class ExternalQuantizer(XGraphBaseSubgraphQuantizer, ABC):
xgraph_factory = XGraphFactory()
xgraph_partitioner = XGraphPartitioner()
def __init__(self,
xgraph,
inputs_func,
work_dir=os.path.join(os.getcwd(), 'work')):
super(ExternalQuantizer, self).__init__(xgraph, inputs_func, work_dir)
self.gen = TfGenerator()
self.partition_graphs = {}
self.res = {}
self.q_output = QuantizerOutput(name=xgraph.get_name())
def _propagate_quant_info(self, xgraph):
# setup empty vqi and vqo for every layer w/o vai_quant
for layer in xgraph.get_layers():
if 'vai_quant' not in layer.attrs:
layer.attrs['vai_quant'] = ['vai_quant_in', 'vai_quant_out']
layer.attrs['vai_quant_in'] = ''
layer.attrs['vai_quant_out'] = ''
# for every layer
for layer in xgraph.get_layers():
# if the layer has non empty vqo, propagate it to the output layers
if layer.attrs['vai_quant_out'] != '':
l_vqo = layer.attrs['vai_quant_out']
# for every output layer
for t_idx, t_name in enumerate(layer.tops):
t_layer = xgraph.get(t_name)
# if the input quant is not specified in the output layer
if t_layer.attrs['vai_quant_in'] == '':
# get quant info from current layer, two by two
t_vqi = [l_vqo[2 * t_idx], l_vqo[2 * t_idx + 1]]
t_layer.attrs['vai_quant_in'] = t_vqi
# if the layer has non empty vqi, propagate it to the input layers
if layer.attrs['vai_quant_in'] != '':
l_vqi = layer.attrs['vai_quant_in']
# for every input layer
for b_idx, b_name in enumerate(layer.bottoms):
b_layer = xgraph.get(b_name)
if b_layer.attrs['vai_quant_out'] == '':
b_vqo = [l_vqi[2 * b_idx], l_vqi[2 * b_idx + 1]]
b_layer.attrs['vai_quant_out'] = b_vqo
def quantize(self):
# NOTE For Conv2Dtranspose layers we need the specific batch size in tensorflow 1.13
batch_size = list(self.inputs_func(0).values())[0].shape[0]
fs = self.gen.generate(
self.xgraph,
'graph',
subgraphs_only=True,
layout='NHWC',
batch_size=batch_size)
assert len(fs) == 1, 'Too many partitions'
partition_key = list(fs.keys())[0]
pb_path = list(fs.values())[0]
self.partition_graphs[partition_key] = pb_path
q_xgraph = super(ExternalQuantizer, self).quantize()
self.xgraph.meta_attrs["is_quantized"] = True
for qkey in self.q_output.keys():
if 'quant_keys' not in self.xgraph.meta_attrs:
self.xgraph.meta_attrs['quant_keys'] = [qkey]
else:
self.xgraph.meta_attrs['quant_keys'].append(qkey)
quant_file = self.q_output.get_q_file(qkey)
quant_info_file = self.q_output.get_q_info(qkey)
quant_orig_pb = self.q_output.get_orig_pb(qkey)
self.xgraph.meta_attrs[qkey] = {
'q_file': quant_file,
'q_info': quant_info_file,
'orig_pb': quant_orig_pb}
return q_xgraph
class DECENTQuantizer(XGraphBaseSubgraphQuantizer):
# try:
# if hasattr(tf.contrib, 'decent_q'):
# from tensorflow.contrib import decent_q
# except Exception as e:
# warnings.warn("Could not import decent_q module")
try:
# from tensorflow.contrib import decent_q
import tensorflow as tf
if hasattr(tf, 'contrib') and hasattr(tf.contrib, 'decent_q'):
from tensorflow.contrib import decent_q
else:
warnings.warn("Could not import decent_q module. Please check"
" if installed.")
except ImportError:
warnings.warn("Could not import decent_q module. Please check"
" if installed.")
