def build(self, params):
depth = params[0]
nb_dense_block = params[1]
growth_rate = params[2]
dropout_rate = params[3]
nb_filter = params[4]
lr = 10. ** params[5]
densenet = DenseNet(nb_classes=3, img_dim=self.input_shape, depth=depth, nb_dense_block=nb_dense_block,
growth_rate = growth_rate, dropout_rate = dropout_rate, nb_filter = nb_filter)
optimizer = Adam(lr = lr)
densenet.compile(loss='categorical_crossentropy', optimizer = optimizer)
return densenet.to_json()
def get_model(model_name, pho_size=299, num_classes=110):
if model_name == "vgg16":
model = VGG(num_classes=num_classes, pho_size=299)
elif model_name == "resnet101":
model = resnet101(num_classes=num_classes)
elif model_name == "densenet":
model = DenseNet(growth_rate=12,
block_config=[(100 - 4) // 6 for _ in range(3)],
num_classes=num_classes,
small_inputs=False,
efficient=True,
pho_size=pho_size)
elif model_name == "InceptionResNetV2":
model = InceptionResNetV2(num_classes=num_classes)
elif model_name == "InceptionV4":
model = InceptionV4(num_classes=num_classes)
elif model_name == "Inception3":
model = Inception3(num_classes=num_classes)
elif model_name == "denoise":
model = get_denoise()
elif model_name == "Mymodel":
model = Mymodel()
elif model_name == 'Comdefend':
model = ComDefend()
elif model_name == 'Rectifi':
model = Rectifi()
return model
def __init__(self):
super(ModuleJudgerDense, self).__init__()
self.module_name = "judger"
self.module_shape = (2, 1)
self.data_type_in = (("object", "objects", "none"),
("object", "objects", "none"), "image")
self.data_type_out = "answer"
self.trainable = True
self.weight_frozen = False
self.weight_frozen_tmp = False
self.color_refs = {}
self.independent_branch = True
if self.independent_branch:
self.densenet = DenseNetSP()
else:
self.densenet = DenseNet()
def test_densenet(nb_classes=3,
img_dim=(150, 94, 5),
depth=10,
nb_dense_block=3,
growth_rate=12,
dropout_rate=0.00,
nb_filter=16,
lr=1e-3):
densenet = DenseNet(nb_classes=nb_classes,
img_dim=img_dim,
depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
dropout_rate=dropout_rate,
nb_filter=nb_filter)
optimizer = Adam(lr=lr)
densenet.compile(loss='categorical_crossentropy', optimizer=optimizer)
return densenet.to_json()
def build_module(self):
x = torch.zeros(self.densenetParameters.input_shape)
out = x
self.layer_dict['encoder'] = DenseNet(
input_shape=self.densenetParameters.input_shape,
growth_rate=self.densenetParameters.growth_rate,
block_config=self.densenetParameters.block_config,
compression=self.densenetParameters.compression,
num_init_features=self.densenetParameters.num_init_features,
bottleneck_factor=self.densenetParameters.bottleneck_factor,
drop_rate=self.densenetParameters.drop_rate,
num_classes=self.densenetParameters.num_classes,
no_classification=True,
use_se=self.densenetParameters.use_se,
increasing_dilation=True,
small_inputs=False)
out = self.layer_dict['encoder'].forward(out)
self.layer_dict['decoder'] = AutoDecoder(
block_config=self.densenetParameters.block_config,
use_bias=self.densenetParameters.use_bias,
input_shape=out.shape,
growth_rate=self.densenetParameters.growth_rate,
compression=self.densenetParameters.compression,
num_of_layers=1)
out = self.layer_dict['decoder'].forward(out)
self.layer_dict['conv_final_a'] = nn.ConvTranspose2d(out.shape[1],
out_channels=3,
kernel_size=7,
stride=4,
padding=3,
output_padding=3,
bias=True)
out = self.layer_dict['conv_final_a'].forward(out)
self.layer_dict['bn_1'] = nn.BatchNorm2d(out.shape[1])
out = self.layer_dict['bn_1'].forward(out)
self.layer_dict['conv_final_b'] = nn.ConvTranspose2d(out.shape[1],
out_channels=3,
kernel_size=7,
stride=1,
padding=3,
bias=True)
out = self.layer_dict['conv_final_b'].forward(out)
# self.layer_dict['bn_2'] = nn.BatchNorm2d(out.shape[1])
# out = self.layer_dict['bn_2'].forward(out)
return out
def load_model(path):
"""Load and return a copy of the model stored at the specified path."""
