def make_frontend_vgg(options, is_training):
batch_size = options.train_batch if is_training else options.test_batch
image_path = options.train_image if is_training else options.test_image
label_path = options.train_label if is_training else options.test_label
net = caffe.NetSpec()
net.data, net.label = network.make_image_label_data(
image_path, label_path, batch_size, is_training, options.crop_size,
options.mean)
last = network.build_frontend_vgg(net, net.data, options.classes)[0]
if options.up:
net.upsample = network.make_upsample(last, options.classes)
last = net.upsample
net.loss = network.make_softmax_loss(last, net.label)
if not is_training:
net.accuracy = network.make_accuracy(last, net.label)
return net.to_proto()
def make_context(options, is_training):
batch_size = options.train_batch if is_training else options.test_batch
image_path = options.train_image if is_training else options.test_image
label_path = options.train_label if is_training else options.test_label
net = caffe.NetSpec()
net.data, net.label = network.make_bin_label_data(
image_path, label_path, batch_size,
options.label_shape, options.label_stride)
last = network.build_context(
net, net.data, options.classes, options.layers)[0]
if options.up:
net.upsample = network.make_upsample(last, options.classes)
last = net.upsample
net.loss = network.make_softmax_loss(last, net.label)
if not is_training:
net.accuracy = network.make_accuracy(last, net.label)
return net.to_proto()
def make_context(options, is_training):
batch_size = options.train_batch if is_training else options.test_batch
image_path = options.train_image if is_training else options.test_image
label_path = options.train_label if is_training else options.test_label
net = caffe.NetSpec()
net.data, net.label = network.make_bin_label_data(image_path, label_path,
batch_size,
options.label_shape,
options.label_stride)
# last = network.build_context(
# net, net.data, options.classes, options.layers)[0]
last = network.build_context_large(net, net.data, options.classes,
options.layers)[0]
if options.up:
net.upsample = network.make_upsample(last, options.classes)
last = net.upsample
net.loss = network.make_softmax_loss(last, net.label)
if not is_training:
net.accuracy = network.make_accuracy(last, net.label)
return net.to_proto()