xgraph_factory = XGraphFactory()
xgraph_partitioner = XGraphPartitioner()
def __init__(self,
xgraph,
inputs_func,
work_dir=os.path.join(os.getcwd(), 'work'),
quant_iter=1,
**kwargs):
super(DECENTQuantizer, self).__init__(xgraph, inputs_func, work_dir)
self.quant_iter = quant_iter
self.gen = TfGenerator()
self.partition_graphs = {}
self.res = {}
self.kwargs = kwargs
self.q_output = QuantizerOutput(name=xgraph.get_name())
def quantize_subgraph(self, xgraph, inputs, input_names, output_names):
# type: (XGraph, Dict[str, numpy.ndarray])
""" Quantize subgraph with inputs """
# Import Tensorflow only when needed to avoid strict dependency
import tensorflow as tf
frozen_graph = self.partition_graphs[xgraph.get_name()]
logger.info("Load frozen graph from: {}".format(frozen_graph))
input_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(frozen_graph, "rb") as f:
input_graph_def.ParseFromString(f.read())
logger.info("Quantization input: {} and output names: {}".format(
input_names, output_names))
input_shapes = [X.shapes.tolist() for X in xgraph.get_input_layers()]
def inputs_func(iter):
import numpy as np
nonlocal inputs
return inputs
logger.info("START decent quantization for graph partition: {}".format(
xgraph.get_name()))
q_config = self.decent_q.QuantizeConfig(input_nodes=input_names,
output_nodes=output_names,
input_shapes=input_shapes,
output_dir=self.work_dir,
method='1',
calib_iter=self.quant_iter)
self.decent_q.quantize_frozen(input_graph_def, inputs_func, q_config)
netcfg = os.path.join(self.work_dir, "deploy_model.pb")
q_eval_file = os.path.join(self.work_dir, "quantize_eval_model.pb")
quant_info_file = os.path.join(
self.work_dir, 'quant_info_{}.txt'.format(xgraph.get_name()))
self._save_quant_info(netcfg, quant_info_file)
self.q_output.add(xgraph.get_name(), netcfg, quant_info_file,
frozen_graph, q_eval_file)
# TODO
# Add quantization info to corresponding XLayers
self._add_quant_info_to_xgraph(netcfg)
def quantize(self) -> None:
"""Quantize the XGraph model using the decent_q quantizer"""
# NOTE For Conv2Dtranspose layers we need the specific batch size in
# tensorflow 1.13
batch_size = list(self.inputs_func(0).values())[0].shape[0]
fs = self.gen.generate(self.xgraph,
'graph',
subgraphs_only=True,
layout='NHWC',
batch_size=batch_size,
out_dir=self.work_dir,
**self.kwargs)
if len(fs) != 1:
raise ValueError("DECENT quantization currently only supports"
" models with one DPU compatible partition,"
" but got: {}".format(len(fs)))
partition_key = list(fs.keys())[0]
pb_path = list(fs.values())[0]
self.partition_graphs[partition_key] = pb_path
q_xgraph = super(DECENTQuantizer, self).quantize()
self.xgraph.meta_attrs["is_quantized"] = True
for qkey in self.q_output.keys():
if 'quant_keys' not in self.xgraph.meta_attrs:
self.xgraph.meta_attrs['quant_keys'] = [qkey]
else:
self.xgraph.meta_attrs['quant_keys'].append(qkey)
quant_file = self.q_output.get_q_file(qkey)
quant_info_file = self.q_output.get_q_info(qkey)
quant_orig_pb = self.q_output.get_orig_pb(qkey)
quant_eval_file = self.q_output.get_q_eval(qkey)
self.xgraph.meta_attrs[qkey] = {
'q_file': quant_file,
'q_info': quant_info_file,
'orig_pb': quant_orig_pb,
'q_eval': quant_eval_file
}
self.xgraph.set_quantizer_output(self.q_output)
# import pdb; pdb.set_trace()
return q_xgraph
def _add_quant_info_to_xgraph(self, deploy_frozen_graph: str) -> None:
"""
Retrieve the quantization info from the provided quantized model and
add the information to the corresponding XLayers
"""
# Import tensorflow only when needed to avoid strict dependency
import tensorflow as tf
quant_info = []
input_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(deploy_frozen_graph, "rb") as f:
input_graph_def.ParseFromString(f.read())
for idx, node in enumerate(input_graph_def.node):
if node.name in self.xgraph:
X = self.xgraph.get(node.name)
X.attrs['vai_quant_idx'] = idx + 1
if 'ipos' in node.attr.keys():
X.attrs['vai_quant'] = ['vai_quant_in']
X.attrs['vai_quant_in'] = \
[int(v) for v in node.attr['ipos'].list.i]
if 'opos' in node.attr.keys():
X.attrs['vai_quant'].append('vai_quant_out')
X.attrs['vai_quant_out'] = \
[int(v) for v in node.attr['opos'].list.i]
if 'wpos' in node.attr.keys():
X.attrs['vai_quant'].append('vai_quant_weights')
X.attrs['vai_quant_weights'] = \
[int(v) for v in node.attr['wpos'].list.i]
if 'bpos' in node.attr.keys():
X.attrs['vai_quant'].append('vai_quant_biases')
X.attrs['vai_quant_biases'] = \
[int(v) for v in node.attr['bpos'].list.i]
def _save_quant_info(self, deploy_frozen_graph, filename):
# type: (str) -> None
"""
Retrieve the quantization info from the provided quantized model
"""
quant_info = self._get_quant_info(deploy_frozen_graph)
lines = [[q_op['idx']] + [q_op['name']] +
