datasets = ('CIFAR10', 'CIFAR100') + data.imagenet.names + data.custom.names
parser = argparse.ArgumentParser(description='PyTorch CIFAR Training')
parser.add_argument('--batch-size',
default=512,
type=int,
help='Batch size used for training')
parser.add_argument('--epochs',
'-e',
default=200,
type=int,
help='By default, lr schedule is scaled accordingly')
parser.add_argument('--dataset', default='CIFAR10', choices=datasets)
parser.add_argument('--arch',
default='ResNet18',
choices=list(models.get_model_choices()))
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume',
'-r',
action='store_true',
help='resume from checkpoint')
# extra general options for main script
parser.add_argument('--path-resume',
default='',
help='Overrides checkpoint path generation')
parser.add_argument('--name',
default='',
help='Name of experiment. Used for checkpoint filename')
parser.add_argument(
'--pretrained',
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataset',
help='Must be a folder nbdt/wnids/{dataset}.txt containing wnids',
choices=DATASETS,
default='MNIST')
parser.add_argument(
'--extra',
type=int,
default=0,
help='Percent extra nodes to add to the tree. If 100, the number of '
'nodes in tree are doubled. Note this is an integral percent.')
parser.add_argument('--multi-path',
action='store_true',
help='Allows each leaf multiple paths to the root.')
parser.add_argument('--no-prune',
action='store_true',
help='Do not prune.')
parser.add_argument(
'--fname',
type=str,
choices=FNAME,
help='Override all settings and just provide a path to a graph',
default='graph-MNIST')
parser.add_argument(
'--method',
choices=METHODS,
help=
'structure_released.xml apparently is missing many CIFAR100 classes. '
'As a result, pruning does not work for CIFAR100. Random will randomly '
'join clusters together, iteratively, to make a roughly-binary tree.',
default='induced')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--branching-factor', type=int, default=2)
parser.add_argument(
'--checkpoint',
type=str,
choices=CHECKPOINT,
default='./mnist_cnn.pth',
help='(induced hierarchy) Checkpoint to load into model. The fc weights'
' are used for clustering.')
parser.add_argument(
'--arch',
type=str,
default='ResNet18',
help='(induced hierarchy) Model name to get pretrained fc weights for.',
choices=list(models.get_model_choices()))
parser.add_argument(
'--induced-linkage',
type=str,
default='ward',
help=
'(induced hierarchy) Linkage type used for agglomerative clustering "ward", "complete", "average", "single"'
)
parser.add_argument(
'--induced-affinity',
type=str,
default='euclidean',
help=
'(induced hierarchy) Metric used for computing similarity "l1", "l2", "manhattan", "cosine","euclidean"'
)
parser.add_argument('--vis-zoom', type=float, default=1.0)
parser.add_argument('--vis-curved',
action='store_true',
help='Use curved lines for edges')
parser.add_argument('--vis-sublabels',
action='store_true',
help='Show sublabels')
parser.add_argument(
'--color',
choices=('blue', 'blue-green'),
default='blue',
help='Color to use, for colored flags. Note this takes NO effect if '
'nodes are not colored.')
parser.add_argument('--vis-no-color-leaves',
action='store_true',
help='Do NOT highlight leaves with special color.')
parser.add_argument(
'--vis-color-path-to',
type=str,
help='Vis all nodes on path from leaf to root, as blue. Pass leaf name.'
)
parser.add_argument('--vis-color-nodes',
nargs='*',
help='Nodes to color. Nodes are identified by label')
parser.add_argument('--vis-force-labels-left',
nargs='*',
help='Labels to force text left of the node.')
parser.add_argument('--vis-leaf-images',
action='store_true',
help='Include sample images for each leaf/class.')
parser.add_argument(
'--vis-image-resize-factor',
type=float,
default=1.,
help='Factor to resize image size by. Default image size is provided '
'by the original image. e.g., 32 for CIFAR10, 224 for Imagenet')
parser.add_argument('--vis-height',
type=int,
default=750,
help='Height of the outputted visualization')
parser.add_argument('--vis-dark', action='store_true', help='Dark mode')
return parser
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset",
help="Must be a folder nbdt/wnids/{dataset}.txt containing wnids",
choices=DATASETS,
default="CIFAR10",
)
parser.add_argument(
"--extra",
type=int,
default=0,
help="Percent extra nodes to add to the tree. If 100, the number of "
"nodes in tree are doubled. Note this is an integral percent.",
)
parser.add_argument(
"--multi-path",
action="store_true",
help="Allows each leaf multiple paths to the root.",
)
parser.add_argument("--no-prune",
action="store_true",
help="Do not prune.")
parser.add_argument(
"--fname",
type=str,
help="Override all settings and just provide graph name")
parser.add_argument(
"--path",
type=str,
help="Override all settings and just provide a path to a graph",
)
parser.add_argument(
"--method",
choices=METHODS,
help=
"structure_released.xml apparently is missing many CIFAR100 classes. "
"As a result, pruning does not work for CIFAR100. Random will randomly "
"join clusters together, iteratively, to make a roughly-binary tree.",
default="induced",
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--branching-factor", type=int, default=2)
parser.add_argument(
"--checkpoint",
type=str,
help="(induced hierarchy) Checkpoint to load into model. The fc weights"
" are used for clustering.",
)
parser.add_argument(
"--arch",
type=str,
default="ResNet18",
help="(induced hierarchy) Model name to get pretrained fc weights for.",
choices=list(models.get_model_choices()),
)
parser.add_argument(
"--induced-linkage",
type=str,
default="ward",
help=
"(induced hierarchy) Linkage type used for agglomerative clustering",
)
parser.add_argument(
"--induced-affinity",
type=str,
default="euclidean",
help="(induced hierarchy) Metric used for computing similarity",
)
parser.add_argument("--vis-out-fname",
type=str,
help="Base filename for vis output file")
parser.add_argument(
"--vis-zoom",
type=float,
default=1.0,
help="How large individual elements are, relative to the whole screen",
)
parser.add_argument(
"--vis-scale",
type=float,
default=1.0,
help="Initial scale for the svg. Like scaling an image.",
)
parser.add_argument("--vis-curved",
action="store_true",
help="Use curved lines for edges")
parser.add_argument("--vis-sublabels",
action="store_true",
help="Show sublabels")
parser.add_argument("--vis-fake-sublabels",
action="store_true",
help="Show fake sublabels")
parser.add_argument(
"--color",
choices=("blue", "blue-green", "blue-minimal"),
default="blue",
help="Color to use, for colored flags. Note this takes NO effect if "
"nodes are not colored.",
)
parser.add_argument(
"--vis-no-color-leaves",
action="store_true",
help="Do NOT highlight leaves with special color.",
)
parser.add_argument(
"--vis-color-path-to",
type=str,
help=
"Vis all nodes on path from leaf to root, as blue. Pass leaf name.",
)
parser.add_argument(
"--vis-color-nodes",
nargs="*",
help="Nodes to color. Nodes are identified by label",
)
parser.add_argument(
"--vis-force-labels-left",
nargs="*",
help="Labels to force text left of the node.",
)
parser.add_argument(
"--vis-leaf-images",
action="store_true",
help="Include sample images for each leaf/class.",
)
parser.add_argument(
"--vis-image-resize-factor",
type=float,
default=1.0,
help="Factor to resize image size by. Default image size is provided "
"by the original image. e.g., 32 for CIFAR10, 224 for Imagenet",
)
parser.add_argument(
"--vis-height",
type=int,
default=750,
help="Height of the outputted visualization",
)
parser.add_argument("--vis-width", type=int, default=3000)
parser.add_argument("--vis-theme",
choices=("dark", "minimal", "regular"),
default="regular")
parser.add_argument("--vis-root", type=str, help="Which node is root")
parser.add_argument("--vis-margin-top", type=int, default=20)
parser.add_argument("--vis-margin-left", type=int, default=250)
parser.add_argument("--vis-hide", nargs="*", help="IDs of nodes to hide")
parser.add_argument(
"--vis-node-conf",
nargs=3,
action="append",
help="Key-value pairs to add: <node> <key> <value>",
)
parser.add_argument(
"--vis-above-dy",
type=int,
default=325,
help="Amount to offset images above nodes by",
)
parser.add_argument(
"--vis-below-dy",
type=int,
default=200,
help="Amount to offset images below nodes by",
)
parser.add_argument("--vis-colormap", help="Path to colormap image")
parser.add_argument("--vis-root-y",
type=int,
help="root position y",
default=-1)
return parser
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataset',
help='Must be a folder nbdt/wnids/{dataset}.txt containing wnids',
choices=DATASETS,
default='CIFAR10')
parser.add_argument(
'--extra',
type=int,
default=0,
help='Percent extra nodes to add to the tree. If 100, the number of '
'nodes in tree are doubled. Note this is an integral percent.')
parser.add_argument('--multi-path',
action='store_true',
help='Allows each leaf multiple paths to the root.')
parser.add_argument('--no-prune',
action='store_true',
help='Do not prune.')
parser.add_argument(
'--fname',
type=str,
help='Override all settings and just provide graph name')
parser.add_argument(
'--path',
type=str,
help='Override all settings and just provide a path to a graph')
parser.add_argument(
'--method',
choices=METHODS,
help=
'structure_released.xml apparently is missing many CIFAR100 classes. '
'As a result, pruning does not work for CIFAR100. Random will randomly '
'join clusters together, iteratively, to make a roughly-binary tree.',
default='induced')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--branching-factor', type=int, default=2)
parser.add_argument(
'--checkpoint',
type=str,
help='(induced hierarchy) Checkpoint to load into model. The fc weights'
' are used for clustering.')
parser.add_argument(
'--arch',
type=str,
default='ResNet18',
help='(induced hierarchy) Model name to get pretrained fc weights for.',
choices=list(models.get_model_choices()))
parser.add_argument(
'--induced-linkage',
type=str,
default='ward',
help=
'(induced hierarchy) Linkage type used for agglomerative clustering')
parser.add_argument(
'--induced-affinity',
type=str,
default='euclidean',
help='(induced hierarchy) Metric used for computing similarity')
parser.add_argument('--vis-out-fname',
type=str,
help='Base filename for vis output file')
parser.add_argument(
'--vis-zoom',
type=float,
default=1.0,
help='How large individual elements are, relative to the whole screen')
parser.add_argument(
'--vis-scale',
type=float,
default=1.0,
help='Initial scale for the svg. Like scaling an image.')
parser.add_argument('--vis-curved',
action='store_true',
help='Use curved lines for edges')
parser.add_argument('--vis-sublabels',
action='store_true',
help='Show sublabels')
parser.add_argument('--vis-fake-sublabels',
action='store_true',
help='Show fake sublabels')
parser.add_argument(
'--color',
choices=('blue', 'blue-green'),
default='blue',
help='Color to use, for colored flags. Note this takes NO effect if '
'nodes are not colored.')
parser.add_argument('--vis-no-color-leaves',
action='store_true',
help='Do NOT highlight leaves with special color.')
parser.add_argument(
'--vis-color-path-to',
type=str,
help='Vis all nodes on path from leaf to root, as blue. Pass leaf name.'
