def quant_aware(program,
place,
config=None,
scope=None,
for_test=False,
weight_quantize_func=None,
act_quantize_func=None,
weight_preprocess_func=None,
act_preprocess_func=None,
optimizer_func=None,
executor=None,
return_program=False):
"""Add quantization and dequantization operators to "program"
for quantization training or testing.
Args:
program(paddle.static.Program): training or testing ``program``.
place(paddle.CPUPlace or paddle.CUDAPlace): This parameter represents
the executor run on which device.
config(dict, optional): configs for quantization. if None, will use default config.
Default: None.
scope(paddle.static.Scope): Scope records the mapping between variable names and variables,
similar to brackets in programming languages. Usually users can use
`paddle.static.global_scope <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_. When ``None`` will use `paddle.static.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_ . Default: ``None``.
for_test(bool): If the 'program' parameter is a test program, this parameter should be set to ``True``.
Otherwise, set to ``False``.Default: False
weight_quantize_func(function): Function that defines how to quantize weight. Using this
can quickly test if user's quantization method works or not. In this function, user should
both define quantization function and dequantization function, that is, the function's input
is non-quantized weight and function returns dequantized weight. If None, will use
quantization op defined by 'weight_quantize_type'.
Default is None.
act_quantize_func(function): Function that defines how to quantize activation. Using this
can quickly test if user's quantization method works or not. In this function, user should
both define quantization and dequantization process, that is, the function's input
is non-quantized activation and function returns dequantized activation. If None, will use
quantization op defined by 'activation_quantize_type'.
Default is None.
weight_preprocess_func(function): Function that defines how to preprocess weight before quantization. Using this
can quickly test if user's preprocess method works or not. The function's input
is non-quantized weight and function returns processed weight to be quantized. If None, the weight will
be quantized directly.
Default is None.
act_preprocess_func(function): Function that defines how to preprocess activation before quantization. Using this
can quickly test if user's preprocess method works or not. The function's input
is non-quantized activation and function returns processed activation to be quantized. If None, the activation will
be quantized directly.
Default is None.
optimizer_func(function): Fuction return a optimizer. When 'is_test' is False and user want to use self-defined
quantization function and preprocess function, this function must be set. Default is None.
exe(paddle.static.Executor): If user want to use self-defined quantization function and preprocess function, exe must be set for
initialization. Default is None.
return_program(bool): If user want return value is a Program rather than Compiled Program, This argument should be set True.
Default is False.
Returns:
paddle.static.CompiledProgram | paddle.static.Program: Program with quantization and dequantization ``operators``
"""
scope = paddle.static.global_scope() if not scope else scope
if config is None:
config = _quant_config_default
else:
assert isinstance(config, dict), "config must be dict"
config = _parse_configs(config)
_logger.info("quant_aware config {}".format(config))
main_graph = IrGraph(core.Graph(program.desc), for_test=for_test)
transform_pass_ops = []
quant_dequant_ops = []
for op_type in config['quantize_op_types']:
if op_type in TRANSFORM_PASS_OP_TYPES:
transform_pass_ops.append(op_type)
elif op_type in QUANT_DEQUANT_PASS_OP_TYPES:
quant_dequant_ops.append(op_type)
if len(transform_pass_ops) > 0:
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
weight_bits=config['weight_bits'],
activation_bits=config['activation_bits'],
activation_quantize_type=config['activation_quantize_type'],
weight_quantize_type=config['weight_quantize_type'],
window_size=config['window_size'],
moving_rate=config['moving_rate'],
quantizable_op_type=transform_pass_ops,
skip_pattern=config['not_quant_pattern'],
weight_quantize_func=weight_quantize_func,
act_quantize_func=act_quantize_func,
weight_preprocess_func=weight_preprocess_func,
act_preprocess_func=act_preprocess_func,
optimizer_func=optimizer_func,
executor=executor)
transform_pass.apply(main_graph)
if len(quant_dequant_ops) > 0:
quant_dequant_pass = AddQuantDequantPass(
scope=scope,
place=place,
moving_rate=config['moving_rate'],
quant_bits=config['activation_bits'],
skip_pattern=config['not_quant_pattern'],
quantizable_op_type=quant_dequant_ops)
quant_dequant_pass.apply(main_graph)
out_scale_training_pass = OutScaleForTrainingPass(
scope=scope, place=place, moving_rate=config['moving_rate'])
out_scale_training_pass.apply(main_graph)
if (weight_preprocess_func is not None
or act_preprocess_func is not None) and not for_test:
_logger.info(
"When a preprocess_func is used in quant_aware, Need to save a mapping table to match variable names in the convert phase."
