def setUpClass(
cls,
image_url='./assets/dog.jpg',
mx_prefix='mxnet_models/vgg16_ssd_300_voc0712_trainval/ssd_300',
mx_epoch=0,
caffe_mean=[0, 0, 0],
caffe_prototxt='caffe_models/deploy.prototxt',
caffe_weights='caffe_models/weights.caffemodel'):
cls.image_url = image_url
cls.caffe_mean = caffe_mean
cls.caffe_prototxt = caffe_prototxt
cls.caffe_weights = caffe_weights
# Download mxnet model if not found
filename = mx_prefix + '-symbol.json'
link = "https://goo.gl/fNonDd"
model_dir = os.path.dirname(mx_prefix)
if not os.path.isfile(filename):
if not os.path.isdir(model_dir):
logger.info('Creating: ' + model_dir)
os.makedirs(model_dir)
logger.info('Downloading mxnet model to test converter')
try:
urllib.urlretrieve(link,
model_dir + '/model.zip',
reporthook=print_progress)
with zipfile.ZipFile(model_dir + '/model.zip', 'r') as zip_ref:
zip_ref.extractall('mxnet_models/')
except Exception as inst:
logger.info(inst)
# Load mxnet model
logger.info('Loading mxnet model')
cls.sym, cls.arg_params, cls.aux_params = mx.model.load_checkpoint(
mx_prefix, mx_epoch)
# Convert model to caffe
logger.info('Converting to caffe model')
cls.convertor = MxNetToCaffe(mx_prefix, mx_epoch, caffe_prototxt,
caffe_weights)
cls.layer_to_record, cls.top_to_layers = read_network_dag(
cls.caffe_prototxt)
def convert_and_compare_caffe_to_mxnet(image_url, gpu, caffe_prototxt_path, caffe_model_path,
caffe_mean, mean_diff_allowed, max_diff_allowed):
"""
Run the layer comparison on a caffe model, given its prototxt, weights and mean.
The comparison is done by inferring on a given image using both caffe and mxnet model
:param image_url: image file or url to run inference on
:param gpu: gpu to use, -1 for cpu
:param caffe_prototxt_path: path to caffe prototxt
:param caffe_model_path: path to caffe weights
:param caffe_mean: path to caffe mean file
"""
import caffe
from caffe_proto_utils import read_network_dag, process_network_proto, read_caffe_mean
from convert_model import convert_model
if isinstance(caffe_mean, str):
caffe_mean = read_caffe_mean(caffe_mean)
elif caffe_mean is None:
pass
elif len(caffe_mean) == 3:
# swap channels from Caffe BGR to RGB
caffe_mean = caffe_mean[::-1]
# get caffe root location, this is needed to run the upgrade network utility, so we only need
# to support parsing of latest caffe
caffe_root = os.path.dirname(os.path.dirname(caffe.__path__[0]))
caffe_prototxt_path = process_network_proto(caffe_root, caffe_prototxt_path)
_, layer_name_to_record, top_to_layers = read_network_dag(caffe_prototxt_path)
caffe.set_mode_cpu()
caffe_net = caffe.Net(caffe_prototxt_path, caffe_model_path, caffe.TEST)
image_dims = tuple(caffe_net.blobs['data'].shape)[2:4]
logging.info('getting image %s', image_url)
img_rgb = read_image(image_url, image_dims, caffe_mean)
img_bgr = img_rgb[:, ::-1, :, :]
caffe_net.blobs['data'].reshape(*img_bgr.shape)
caffe_net.blobs['data'].data[...] = img_bgr
_ = caffe_net.forward()
# read sym and add all outputs
sym, arg_params, aux_params, _ = convert_model(caffe_prototxt_path, caffe_model_path)
sym = sym.get_internals()
# now mxnet
if gpu < 0:
ctx = mx.cpu(0)
else:
ctx = mx.gpu(gpu)
arg_params, aux_params = _ch_dev(arg_params, aux_params, ctx)
arg_params["data"] = mx.nd.array(img_rgb, ctx)
arg_params["prob_label"] = mx.nd.empty((1,), ctx)
exe = sym.bind(ctx, arg_params, args_grad=None, grad_req="null", aux_states=aux_params)
exe.forward(is_train=False)
compare_layers_from_nets(caffe_net, arg_params, aux_params, exe, layer_name_to_record,
top_to_layers, mean_diff_allowed, max_diff_allowed)
return
def convert_and_compare_caffe_to_mxnet(image_url, gpu, caffe_prototxt_path,
caffe_model_path, caffe_mean,
mean_diff_allowed, max_diff_allowed):
"""
Run the layer comparison on a caffe model, given its prototxt, weights and mean.
The comparison is done by inferring on a given image using both caffe and mxnet model
:param image_url: image file or url to run inference on
:param gpu: gpu to use, -1 for cpu
:param caffe_prototxt_path: path to caffe prototxt
:param caffe_model_path: path to caffe weights
:param caffe_mean: path to caffe mean file
"""
import caffe
from caffe_proto_utils import read_network_dag, process_network_proto, read_caffe_mean
from convert_model import convert_model
if isinstance(caffe_mean, str):
caffe_mean = read_caffe_mean(caffe_mean)
elif caffe_mean is None:
pass
elif len(caffe_mean) == 3:
# swap channels from Caffe BGR to RGB
caffe_mean = caffe_mean[::-1]
# get caffe root location, this is needed to run the upgrade network utility, so we only need
# to support parsing of latest caffe
caffe_root = os.path.dirname(os.path.dirname(caffe.__path__[0]))
caffe_prototxt_path = process_network_proto(caffe_root,
caffe_prototxt_path)
_, layer_name_to_record, top_to_layers = read_network_dag(
caffe_prototxt_path)
caffe.set_mode_cpu()
caffe_net = caffe.Net(caffe_prototxt_path, caffe_model_path, caffe.TEST)
image_dims = tuple(caffe_net.blobs['data'].shape)[2:4]
logging.info('getting image %s', image_url)
img_rgb = read_image(image_url, image_dims, caffe_mean)
img_bgr = img_rgb[:, ::-1, :, :]
caffe_net.blobs['data'].reshape(*img_bgr.shape)
caffe_net.blobs['data'].data[...] = img_bgr
_ = caffe_net.forward()
# read sym and add all outputs
sym, arg_params, aux_params, _ = convert_model(caffe_prototxt_path,
caffe_model_path)
sym = sym.get_internals()
# now mxnet
if gpu < 0:
ctx = mx.cpu(0)
else:
ctx = mx.gpu(gpu)
arg_params, aux_params = _ch_dev(arg_params, aux_params, ctx)
arg_params["data"] = mx.nd.array(img_rgb, ctx)
arg_params["prob_label"] = mx.nd.empty((1, ), ctx)
exe = sym.bind(ctx,
arg_params,
args_grad=None,
grad_req="null",
aux_states=aux_params)
exe.forward(is_train=False)
compare_layers_from_nets(caffe_net, arg_params, aux_params, exe,
layer_name_to_record, top_to_layers,
mean_diff_allowed, max_diff_allowed)
return