def test_anchor_target_layer(backend_default, fargs):
(height, width) = fargs
manifest_path = os.environ['PASCAL_MANIFEST_PATH']
assert manifest_path is not None, "Please set the PASCAL_MANIFEST_PATH variable."
manifest_root = os.environ['PASCAL_MANIFEST_ROOT']
assert manifest_root is not None, "Please set the PASCAL_MANIFEST_ROOT variable."
config = PASCALVOC(manifest_path, manifest_root, cache_dir='',
height=height, width=width, inference=False)
config['subset_fraction'] = 0.1
dl = DataLoader(config, backend_default)
dl = TypeCast(dl, index=0, dtype=np.float32)
train_set = ObjectLocalization(dl, frcn_rois_per_img=128)
for idx, (X, Y) in enumerate(train_set):
reference_test(train_set, X, Y)
assert args.model_file is not None, "Model file required for Faster-RCNN testing"
assert 'val' in args.manifest, "Path to manifest file requred"
# hyperparameters
assert args.batch_size is 1, "Faster-RCNN only supports batch size 1"
rpn_rois_per_img = 256
frcn_rois_per_img = 128
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
# build data loader
cache_dir = get_data_cache_dir(args.data_dir, subdir='pascalvoc_cache')
config = PASCALVOC(args.manifest['val'],
args.manifest_root,
width=args.width,
height=args.height,
rois_per_img=rpn_rois_per_img,
inference=True)
valid_set = faster_rcnn.build_dataloader(config, frcn_rois_per_img)
num_classes = valid_set.num_classes
# build the Faster-RCNN network
(model, proposalLayer) = faster_rcnn.build_model(valid_set,
frcn_rois_per_img,
inference=True)
# load parameters and initialize model
model.load_params(args.model_file, load_states=False)
model.initialize(dataset=valid_set)
rpn_rois_per_img = 256 # number of rois to sample to train rpn
frcn_rois_per_img = 128 # number of rois to sample to train frcn
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
be.enable_winograd = 4 # default to winograd 4 for fast autotune
# directory to store VGG weights
cache_dir = get_data_cache_dir(args.data_dir, subdir='pascalvoc_cache')
# build data loader
# get config file for PASCALVOC
config = PASCALVOC(args.manifest['train'],
args.manifest_root,
width=args.width,
height=args.height,
rois_per_img=rpn_rois_per_img,
inference=False)
config['subset_fraction'] = float(args.subset_pct / 100.0)
train_set = faster_rcnn.build_dataloader(config, frcn_rois_per_img)
# build the Faster-RCNN model
model = faster_rcnn.build_model(train_set, frcn_rois_per_img, inference=False)
# set up cost different branches, respectively
weights = 1.0 / (rpn_rois_per_img)
roi_w = 1.0 / (frcn_rois_per_img)
frcn_tree_cost = Multicost(costs=[
GeneralizedCostMask(costfunc=CrossEntropyMulti(), weights=roi_w),