)
parser.add_argument('--vis-color-nodes',
nargs='*',
help='Nodes to color. Nodes are identified by label')
parser.add_argument('--vis-force-labels-left',
nargs='*',
help='Labels to force text left of the node.')
parser.add_argument('--vis-leaf-images',
action='store_true',
help='Include sample images for each leaf/class.')
parser.add_argument(
'--vis-image-resize-factor',
type=float,
default=1.,
help='Factor to resize image size by. Default image size is provided '
'by the original image. e.g., 32 for CIFAR10, 224 for Imagenet')
parser.add_argument('--vis-height',
type=int,
default=750,
help='Height of the outputted visualization')
parser.add_argument('--vis-width', type=int, default=3000)
parser.add_argument('--vis-dark', action='store_true', help='Dark mode')
parser.add_argument('--vis-root', type=str, help='Which node is root')
parser.add_argument('--vis-margin-top', type=int, default=20)
parser.add_argument('--vis-margin-left', type=int, default=250)
parser.add_argument('--vis-hide', nargs='*', help='IDs of nodes to hide')
parser.add_argument('--vis-node-conf',
nargs=3,
action='append',
help='Key-value pairs to add: <node> <key> <value>')
parser.add_argument('--vis-above-dy',
type=int,
default=325,
help='Amount to offset images above nodes by')
parser.add_argument('--vis-below-dy',
type=int,
default=200,
help='Amount to offset images below nodes by')
parser.add_argument('--vis-colormap', help='Path to colormap image')
parser.add_argument('--vis-root-y',
type=int,
help='root position y',
default=-1)
return parser
from nbdt.utils import (
progress_bar, generate_fname, generate_kwargs, Colors, maybe_install_wordnet
)
maybe_install_wordnet()
datasets = ('CIFAR10', 'CIFAR100') + data.imagenet.names + data.custom.names
parser = argparse.ArgumentParser(description='PyTorch CIFAR Training')
parser.add_argument('--batch-size', default=512, type=int,
help='Batch size used for training')
parser.add_argument('--epochs', '-e', default=200, type=int,
help='By default, lr schedule is scaled accordingly')
parser.add_argument('--dataset', default='CIFAR10', choices=datasets)
parser.add_argument('--arch', default='ResNet18', choices=list(models.get_model_choices()))
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
# extra general options for main script
parser.add_argument('--path-resume', default='',
help='Overrides checkpoint path generation')
parser.add_argument('--name', default='',
help='Name of experiment. Used for checkpoint filename')
parser.add_argument('--pretrained', action='store_true',
help='Download pretrained model. Not all models support this.')
parser.add_argument('--eval', help='eval only', action='store_true')
# options specific to this project and its dataloaders
parser.add_argument('--loss', choices=loss.names, default='CrossEntropyLoss')
parser.add_argument('--analysis', choices=analysis.names, help='Run analysis after each epoch')
def main():
maybe_install_wordnet()
datasets = data.cifar.names + data.imagenet.names + data.custom.names
parser = argparse.ArgumentParser(description="PyTorch CIFAR Training")
parser.add_argument("--batch-size",
default=512,
type=int,
help="Batch size used for training")
parser.add_argument(
"--epochs",
"-e",
default=200,
type=int,
help="By default, lr schedule is scaled accordingly",
)
parser.add_argument("--dataset", default="CIFAR10", choices=datasets)
parser.add_argument("--arch",
default="ResNet18",
choices=list(models.get_model_choices()))
parser.add_argument("--lr", default=0.1, type=float, help="learning rate")
parser.add_argument("--resume",
"-r",
action="store_true",
help="resume from checkpoint")
# extra general options for main script
parser.add_argument("--path-resume",
default="",
help="Overrides checkpoint path generation")
parser.add_argument(
"--name",
default="",
help="Name of experiment. Used for checkpoint filename")
parser.add_argument(
"--pretrained",
action="store_true",
help="Download pretrained model. Not all models support this.",
)
parser.add_argument("--eval", help="eval only", action="store_true")
parser.add_argument(
"--dataset-test",
choices=datasets,
help="If not set, automatically set to train dataset",
)
parser.add_argument(
"--disable-test-eval",
help="Allows you to run model inference on a test dataset "
" different from train dataset. Use an anlayzer to define "
"a metric.",
action="store_true",
)
# options specific to this project and its dataloaders
parser.add_argument("--loss",
choices=loss.names,
default=["CrossEntropyLoss"],
nargs="+")
parser.add_argument("--metric", choices=metrics.names, default="top1")
parser.add_argument("--analysis",
choices=analysis.names,
help="Run analysis after each epoch")
# other dataset, loss or analysis specific options
data.custom.add_arguments(parser)
T.add_arguments(parser)
loss.add_arguments(parser)
analysis.add_arguments(parser)
args = parser.parse_args()
loss.set_default_values(args)
device = "cuda" if torch.cuda.is_available() else "cpu"
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print("==> Preparing data..")