cp = torch.load(path)
model = DenseNet(**cp['model_args'])
model.load_state_dict(cp['state_dict'])
model.device = cp['model_device']
model.to(model.device)
return model
def test_hello():
train_x, train_y, test_x, test_y = get_data()
x = Variable(torch.FloatTensor(convert(train_x[0:32])))
model: nn.Module = DenseNet(
24, 0.5, n_class=2, blocks=[20, 20, 20],
fc_size=13632) # TransitionLayer(in_filter=3, out_filter=3)
pred = model(x)
print('Model Layer:', len(list(model.parameters())))
print(pred.size(), x.size())
def get_densenet(batchsize):
model = DenseNet(161)
model = Wrapper(model)
x = np.random.uniform(size=(batchsize, 3, 224, 224)).astype('f')
x = chainer.as_variable(x)
t = np.random.randint(size=(batchsize, ), low=0,
high=1000).astype(np.int32)
t = chainer.as_variable(t)
return [x, t], model
def main():
config_path = '/home2/jypark/CIFAR1000/config.yaml'
keras_session()
config = Config(config_path)
model = DenseNet(config=config)
trainer = Trainer(config=config, model=model())
trainer.train()
def get_new_model(tmp_scale=True, num_classes=args.num_classes):
if args.model == 'resnet18':
return ResNet18(tmp_scale=tmp_scale, num_classes=num_classes)
elif args.model == 'resnet50':
return ResNet50(tmp_scale=tmp_scale, num_classes=num_classes)
elif args.model == 'resnet101':
return ResNet101(tmp_scale=tmp_scale, num_classes=num_classes)
elif args.model == 'inceptionv4':
return inceptionv4(tmp_scale=tmp_scale, num_classes=num_classes)
elif args.model == 'densenet':
return DenseNet(tmp_scale=tmp_scale)
def create_bottleneck(self):
if self.FLAGS.architecture.lower() == 'densenet':
densenet_info = create_densenet_info(
nb_blocks_layers=[6, 12, 48, 32],
filters=24,
output_dim=self.bottleneck_dim)
densenet = DenseNet(self.input_tensor, self.training_flag,
self.FLAGS.dropout_rate, densenet_info)
feature_tensor = densenet.model
elif self.FLAGS.architecture.lower() == 'inception_resnet_v2':
feature_tensor, _ = inception_resnet_v2(self.input_tensor,
self.training_flag, 0.8,
self.bottleneck_dim)
return feature_tensor
def train_densenet_cifar(self,
epochs=200,
n_models=4,
device="cuda:0",
start_num=0):
# Based off: https://github.com/kuangliu/pytorch-cifar
start_num = int(start_num)
PATH = Path('/media/rene/data')
save_path = PATH / 'models'
save_path.mkdir(parents=True, exist_ok=True)
epochs = int(epochs)
num_workers = 4
batch_size = 50
for i in range(start_num, n_models + start_num):
dataloaders, dataset_sizes = make_batch_gen_cifar(
str(PATH), batch_size, num_workers, valid_name='valid')
model_name = 'densenet_' + str(i)
model = DenseNet(growthRate=24,
depth=121,
reduction=0.5,
bottleneck=True,
nClasses=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=.1,
momentum=0.9,
weight_decay=5e-4)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=int(epochs / 3),
gamma=0.1)
best_acc, model = train_model(model, criterion, optimizer,
scheduler, epochs, dataloaders,
dataset_sizes, device)
torch.save(model.state_dict(), str(save_path / model_name))
def get_model(depth=100,
growth_rate=12,
efficient=True,
valid_size=5000,
n_epochs=300,
batch_size=64,
seed=None):
"""
A demo to show off training of efficient DenseNets.
Trains and evaluates a DenseNet-BC on CIFAR-10.
Args:
data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
save (str) - path to save the model to (default /tmp)
depth (int) - depth of the network (number of convolution layers) (default 40)
growth_rate (int) - number of features added per DenseNet layer (default 12)
efficient (bool) - use the memory efficient implementation? (default True)
valid_size (int) - size of validation set
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
seed (int) - manually set the random seed (default None)
"""
depth = config.NET_DEPTH
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_init_features=growth_rate * 2,
num_classes=config.N_CLASS,
small_inputs=True,
efficient=efficient,
)
epoch_start = dir_utils.index_max("cnn_model")
model_path = "cnn_model/" + str(epoch_start) + "model.pth"
print('load cnn model:', model_path)
if (epoch_start > 0):
model.load_state_dict(
torch.load(model_path, map_location=torch.device('cpu')))
# model.cuda()
model.eval()
return model.eval()
def get_model(learning_rate=1e-3):
model = DenseNet(growth_rate=12,
block_config=(8, 12, 10),
num_init_features=48,
bn_size=4,
drop_rate=0.25,
final_drop_rate=0.25,
num_classes=200)
# set the first layer not trainable
model.features.conv0.weight.requires_grad = False
# the last fc layer
weights = [
p for n, p in model.named_parameters() if 'classifier.weight' in n
]
biases = [model.classifier.bias]
# all conv layers except the first
weights_to_be_quantized = [
p for n, p in model.named_parameters()
if 'conv' in n and ('dense' in n or 'transition' in n)
]
# parameters of batch_norm layers
bn_weights = [
p for n, p in model.named_parameters() if 'norm' in n and 'weight' in n
]
bn_biases = [
p for n, p in model.named_parameters() if 'norm' in n and 'bias' in n
]
params = [{
'params': weights,
'weight_decay': 1e-4
}, {
'params': weights_to_be_quantized
}, {
'params': biases
}, {
'params': bn_weights
}, {
'params': bn_biases
}]
optimizer = optim.Adam(params, lr=learning_rate)
loss = nn.CrossEntropyLoss().cuda()
model = model.cuda() # move the model to gpu
return model, loss, optimizer
def __init__(self, img_size, output_size):
self.losses = []
base_model = DenseNet(weights=None,
input_shape=(img_size[0], img_size[1], 3),
include_top=False)
self.classifier = Sequential()
# Batchnorm input
self.classifier.add(
BatchNormalization(input_shape=(img_size[0], img_size[1], 3)))
# Base model
self.classifier.add(base_model)
# Classifier
#self.classifier.add(Flatten())
self.classifier.add(Dense(output_size, activation='sigmoid'))
def ConsturctNetwork(NetworkType, resume):
""" Creates a Neural Network of the specified type.
Parameters
----------
NetworkType : :obj:`str`
the type of Neural Network
resume : :obj:'str'
the path of trained model
"""
if NetworkType == 'CifarResNeXt':
model = CifarResNeXt(num_classes=17, depth=29, cardinality=8)
elif NetworkType == 'ShallowResNeXt':
model = ShallowResNeXt(num_classes=17, depth=11, cardinality=16)
elif NetworkType == 'se_resnet50_shallow_sia':
model = se_resnet50_shallow_sia(17, None)
elif NetworkType == 'se_resnext50_32x4d':
model = se_resnet50_shallow_sia(17, None)
elif NetworkType == 'SimpleNet':
model = SimpleNet(17)
elif NetworkType == 'SimpleNetGN':
model = SimpleNetGN(17)
elif NetworkType == 'DenseNet':
model = DenseNet(num_classes=17)
elif NetworkType == 'DenseNetSia':
model = DenseNetSia(num_classes=17)
elif NetworkType == 'nasnetamobile':
model = nasnetamobile(num_classes=17, pretrained=None)
elif NetworkType == 'Shake_Shake':
model = Shake_Shake(num_classes=17)
else:
raise ValueError('Neural Network type %s not supported' %
(NetworkType))
if resume:
model.load_state_dict(torch.load(args.resume))
return model
def make_model(dataloader_args, model_args, training_args):
"""Make model function.
Data loaders are first created using dataloader_args dict (See get_dataloaders function for arguments).
DenseNet model is initialized with model_args dict (See DenseNet class for initialization arguments).
Training on the model is carried out according to training_args dict (See train_model function for arguments).
The learning curve is plotted after training is completed.
Finally, the trained model is returned.
"""
train_loader, test_loader, _ = get_dataloaders(**dataloader_args)
model = DenseNet(**model_args)
history = train_model(model, train_loader, test_loader, **training_args)
plot_history(history, metric='accuracy')
return model
def evaluate_hw2(path_=None):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn = DenseNet(growthRate=12, depth=48, reduction=0.5,bottleneck=True, nClasses=1)
cnn = cnn.to(device)
params = {'batch_size': 50,
'shuffle': False,
'num_workers': 4}
if path_:
data = glob.glob(os.path.join(path_, '*.jpg'))
else:
data = glob.glob(os.path.join(data_root, 'test', '*.jpg'))
list_IDs = list(map(lambda x: x[-12:-6], data))
labels = list(map(lambda x: int(x[-5:-4]), data))
channel_means = (0.5226, 0.4494, 0.4206)
channel_stds = (0.2411, 0.2299, 0.2262)
testTransform = transforms.Compose([
transforms.Resize((64, 64), interpolation=Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(channel_means, channel_stds)
])
# Generators
testing_set = MaskDataset(list_IDs, labels, testTransform,train=False)
testing_generator = DataLoader(testing_set, **params)
cnn.load_state_dict(torch.load('latest.pkl', map_location=lambda storage, loc: storage))
cnn.eval()
# Test the Model
correct = 0
total = 0
predictions = []
y_true =[]
for idx, (images, labels) in enumerate(testing_generator):
images = images.to(device)
outputs = cnn(images).view(-1).detach().cpu()
predicted = outputs.round().numpy().astype(int)
predictions.extend(predicted)
y_true.extend(labels)
print('F1 score of the model on the {} test images: {:.4f}'.format(testing_generator.__len__(),f1_score(y_true,predictions)))
data = [os.path.split(f)[-1] for f in data]
return zip(np.array(data),predictions)
def test_batches_with_labels(config_file, **kwargs):
test_data = kwargs.get('testing_data')
x = T.tensor4('input', theano.config.floatX)
y = T.ivector('output')
net = DenseNet(config_file)
net.load_params()
shape = (net.testing_batch_size, net.input_shape[1], net.input_shape[2],
net.input_shape[3])
placeholder_x = theano.shared(np.zeros(shape, 'float32'),
'input_placeholder')
placeholder_y = theano.shared(
np.zeros((net.testing_batch_size, ), 'int32'), 'label_placeholder')
p_y_given_x_test = net.inference(x)
cost = net.build_cost(p_y_given_x_test, y, **{'params': net.regularizable})
accuracy = (1. -
metrics.mean_classification_error(p_y_given_x_test, y)) * 100.
