def get_model(args, num_classes, test=False, tiny=False):
"""
Create computation graph and variables.
Args:
tiny: Tiny ImageNet mode if True.
"""
data_size = 320
nn_in_size = 224
if tiny:
data_size = 64
nn_in_size = 56
image = nn.Variable([args.batch_size, 3, data_size, data_size])
label = nn.Variable([args.batch_size, 1])
pimage = image_preprocess(image, nn_in_size, data_size, test)
pred, hidden = model_resnet.resnet_imagenet(pimage,
num_classes,
args.num_layers,
args.shortcut_type,
test=test,
tiny=tiny)
loss = F.mean(F.softmax_cross_entropy(pred, label))
Model = namedtuple('Model', ['image', 'label', 'pred', 'loss', 'hidden'])
return Model(image, label, pred, loss, hidden)
def get_model(args, num_classes):
"""
Create computation graph and variables.
"""
data_size = 224
image = nn.Variable([1, 3, data_size, data_size])
pimage = image_preprocess(image)
pred, hidden = model_resnet.resnet_imagenet(pimage,
num_classes,
args.num_layers,
args.shortcut_type,
test=True,
tiny=False)
Model = namedtuple('Model', ['image', 'pred', 'hidden'])
return Model(image, pred, hidden)
def get_model(args, num_classes, n_devices, accum_grad, test=False):
"""
Create computation graph and variables.
"""
nn_in_size = 224
image = nn.Variable([args.batch_size, 3, nn_in_size, nn_in_size])
label = nn.Variable([args.batch_size, 1])
pred, hidden = model_resnet.resnet_imagenet(image,
num_classes,
args.num_layers,
args.shortcut_type,
test=test,
tiny=False)
loss = F.mean(F.softmax_cross_entropy(pred, label)) / \
(n_devices * accum_grad)
Model = namedtuple('Model', ['image', 'label', 'pred', 'loss', 'hidden'])
return Model(image, label, pred, loss, hidden)
def get_model(args, num_classes, test=False, tiny=False):
"""
Create computation graph and variables.
Args:
tiny: Tiny ImageNet mode if True.
"""
data_size = 320
nn_in_size = 224
if tiny:
data_size = 64
nn_in_size = 56
image = nn.Variable([args.batch_size, 3, data_size, data_size])
label = nn.Variable([args.batch_size, 1])
pimage = image_preprocess(image, nn_in_size)
pred, hidden = model_resnet.resnet_imagenet(
pimage, num_classes, args.num_layers, args.shortcut_type, test=test, tiny=tiny)
loss = F.mean(F.softmax_cross_entropy(pred, label))
Model = namedtuple('Model', ['image', 'label', 'pred', 'loss', 'hidden'])
return Model(image, label, pred, loss, hidden)