def test_quantization_saved(self):
from lpot.utils.pytorch import load
model = copy.deepcopy(self.model)
for fake_yaml in ['qat_yaml.yaml', 'ptq_yaml.yaml']:
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 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 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 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_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 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_loss_calculation(self):
from lpot.strategy.tpe import TpeTuneStrategy
from lpot 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)
def test_matmul_biasadd_relu_requantize_fusion(self):
tf.disable_v2_behavior()
g = tf.Graph()
with g.as_default():
from lpot import Quantization
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')
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()
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(2, 2), label=True)
dataloader = quantizer.dataloader(dataset, batch_size=2)
output_graph = quantizer(float_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
for i in output_graph.as_graph_def().node:
if i.op == 'QuantizedMatMulWithBiasAndReluAndRequantize':
found_quantized_matmul = True
break
self.assertEqual(found_quantized_matmul, True)
def test_tuning_ipex(self):
from lpot import Quantization
model = torchvision.models.resnet18()
model = MODELS['pytorch_ipex'](model)
quantizer = Quantization('ipex_yaml.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)
lpot_model = quantizer()
lpot_model.save("./saved")
new_model = MODELS['pytorch_ipex'](model.model, {
"workspace_path": "./saved"
})
new_model.model.to(ipex.DEVICE)
try:
script_model = torch.jit.script(new_model.model)
except:
script_model = torch.jit.trace(
new_model.model,
torch.randn(10, 3, 224, 224).to(ipex.DEVICE))
from lpot import Benchmark
evaluator = Benchmark('ipex_yaml.yaml')
evaluator.model = common.Model(script_model)
evaluator.b_dataloader = common.DataLoader(dataset)
results = evaluator()
def test_dump_tensor_to_disk(self):
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 30, 30, 1), label=True)
dataloader = quantizer.dataloader(dataset)
output_graph = quantizer(
self.constant_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader
)
with open(self.calibration_log_path) as f:
data = f.readlines()
found_min_str = False
found_max_str = False
for i in data:
if i.find('__print__;__max') != -1:
found_max_str = True
if i.find('__print__;__min') != -1:
found_min_str = True
self.assertEqual(os.path.exists(self.calibration_log_path), True)
self.assertGreater(len(data), 1)
self.assertEqual(found_min_str, True)
self.assertEqual(found_max_str, True)
def test_autodump(self):
from lpot import Quantization, common
quantizer = Quantization('fake_yaml3.yaml')
dataset = quantizer.dataset('dummy', shape=(100, 3, 3, 1), label=True)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.model = self.constant_graph
output_graph = quantizer()
def test_ru_mse_max_trials(self):
from lpot import Quantization, common
quantizer = Quantization('fake_yaml2.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
quantizer()
def test_ru_exhaustive_one_trial(self):
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 3, 1), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer(self.constant_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader)
def test_run_bayesian_max_trials(self):
from lpot import Quantization
quantizer = Quantization('fake_yaml2.yaml')
dataset = quantizer.dataset('dummy', (1, 224, 224, 3), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer(self.test_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader)
def test_run_basic_max_trials(self):
from lpot import Quantization
quantizer = Quantization('fake_yaml2.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 3, 1), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer(self.constant_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader)
def test_tensorflow_graph_meta_pass(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
top_relu = tf.nn.relu(x)
conv_weights = tf.compat.v1.get_variable(
"weight", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv = tf.nn.conv2d(top_relu,
conv_weights,
strides=[1, 2, 2, 1],
padding="VALID")
normed = tf.compat.v1.layers.batch_normalization(conv)
relu = tf.nn.relu(normed)
sq = tf.squeeze(relu, [0])
reshape = tf.reshape(sq, [1, 27, 27, 16])
conv_weights2 = tf.compat.v1.get_variable(
"weight2", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv2 = tf.nn.conv2d(reshape,
conv_weights2,
strides=[1, 2, 2, 1],
padding="VALID")
normed2 = tf.compat.v1.layers.batch_normalization(conv2)
relu6 = tf.nn.relu6(normed2, name='op_to_store')
out_name = relu6.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 import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 56, 56, 16),
label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = output_graph_def
output_graph = quantizer()
quantize_count = 0
dequantize_count = 0
for i in output_graph.graph_def.node:
if i.op == 'QuantizeV2':
quantize_count += 1
if i.op == 'Dequantize':
dequantize_count += 1
self.assertEqual(quantize_count, 1)
self.assertEqual(dequantize_count, 1)
def test_conv_fusion_with_last_matmul(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
top_relu = tf.nn.relu(x)
# paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
# x_pad = tf.pad(top_relu, 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(top_relu,
conv_weights,
strides=[1, 2, 2, 1],
padding="VALID")
normed = tf.compat.v1.layers.batch_normalization(conv)
relu = tf.nn.relu(normed)
pooling = tf.nn.max_pool(relu,
ksize=1,
strides=[1, 2, 2, 1],
padding="SAME")
reshape = tf.reshape(pooling, [-1, 3136])
y_data = np.random.random([3136, 1])
y = tf.constant(y_data, dtype=tf.float32, shape=[3136, 1])
z = tf.matmul(reshape, y)
y_data_1 = np.random.random([1, 1])
y_1 = tf.constant(y_data_1, dtype=tf.float32, shape=[1, 1])
z_2nd_matmul = tf.matmul(z, y_1)
relu6 = tf.nn.relu6(z_2nd_matmul, name='op_to_store')
out_name = relu6.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 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()
quantize_v2_count = 0
for i in output_graph.graph_def.node:
if i.op == 'QuantizeV2':
quantize_v2_count += 1
break
self.assertEqual(quantize_v2_count, 1)
def test_run_basic_one_trial(self):
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 3, 1), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer(self.constant_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader)
self.assertTrue(True if len(os.listdir("./runs/eval")) == 2 else False)
def test_conv_biasadd_addv2_relu_fusion(self):
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
top_relu = tf.nn.relu(x)
paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
x_pad = tf.pad(top_relu, 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)
conv_weights2 = tf.compat.v1.get_variable(
"weight2", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv2 = tf.nn.conv2d(top_relu,
conv_weights2,
strides=[1, 2, 2, 1],
padding="SAME")
normed2 = tf.compat.v1.layers.batch_normalization(conv2)
# relu2 = tf.nn.relu(normed2)
add = tf.raw_ops.AddV2(x=normed, y=normed2, name='addv2')
relu = tf.nn.relu(add)
relu6 = tf.nn.relu6(relu, name='op_to_store')
out_name = relu6.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 import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 56, 56, 16),
label=True)
dataloader = quantizer.dataloader(dataset)
output_graph = quantizer(output_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
found_conv_fusion = False
for i in output_graph.as_graph_def().node:
if i.op == 'QuantizedConv2DWithBiasSignedSumAndReluAndRequantize':
found_conv_fusion = True
break
self.assertEqual(found_conv_fusion, True)
def test_run_basic_one_trial(self):
from lpot import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (1, 224, 224, 3), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = self.constant_graph
quantizer()
self.assertTrue(True if len(os.listdir("./runs/eval")) > 2 else False)
def test_disable_scale_propagation(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 30, 30, 1], name="input")
conv_weights = tf.compat.v1.get_variable(
"weight", [2, 2, 1, 1],
initializer=tf.compat.v1.random_normal_initializer())
conv_bias = tf.compat.v1.get_variable(
"bias", [1], initializer=tf.compat.v1.random_normal_initializer())
x = tf.nn.relu(x)
conv = tf.nn.conv2d(x,
conv_weights,
strides=[1, 2, 2, 1],
padding="SAME",
name='last')
normed = tf.compat.v1.layers.batch_normalization(conv)
relu = tf.nn.relu(normed)
pool = tf.nn.avg_pool(relu,
ksize=1,
strides=[1, 2, 2, 1],
padding="SAME")
conv1 = tf.nn.conv2d(pool,
conv_weights,
strides=[1, 2, 2, 1],
padding="SAME",
name='last')
conv_bias = tf.nn.bias_add(conv1, conv_bias)
x = tf.nn.relu(conv_bias)
final_node = tf.nn.relu(x, name='op_to_store')
out_name = final_node.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 import Quantization, common
quantizer = Quantization(
'fake_yaml_disable_scale_propagation.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 30, 30, 1),
label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = output_graph_def
output_graph = quantizer()
max_freezed_out = []
for i in output_graph.graph_def.node:
if i.op == 'QuantizedConv2DWithBiasAndReluAndRequantize':
max_freezed_out.append(i.input[-1])
self.assertEqual(2, len(set(max_freezed_out)))
def test_quantizate(self):
from lpot import Quantization
for fake_yaml in ["static_yaml.yaml", "dynamic_yaml.yaml"]:
quantizer = Quantization(fake_yaml)
dataset = quantizer.dataset("dummy", (100, 3, 224, 224),
label=True)
dataloader = quantizer.dataloader(dataset)
q_model = quantizer(self.cnn_model,
q_dataloader=dataloader,
eval_dataloader=dataloader)
eval_func(q_model)
def test_run_bayesian_max_trials(self):
from lpot import Quantization, common
quantizer = Quantization('fake_yaml2.yaml')
dataset = quantizer.dataset('dummy',
shape=(1, 224, 224, 3),
label=True)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.model = self.test_graph
output_graph = quantizer()
def test_fold_pad_conv2(self):
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
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)
paddings2 = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
x_pad2 = tf.pad(x, paddings2, "CONSTANT")
conv_weights2 = tf.compat.v1.get_variable(
"weight2", [3, 3, 16, 16],
initializer=tf.compat.v1.random_normal_initializer())
conv2 = tf.nn.conv2d(x_pad2,
conv_weights2,
strides=[1, 2, 2, 1],
padding="VALID")
normed2 = tf.compat.v1.layers.batch_normalization(conv2)
relu2 = tf.nn.relu(normed2)
add = tf.math.add(relu, relu2, name='op_to_store')
out_name = add.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 import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 56, 56, 16),
label=True)
dataloader = quantizer.dataloader(dataset)
output_graph = quantizer(output_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
found_pad = False
if tf.__version__ >= "2.0.0":
for i in output_graph.as_graph_def().node:
if i.op == 'Pad':
found_pad = True
break
self.assertEqual(found_pad, True)
def test_quantizate(self):
from lpot import Quantization, common
for fake_yaml in ["static_yaml.yaml", "dynamic_yaml.yaml"]:
quantizer = Quantization(fake_yaml)
dataset = quantizer.dataset("dummy", (100, 3, 224, 224),
low=0.,
high=1.,
label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
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)
dataset = quantizer.dataset("dummy", (100, 3, 224, 224),
low=0.,
high=1.,
label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = common.Model(self.mb_v2_model)
q_model = quantizer()
eval_func(q_model)
def test_bf16_fallback(self):
os.environ['FORCE_BF16'] = '1'
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (1, 224, 224, 3), label=True)
dataloader = quantizer.dataloader(dataset)
quant_model = quantizer(self.test_graph,
q_dataloader=dataloader,
eval_dataloader=dataloader)
cast_op_count = 0
for node in quant_model.as_graph_def().node:
if node.op == 'Cast':
cast_op_count += 1
self.assertTrue(cast_op_count >= 1)
def test_no_input_output_config(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
float_graph_def = g.dump_graph()
from lpot import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(20, 224, 224, 3), 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()
self.assertGreater(len(output_graph.graph_def.node), 0)
def test_tensor_dump(self):
model = copy.deepcopy(self.lpot_model)
model.model.eval().fuse_model()
quantizer = Quantization('dump_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 256, 256), label=True)
quantizer.model = common.Model(model.model)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_func = eval_func
quantizer()
self.assertTrue(
True if os.path.exists('runs/eval/baseline_acc0.0') else False)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer()
self.assertTrue(
True if os.path.exists('runs/eval/baseline_acc0.0') else False)
def test_invalid_input_output_config(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
float_graph_def = g.dump_graph()
from lpot import Quantization, common
quantizer = Quantization('fake_yaml_2.yaml')
dataset = quantizer.dataset('dummy', shape=(20, 224, 224, 3), 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
model = quantizer()
# will detect the right inputs/outputs
self.assertNotEqual(model.input_node_names, ['x'])
self.assertNotEqual(model.output_node_names, ['op_to_store'])
def test_bf16_fallback(self):
os.environ['FORCE_BF16'] = '1'
from lpot import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(1, 224, 224, 3),
label=True)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.model = self.test_graph
output_graph = quantizer()
cast_op_count = 0
for node in output_graph.graph_def.node:
if node.op == 'Cast':
cast_op_count += 1
self.assertTrue(cast_op_count >= 1)
def test_enable_first_quantization(self):
x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
top_relu = tf.nn.relu(x)
paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
x_pad = tf.pad(top_relu, 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)
relu6 = tf.nn.relu6(relu, name='op_to_store')
out_name = relu6.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 import Quantization, common
quantizer = Quantization(
'fake_yaml_enable_first_quantization.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 56, 56, 16),
label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = output_graph_def
output_graph = quantizer()
found_fp32_conv = False
for i in output_graph.graph_def.node:
if i.op == 'Conv2D':
found_fp32_conv = True
break
self.assertEqual(found_fp32_conv, False)
def test_autosave(self):
from lpot import Quantization, common
from lpot.utils.utility import get_size
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 256, 256, 1), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset)
quantizer.eval_dataloader = common.DataLoader(dataset)
quantizer.model = self.constant_graph
quantizer()
q_model = quantizer()
quantizer.model = self.constant_graph_1
q_model_1 = quantizer()
self.assertTrue((get_size(q_model_1.sess.graph) -
get_size(q_model.sess.graph)) > 0)
def test_tuning_ipex(self):
from lpot import Quantization
model = torchvision.models.resnet18()
quantizer = Quantization('ipex_yaml.yaml')
dataset = quantizer.dataset('dummy', (100, 3, 256, 256), label=True)
dataloader = quantizer.dataloader(dataset)
quantizer(
model,
eval_dataloader=dataloader,
q_dataloader=dataloader,
)
model.to(ipex.DEVICE)
try:
script_model = torch.jit.script(model)
except:
script_model = torch.jit.trace(
model,
torch.randn(10, 3, 224, 224).to(ipex.DEVICE))
from lpot import Benchmark
evaluator = Benchmark('ipex_yaml.yaml')
results = evaluator(model=script_model, b_dataloader=dataloader)