test_network = net.compile([], [cost, accuracy],
givens={
x: placeholder_x,
y: placeholder_y
},
name='test_densenet',
allow_input_downcast=True)
num_test_batches = test_data[0].shape[0] // net.testing_batch_size
cost = 0.
accuracy = 0.
dm = utils.DataManager(config_file, (placeholder_x, placeholder_y))
dm.testing_set = test_data
dm.num_test_data = test_data[0].shape[0]
dm.batch_size = net.testing_batch_size
batches = dm.get_batches(training=False)
print('Testing %d batches...' % num_test_batches)
for x, y in batches:
dm.update_input((x, y))
c, a = test_network()
cost += c
accuracy += a
cost /= num_test_batches
accuracy /= num_test_batches
print(
'Testing finished. Testing cost is %.2f. Testing accuracy is %.2f %%.'
% (cost, accuracy))
def main(config=None, init_path='', out_path='', batchsize=64, dataset='C10',
n=31, growth=40, bottleneck=True, neck_size=None, compression=1,
dropout=0):
# network
assert dataset in ('C10', 'C100', 'SVHN')
classes = 100 if dataset == 'C100' else 10
model = DenseNet.cifar_model(
n=n, growth=growth, bottleneck=bottleneck, neck_size=neck_size,
compression=compression, dropout=dropout, classes=classes
)
# trainer
if dataset == 'SVHN':
trainer_cls = SVHN_DenseNetTrainer
else:
trainer_cls = CIFAR_DenseNetTrainer
if init_path:
trainer = trainer_cls.load_state(model, init_path, batchsize=batchsize)
else:
trainer = trainer_cls(model, batchsize=batchsize)
# dataset
if not trainer.dataset:
if dataset == 'C10':
trainer.dataset = CIFAR10(testsplit=0.1)
elif dataset == 'C100':
trainer.dataset = CIFAR100(testsplit=0.1)
else:
raise NotImplementedError(
'The SVHN dataset is not yet implemented.')
# training the network
print('Training model ({} parameters) ...'.format(
count_params(model, trainable=True)))
trainer.train(config)
# save the network, the updates and the journal
if not out_path:
_, acc = trainer.validate()
date = datetime.now().strftime('%Y-%m-%d_%H:%M')
bn_str = 'bottleneck' if bottleneck else 'no_bottleneck'
tmpl = 'densenet-{}_-_n_{}_-_k_{}_-_{}_-_t_{:.2f}_-_acc_{:.2f}_{}'
out_path = tmpl.format(dataset, n, growth, bn_str, compression,
acc * 100, date)
trainer.save_state(out_path, resume=True)
def __init__(self):
super(NewDensenet, self).__init__()
self.newdense = DenseNet(growth_rate = 12, block_config = (6, 12, 12, 12),\
num_init_features=48, bn_size=4, drop_rate=0)
num_init_features = 48
growth_rate = 12
block_config = [6, 12, 12, 12]
feature_dim = num_init_features
feature_dim += growth_rate * block_config[0]
feature_dim //= 2
feature_dim += growth_rate * block_config[1]
feature_dim //= 2
feature_dim += growth_rate * block_config[2]
feature_dim //= 2
feature_dim += growth_rate * block_config[3]
print('feature_dim ', feature_dim)
self.softmax = nn.LogSoftmax(dim=1)
self.conv_segment = nn.Conv2d(feature_dim, 4, 1, stride=1, padding=0)
def load_DN_model():
base_model = DenseNet(depth=10,
weights=None,
growth_rate=12,
dropout_rate=0.4,
nb_dense_block=3,
classes=64) # Default GR = 12
output_layer = base_model.output
predictions = Dense(nb_classes, activation='sigmoid',
name='meta_preds')(output_layer)
model = Model(inputs=base_model.input, outputs=predictions)
adam = Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def test_image(config_file, image):
image = np.array(image)
assert len(image.shape) == 3 and image.shape[
-1] == 3, 'Testing image must be an RGB color image.'
