pt_transforms.RandomRotation((0, 0)),
pt_transforms.RandomRotation((0, 0)),
pt_transforms.RandomAffine(0.7),
pt_transforms.Grayscale(num_output_channels=1),
pt_transforms.RandomGrayscale(p=0.5),
# pt_transforms.RandomAffine(degrees=[-45, 45],translate=[0.15, 0.15],scale=[1.0, 1.2])
])
result_pt = transform_pt(img)
print(result_pt)
# Transform TF
transform_tf = tf_transforms.Compose([
# tf_transforms.FiveCrop(5),
# tf_transforms.FiveCrop((2,2)),
# tf_transforms.TenCrop(5),
# tf_transforms.TenCrop((2,2)),
tf_transforms.ToTensor(),
tf_transforms.ToPILImage(),
tf_transforms.ToTensor(),
def __init__(self, mom, do, wd, name, index):
momentum = mom
dropout = do
weightdecay = wd
name = name
index = index
learningRate = 0.1
#momentum = float(sys.argv[1]) #0.5
#dropout = float(sys.argv[2]) #0.2
#weightdecay = float(sys.argv[3]) #0
model = Net(dropout).to(device)
optimizer = torch.optim.SGD(model.parameters(),
lr=learningRate,
momentum=momentum,
weight_decay=weightdecay)
criterion = nn.CrossEntropyLoss()
print(model)
batch_size = 32
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor()
])
train_dataset = datasets.CIFAR10('./data',
train=True,
download=True,
transform=transform)
# train_dataset, validation_dataset = torch.utils.data.random_split(train_dataset, [40000, 10000])
# validation_dataset = torch.utils.data.Subset(train_dataset, [])
validation_dataset = datasets.CIFAR10('./data',
train=True,
transform=transform)
testing_dataset = datasets.CIFAR10('./data',
train=False,
transform=transform,
download=True)
num_train = len(train_dataset)
indices = list(range(num_train))
split = 40000
train_idx, valid_idx = indices[:split], indices[split:]
train_dataset = Subset(train_dataset, train_idx)
validation_dataset = Subset(validation_dataset, valid_idx)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size)
validation_loader = torch.utils.data.DataLoader(
dataset=validation_dataset, batch_size=batch_size)
testing_loader = torch.utils.data.DataLoader(dataset=testing_dataset,
batch_size=batch_size)
for (X_train, y_train) in train_loader:
print('X_train:', X_train.size(), 'type:', X_train.type())
print('y_train:', y_train.size(), 'type:', y_train.type())
break
print('validationsize: ' + str(len(validation_loader)))
print('trainsize: ' + str(len(train_loader)))
def train(epoch, log_interval=200):
# Set model to training mode
model.train()
# Loop over each batch from the training set
for batch_idx, (data, target) in enumerate(train_loader):
# Copy data to GPU if needed
data = data.to(device)
target = target.to(device)
# Zero gradient buffers
optimizer.zero_grad()
# Pass data through the network
output = model(data)
# Calculate loss
loss = criterion(output, target)
# Backpropagate
loss.backward()
# Update weights
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.