dataset_train = getattr(data, args.dataset)
dataset_test = getattr(data, args.dataset_test or args.dataset)
transform_train = dataset_train.transform_train()
transform_test = dataset_test.transform_val()
dataset_train_kwargs = generate_kwargs(
args,
dataset_train,
name=f"Dataset {dataset_train.__class__.__name__}",
globals=locals(),
)
dataset_test_kwargs = generate_kwargs(
args,
dataset_test,
name=f"Dataset {dataset_test.__class__.__name__}",
globals=locals(),
)
trainset = dataset_train(
**dataset_train_kwargs,
root="./data",
train=True,
download=True,
transform=transform_train,
)
testset = dataset_test(
**dataset_test_kwargs,
root="./data",
train=False,
download=True,
transform=transform_test,
)
assert trainset.classes == testset.classes or args.disable_test_eval, (
trainset.classes,
testset.classes,
)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=2)
Colors.cyan(
f"Training with dataset {args.dataset} and {len(trainset.classes)} classes"
)
Colors.cyan(
f"Testing with dataset {args.dataset_test or args.dataset} and {len(testset.classes)} classes"
)
# Model
print("==> Building model..")
model = getattr(models, args.arch)
if args.pretrained:
print("==> Loading pretrained model..")
model = make_kwarg_optional(model, dataset=args.dataset)
net = model(pretrained=True, num_classes=len(trainset.classes))
else:
net = model(num_classes=len(trainset.classes))
net = net.to(device)
if device == "cuda":
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
checkpoint_fname = generate_checkpoint_fname(**vars(args))
checkpoint_path = "./checkpoint/{}.pth".format(checkpoint_fname)
print(f"==> Checkpoints will be saved to: {checkpoint_path}")
resume_path = args.path_resume or checkpoint_path
if args.resume:
# Load checkpoint.
print("==> Resuming from checkpoint..")
assert os.path.isdir(
"checkpoint"), "Error: no checkpoint directory found!"
if not os.path.exists(resume_path):
print("==> No checkpoint found. Skipping...")
else:
checkpoint = torch.load(resume_path,
map_location=torch.device(device))
if "net" in checkpoint:
load_state_dict(net, checkpoint["net"])
best_acc = checkpoint["acc"]
start_epoch = checkpoint["epoch"]
Colors.cyan(
f"==> Checkpoint found for epoch {start_epoch} with accuracy "
f"{best_acc} at {resume_path}")
else:
load_state_dict(net, checkpoint)
Colors.cyan(f"==> Checkpoint found at {resume_path}")
# hierarchy
tree = Tree.create_from_args(args, classes=trainset.classes)
# loss
criterion = None
for _loss in args.loss:
if criterion is None and not hasattr(nn, _loss):
criterion = nn.CrossEntropyLoss()
class_criterion = getattr(loss, _loss)
loss_kwargs = generate_kwargs(
args,
class_criterion,
name=f"Loss {args.loss}",
globals=locals(),
)
criterion = class_criterion(**loss_kwargs)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(3 / 7.0 * args.epochs),
int(5 / 7.0 * args.epochs)])
class_analysis = getattr(analysis, args.analysis or "Noop")
analyzer_kwargs = generate_kwargs(
args,
class_analysis,
name=f"Analyzer {args.analysis}",
globals=locals(),
)
analyzer = class_analysis(**analyzer_kwargs)
metric = getattr(metrics, args.metric)()
# Training
@analyzer.train_function
def train(epoch):
if hasattr(criterion, "set_epoch"):
criterion.set_epoch(epoch, args.epochs)
print("\nEpoch: %d / LR: %.04f" % (epoch, scheduler.get_last_lr()[0]))
net.train()
train_loss = 0
metric.clear()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
metric.forward(outputs, targets)