x = T.tensor4('input', theano.config.floatX)
net = DenseNet(config_file)
net.load_params()
p_y_given_x_test = net.inference(x)
prediction = T.argmax(
p_y_given_x_test,
1) if p_y_given_x_test.ndim > 1 else p_y_given_x_test >= 0.5
test_network = net.compile([x],
prediction,
name='test_densenet',
allow_input_downcast=True)
prediction = test_network(np.expand_dims(image, 0).transpose((0, 3, 1, 2)))
print('The image belongs to class %d' % prediction)
def get_model():
model = DenseNet(
growth_rate=12, block_config=(8, 12, 10),
num_init_features=48, bn_size=4, drop_rate=0.25,
final_drop_rate=0.25, num_classes=200
)
# create different parameter groups
weights = [
p for n, p in model.named_parameters()
if 'conv' in n or 'classifier.weight' in n
]
biases = [model.classifier.bias]
bn_weights = [
p for n, p in model.named_parameters()
if 'norm' in n and 'weight' in n
]
bn_biases = [
p for n, p in model.named_parameters()
if 'norm' in n and 'bias' in n
]
# parameter initialization
for p in weights:
kaiming_uniform(p)
for p in biases:
constant(p, 0.0)
for p in bn_weights:
constant(p, 1.0)
for p in bn_biases:
constant(p, 0.0)
params = [
{'params': weights, 'weight_decay': 1e-4},
{'params': biases},
{'params': bn_weights},
{'params': bn_biases}
]
optimizer = optim.SGD(params, lr=1e-1, momentum=0.95, nesterov=True)
loss = nn.CrossEntropyLoss().cuda()
model = model.cuda() # move the model to gpu
return model, loss, optimizer
def build_densenet():
model = DenseNet(input_shape=(None, utils.STRIP_SIZE * 2, 1),
depth=19,
nb_dense_block=3,
growth_rate=12,
nb_filter=-1,
nb_layers_per_block=-1,
bottleneck=False,
reduction=0.0,
dropout_rate=0.0,
weight_decay=1e-4,
subsample_initial_block=False,
include_top=True,
weights=None,
input_tensor=None,
classes=1,
activation='sigmoid')
model.compile(
keras.optimizers.SGD(lr=0.1, momentum=0.9),
loss='binary_crossentropy', # TODO
metrics=['accuracy'],
)
model.summary()
return model
def main():
# Training settings
parser = argparse.ArgumentParser(
description='Obtain confidence scores from trained networks')
parser.add_argument('--test-batch-size',
type=int,
default=200,
metavar='N',
help='input batch size for testing (default: 200)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--arch',
type=str,
default='wrn',
metavar='ARC',
help='neural network arch')
parser.add_argument('--data',
type=str,
default='cifar10',
metavar='DT',
help='dataset the classifier was trained with')
parser.add_argument('--test-data',
type=str,
default='fakedata',
metavar='DT',
help='dataset used to test the classifier')
parser.add_argument('--lsun-path', type=str, help='path to LSUN dataset')
parser.add_argument('--save_path',
type=str,
default='data',
metavar='SP',
help='path to save the produced confidence')
parser.add_argument('--print-time',
type=bool,
default=False,
metavar='PT',
help='print elapsed time per batch')
parser.add_argument(
'--mode',
type=str,
default='msp',
metavar='M',
help=
'available modes: msp (maximum softmax probability), tmsp (t-softmax msp), tmsp0.5, tmsp5, tmsp10 (where number indicates nu value) odin. default: msp'
)
parser.add_argument('--T',
type=float,
default=1000.0,
metavar='T',
help='ODIN temperature scaling')
parser.add_argument('--eps',
type=float,
default=0.001,
metavar='EPS',
help='ODIN epsilon value')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if device.type == 'cuda':
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
mean, std = get_normal(args.data)
if args.test_data == "cifar10":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('../data',
download=True,
train=False,
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "cifar10_bw":
test_transform = trn.Compose([
trn.Grayscale(),
trn.Resize((28, 28)),
trn.ToTensor(),
trn.Normalize(mean, std)
])
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('../data',
download=True,
train=False,
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "svhn":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.SVHN('../data',
download=True,
split='test',
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "fakedata":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.FakeData(size=10000,
image_size=(3, 32, 32),
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "fakedata_bw":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.FakeData(size=10000,
image_size=(1, 28, 28),
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "fakedata_wm":
# wrong mean normalization
mean = (50, 50, 50)
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.FakeData(size=10000,
image_size=(3, 32, 32),
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "fmnist":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST('../data',
download=True,
train=False,
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "kmnist":
test_transform = trn.Compose(
[trn.ToTensor(), trn.Normalize(mean, std)])
test_loader = torch.utils.data.DataLoader(
datasets.KMNIST('../data',
download=True,
train=False,
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "mnist":
test_transform = trn.Compose([
trn.ToTensor(),
# lambda x : x.repeat(3,1,1) * torch.rand(3,1,1),
trn.Normalize(mean, std)
])
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data',
download=True,
train=False,
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
elif args.test_data == "lsun":
test_transform = trn.Compose([
trn.Resize((32, 32)), #trn.CenterCrop(32),
trn.ToTensor(),
trn.Normalize(mean, std)
])
test_loader = torch.utils.data.DataLoader(
datasets.LSUN(args.lsun_path,
classes='test',
transform=test_transform),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
if args.data == "cifar10":
Nc = 10
channels = 3
elif args.data == "svhn":
Nc = 10
channels = 3
elif args.data == "fmnist":
Nc = 10
channels = 1
elif args.data == "kmnist":
Nc = 10
channels = 1
if (args.mode == 'msp') | (args.mode == 'odin') | (args.mode == 'cb'):
nu = 0.0
elif args.mode == 'tmsp':
nu = 1.0
elif args.mode == 'tmsp0.5':
nu = 0.5
elif args.mode == 'tmsp5.0':
nu = 5.0
elif args.mode == 'tmsp10.0':
nu = 10.0
else:
print('mode not recognized!')
quit()
model_path = 'models'
model_path += '/' + args.data
model_name = args.arch + 'nu{}'.format(nu)
if args.arch == 'densenet':
densenet_depth = 100
model = DenseNet(densenet_depth, Nc, nu=nu).to(device)
elif args.arch == 'densenet_small':
densenet_depth = 10
model = DenseNet(densenet_depth, Nc, nu=nu).to(device)
elif args.arch == 'convnet':
model = ConvNet(Nc, channels=channels, nu=nu).to(device)
model.load_state_dict(
torch.load(model_path + '/' + model_name + '.pt', map_location=device))
score, is_hit = test(args, model, device, test_loader)
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
df = pd.DataFrame(data={'score': score, 'is_hit': is_hit})
df.to_csv('{}/{}_train{}_test{}_{}.csv'.format(args.save_path, args.arch,
args.data, args.test_data,
args.mode))
def main(chosen_model, model_checkpoint, alternate_scaling=False):
data = "/data2/cdcm/preprocessed/test" if not alternate_scaling else "/data2/cdcm/rescaled/preprocessed/test"
test_dataset = ImageAndPathDataset(root=data,
loader=npy_loader,
extensions=".npy")
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=128,
# pin_memory=True
)
# device = torch.device("cpu")
device = (torch.device('cuda')
if torch.cuda.is_available() else torch.device('cpu'))
# model = _vgg("vgg16", "D", True)
if "resnet50" in chosen_model.lower():
model = ResNet(Bottleneck, [3, 4, 6, 3], 4)
elif chosen_model.lower() == "resnet34":
model = ResNet(BasicBlock, [3, 4, 6, 3], 4)
elif "resnet18" in chosen_model.lower():
model = ResNet(BasicBlock, [2, 2, 2, 2], 4)
elif chosen_model.lower() == "basic":
model = BasicNet()
elif "vgg_dropout" in chosen_model.lower():
model = _vgg("vgg16", "D", True, dropout=True)
elif "vgg" in chosen_model.lower():
model = _vgg("vgg16", "D", True)
elif chosen_model.lower() == "mnasnet":
model = MNASNet(1.0)
elif chosen_model.lower() == "densenet":
model = DenseNet()
else:
print(f"Invalid option for model: {chosen_model}")
exit(0)
model = nn.DataParallel(model)
weight_path = f"/data2/cdcm/models/{chosen_model}-{model_checkpoint}.pt" if not alternate_scaling else f"/data2/cdcm/models/rescaled/{chosen_model}-{model_checkpoint}.pt"
checkpoint = torch.load(weight_path, map_location=device)
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in checkpoint["model_state_dict"].items():
# name = k[7:] # remove module.
# new_state_dict[name] = v
model = model.to(device=device)
# model.load_state_dict(new_state_dict)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
batch = next(iter(test_loader))
times = []
with torch.no_grad():
for sample in batch[0]:
# print(sample)
start = time.time()
sample = torch.unsqueeze(sample, 0)
end = time.time()
times.append(end - start)
prediction_time = mean(times)
with open(
f"./results/{chosen_model}-{model_checkpoint}-prediction-time.txt",
"w") as f:
f.write(str(prediction_time))
f.write("\n")
# return
evaluation.evaluate(model, test_loader, test_dataset.classes, device,
"test", f"{chosen_model}-{model_checkpoint}")
times = []
def main():
if args.data_type == "imagenet":
args.num_classes = 1000
if args.depth == 121:
units = [6, 12, 24, 16]
elif args.depth == 169:
units = [6, 12, 32, 32]
elif args.depth == 201:
units = [6, 12, 48, 32]
elif args.depth == 161:
units = [6, 12, 36, 24]
else:
raise ValueError(
"no experiments done on detph {}, you can do it youself".
format(args.depth))
symbol = DenseNet(
units=units,
num_stage=4,
growth_rate=48 if args.depth == 161 else args.growth_rate,
num_class=args.num_classes,
data_type="imagenet",
reduction=args.reduction,
drop_out=args.drop_out,
bottle_neck=True,
bn_mom=args.bn_mom,
workspace=args.workspace)
elif args.data_type == "vggface":
args.num_classes = 2613
if args.depth == 121:
units = [6, 12, 24, 16]
elif args.depth == 169:
units = [6, 12, 32, 32]
elif args.depth == 201:
units = [6, 12, 48, 32]
elif args.depth == 161:
units = [6, 12, 36, 24]
else:
raise ValueError(
"no experiments done on detph {}, you can do it youself".