)
_logger.info(
"The mapping table is saved as '{}'.".format(VARS_MAPPING_TABLE))
save_dict(main_graph.out_node_mapping_table)
if for_test or return_program:
quant_program = main_graph.to_program()
else:
quant_program = paddle.static.CompiledProgram(main_graph.graph)
return quant_program
def check_output_with_option(self,
use_gpu,
atol=1e-5,
flatten=False,
quant=False,
rtol=1e-5):
'''
Check whether calculating on CPU and GPU, enable TensorRT
or disable TensorRT, enable MKLDNN or disable MKLDNN
are all the same.
'''
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
executor = fluid.Executor(place)
scope = fluid.Scope()
device = "GPU" if use_gpu else "CPU"
with fluid.scope_guard(scope):
executor.run(self.startup_program)
executor.run(self.test_startup_program)
main_graph = IrGraph(core.Graph(self.main_program.desc),
for_test=False)
test_graph = IrGraph(core.Graph(self.test_main_program.desc),
for_test=True)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=self.activation_quantize_type,
weight_quantize_type=self.weight_quantize_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
add_quant_dequant_pass.apply(test_graph)
scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
scale_training_pass.apply(main_graph)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph)
iters = 10
batch_size = 1
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=[self.data, self.label],
place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = executor.run(binary,
feed=feeder.feed(data),
fetch_list=[self.loss])
scale_inference_pass = OutScaleForInferencePass(scope=scope)
scale_inference_pass.apply(test_graph)
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope,
place=place,
weight_quantize_type=self.weight_quantize_type)
freeze_pass.apply(test_graph)
self.main_program = test_graph.to_program()
with fluid.scope_guard(scope):
self.main_program = self._normalize_program(
self.main_program, self.data, self.fetch_list)
self._save_models(self.path, list(self.feeds.keys()), self.fetch_list,
executor, self.main_program, scope)
paddle_outs = self._get_paddle_outs(self.feeds, self.fetch_list,
executor, self.main_program, scope)
inference_outs = self._get_inference_outs(
self._get_analysis_config(use_gpu=use_gpu))
# Check whether the results calculated on CPU and on GPU are the same.
self.assertTrue(
len(paddle_outs) == len(inference_outs),
"The number of outputs is different between inference and training forward at {}"
.format(device))
for out, inference_out in zip(paddle_outs, inference_outs):
paddle_out = np.array(out)
if flatten:
paddle_out = paddle_out.flatten()
inference_out = inference_out.flatten()
self.assertTrue(
np.allclose(paddle_out, inference_out, atol=atol),
"Output has diff between inference and training forward at {} "
.format(device))
# Check whether the trt results and the GPU results are the same.
if use_gpu and self.enable_trt:
tensorrt_outputs = self._get_inference_outs(
self._get_analysis_config(use_gpu=use_gpu,
use_trt=self.enable_trt))
if self.trt_parameters.use_static:
#deserialize
tensorrt_outputs = self._get_inference_outs(
self._get_analysis_config(use_gpu=use_gpu,
use_trt=self.enable_trt))
self.assertTrue(
len(tensorrt_outputs) == len(paddle_outs),
"The number of outputs is different between GPU and TensorRT. "
)
for paddle_out, tensorrt_output in zip(paddle_outs,
tensorrt_outputs):
paddle_out = np.array(paddle_out)
if flatten:
paddle_out = paddle_out.flatten()
tensorrt_output = tensorrt_output.flatten()
self.assertTrue(
np.allclose(paddle_out,
tensorrt_output,
rtol=rtol,
atol=atol),
"Output has diff between GPU and TensorRT. ")