transform = trainset.transform_val_inverse().to(device)
stat = analyzer.update_batch(outputs, targets, transform(inputs))
progress_bar(
batch_idx,
len(trainloader),
"Loss: %.3f | Acc: %.3f%% (%d/%d) %s" % (
train_loss / (batch_idx + 1),
100.0 * metric.report(),
metric.correct,
metric.total,
f"| {analyzer.name}: {stat}" if stat else "",
),
)
scheduler.step()
@analyzer.test_function
def test(epoch, checkpoint=True):
nonlocal best_acc
net.eval()
test_loss = 0
metric.clear()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
if not args.disable_test_eval:
loss = criterion(outputs, targets)
test_loss += loss.item()
metric.forward(outputs, targets)
transform = testset.transform_val_inverse().to(device)
stat = analyzer.update_batch(outputs, targets,
transform(inputs))
progress_bar(
batch_idx,
len(testloader),
"Loss: %.3f | Acc: %.3f%% (%d/%d) %s" % (
test_loss / (batch_idx + 1),
100.0 * metric.report(),
metric.correct,
metric.total,
f"| {analyzer.name}: {stat}" if stat else "",
),
)
# Save checkpoint.
acc = 100.0 * metric.report()
print("Accuracy: {}, {}/{} | Best Accurracy: {}".format(
acc, metric.correct, metric.total, best_acc))
if acc > best_acc and checkpoint:
Colors.green(f"Saving to {checkpoint_fname} ({acc})..")
state = {
"net": net.state_dict(),
"acc": acc,
"epoch": epoch,
}
os.makedirs("checkpoint", exist_ok=True)
torch.save(state, f"./checkpoint/{checkpoint_fname}.pth")
best_acc = acc
if args.disable_test_eval and (not args.analysis
or args.analysis == "Noop"):
Colors.red(
" * Warning: `disable_test_eval` is used but no custom metric "
"`--analysis` is supplied. I suggest supplying an analysis to perform "
" custom loss and accuracy calculation.")
if args.eval:
if not args.resume and not args.pretrained:
Colors.red(" * Warning: Model is not loaded from checkpoint. "
"Use --resume or --pretrained (if supported)")
with analyzer.epoch_context(0):
test(0, checkpoint=False)
else:
for epoch in range(start_epoch, args.epochs):
with analyzer.epoch_context(epoch):
train(epoch)
test(epoch)
print(f"Best accuracy: {best_acc} // Checkpoint name: {checkpoint_fname}")
from nbdt.utils import (
progress_bar, generate_fname, generate_kwargs, Colors, maybe_install_wordnet
)
maybe_install_wordnet()
datasets = ('CIFAR10', 'CIFAR100', 'NeuronData') + data.imagenet.names + data.custom.names
parser = argparse.ArgumentParser(description='PyTorch CIFAR Training')
parser.add_argument('--batch-size', default=64, type=int,
help='Batch size used for training')
parser.add_argument('--epochs', '-e', default=100, type=int,
help='By default, lr schedule is scaled accordingly')
parser.add_argument('--dataset', default='NeuronData', choices=datasets)
parser.add_argument('--arch', default='wrn28_10_cifar10', choices=list(models.get_model_choices()))
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
# extra general options for main script
parser.add_argument('--path-resume', default='',
help='Overrides checkpoint path generation')
parser.add_argument('--name', default='',
help='Name of experiment. Used for checkpoint filename')
parser.add_argument('--pretrained', action='store_true',
help='Download pretrained model. Not all models support this.')
parser.add_argument('--eval', help='eval only', action='store_true')
# options specific to this project and its dataloaders
parser.add_argument('--loss', choices=loss.names, default='CrossEntropyLoss')
parser.add_argument('--analysis', choices=analysis.names, help='Run analysis after each epoch')