def model(is_training):
_IMAGE_SIZE = 32
_IMAGE_CHANNELS = 3
_NUM_CLASSES = 10
with open(graph_file, 'r') as f:
graph = json.load(f)
params = np.load(param_file, allow_pickle=True)['arr_0'][()]
params = transform_weight_from_mxnet_to_tensorflow(params)
with tf.name_scope('main_params'):
x = tf.placeholder(tf.float32, shape=[None, _IMAGE_SIZE * _IMAGE_SIZE * _IMAGE_CHANNELS], name='Input')
y = tf.placeholder(tf.float32, shape=[None, _NUM_CLASSES], name='Output')
x_image = tf.reshape(x, [-1, _IMAGE_SIZE, _IMAGE_SIZE, _IMAGE_CHANNELS], name='images')
global_step = tf.Variable(initial_value=0, trainable=False, name='global_step')
learning_rate = tf.placeholder(tf.float32, shape=[], name='learning_rate')
with tf.variable_scope('resnet50') as scope:
trc = TFReconstructorTrain(graph, params, output_node_ids=['502'])
featmap = trc.model(is_training=is_training, input_dict={'0': x_image})
with tf.variable_scope('fully_connected') as scope:
flat = tf.reshape(featmap['502'], [-1, 2048])
softmax = tf.layers.dense(inputs=flat, units=_NUM_CLASSES, name=scope.name)
y_pred_cls = tf.argmax(softmax, axis=1)
return x, y, softmax, y_pred_cls, global_step, learning_rate
def create_model():
with open('resnet50_v1b/IR_fused_for_CModel_graph.json', 'r') as f:
jgraph = json.load(f)
params = np.load('resnet50_v1b/IR_fused_for_CModel_params.npz', allow_pickle=True)['arr_0'][()]
params = src.transform_weight_from_mxnet_to_tensorflow(params)
trc = src.TFReconstructor(jgraph, params)
trc._execute()
x = tf.get_default_graph().get_tensor_by_name("0:0")
y = tf.placeholder(tf.int64, (None,))
logits = tf.layers.flatten(tf.get_default_graph().get_tensor_by_name("506:0"))
op_names = []
in_tensors = []
out_tensors = []
kernel_list = []
bias_list = []
for operation in tf.get_default_graph().get_operations():
if operation.type == 'Conv2D':
in_tensors.append(operation.inputs._inputs[0])
out_tensors.append(operation.outputs[0])
kernel_list.append(operation.inputs._inputs[1])
bias_list.append(None)
op_names.append(operation.inputs._inputs[1].name)
# replace all the kernel by variable
kernel_name_val_mapping = {kernel.name: tf.make_ndarray(kernel.op.get_attr('value')) for kernel in kernel_list}
tf.io.write_graph(tf.get_default_graph(), './', 'temp.pb', as_text=False)
tf.reset_default_graph()
graphdef = tf.GraphDef()
with open('./temp.pb', 'rb') as f:
graphdef.ParseFromString(f.read())
kernel_var_mapping = {name: tf.Variable(val) for name, val in kernel_name_val_mapping.items()}
tf.graph_util.import_graph_def(graphdef, input_map=kernel_var_mapping)
x = tf.get_default_graph().get_tensor_by_name("import/0:0")
y = tf.placeholder(tf.int64, (None, ))
logits = tf.layers.flatten(tf.get_default_graph().get_tensor_by_name("import/506:0"))
kernel_list = [kernel_var_mapping[kernel.name] for kernel in kernel_list]
op_names = []
in_tensors = []
out_tensors = []
# kernel_list = []
bias_list = []
for operation in tf.get_default_graph().get_operations():
if operation.type == 'Conv2D':
in_tensors.append(operation.inputs._inputs[0])
out_tensors.append(operation.outputs[0])
# kernel_list.append(operation.inputs._inputs[1])
bias_list.append(None)
op_names.append(operation.inputs._inputs[1].name)
return x, y, logits, op_names, kernel_list, bias_list, in_tensors, out_tensors
def get_graph(graph, params, config):
params = src.transform_weight_from_mxnet_to_tensorflow(params)
tf_graph = tf.Graph()
with tf_graph.as_default():
quan_instance = src.get_quan(config.QUAN.strategy, config.QUAN.qconfig,
graph, params)
quan_instance.execute()
input_tensor = quan_instance.node_dict[config.EVALUATION.input_node]
output_tensor = quan_instance.node_dict[config.EVALUATION.output_node]
return tf_graph, input_tensor, output_tensor
def run(graph, params, config, evaluation, save_path):
params = src.transform_weight_from_mxnet_to_tensorflow(params)
tf_graph = tf.Graph()
with tf_graph.as_default():
quan_instance = src.get_quan(config.strategy, config.qconfig, graph,
params)
quan_instance.execute()
with tf.Session(graph=tf_graph) as sess:
input_arr = src.load_images(evaluation.input_images)
# input_arr = src.load_cifar10(evaluation.input_images)
output_arr = sess.run(
quan_instance.node_dict[evaluation.output_node],
feed_dict={
quan_instance.node_dict[evaluation.input_node]: input_arr
})
if save_path:
tf.io.write_graph(tf_graph,
os.path.dirname(save_path),
os.path.basename(save_path),
as_text=False)
return output_arr, quan_instance.act_list
'--preprocess-config',
default='configs/mxnet_imagenet_trans.json',
help='json config of preprocess')
parser.add_argument('-o',
'--output',
default='calibrations/resnet50_v1b.json')
parser.add_argument('--use-kl',
action='store_true',
default=False,
help='flag of use kl')
args = parser.parse_args()
with open(args.graph_json, 'r') as f:
json_graph = json.load(f)
params = np.load(args.params_file, allow_pickle=True)['arr_0'][()]
params = src.transform_weight_from_mxnet_to_tensorflow(params)
image_list = src.utils.get_image_list(args.image_dir)
preprocessor = src.transform.JsonTrans(args.preprocess_config)
calibrate_dataset = src.CalibDataset(args.image_dir,
25,
transformer=preprocessor)
calibrator = src.quantization.Calibration(json_graph, params,
calibrate_dataset)
# table = calibrator.run()
if args.use_kl:
table = calibrator.run_kl()
else:
table = calibrator.run()
with open(args.output, 'w') as f: