def test_autodump(self):
from lpot import Quantization
quantizer = Quantization('fake_yaml3.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 3, 3, 1), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer.model = self.constant_graph
output_graph = quantizer(self.constant_graph, \
q_dataloader=dataloader, eval_dataloader=dataloader)
def main():
from lpot.experimental import Quantization, common
quantizer = Quantization('./conf.yaml')
# Do quantization
quantizer.model = common.Model('./inception_v1.ckpt')
quantized_model = quantizer()
def main():
quantizer = Quantization('./conf.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 100, 100, 3), label=True)
quantizer.model = common.Model(
'./model/public/rfcn-resnet101-coco-tf/rfcn_resnet101_coco_2018_01_28/'
)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantized_model = quantizer()
def main():
class CalibrationDL():
def __init__(self):
path = os.path.abspath(
os.path.expanduser('./brats_cal_images_list.txt'))
with open(path, 'r') as f:
self.preprocess_files = [line.rstrip() for line in f]
self.loaded_files = {}
self.batch_size = 1
def __getitem__(self, sample_id):
file_name = self.preprocess_files[sample_id]
print("Loading file {:}".format(file_name))
with open(
os.path.join('build/calib_preprocess/',
"{:}.pkl".format(file_name)), "rb") as f:
self.loaded_files[sample_id] = pickle.load(f)[0]
# note that calibration phase does not care label, here we return 0 for label free case.
return self.loaded_files[sample_id], 0
def __len__(self):
self.count = len(self.preprocess_files)
return self.count
args = get_args()
assert args.backend == "pytorch"
model_path = os.path.join(args.model_dir, "plans.pkl")
assert os.path.isfile(
model_path), "Cannot find the model file {:}!".format(model_path)
trainer, params = load_model_and_checkpoint_files(
args.model_dir,
folds=1,
fp16=False,
checkpoint_name='model_final_checkpoint')
trainer.load_checkpoint_ram(params[0], False)
model = trainer.network
if args.tune:
quantizer = Quantization('conf.yaml')
quantizer.model = common.Model(model)
quantizer.eval_func = eval_func
quantizer.calib_dataloader = common.DataLoader(CalibrationDL())
q_model = quantizer()
q_model.save('./lpot_workspace')
exit(0)
if args.benchmark:
model.eval()
if args.int8:
from lpot.utils.pytorch import load
new_model = load(
os.path.abspath(os.path.expanduser('./lpot_workspace')), model)
else:
new_model = model
eval_func(new_model)
def main(_):
arg_parser = ArgumentParser(description='Parse args')
arg_parser.add_argument("--input-graph",
help='Specify the slim model',
dest='input_graph')
arg_parser.add_argument("--output-graph",
help='Specify tune result model save dir',
dest='output_graph')
arg_parser.add_argument("--config", default=None, help="tuning config")
arg_parser.add_argument('--benchmark',
dest='benchmark',
action='store_true',
help='run benchmark')
arg_parser.add_argument('--tune',
dest='tune',
action='store_true',
help='use lpot to tune.')
args = arg_parser.parse_args()
factory = TFSlimNetsFactory()
# user specific model can register to slim net factory
input_shape = [None, 299, 299, 3]
factory.register('inception_v4', inception_v4, input_shape,
inception_v4_arg_scope)
if args.tune:
from lpot.experimental import Quantization
quantizer = Quantization(args.config)
quantizer.model = args.input_graph
q_model = quantizer()
q_model.save(args.output_graph)
if args.benchmark:
from lpot.experimental import Benchmark
evaluator = Benchmark(args.config)
evaluator.model = args.input_graph
results = evaluator()
for mode, result in results.items():
acc, batch_size, result_list = result
latency = np.array(result_list).mean() / batch_size
print('\n{} mode benchmark result:'.format(mode))
print('Accuracy is {:.3f}'.format(acc))
print('Batch size = {}'.format(batch_size))
print('Latency: {:.3f} ms'.format(latency * 1000))
print('Throughput: {:.3f} images/sec'.format(1. / latency))
def quantize(model, q_data, e_data):
from lpot.experimental import Quantization, common
from lpot.experimental.common import DataLoader
quantizer = Quantization('fake_yaml.yaml')
q_dataloader = DataLoader(dataset=list(zip(q_data[0], q_data[1])))
e_dataloader = DataLoader(dataset=list(zip(e_data[0], e_data[1])))
quantizer.model = common.Model(model)
quantizer.calib_dataloader = q_dataloader
quantizer.eval_dataloader = e_dataloader
quantized_model = quantizer()
return quantized_model
def main(_):
if FLAGS.benchmark:
run_benchmark()
else:
FLAGS.batch_size = 1
from lpot.experimental import Quantization, common
quantizer = Quantization(FLAGS.config)
quantizer.model = common.Model(FLAGS.input_graph)
kwargs = {'conf_threshold': FLAGS.conf_threshold,
'iou_threshold': FLAGS.iou_threshold}
quantizer.postprocess = common.Postprocess(NMS, 'NMS', **kwargs)
q_model = quantizer()
q_model.save(FLAGS.output_graph)
def run(self):
if self.args.tune:
from lpot.experimental import Quantization
quantizer = Quantization(self.args.config)
quantizer.model = self.args.input_graph
q_model = quantizer()
q_model.save(self.args.output_model)
if self.args.benchmark:
from lpot.experimental import Benchmark
evaluator = Benchmark(self.args.config)
evaluator.model = self.args.input_graph
evaluator(self.args.mode)
def test_fx_dynamic_quant(self):
# Model Definition
class LSTMModel(nn.Module):
"""Container module with an encoder, a recurrent module, and a decoder."""
def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
super(LSTMModel, self).__init__()
self.drop = nn.Dropout(dropout)
self.encoder = nn.Embedding(ntoken, ninp)
self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
self.decoder = nn.Linear(nhid, ntoken)
self.init_weights()
self.nhid = nhid
self.nlayers = nlayers
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, input, hidden):
emb = self.drop(self.encoder(input))
output, hidden = self.rnn(emb, hidden)
output = self.drop(output)
decoded = self.decoder(output)
return decoded, hidden
version = get_torch_version()
if version >= '1.8':
model = LSTMModel(
ntoken=10,
ninp=512,
nhid=256,
nlayers=5,
)
# run fx_quant in lpot and save the quantized GraphModule
model.eval()
quantizer = Quantization('fx_dynamic_yaml.yaml')
quantizer.model = common.Model(model, **{'a': 1})
q_model = quantizer()
q_model.save('./saved_dynamic_fx')
# Load configure and weights by lpot.utils
model_fx = load("./saved_dynamic_fx", model, **{'a': 1})
if version >= '1.8':
self.assertTrue(
isinstance(model_fx, torch.fx.graph_module.GraphModule))
else:
self.assertTrue(
isinstance(model_fx, torch._fx.graph_module.GraphModule))
def test_bf16_rnn(self):
os.environ['FORCE_BF16'] = '1'
inp = tf.keras.layers.Input(shape=(None, 4))
lstm_1 = tf.keras.layers.LSTM(units=10, return_sequences=True)(inp)
dropout_1 = tf.keras.layers.Dropout(0.2)(lstm_1)
lstm_2 = tf.keras.layers.LSTM(units=10,
return_sequences=False)(dropout_1)
dropout_2 = tf.keras.layers.Dropout(0.2)(lstm_2)
out = tf.keras.layers.Dense(1)(dropout_2)
model = tf.keras.models.Model(inputs=inp, outputs=out)
model.compile(loss="mse", optimizer=tf.keras.optimizers.RMSprop())
# input_names = [t.name.split(":")[0] for t in model.inputs]
output_names = [t.name.split(":")[0] for t in model.outputs]
q_data = np.random.randn(64, 10, 4)
label = np.random.randn(64, 1)
model.predict(q_data)
sess = tf.keras.backend.get_session()
graph = sess.graph
from tensorflow.python.framework import graph_util
graph_def = graph_util.convert_variables_to_constants(
sess,
graph.as_graph_def(),
output_names,
)
quant_data = (q_data, label)
evl_data = (q_data, label)
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_bf16_rnn.yaml')
quantizer.calib_dataloader = common.DataLoader(
dataset=list(zip(quant_data[0], quant_data[1])))
quantizer.eval_dataloader = common.DataLoader(
dataset=list(zip(evl_data[0], evl_data[1])))
quantizer.model = graph_def
quantized_model = quantizer()
convert_to_bf16_flag = False
for i in quantized_model.graph_def.node:
if i.name == 'lstm/while/MatMul_3' and \
i.attr['T'].type == dtypes.bfloat16.as_datatype_enum:
convert_to_bf16_flag = True
self.assertEqual(convert_to_bf16_flag, True)
def run(self):
""" This is lpot function include tuning and benchmark option """
if self.args.tune:
from lpot.experimental import Quantization, common
quantizer = Quantization(self.args.config)
quantizer.model = common.Model(self.args.input_graph)
q_model = quantizer()
q_model.save(self.args.output_graph)
if self.args.benchmark:
from lpot.experimental import Benchmark, common
evaluator = Benchmark(self.args.config)
evaluator.model = common.Model(self.args.input_graph)
evaluator(self.args.mode)
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
if FLAGS.benchmark:
from lpot.experimental import Benchmark
evaluator = Benchmark(FLAGS.config)
evaluator.model = FLAGS.input_model
evaluator(FLAGS.mode)
elif FLAGS.tune:
from lpot.experimental import Quantization
quantizer = Quantization(FLAGS.config)
quantizer.model = FLAGS.input_model
q_model = quantizer()
q_model.save(FLAGS.output_model)
def main(_):
arg_parser = ArgumentParser(description='Parse args')
arg_parser.add_argument("--input-graph",
help='Specify the slim model',
dest='input_graph')
arg_parser.add_argument("--output-graph",
help='Specify tune result model save dir',
dest='output_graph')
arg_parser.add_argument("--config", default=None, help="tuning config")
arg_parser.add_argument('--benchmark',
dest='benchmark',
action='store_true',
help='run benchmark')
arg_parser.add_argument('--mode',
dest='mode',
default='performance',
help='benchmark mode')
arg_parser.add_argument('--tune',
dest='tune',
action='store_true',
help='use lpot to tune.')
args = arg_parser.parse_args()
factory = TFSlimNetsFactory()
# user specific model can register to slim net factory
input_shape = [None, 299, 299, 3]
factory.register('inception_v4', inception_v4, input_shape,
inception_v4_arg_scope)
if args.tune:
from lpot.experimental import Quantization
quantizer = Quantization(args.config)
quantizer.model = args.input_graph
q_model = quantizer()
q_model.save(args.output_graph)
if args.benchmark:
from lpot.experimental import Benchmark
evaluator = Benchmark(args.config)
evaluator.model = args.input_graph
evaluator(args.mode)
def test_quantizate(self):
from lpot.experimental import Quantization, common
for fake_yaml in ["static_yaml.yaml", "dynamic_yaml.yaml"]:
quantizer = Quantization(fake_yaml)
quantizer.calib_dataloader = self.cv_dataloader
quantizer.eval_dataloader = self.cv_dataloader
quantizer.model = common.Model(self.rn50_model)
q_model = quantizer()
eval_func(q_model)
for fake_yaml in ["non_MSE_yaml.yaml"]:
quantizer = Quantization(fake_yaml)
quantizer.calib_dataloader = self.cv_dataloader
quantizer.eval_dataloader = self.cv_dataloader
quantizer.model = common.Model(self.mb_v2_model)
q_model = quantizer()
eval_func(q_model)
def auto_tune(self):
"""This is lpot tuning part to generate a quantized pb
Returns:
graph: it will return a quantized pb
"""
from lpot.experimental import Quantization
infer_graph = load_graph(self.args.input_graph)
quantizer = Quantization(self.args.config)
if self.args.calib_data:
quantizer.model = infer_graph
quantizer.calib_dataloader = Dataloader(self.args.calib_data,
self.args.batch_size)
quantizer.eval_func = self.eval_inference
q_model = quantizer()
return q_model
else:
print("Please provide calibration dataset!")
def test_quantization_saved(self):
for fake_yaml in [
'dynamic_yaml.yaml', 'qat_yaml.yaml', 'ptq_yaml.yaml'
]:
if fake_yaml == 'dynamic_yaml.yaml':
model = torchvision.models.resnet18()
else:
model = copy.deepcopy(self.model)
if fake_yaml == 'ptq_yaml.yaml':
model.eval().fuse_model()
quantizer = Quantization(fake_yaml)
dataset = quantizer.dataset('dummy', (100, 3, 256, 256),
label=True)
quantizer.model = common.Model(model)
if fake_yaml == 'qat_yaml.yaml':
quantizer.q_func = q_func
else:
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
q_model = quantizer()
q_model.save('./saved')
# Load configure and weights by lpot.utils
saved_model = load("./saved", model)
eval_func(saved_model)
shutil.rmtree('./saved', ignore_errors=True)
from lpot.experimental import Benchmark
evaluator = Benchmark('ptq_yaml.yaml')
# Load configure and weights by lpot.model
evaluator.model = common.Model(model)
evaluator.b_dataloader = common.DataLoader(dataset)
evaluator()
evaluator.model = common.Model(model)
evaluator()
def test_quantization_saved(self):
from lpot.utils.pytorch import load
for fake_yaml in [
'dynamic_yaml.yaml', 'qat_yaml.yaml', 'ptq_yaml.yaml'
]:
if fake_yaml == 'dynamic_yaml.yaml':
model = torchvision.models.resnet18()
else:
model = copy.deepcopy(self.model)
if fake_yaml == 'ptq_yaml.yaml':
model.eval().fuse_model()
quantizer = Quantization(fake_yaml)
dataset = quantizer.dataset('dummy', (100, 3, 256, 256),
label=True)
quantizer.model = common.Model(model)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
if fake_yaml == 'qat_yaml.yaml':
quantizer.q_func = q_func
q_model = quantizer()
q_model.save('./saved')
# Load configure and weights by lpot.utils
saved_model = load("./saved", model)
eval_func(saved_model)
from lpot.experimental import Benchmark
evaluator = Benchmark('ptq_yaml.yaml')
# Load configure and weights by lpot.model
evaluator.model = common.Model(model)
evaluator.b_dataloader = common.DataLoader(dataset)
results = evaluator()
evaluator.model = common.Model(model)
fp32_results = evaluator()
self.assertTrue(
(fp32_results['accuracy'][0] - results['accuracy'][0]) < 0.01)
def tune_model(
input_graph: str,
output_graph: str,
config: str,
framework: str,
) -> None:
"""Execute tuning."""
from lpot.experimental import Quantization, common
if framework == "onnxrt":
import onnx
input_graph = onnx.load(input_graph)
quantizer = Quantization(config)
quantizer.model = common.Model(input_graph)
quantized_model = quantizer()
quantized_model.save(output_graph)
def main():
from lpot.experimental import Quantization, common
quantizer = Quantization('./conf.yaml')
quantizer.model = common.Model("./mobilenet_v1_1.0_224_frozen.pb")
quantized_model = quantizer()
# Optional, run benchmark
from lpot.experimental import Benchmark
evaluator = Benchmark('./conf.yaml')
evaluator.model = common.Model(quantized_model.graph_def)
results = evaluator()
batch_size = 1
for mode, result in results.items():
acc, batch_size, result_list = result
latency = np.array(result_list).mean() / batch_size
print('Accuracy is {:.3f}'.format(acc))
print('Latency: {:.3f} ms'.format(latency * 1000))
def test_tensor_dump(self):
model = copy.deepcopy(self.lpot_model)
model.model.eval().fuse_model()
quantizer = Quantization('ptq_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 256, 256), label=True)
dataloader = common.DataLoader(dataset)
dataloader = common._generate_common_dataloader(dataloader, 'pytorch')
self.adaptor.inspect_tensor(model,
dataloader,
op_list=['conv1', 'layer1.0.conv1'],
iteration_list=[1, 2],
weights=True,
save_to_disk=True)
load_array = lambda *a, **k: np.load(*a, allow_pickle=True, **k)
a = load_array('dump_tensor/activation_iter1.npz')
w = load_array('dump_tensor/weight.npz')
self.assertTrue(w['conv1'].item()['conv1.0.weight'].shape[0] ==
a['conv1'].item()['conv1.output0'].shape[1])
shutil.rmtree('./dump_tensor', ignore_errors=True)
def test_performance(self):
from lpot.data import DATASETS
dataset = DATASETS('tensorflow')['dummy']((100, 256, 256, 1),
label=True)
from lpot.experimental import Quantization, common
from lpot.utils.utility import get_size
quantizer = Quantization('fake_yaml.yaml')
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = self.constant_graph
q_model = quantizer()
from lpot.experimental import Benchmark, common
benchmarker = Benchmark('fake_yaml.yaml')
benchmarker.b_dataloader = common.DataLoader(dataset)
benchmarker.model = self.constant_graph_1
benchmarker()
def main(_):
graph = load_graph(FLAGS.input_graph)
if FLAGS.mode == 'tune':
from lpot.experimental import Quantization, common
quantizer = Quantization(FLAGS.config)
ds = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
quantizer.calib_dataloader = common.DataLoader(ds, collate_fn=collate_fn, \
batch_size=FLAGS.batch_size)
quantizer.model = common.Model(graph)
quantizer.eval_func = eval_func
q_model = quantizer()
try:
q_model.save(FLAGS.output_model)
except Exception as e:
print("Failed to save model due to {}".format(str(e)))
elif FLAGS.mode == 'benchmark':
eval_func(graph, FLAGS.iters)
elif FLAGS.mode == 'accuracy':
eval_func(graph, -1)
def test_set_tensor(self):
quantizer = Quantization("static.yaml")
quantizer.calib_dataloader = self.cv_dataloader
quantizer.eval_dataloader = self.cv_dataloader
quantizer.model = common.Model(self.mb_v2_model)
q_model = quantizer()
framework_specific_info = {"device": "cpu",
"approach": "post_training_static_quant",
"random_seed": 1234,
"q_dataloader": None,
"backend": "qlinearops",
"workspace_path": './lpot_workspace/{}/{}/'.format(
'onnxrt',
'imagenet')}
framework = "onnxrt_qlinearops"
adaptor = FRAMEWORKS[framework](framework_specific_info)
q_config = {'fused Conv_0': {'weight': {'granularity': 'per_channel', 'dtype': onnx_proto.TensorProto.INT8}}}
adaptor.q_config = q_config
adaptor.set_tensor(q_model, {'ConvBnFusion_W_features.0.0.weight': np.random.random([32, 3, 3, 3])})
adaptor.set_tensor(q_model, {'ConvBnFusion_BN_B_features.0.1.bias': np.random.random([32])})
def main():
arg_parser = ArgumentParser(description='Parse args')
arg_parser.add_argument('--benchmark',
action='store_true',
help='run benchmark')
arg_parser.add_argument('--tune', action='store_true', help='run tuning')
args = arg_parser.parse_args()
if args.tune:
from lpot.experimental import Quantization, common
quantizer = Quantization('./conf.yaml')
quantizer.model = common.Model("./mobilenet_v1_1.0_224_frozen.pb")
quantized_model = quantizer()
quantized_model.save('./int8.pb')
if args.benchmark:
from lpot.experimental import Benchmark, common
evaluator = Benchmark('./conf.yaml')
evaluator.model = common.Model('int8.pb')
evaluator(mode='accuracy')
def test_matmul_with_nan(self):
g = tf.Graph()
with g.as_default():
x_data = np.array([[0.1, 0.2], [0.2, 0.3]])
nan_array = np.empty((2, 2), dtype=np.float32)
nan_array[:] = np.NaN
x = tf.placeholder(tf.float32, shape=[2, 2], name='x')
z = tf.matmul(x, nan_array, name='no_quant_matmul')
z = tf.identity(z, name='op_to_store')
found_quantized_matmul = True
with tf.Session() as sess:
sess.run(z, feed_dict={x: x_data})
float_graph_def = sess.graph.as_graph_def()
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(2, 2), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.eval_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.model = float_graph_def
output_graph = quantizer()
for i in output_graph.graph_def.node:
if i.op == 'MatMul':
found_quantized_matmul = False
break
self.assertEqual(found_quantized_matmul, False)
def test_fold_pad_conv(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
x_pad = tf.pad(x, paddings, "CONSTANT")
conv_weights = tf.compat.v1.get_variable("weight", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv = tf.nn.conv2d(x_pad, conv_weights, strides=[1, 2, 2, 1], padding="VALID")
normed = tf.compat.v1.layers.batch_normalization(conv)
relu = tf.nn.relu(normed, name='op_to_store')
out_name = relu.name.split(':')[0]
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
output_graph_def = graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=sess.graph_def,
output_node_names=[out_name])
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 56, 56, 16), label=True)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.model = output_graph_def
output_graph = quantizer()
found_pad = False
if tf.__version__ >= "2.0.0":
for i in output_graph.graph_def.node:
if i.op == 'Pad':
found_pad = True
break
self.assertEqual(found_pad, True)
def test_conv_fusion_with_max_pooling(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
relu = tf.nn.relu(x)
pooling = tf.nn.max_pool(relu, ksize=1, strides=[1, 2, 2, 1], padding="SAME")
conv_weights = tf.compat.v1.get_variable("weight2", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv = tf.nn.conv2d(pooling, conv_weights, strides=[1, 2, 2, 1], padding="VALID")
biasadd = tf.compat.v1.layers.batch_normalization(conv, name='op_to_store')
out_name = biasadd.name.split(':')[0]
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
output_graph_def = graph_util.convert_variables_to_constants(
sess=sess,
input_graph_def=sess.graph_def,
output_node_names=[out_name])
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 56, 56, 16), label=True)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.model = output_graph_def
output_graph = quantizer()
quantized_pool_data_type = None
quantized_conv_data_type = None
for i in output_graph.graph_def.node:
if i.op.find("QuantizedMaxPool") != -1:
quantized_pool_data_type = i.attr['T'].type
if i.op.find("QuantizedConv2D") != -1:
quantized_conv_data_type = i.attr['Tinput'].type
self.assertNotEqual(quantized_pool_data_type, None)
self.assertEqual(quantized_pool_data_type, quantized_conv_data_type)
def test_disable_matmul_fusion(self):
g = tf.Graph()
with g.as_default():
x_data = np.array([[0.1, 0.2], [0.2, 0.3]])
y_data = np.array([[1, 2], [3, 4]], dtype=np.float)
x = tf.placeholder(tf.float32, shape=[2, 2], name='x')
y = tf.constant(y_data, dtype=tf.float32, shape=[2, 2])
z = tf.matmul(x, y, name='no_quant_matmul')
z = tf.nn.relu6(z, name='op_to_store')
found_quantized_matmul = False
with tf.Session() as sess:
sess.run(z, feed_dict={x: x_data, y: y_data})
float_graph_def = sess.graph.as_graph_def()
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(2, 2), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.eval_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.model = float_graph_def
output_graph = quantizer()
for i in output_graph.graph_def.node:
if i.op == 'QuantizedMatMulWithBiasAndDequantize' and i.name == 'op_to_store':
found_quantized_matmul = True
break
self.assertEqual(found_quantized_matmul, False)
def test_first_matmul_biasadd_relu_fusion(self):
x_data = np.array([[0.1, 0.2], [0.2, 0.3]])
y_data = np.array([[1, 2], [3, 4]], dtype=np.float)
x = tf.placeholder(tf.float32, shape=[2, 2], name='x')
y = tf.constant(y_data, dtype=tf.float32, shape=[2, 2])
z = tf.matmul(x, y)
z = tf.nn.bias_add(z, [1, 2])
z = tf.nn.relu(z, name='op_to_store')
with tf.Session() as sess:
sess.run(z, feed_dict={x: x_data, y: y_data})
float_graph_def = sess.graph.as_graph_def()
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(2, 2), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.eval_dataloader = common.DataLoader(dataset,
batch_size=2)
quantizer.model = float_graph_def
output_graph = quantizer()
found_quantized_matmul = False
for i in output_graph.graph_def.node:
if i.op == 'QuantizeV2' and i.name == 'MatMul_eightbit_quantize_x' and i.attr[
"T"].type == dtypes.quint8:
found_quantized_matmul = True
break
self.assertEqual(found_quantized_matmul, True)
def test_loss_calculation(self):
from lpot.strategy.tpe import TpeTuneStrategy
from lpot.experimental import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 3, 1), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = self.constant_graph
testObject = TpeTuneStrategy(quantizer.model, quantizer.conf,
quantizer.calib_dataloader)
testObject._calculate_loss_function_scaling_components(
0.01, 2, testObject.loss_function_config)
# check if latency difference between min and max corresponds to 10 points of loss function
tmp_val = testObject.calculate_loss(0.01, 2,
testObject.loss_function_config)
tmp_val2 = testObject.calculate_loss(0.01, 1,
testObject.loss_function_config)
self.assertTrue(True if int(tmp_val2 - tmp_val) == 10 else False)
# check if 1% of acc difference corresponds to 10 points of loss function
tmp_val = testObject.calculate_loss(0.02, 2,
testObject.loss_function_config)
tmp_val2 = testObject.calculate_loss(0.03, 2,
testObject.loss_function_config)
self.assertTrue(True if int(tmp_val2 - tmp_val) == 10 else False)