format(epoch, batch_idx * len(data),
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.data.item()))
def validate(loss_vector, accuracy_vector, loader):
model.eval()
val_loss, correct = 0, 0
for data, target in loader:
data = data.to(device)
target = target.to(device)
output = model(data)
val_loss += criterion(output, target).data.item()
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
val_loss /= len(loader)
loss_vector.append(val_loss)
accuracy = 100. * correct.to(torch.float32) / len(loader.dataset)
accuracy_vector.append(accuracy)
print(
'\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
.format(val_loss, correct, len(loader.dataset), accuracy))
return accuracy
#%%time
epochs = 5
lossv, accv = [], []
for epoch in range(1, epochs + 1):
train(epoch)
validate(lossv, accv, validation_loader)
testv, taccv = [], []
print("THE BELOW IS TEST ACCURACY")
self.accr = validate(testv, taccv, testing_loader)
# Generate plot of loss as a function of training epochs
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.plot(np.arange(1, epochs + 1), lossv, label="Loss")
plt.title('Validation Loss')
plt.legend(loc='best')
plt.savefig('q3plot1' + name + str(index))
# Generate plot of accuracy as a function of training epochs
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Accuracy Percentage')
plt.plot(np.arange(1, epochs + 1), accv, label="Accuracy")
plt.title('Validation Accuracy')
plt.legend(loc='best')
plt.savefig('q3plot2' + name + str(index))
def main():
# # Parsing command line args
# parser = argparse.ArgumentParser(description='CIFAR10 example')
# parser.add_argument('--kernel', type=str, default=None,
# help='Kernel type to use: [gaussian, polynomial, sigmoid, DCT] (default: None)')
#
# parser.add_argument('--epoch', type=int, default=2, help='Number of epochs (default: 2)')
# parser.add_argument('--batch_size', type=int, default=4, help='Batch suze (default: 4)')
# parser.add_argument('--gpu', type=bool, default=True, help='Use GPU? (default: True)')
#
# args = parser.parse_args()
#
# device = 'cpu'
# if args.gpu:
# device = 'cuda'
#
# # Initiating network
# resnet50 = torchvision.models.resnet50()
# resnet50._modules['fc'] = torch.nn.Linear(2048, 10, True)
# net = resnet50
#
# if args.kernel == 'gaussian':
# kernel_wrapper(net, GaussianKernel())
# elif args.kernel == 'polynomial':
# kernel_wrapper(net, PolynomialKernel())
# elif args.kernel == 'sigmoid':
# kernel_wrapper(net, SigmoidKernel())
# elif args.kernel == "DCT":
# kernel_wrapper(net, SigmoidKernel())
# elif args.kernel is not None:
# raise Exception('Invalid kernel')
net = SharpnessModelUnfold(num_classes=3)
net2 = SharpnessModelConv(num_classes=3)
# net.to(device)
# Loading datasets
transform = transforms.Compose([
transforms.Grayscale(),
transforms.ToTensor()
# transforms.Normalize((0.49, 0.48, 0.45), (0.25, 0.24, 0.26))
])
# trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
# trainloader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=True, num_workers=2)
testset = torchvision.datasets.FakeData(transform=transform)
# testset = torchvision.datasets.(root='./data', train=False, download=True, transform=transform)
# testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset,
batch_size=1,
shuffle=False,
num_workers=2)
img = plt.imread("./resourses/image_blur3.jpg")[:200, :200, 0:1]
torch_images = torch.Tensor(img).transpose(0, 2).transpose(1,
2).unsqueeze(0)
with torch.no_grad():
net.eval()
# print(net)
# for (images, labels) in testloader:
# images = images.to(device)
# labels = labels.to(device)
outputs = net(torch_images)
outputs2 = net2(torch_images)
# dct_map_2 = compute_dct_pytorch(img[:, :, 0].astype(np.float))
# dct_map_2[0][0, 0] = dct_map[0].max()
# print(np.abs(dct_map).mean())
# print(np.abs(dct_map_2[0]).mean())
# print(np.abs(dct_map_conv).mean())
dct_map = outputs.data.numpy()[0, 0]
dct_map_conv = outputs2.data.numpy()[0, 0]
assert outputs.shape == outputs2.shape
assert np.abs(dct_map_conv).mean() == np.abs(dct_map).mean()
assert np.abs(dct_map_conv).max() == np.abs(dct_map).max()
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import os
from torchvision import datasets, transforms
import time
from PIL import Image
from torchvision import models
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Lambda(lambda x: torch.cat([x, x, x], 0))
# transforms.Normalize()
]),
'test': transforms.Compose([
# transforms.Resize(256),
transforms.CenterCrop(42),
transforms.Grayscale(),
transforms.ToTensor(),
transforms.Lambda(lambda x: torch.cat([x, x, x], 0))
# transforms.Normalize()
])
}
data_dir = '../Datasets/'
def get_dataloaders(
dataset,
root=None,
seed=0,
device=torch.device("cpu"),
train_batch_size=1,
test_batch_size=1,
num_workers=0,
**kwargs,
):
"""
Args:
dataset: string, dataset identifier (only 'mnist' supported)
root: string (optional), folder where the data will be downloaded.