format(args.depth))
symbol = DenseNet(
units=units,
num_stage=4,
growth_rate=48 if args.depth == 161 else args.growth_rate,
num_class=args.num_classes,
data_type="vggface",
reduction=args.reduction,
drop_out=args.drop_out,
bottle_neck=True,
bn_mom=args.bn_mom,
workspace=args.workspace)
elif args.data_type == "msface":
args.num_classes = 79051
if args.depth == 121:
units = [6, 12, 24, 16]
elif args.depth == 169:
units = [6, 12, 32, 32]
elif args.depth == 201:
units = [6, 12, 48, 32]
elif args.depth == 161:
units = [6, 12, 36, 24]
else:
raise ValueError(
"no experiments done on detph {}, you can do it youself".
format(args.depth))
symbol = DenseNet(
units=units,
num_stage=4,
growth_rate=48 if args.depth == 161 else args.growth_rate,
num_class=args.num_classes,
data_type="msface",
reduction=args.reduction,
drop_out=args.drop_out,
bottle_neck=True,
bn_mom=args.bn_mom,
workspace=args.workspace)
elif args.data_type == 'cifar10':
args.num_classes = 10
N = (args.depth - 4) // 6
units = [16, 16, 16]
symbol = DenseNet(units, 3, 12, 10, 'cifar10')
else:
raise ValueError("do not support {} yet".format(args.data_type))
kv = mx.kvstore.create(args.kv_store)
devs = mx.cpu() if args.gpus is None else [
mx.gpu(int(i)) for i in args.gpus.split(',')
]
epoch_size = max(int(args.num_examples / args.batch_size / kv.num_workers),
1)
begin_epoch = args.model_load_epoch if args.model_load_epoch else 0
if not os.path.exists("./model"):
os.mkdir("./model")
model_prefix = "model/densenet-{}-{}-{}".format(args.data_type, args.depth,
kv.rank)
checkpoint = mx.callback.do_checkpoint(model_prefix)
arg_params = None
aux_params = None
if args.retrain:
_, arg_params, aux_params = mx.model.load_checkpoint(
model_prefix, args.model_load_epoch)
# import pdb
# pdb.set_trace()
# print(symbol.debug_str())
# mx.viz.plot_network(symbol)
train = mx.io.ImageRecordIter(
path_imgrec=os.path.join(args.data_dir, "train.rec")
if args.data_type == 'cifar10' else
os.path.join(args.data_dir, "train_256_q90.rec") if args.aug_level == 1
else os.path.join(args.data_dir, "train_480_q90.rec"),
label_width=1,
data_name='data',
label_name='softmax_label',
data_shape=(3, 32, 32) if args.data_type == "cifar10" else
(3, 224, 224),
batch_size=args.batch_size,
pad=4 if args.data_type == "cifar10" else 0,
fill_value=127, # only used when pad is valid
rand_crop=True,
max_random_scale=
1.0, # 480 with imagnet and vggface, 384 with msface, 32 with cifar10
min_random_scale=1.0 if args.data_type == "cifar10" else 1.0 if
args.aug_level == 1 else 0.667, # 256.0/480.0=0.533, 256.0/384.0=0.667
max_aspect_ratio=0
if args.data_type == "cifar10" else 0 if args.aug_level == 1 else 0.25,
random_h=0 if args.data_type == "cifar10" else
0 if args.aug_level == 1 else 36, # 0.4*90
random_s=0 if args.data_type == "cifar10" else
0 if args.aug_level == 1 else 50, # 0.4*127
random_l=0 if args.data_type == "cifar10" else
0 if args.aug_level == 1 else 50, # 0.4*127
max_rotate_angle=0 if args.aug_level <= 2 else 10,
max_shear_ratio=0 if args.aug_level <= 2 else 0.1,
rand_mirror=True,
shuffle=True,
num_parts=kv.num_workers,
part_index=kv.rank)
val = mx.io.ImageRecordIter(
path_imgrec=os.path.join(args.data_dir, "val.rec") if args.data_type
== 'cifar10' else os.path.join(args.data_dir, "val_256_q90.rec"),
label_width=1,
data_name='data',
label_name='softmax_label',
batch_size=args.batch_size,
data_shape=(3, 32, 32) if args.data_type == "cifar10" else
(3, 224, 224),
rand_crop=False,
rand_mirror=False,
num_parts=kv.num_workers,
part_index=kv.rank)
dshape = (64, 3, 32, 32)
net_planned = memonger.search_plan(symbol, 1, data=dshape)
c = get_cost(symbol, data=dshape)
print('cost %d MB' % c)
# dshape = (64, 3, 32, 32)
# old_cost = memonger.get_cost(symbol, data=dshape)
new_cost = memonger.get_cost(net_planned, data=dshape)
# print('Old feature map cost=%d MB' % old_cost)
print('New feature map cost=%d MB' % new_cost)
model = mx.model.FeedForward(
# net_planned,
ctx=devs,
symbol=net_planned,
arg_params=arg_params,
