def vgg(**config):
dataset = config.pop('dataset', 'imagenet')
depth = config.pop('depth', 16)
bn = config.pop('bn', True)
if dataset == 'imagenet':
config.setdefault('num_classes', 1000)
if depth == 11:
if bn is False:
return vgg11(pretrained=False, **config)
else:
return vgg11_bn(pretrained=False, **config)
if depth == 13:
if bn is False:
return vgg13(pretrained=False, **config)
else:
return vgg13_bn(pretrained=False, **config)
if depth == 16:
if bn is False:
return vgg16(pretrained=False, **config)
else:
return vgg16_bn(pretrained=False, **config)
if depth == 19:
if bn is False:
return vgg19(pretrained=False, **config)
else:
return vgg19_bn(pretrained=False, **config)
elif dataset == 'cifar10':
config.setdefault('num_classes', 10)
elif dataset == 'cifar100':
config.setdefault('num_classes', 100)
config.setdefault('batch_norm', bn)
return VGG(model_name[depth], **config)
def __init__(self, dataset, arch='resnet18'):
super().__init__()
self.num_cam = dataset.num_cam
self.img_shape, self.reducedgrid_shape = dataset.img_shape, dataset.reducedgrid_shape
imgcoord2worldgrid_matrices = self.get_imgcoord2worldgrid_matrices(
dataset.base.intrinsic_matrices, dataset.base.extrinsic_matrices,
dataset.base.worldgrid2worldcoord_mat)
self.coord_map = self.create_coord_map(self.reducedgrid_shape + [1])
# img
self.upsample_shape = list(
map(lambda x: int(x / dataset.img_reduce), self.img_shape))
img_reduce = np.array(self.img_shape) / np.array(self.upsample_shape)
img_zoom_mat = np.diag(np.append(img_reduce, [1]))
# map
map_zoom_mat = np.diag(
np.append(np.ones([2]) / dataset.grid_reduce, [1]))
# projection matrices: img feat -> map feat
self.proj_mats = [
torch.from_numpy(
map_zoom_mat @ imgcoord2worldgrid_matrices[cam] @ img_zoom_mat)
for cam in range(self.num_cam)
]
if arch == 'vgg11':
base = vgg11().features
base[-1] = nn.Sequential()
base[-4] = nn.Sequential()
split = 10
self.base_pt1 = base[:split].to('cuda:1')
self.base_pt2 = base[split:].to('cuda:0')
out_channel = 512
elif arch == 'resnet18':
base = nn.Sequential(*list(
resnet18(replace_stride_with_dilation=[False, True, True
]).children())[:-2])
split = 7
self.base_pt1 = base[:split].to('cuda:1')
self.base_pt2 = base[split:].to('cuda:0')
out_channel = 512
else:
raise Exception('architecture currently support [vgg11, resnet18]')
# 2.5cm -> 0.5m: 20x
self.img_classifier = nn.Sequential(nn.Conv2d(out_channel, 64, 1),
nn.ReLU(),
nn.Conv2d(64, 2, 1,
bias=False)).to('cuda:0')
self.map_classifier = nn.Sequential(
nn.Conv2d(self.num_cam + 2, 512, 3, padding=1),
nn.ReLU(),
# nn.Conv2d(512, 512, 5, 1, 2), nn.ReLU(),
nn.Conv2d(512, 512, 3, padding=2, dilation=2),
nn.ReLU(),
nn.Conv2d(512, 1, 3, padding=4, dilation=4,
bias=False)).to('cuda:0')
pass
def vgg_11(batch_norm=True, pretrained=False, fixed_feature=True):
""" VGG 11-layer model from torchvision's vgg model.
:param batch_norm: train model with batch normalization
:param pretrained: if true, return a model pretrained on ImageNet
:param fixed_feature: if true and pretrained is true, model features are fixed while training.
"""
if batch_norm:
from torchvision.models.vgg import vgg11_bn
model = vgg11_bn(pretrained)
else:
from torchvision.models.vgg import vgg11
model = vgg11(pretrained)
ff = True if pretrained and fixed_feature else False
return _VGG(model, model.features, ff)
def test_integration():
transform_pipeline = Compose([Resize((64, 64)), ToTensor()])
cifar10_train = DummyDataset(transform_pipeline)
cifar10_test = DummyDataset(transform_pipeline)
al_dataset = ActiveLearningDataset(cifar10_train, pool_specifics={'transform': transform_pipeline})
al_dataset.label_randomly(10)
use_cuda = False
model = vgg.vgg11(pretrained=False,
num_classes=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0005)
# We can now use BaaL to create the active learning loop.
model = ModelWrapper(model, criterion)
# We create an ActiveLearningLoop that will automatically label the most uncertain samples.
# In this case, we use the widely used BALD heuristic.
active_loop = ActiveLearningLoop(al_dataset,
model.predict_on_dataset,
heuristic=heuristics.BALD(),
query_size=10,
batch_size=10,
iterations=2,
use_cuda=use_cuda,
workers=4)
# We're all set!
num_steps = 10
for step in range(num_steps):
old_param = list(map(lambda x: x.clone(), model.model.parameters()))
model.train_on_dataset(al_dataset, optimizer=optimizer, batch_size=10,
epoch=1, use_cuda=use_cuda, workers=2)
model.test_on_dataset(cifar10_test, batch_size=10, use_cuda=use_cuda,
workers=2)
if not active_loop.step():
break
new_param = list(map(lambda x: x.clone(), model.model.parameters()))
assert any([not np.allclose(i.detach(), j.detach())
for i, j in zip(old_param, new_param)])
assert step == 4 # 10 + (4 * 10) = 50, so it stops at iterations 4
def test_anneal_lr(self):
net = vgg11()
opt = torch.optim.SGD(net.parameters(), lr = .1)
anneal_lr(opt, 10)
for o in opt.param_groups:
self.assertEqual(o["lr"], 0.01)
def recordVGG(info):
global SKIP
import torchvision.models.vgg as vggGen
if not (SKIP and 'vgg11' in info['name_list']):
INFO("proceeding for VGG11...")
net = vggGen.vgg11(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg11')
else:
INFO("Skip VGG11")
if not (SKIP and 'vgg13' in info['name_list']):
INFO("proceeding for VGG13...")
net = vggGen.vgg13(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg13')
else:
INFO("Skip VGG13")
if not (SKIP and 'vgg16' in info['name_list']):
INFO("proceeding for VGG16...")
net = vggGen.vgg16(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg16')
else:
INFO("Skip VGG16")
if not (SKIP and 'vgg19' in info['name_list']):
INFO("proceeding for VGG19...")
net = vggGen.vgg19(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg19')
else:
INFO("Skip VGG19")
if not (SKIP and 'vgg11_bn' in info['name_list']):
INFO("proceeding for VGG11_bn...")
net = vggGen.vgg11_bn(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg11_bn')
else:
INFO("Skip VGG11_bn")
if not (SKIP and 'vgg13_bn' in info['name_list']):
INFO("proceeding for VGG13_bn...")
net = vggGen.vgg13_bn(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg13_bn')
else:
INFO("Skip VGG13_bn")
if not (SKIP and 'vgg16_bn' in info['name_list']):
INFO("proceeding for VGG16_bn...")
net = vggGen.vgg16_bn(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg16_bn')
else:
INFO("Skip VGG16_bn")
if not (SKIP and 'vgg19_bn' in info['name_list']):
INFO("proceeding for VGG19_bn...")
net = vggGen.vgg19_bn(pretrained=True).cuda()
sum = __summary(net, [3, 224, 224], verbose=True)
__writeInfoJSON(sum, 'vgg19_bn')
else:
INFO("Skip VGG19_bn")
def load_model(model_name, classes=1000, pretrained=True, in_channels=3):
"""Load the specified VGG architecture for ImageNet
Args:
model_name: VGG architecture type
classes: number of predicted classes
pretrained: load pretrained network on ImageNet
"""
if pretrained:
assert classes == 1000, "Pretrained models are provided only for Imagenet."
kwargs = {'num_classes': classes}
if model_name == 'vgg11':
net = VGG.vgg11(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg13':
net = VGG.vgg13(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg16':
net = VGG.vgg16(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg19':
net = VGG.vgg19(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg11bn':
net = VGG.vgg11_bn(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg13bn':
net = VGG.vgg13_bn(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg16bn':
net = VGG.vgg16_bn(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg19bn':
net = VGG.vgg19_bn(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'vgg19_orig':
net = VGG.vgg19(pretrained=False, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
net.features[0] = input_layer
init_weights_vgg_orig(net)
elif model_name == 'alexnet':
net = AlexNet(pretrained=pretrained, **kwargs)
if in_channels != 3:
input_layer = nn.Conv2d(in_channels,
64,
kernel_size=11,
stride=4,
padding=2)
nn.init.kaiming_normal_(input_layer.weight,
mode='fan_out',
nonlinearity='relu')
input_layer.bias.data.zero_()
net.features[0] = input_layer
elif model_name == 'lenet':
kwargs['in_channels'] = in_channels
net = lenet(**kwargs)
else:
raise ValueError("Unsupported model architecture.")
return net
test_data = CIFAR10(root="./tmp",
train=False,
download=True,
transform=Compose([ToTensor()]))
train_loader = DataLoader(train_data,
batch_size=1024,
shuffle=True,
pin_memory=True)
test_loader = DataLoader(test_data,
batch_size=1024,
shuffle=False,
pin_memory=True)
# instantiate model
model = vgg11(num_classes=10).to(device)
# instantiate optimizer and loss
optimizer = Adam(params=model.parameters())
criterion = CrossEntropyLoss().to(device)
# initialize delve
tracker = SaturationTracker("experiment",
save_to="plotcsv",
stats=["lsat"],
modules=model,
device=device)
# begin training
for epoch in range(10):
# only record saturation for uneven epochs
plt.show()
train_monitors[1].plot(res)
plt.show()
train_monitors[5].plot(res)
plt.show()
train_monitors[6].plot(res)
plt.show()
# You can still have access to test_errors etc by doing "te, _ = Test_Error(testpred)(net)"
# Note that if you ask again, it will not recompute the net output
# To force recomputation, do "te, _ = Test_Error(testpred)(net, recompute=True)"
if example_3:
from torchvision.models.vgg import vgg11
trainset, valset, testset, trainloader, valloader, testloader, classes = load_plant_seed(
size=(224, 224), validation_size=10)
net = vgg11(pretrained=True)
# for param in net.parameters():
# param.requires_grad = False
#net.fc3 = nn.Linear(4096, len(classes))
net.classifier[6] = nn.Linear(4096, len(classes))
freeze(net, -1)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
res = {}
testpred = Test_Prediction(valloader)
train_monitors = [
Train_Error(10),
Running_Loss(10),
Test_Error(testpred, 50),
Save_Net(100)
] #,
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models.resnet import resnet18
from torchvision.models.vgg import vgg11
from tqdm import tqdm
if __name__ == '__main__':
transform = transforms.Compose((transforms.ToTensor(), transforms.Normalize(0.5, 0.5, 0.5)))
train_dataset = CIFAR10('data', train=True, transform=transform, download=False)
test_dataset = CIFAR10('data', train=False, transform=transform, download=False)
max_epochs = 100
batch_size = 64
net = vgg11(pretrained=False, num_classes=10).cuda()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-5)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_epochs, eta_min=1e-10)
loss_f = CrossEntropyLoss()
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=3)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=3)
for epoch in range(max_epochs):
train_acc = 0
test_acc = 0
net.train()
for idx, data in enumerate(tqdm(train_dataloader)):
optimizer.zero_grad()
inputs = data[0].cuda()
def main():
vgg = vgg11()
dataset = Cifar10Dataset()
kubenet = KubeVGG(vgg, dataset)
return kubenet.start()
def __init__(self):
super().__init__()
vgg = vgg11()
self.features = vgg.features # We only need the convolutional part of vgg11
