def make_frontend_vgg(options): deploy_net = caffe.NetSpec() deploy_net.data = network.make_input_data(options.input_size) last, final_name = network.build_frontend_vgg( deploy_net, deploy_net.data, options.classes) if options.up: deploy_net.upsample = network.make_upsample(last, options.classes) last = deploy_net.upsample deploy_net.prob = network.make_prob(last) deploy_net = deploy_net.to_proto() return deploy_net, final_name
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()