# Check whether the mkldnn results and the CPU results are the same.
if (not use_gpu) and self.enable_mkldnn:
mkldnn_outputs = self._get_inference_outs(
self._get_analysis_config(use_gpu=use_gpu,
use_mkldnn=self.enable_mkldnn))
self.assertTrue(
len(paddle_outs) == len(mkldnn_outputs),
"The number of outputs is different between CPU and MKLDNN. ")
if self.enable_mkldnn_bfloat16:
atol = 0.01
for paddle_out, mkldnn_output in zip(paddle_outs, mkldnn_outputs):
self.assertTrue(
np.allclose(np.array(paddle_out), mkldnn_output,
atol=atol),
"Output has diff between CPU and MKLDNN. ")
def quantization_scale(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False,
act_preprocess_func=None,
weight_preprocess_func=None,
act_quantize_func=None,
weight_quantize_func=None):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
img.stop_gradient = False
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.SGD(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
def get_optimizer():
return fluid.optimizer.MomentumOptimizer(0.0001, 0.9)
def load_dict():
with open('mapping_table_for_saving_inference_model', 'r') as file:
data = file.read()
data = json.loads(data)
return data
def save_dict(Dict):
with open('mapping_table_for_saving_inference_model', 'w') as file:
file.write(json.dumps(Dict))
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
train_transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type,
act_preprocess_func=act_preprocess_func,
weight_preprocess_func=weight_preprocess_func,
act_quantize_func=act_quantize_func,
weight_quantize_func=weight_quantize_func,
optimizer_func=get_optimizer,
executor=exe)
train_transform_pass.apply(main_graph)
test_transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type,
act_preprocess_func=act_preprocess_func,
weight_preprocess_func=weight_preprocess_func,
act_quantize_func=act_quantize_func,
weight_quantize_func=weight_quantize_func,
optimizer_func=get_optimizer,
executor=exe)
test_transform_pass.apply(test_graph)
save_dict(test_graph.out_node_mapping_table)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
add_quant_dequant_pass.apply(test_graph)
scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
scale_training_pass.apply(main_graph)
dev_name = '_gpu' if use_cuda else '_cpu'
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
out_scale_infer_pass = OutScaleForInferencePass(scope=scope)
out_scale_infer_pass.apply(test_graph)
freeze_pass = QuantizationFreezePass(
scope=scope,
place=place,
weight_bits=8,
activation_bits=8,
weight_quantize_type=weight_quant_type)
mapping_table = load_dict()
test_graph.out_node_mapping_table = mapping_table
if act_quantize_func == None and weight_quantize_func == None:
freeze_pass.apply(test_graph)
def quantization_scale(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = residual_block(img, label, 1)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
add_quant_dequant_pass.apply(test_graph)
scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
scale_training_pass.apply(main_graph)
dev_name = '_gpu' if use_cuda else '_cpu'
if not for_ci:
marked_nodes = set()
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main_scale' + dev_name, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_scale' + dev_name, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name, loss_v))
scale_inference_pass = OutScaleForInferencePass(scope=scope)
scale_inference_pass.apply(test_graph)
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
server_program = test_graph.to_program()
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'quant_scale' + dev_name, marked_nodes)
with open('quant_scale_model' + dev_name + '.txt', 'w') as f:
f.write(str(server_program))
with fluid.scope_guard(scope):
fluid.io.save_inference_model('quant_scale_model' + dev_name,
['image', 'label'], [loss], exe,
server_program)
def test_out_scale_acc(self):
def _build_static_lenet(main, startup, is_test=False, seed=1000):
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
main.random_seed = seed
startup.random_seed = seed
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
prediction = StaticLenet(img)
if not is_test:
loss = fluid.layers.cross_entropy(input=prediction,
label=label)
avg_loss = fluid.layers.mean(loss)
else:
avg_loss = prediction
return img, label, avg_loss
reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=32,
drop_last=True)
weight_quantize_type = 'abs_max'
activation_quant_type = 'moving_average_abs_max'
param_init_map = {}
seed = 1000
lr = 0.1
dynamic_out_scale_list = []
static_out_scale_list = []
# imperative train
_logger.info(
"--------------------------dynamic graph qat--------------------------"
)
imperative_out_scale = ImperativeQuantAware()
with fluid.dygraph.guard():
np.random.seed(seed)
fluid.default_main_program().random_seed = seed
fluid.default_startup_program().random_seed = seed