[str(i)
for i in q_op['ipos']] + [str(i) for i in q_op['opos']] +
[str(i)
for i in q_op['wpos']] + [str(i) for i in q_op['bpos']]
for q_op in quant_info]
s = '\n'.join([' '.join(line) for line in lines])
with open(filename, 'w') as f:
f.write(s)
def _get_quant_info(self, deploy_frozen_graph):
# type: (str) -> List[dict]
"""
Retrieve the quantization info from the provided quantized model
"""
# import tensorflow only when needed to avoid strict dependency
import tensorflow as tf
quant_info = []
input_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(deploy_frozen_graph, "rb") as f:
input_graph_def.ParseFromString(f.read())
for idx, node in enumerate(input_graph_def.node):
q_op = {
'idx': str(idx + 1),
'name': node.name,
'ipos': [],
'opos': [],
'wpos': [],
'bpos': []
}
if 'ipos' in node.attr.keys():
q_op['ipos'].extend(
[int(v) for v in node.attr['ipos'].list.i])
if 'opos' in node.attr.keys():
q_op['opos'].extend(
[int(v) for v in node.attr['opos'].list.i])
if 'wpos' in node.attr.keys():
q_op['wpos'].extend(
[int(v) for v in node.attr['wpos'].list.i])
if 'bpos' in node.attr.keys():
q_op['bpos'].extend(
[int(v) for v in node.attr['bpos'].list.i])
quant_info.append(q_op)
return quant_info
def eval(self,
val_dir,
gold_file,
synset_words,
batch_size,
nb_batches,
class_num=1000,
gpu=0):
#
"""
"""
input_fn_data = {
"prep_key": self.data_prep_key,
"dir": val_dir,
"batch": batch_size,
"inputs": self.xgraph.get_input_names()
}
with open(os.path.join(FILE_PATH, 'calibration.json'), 'w') as f:
json.dump(input_fn_data, f)
with open(gold_file) as f:
val_set = [line.strip('\n').split(' ') for line in f.readlines()]
# frozen_graph_file = os.path.join(os.getcwd(), 'test.pb')
frozen_graph_file = os.path.join(self.output_dir,
"quantize_eval_model.pb")
# TODO
assert (len(self.xgraph.get_input_names()) == 1)
assert (len(self.xgraph.get_output_names()) == 1)
input_node = self.xgraph.get_input_names()[0]
output_node = self.xgraph.get_output_names()[0]
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu)
input_graph_def = tf.Graph().as_graph_def()
input_graph_def.ParseFromString(
tf.gfile.FastGFile(frozen_graph_file, "rb").read())
tf.import_graph_def(input_graph_def, name='')
# Get input tensors
input_tensor = tf.get_default_graph()\
.get_tensor_by_name(input_node+':0')
input_labels = tf.compat.v1.placeholder(tf.float32,
shape=[None, class_num])
# Calculate accuracy
output = tf.get_default_graph().get_tensor_by_name(output_node + ':0')
prediction = tf.reshape(output, [batch_size, class_num])
# correct_labels = tf.argmax(input_labels, 1)
# top1_prediction = tf.nn.in_top_k(prediction, correct_labels, k = 1)
# top5_prediction = tf.nn.in_top_k(prediction, correct_labels, k = 5)
# top1_accuracy = tf.reduce_mean(tf.cast(top1_prediction,'float'))
# top5_accuracy = tf.reduce_mean(tf.cast(top5_prediction,'float'))
# Start evaluation
logger.info("Start Evaluation for {} Batches...".format(nb_batches))
with tf.Session() as sess:
progress = ProgressBar()
top1_sum_acc = 0
top5_sum_acc = 0
for iter in progress(range(0, nb_batches)):
input_data = decent_prepfn.input_fn(iter)
images = input_data[input_node]
# labels = input_data['labels']
logger.debug("IMAGES", images)
labels = [
elem[1] for elem in val_set[iter * batch_size:(iter + 1) *
batch_size]
]
feed_dict = {input_tensor: images}
raw_predictions = sess.run(prediction, feed_dict)
logger.debug(raw_predictions)
# logger.debug("Predictions shape: {}"
# .format(raw_predictions.shape))
# logger.debug("Labels length: {}".format(len(labels)))
top_1 = classification.get_top_k_accuracy(
raw_predictions, synset_words, 1, labels)
top_5 = classification.get_top_k_accuracy(
raw_predictions, synset_words, 5, labels)
top1_sum_acc += top_1
top5_sum_acc += top_5
logger.debug("int: {}, {}".format(top_1, top_5))
final_top1_acc = top1_sum_acc / nb_batches
final_top5_acc = top5_sum_acc / nb_batches
print("Accuracy: Top1: {}, Top5: {}".format(final_top1_acc,
final_top5_acc))
def dump(self, img_dir, input_names, max_dump_batches=1, dump_float=0):
#
"""
TODO: inupt_names
"""
input_fn_data = {
"prep_key": self.data_prep_key,
"dir": img_dir,
"batch": 1,
"inputs": input_names
}
with open(os.path.join(FILE_PATH, 'calibration.json'), 'w') as f:
json.dump(input_fn_data, f)
frozen_graph = os.path.join(self.output_dir, 'quantize_eval_model.pb')
command = """
decent_q dump \
--input_frozen_graph {} \
--input_fn decent_prepfn.input_fn \
--max_dump_batches {} \
--dump_float {} \
--output_dir {}
""".format(frozen_graph, max_dump_batches, dump_float, self.output_dir)
print("COMMAND", command)
process = subprocess.Popen(command.split(),
cwd=FILE_PATH,
stdout=subprocess.PIPE)
output, error = process.communicate()
print(output, error)