device: (optional) a torch.device() where the datasets will be located
(default=torch.device('cpu'))
seed: int (optional) manual seed used for torch and cudnn
train_batch_size: int (optional), batch size for training dataloader
(default: 1)
test_batch_size: int (optional), batch size for test dataloader
(default: 1)
num_workers: int (optional), how many subprocesses to use for data
loading. 0 means that the data will be loaded in the
main process. (default: 0)
**kwargs: allowed arguments include
augment
num_channels
resize_to
Returns:
train_loader: pytorch DataLoader, training set
test_loader: pytorch DataLoader, test set
Raises:
ValueError if dataset.lower is not one of {'mnist', 'cifar-10',
'cifar-100'}
Notes:
Make sure to read the documentation here (https://pytorch.org/docs/stable/data.html)
and here (https://pytorch.org/docs/stable/notes/faq.html#dataloader-workers-random-seed)
when using `num_workers` != 0, and pay particular attention to the role of random
seeds for reproducibility and workers syncing.
The training dataset will be shuffled (deterministically, depending on
seed), while the test set will not be shuffled.
"""
# set seeds and make sure results are deterministic (at least with
# `num_workers` set to 0)
torch.manual_seed(seed)
if device.type == "cuda":
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
# determine transformations to apply depending on dataset nature
if dataset.lower() == "mnist":
# basic image standardization
train_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
]
)
test_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
]
)
if ("resize_to" in kwargs) and (kwargs["resize_to"] is not None):
# TODO: check that resize_to makes sense
resize_transform = [transforms.Resize(kwargs["resize_to"])]
train_transform = transforms.Compose(
resize_transform + train_transform.transforms
)
test_transform = transforms.Compose(
resize_transform + test_transform.transforms
)
# data augmentation strategies
if ("augment" in kwargs) and (kwargs["augment"] == True):
augment_transforms = [
transforms.RandomCrop(28, padding=4),
transforms.RandomHorizontalFlip(),
]
train_transform = transforms.Compose(
augment_transforms + train_transform.transforms
)
# if ("broadcast_channels" in kwargs) and (
# kwargs["broadcast_channels"] == True
# ):
if ("num_channels" in kwargs) and (kwargs["num_channels"] != 1):
broadcast_transform = [
transforms.Grayscale(
num_output_channels=kwargs["num_channels"]
)
]
train_transform = transforms.Compose(
broadcast_transform + train_transform.transforms
)
test_transform = transforms.Compose(
broadcast_transform + test_transform.transforms
)
root = root or "/anonymized/path/"
try:
train_dataset = datasets.MNIST(
root=root,
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.MNIST(
root=root,
train=False,
download=True,
transform=test_transform,
)
except OSError:
train_dataset = datasets.MNIST(
root=os.path.expanduser("~"),
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.MNIST(
root=os.path.expanduser("~"),
train=False,
download=True,
transform=test_transform,
)
elif dataset.lower() in ["cifar-10", "cifar-100"]:
# Construct initial transforms
train_transform = transforms.Compose([transforms.ToTensor()])
test_transform = transforms.Compose([transforms.ToTensor()])
if ("resize_to" in kwargs) and (kwargs["resize_to"] is not None):
print(kwargs["resize_to"])
resize_transform = [transforms.Resize(kwargs["resize_to"])]
train_transform = transforms.Compose(
resize_transform + train_transform.transforms
)
test_transform = transforms.Compose(
resize_transform + test_transform.transforms
)
if ("augment" in kwargs) and (kwargs["augment"] == True):
augment_transforms = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
]
train_transform = transforms.Compose(
augment_transforms + train_transform.transforms
)