lenet = ImperativeLenet()
fixed_state = {}
for name, param in lenet.named_parameters():
p_shape = param.numpy().shape
p_value = param.numpy()
if name.endswith("bias"):
value = np.zeros_like(p_value).astype('float32')
else:
value = np.random.normal(loc=0.0,
scale=0.01,
size=np.product(p_shape)).reshape(
p_shape).astype('float32')
fixed_state[name] = value
param_init_map[param.name] = value
lenet.set_dict(fixed_state)
imperative_out_scale.quantize(lenet)
adam = AdamOptimizer(learning_rate=lr,
parameter_list=lenet.parameters())
dynamic_loss_rec = []
lenet.train()
for batch_id, data in enumerate(reader()):
x_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array([x[1] for x in data
]).astype('int64').reshape(-1, 1)
img = fluid.dygraph.to_variable(x_data)
label = fluid.dygraph.to_variable(y_data)
out = lenet(img)
loss = fluid.layers.cross_entropy(out, label)
avg_loss = fluid.layers.mean(loss)
avg_loss.backward()
adam.minimize(avg_loss)
lenet.clear_gradients()
dynamic_loss_rec.append(avg_loss.numpy()[0])
if batch_id % 100 == 0:
_logger.info('{}: {}'.format('loss', avg_loss.numpy()))
lenet.eval()
path = "./dynamic_outscale_infer_model/lenet"
dynamic_save_dir = "./dynamic_outscale_infer_model"
imperative_out_scale.save_quantized_model(
layer=lenet,
path=path,
input_spec=[
paddle.static.InputSpec(shape=[None, 1, 28, 28],
dtype='float32')
])
_logger.info(
"--------------------------static graph qat--------------------------"
)
static_loss_rec = []
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
main = fluid.Program()
infer = fluid.Program()
startup = fluid.Program()
static_img, static_label, static_loss = _build_static_lenet(
main, startup, False, seed)
infer_img, _, infer_pre = _build_static_lenet(infer, startup, True,
seed)
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
opt = AdamOptimizer(learning_rate=lr)
opt.minimize(static_loss)
scope = core.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
for param in main.all_parameters():
param_tensor = scope.var(param.name).get_tensor()
param_tensor.set(param_init_map[param.name], place)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
infer_graph = IrGraph(core.Graph(infer.desc), for_test=True)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quantize_type,
quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul'])
transform_pass.apply(main_graph)
transform_pass.apply(infer_graph)
outscale_pass = OutScaleForTrainingPass(scope=scope, place=place)
outscale_pass.apply(main_graph)
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=static_loss.name, build_strategy=build_strategy)
feeder = fluid.DataFeeder(feed_list=[static_img, static_label],
place=place)
with fluid.scope_guard(scope):
for batch_id, data in enumerate(reader()):
loss_v, = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[static_loss])
static_loss_rec.append(loss_v[0])
if batch_id % 100 == 0:
_logger.info('{}: {}'.format('loss', loss_v))
scale_inference_pass = OutScaleForInferencePass(scope=scope)
scale_inference_pass.apply(infer_graph)
save_program = infer_graph.to_program()
static_save_dir = "./static_outscale_infer_model"
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
dirname=static_save_dir,
feeded_var_names=[infer_img.name],
target_vars=[infer_pre],
executor=exe,
main_program=save_program,
model_filename="lenet" + INFER_MODEL_SUFFIX,
params_filename="lenet" + INFER_PARAMS_SUFFIX)
rtol = 1e-05
atol = 1e-08
for i, (loss_d,
loss_s) in enumerate(zip(dynamic_loss_rec, static_loss_rec)):
diff = np.abs(loss_d - loss_s)
if diff > (atol + rtol * np.abs(loss_s)):
_logger.info(
"diff({}) at {}, dynamic loss = {}, static loss = {}".
format(diff, i, loss_d, loss_s))
break
self.assertTrue(np.allclose(np.array(dynamic_loss_rec),
np.array(static_loss_rec),
rtol=rtol,
atol=atol,
equal_nan=True),
msg='Failed to do the imperative qat.')
# load dynamic model
[dynamic_inference_program, feed_target_names,
fetch_targets] = (fluid.io.load_inference_model(
dirname=dynamic_save_dir,
executor=exe,
model_filename="lenet" + INFER_MODEL_SUFFIX,
params_filename="lenet" + INFER_PARAMS_SUFFIX))
# load static model
[static_inference_program, feed_target_names,
fetch_targets] = (fluid.io.load_inference_model(
dirname=static_save_dir,
executor=exe,
model_filename="lenet" + INFER_MODEL_SUFFIX,
params_filename="lenet" + INFER_PARAMS_SUFFIX))
dynamic_ops = dynamic_inference_program.global_block().ops
static_ops = static_inference_program.global_block().ops
for op in dynamic_ops[:]:
if op.type == "flatten2" or 'fake' in op.type:
dynamic_ops.remove(op)
for op in static_ops[:]:
if 'fake' in op.type:
static_ops.remove(op)
for i in range(len(dynamic_ops)):
if dynamic_ops[i].has_attr("out_threshold"):
self.assertTrue(dynamic_ops[i].type == static_ops[i].type)
self.assertTrue(dynamic_ops[i].attr("out_threshold") ==
static_ops[i].attr("out_threshold"))