aux_params=aux_params,
num_epoch=300 if args.data_type == "cifar10" else 125,
begin_epoch=begin_epoch,
learning_rate=args.lr,
momentum=args.mom,
wd=args.wd,
optimizer='sgd',
initializer=mx.init.Xavier(rnd_type='gaussian',
factor_type="in",
magnitude=2),
lr_scheduler=multi_factor_scheduler(
begin_epoch, epoch_size, step=[220, 260, 280], factor=0.1)
if args.data_type == 'cifar10' else multi_factor_scheduler(
begin_epoch,
epoch_size,
step=[30, 60, 90, 95, 115, 120],
factor=0.1),
)
# import pdb
# pdb.set_trace()
model.fit(X=train,
eval_data=val,
eval_metric=['acc'] if args.data_type == 'cifar10' else
['acc', mx.metric.create('top_k_accuracy', top_k=5)],
kvstore=kv,
batch_end_callback=mx.callback.Speedometer(
args.batch_size, args.frequent),
epoch_end_callback=checkpoint)
def main(args):
assert((args.depth - args.block - 1) % args.block == 0)
n_layer = int((args.depth - args.block - 1) / args.block)
if args.dataset == 'cifar10':
train, test = cifar.get_cifar10()
n_class = 10
elif args.dataset == 'cifar100':
train, test = cifar.get_cifar100()
n_class = 100
elif args.dataset == 'SVHN':
raise NotImplementedError()
images = convert.concat_examples(train)[0]
mean = images.mean(axis=(0, 2, 3))
std = images.std(axis=(0, 2, 3))
train = PreprocessedDataset(train, mean, std, random=args.augment)
test = PreprocessedDataset(test, mean, std)
train_iter = chainer.iterators.SerialIterator(
train, args.batchsize / args.split_size)
test_iter = chainer.iterators.SerialIterator(
test, args.batchsize / args.split_size, repeat=False, shuffle=False)
model = chainer.links.Classifier(DenseNet(
n_layer, args.growth_rate, n_class, args.drop_ratio, 16, args.block))
if args.init_model:
serializers.load_npz(args.init_model, model)
chainer.cuda.get_device(args.gpu).use()
model.to_gpu()
optimizer = chainer.optimizers.NesterovAG(lr=args.lr, momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
updater = StandardUpdater(
train_iter, optimizer, (args.split_size, 'mean'), device=args.gpu)
trainer = training.Trainer(updater, (300, 'epoch'), out=args.dir)
val_interval = (1, 'epoch')
log_interval = (1, 'epoch')
def lr_shift(): # DenseNet specific!
if updater.epoch == 150 or updater.epoch == 225:
optimizer.lr *= 0.1
return optimizer.lr
trainer.extend(Evaluator(
test_iter, model, device=args.gpu), trigger=val_interval)
trainer.extend(extensions.observe_value(
'lr', lambda _: lr_shift()), trigger=(1, 'epoch'))
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot_object(
model, 'epoch_{.updater.epoch}.model'), trigger=val_interval)
trainer.extend(extensions.snapshot_object(
optimizer, 'epoch_{.updater.epoch}.state'), trigger=val_interval)
trainer.extend(extensions.LogReport(trigger=log_interval))
start_time = time.time()
trainer.extend(extensions.observe_value(
'time', lambda _: time.time() - start_time), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'time', 'epoch', 'iteration', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'lr',
]), trigger=log_interval)
trainer.extend(extensions.observe_value(
'graph', lambda _: create_fig(args.dir)),
trigger=(1, 'epoch'), priority=50)
trainer.extend(extensions.ProgressBar(update_interval=10))
trainer.run()
std = np.std(X[:, :, :, i])
X_train[:, :, :, i] = (X_train[:, :, :, i] - mean) / std
X_test[:, :, :, i] = (X_test[:, :, :, i] - mean) / std
# set default parameters
batch_size = 64
nb_epoch = 300
depth = 40
nb_dense_block = 3
nb_filter = 16
growth_rate = 12
dropout_rate = 0.2
learning_rate = 0.1
weight_decay = 1e-4
model = DenseNet(10, img_dim, depth, nb_dense_block, growth_rate,
nb_filter, dropout_rate, weight_decay)
opt = SGD(lr=learning_rate, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=["accuracy"])
offset = 0
save_path = os.path.join('./log', 'densenet')
callbacks = []
callbacks.append(ModelCheckpoint(save_path + '.h5'))
steps = [nb_epoch / 2 - offset, 3 * nb_epoch / 4 - offset]
schedule = Step(steps,
[learning_rate, 0.1 * learning_rate, 0.01 * learning_rate],
verbose=1)
callbacks.append(schedule)