# if ("num_channels" in kwargs) and (kwargs["num_channels"] != 3):
if "num_channels" in kwargs:
if kwargs["num_channels"] == 3:
norm_transform = [
transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
)
]
train_transform = transforms.Compose(
train_transform.transforms + norm_transform
)
test_transform = transforms.Compose(
test_transform.transforms + norm_transform
)
elif kwargs["num_channels"] == 1:
broadcast_transform = [
transforms.Grayscale(
num_output_channels=kwargs["num_channels"]
)
]
norm_transform = [transforms.Normalize((0.4790,), (0.2389,))]
train_transform = transforms.Compose(
broadcast_transform
+ train_transform.transforms
+ norm_transform
)
test_transform = transforms.Compose(
broadcast_transform
+ train_transform.transforms
+ norm_transform
)
else:
raise ValueError(
"Unexpected number of channels: {}".format(
kwargs["num_channels"]
)
)
if dataset.lower() in ["cifar-10"]:
root = root or "/anonymized/path/"
logger.info(
"Data folder for {} dataset: {}".format(dataset, root)
)
try:
train_dataset = datasets.CIFAR10(
root=root,
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.CIFAR10(
root=root,
train=False,
download=True,
transform=test_transform,
)
except OSError:
train_dataset = datasets.CIFAR10(
root=os.path.expanduser("~"),
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.CIFAR10(
root=os.path.expanduser("~"),
train=False,
download=True,
transform=test_transform,
)
elif dataset.lower() == "cifar-100":
root = root or "/anonymized/path/"
logger.info(
"Data folder for {} dataset: {}".format(dataset, root)
)
try:
train_dataset = datasets.CIFAR100(
root=root,
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.CIFAR100(
root=root,
train=False,
download=True,
transform=test_transform,
)
except OSError:
train_dataset = datasets.CIFAR100(
root=os.path.expanduser("~"),
train=True,
download=True,
transform=train_transform,
)
test_dataset = datasets.CIFAR100(
root=os.path.expanduser("~"),
train=False,
download=True,
transform=test_transform,
)
else:
raise ValueError(
'`dataset` must be one of {"mnist, cifar-10, cifar-100"}'
)
# set up dataloaders from the datasets above, specifying the device,
# sampling strategy, number of processes, etc.
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=device.type == "cuda",
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=test_batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=device.type == "cuda",
)
return train_loader, test_loader
self.interaction_steps = 0
def select_action(self, state):
self.interaction_steps += 1
return torch.tensor([random.sample([0, 1, 2], 1)],
device=device,
dtype=torch.long)
def evaluate_action(self, state):
return torch.tensor([random.sample([0, 1, 2], 1)],
device=device,
dtype=torch.long)
frame_proc = T.Compose([T.ToPILImage(),
T.Grayscale(), \
T.Resize((84,84), interpolation=Image.BILINEAR), \
T.ToTensor()])
class Atari(object):
def __init__(self,
env_name="FreewayDeterministic-v4",
agent_history_length=4):
self.env = gym.make(env_name)
self.state = None
self.agent_history_length = agent_history_length
def reset(self):
observation = self.env.reset()
frame = self.image_proc(observation).to(device)
plt.ylabel('Reward')
plt.grid()
plt.legend()
plt.savefig('random.png')
def select_action(state):
global steps_done
steps_done += 1
return torch.tensor([[random.randrange(3)]],
device=device,
dtype=torch.long)
transform1 = T.Compose([T.ToPILImage(), T.CenterCrop(160), T.Grayscale()])
transform2 = T.Compose([T.Resize((84, 84)), T.ToTensor()])
def get_state(obs, prev_state=None):
state = np.array(obs)
state = state.transpose((2, 0, 1))
state = torch.from_numpy(state)
# print(state.shape)
state = transform1(state)
state = T.functional.adjust_contrast(state, 10)
state = transform2(state)
if prev_state is None:
state = torch.cat([state, state, state], 0)
else:
state = torch.cat([prev_state.squeeze(0)[-2:], state], 0)