def test_net(imageset, year, root_path, devkit_path, prefix, epoch, ctx):
"""
wrapper for detector
:param imageset: image set to test on
:param year: year of image set
:param root_path: 'data' folder path
:param devkit_path: 'VOCdevkit' folder path
:param prefix: new model prefix
:param epoch: new model epoch
:param ctx: context to evaluate in
:return: None
"""
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load testing data
voc, roidb = load_test_roidb(imageset, year, root_path, devkit_path)
test_data = ROIIter(roidb, ctx=ctx, batch_size=1, shuffle=False, mode='test')
# load model
args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)
# load symbol
sym = get_symbol_vgg_test()
# detect
detector = Detector(sym, ctx, args, auxs)
pred_eval(detector, test_data, voc, vis=False)
def test_net(imageset, year, root_path, devkit_path, prefix, epoch, ctx):
"""
wrapper for detector
:param imageset: image set to test on
:param year: year of image set
:param root_path: 'data' folder path
:param devkit_path: 'VOCdevkit' folder path
:param prefix: new model prefix
:param epoch: new model epoch
:param ctx: context to evaluate in
:return: None
"""
# set up logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load testing data
voc, roidb = load_test_roidb(imageset, year, root_path, devkit_path)
test_data = ROIIter(roidb,
ctx=ctx,
batch_size=1,
shuffle=False,
mode='test')
# load model
args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)
# load symbol
sym = get_symbol_vgg_test()
# detect
detector = Detector(sym, ctx, args, auxs)
pred_eval(detector, test_data, voc, vis=False)
def get_net(prefix, epoch, ctx):
args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)
sym = get_symbol_vgg_test()
detector = Detector(sym, ctx, args, auxs)
return detector
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
return arg_params
proto_path = os.path.join(fast_rcnn_path, 'models', 'VGG16', 'test.prototxt')
model_path = os.path.join(fast_rcnn_path, 'data', 'fast_rcnn_models',
'vgg16_fast_rcnn_iter_40000.caffemodel')
symbol = get_symbol_vgg_test()
arg_shapes, out_shapes, aux_shapes = symbol.infer_shape(**{
'data': (1, 3, 224, 224),
'rois': (1, 5)
})
arg_shape_dic = {
name: shape
for name, shape in zip(symbol.list_arguments(), arg_shapes)
}
arg_params = load_model(proto_path, model_path, arg_shape_dic)
model = mx.model.FeedForward(ctx=mx.cpu(),
symbol=symbol,
arg_params=arg_params,
aux_params={},
if weight_name not in arg_shape_dic:
print(weight_name + ' not found in arg_shape_dic.')
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
return arg_params
proto_path = os.path.join(fast_rcnn_path, 'models', 'VGG16', 'test.prototxt')
model_path = os.path.join(fast_rcnn_path, 'data', 'fast_rcnn_models', 'vgg16_fast_rcnn_iter_40000.caffemodel')
symbol = get_symbol_vgg_test()
arg_shapes, out_shapes, aux_shapes = symbol.infer_shape(**{'data': (1, 3, 224, 224), 'rois': (1, 5)})
arg_shape_dic = { name: shape for name, shape in zip(symbol.list_arguments(), arg_shapes) }
arg_params = load_model(proto_path, model_path, arg_shape_dic)
model = mx.model.FeedForward(ctx=mx.cpu(), symbol=symbol, arg_params=arg_params,
aux_params={}, num_epoch=1,
learning_rate=0.01, momentum=0.9, wd=0.0001)
model.save('model/ref')
def get_net(prefix, epoch, ctx):
args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)
sym = get_symbol_vgg_test()
detector = Detector(sym, ctx, args, auxs)
return detector