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PaddleSlim
未验证 提交 bed39157 编写于 作者: B Bai Yifan 提交者： GitHub
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migrate quant code to paddle2.0 (#488) 

* migrate quant code to paddle2.0

* fix quant unittest

* fix demo
上级 3a33f1a4
隐藏空白更改
内联 并排
Showing with 354 addition and 325 deletion
demo/quant/pact_quant_aware/train.py
浏览文件 @ bed39157
...@@ -9,7 +9,6 @@ import time ...@@ -9,7 +9,6 @@ import time
import numpy as np import numpy as np
from collections import defaultdict from collections import defaultdict
import paddle.fluid as fluid
sys.path.append(os.path.dirname("__file__")) sys.path.append(os.path.dirname("__file__"))
sys.path.append( sys.path.append(
os.path.join(os.path.dirname("__file__"), os.path.pardir, os.path.pardir)) os.path.join(os.path.dirname("__file__"), os.path.pardir, os.path.pardir))
...@@ -21,8 +20,6 @@ from utility import add_arguments, print_arguments ...@@ -21,8 +20,6 @@ from utility import add_arguments, print_arguments
from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layer_helper import LayerHelper
quantization_model_save_dir = './quantization_models/' quantization_model_save_dir = './quantization_models/'
from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
_logger = get_logger(__name__, level=logging.INFO) _logger = get_logger(__name__, level=logging.INFO)
parser = argparse.ArgumentParser(description=__doc__) parser = argparse.ArgumentParser(description=__doc__)
...@@ -74,29 +71,32 @@ model_list = [m for m in dir(models) if "__" not in m] ...@@ -74,29 +71,32 @@ model_list = [m for m in dir(models) if "__" not in m]
def piecewise_decay(args): def piecewise_decay(args):
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() places = paddle.static.cuda_places(
) if args.use_gpu else paddle.static.cpu_places()
step = int( step = int(
math.ceil(float(args.total_images) / (args.batch_size * len(places)))) math.ceil(float(args.total_images) / (args.batch_size * len(places))))
bd = [step * e for e in args.step_epochs] bd = [step * e for e in args.step_epochs]
lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)] lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr) learning_rate = paddle.optimizer.lr.PiecewiseDecay(
optimizer = fluid.optimizer.Momentum( boundaries=bd, values=lr, verbose=False)
optimizer = paddle.optimizer.Momentum(
learning_rate=learning_rate, learning_rate=learning_rate,
momentum=args.momentum_rate, momentum=args.momentum_rate,
regularization=fluid.regularizer.L2Decay(args.l2_decay)) weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
return learning_rate, optimizer return learning_rate, optimizer
def cosine_decay(args): def cosine_decay(args):
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() places = paddle.static.cuda_places(
) if args.use_gpu else paddle.static.cpu_places()
step = int( step = int(
math.ceil(float(args.total_images) / (args.batch_size * len(places)))) math.ceil(float(args.total_images) / (args.batch_size * len(places))))
learning_rate = fluid.layers.cosine_decay( learning_rate = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs) learning_rate=args.lr, T_max=step * args.num_epochs, verbose=False)
optimizer = fluid.optimizer.Momentum( optimizer = paddle.optimizer.Momentum(
learning_rate=learning_rate, learning_rate=learning_rate,
momentum=args.momentum_rate, momentum=args.momentum_rate,
regularization=fluid.regularizer.L2Decay(args.l2_decay)) weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
return learning_rate, optimizer return learning_rate, optimizer
...@@ -127,41 +127,43 @@ def compress(args): ...@@ -127,41 +127,43 @@ def compress(args):
image_shape = [int(m) for m in image_shape.split(",")] image_shape = [int(m) for m in image_shape.split(",")]
assert args.model in model_list, "{} is not in lists: {}".format(args.model, assert args.model in model_list, "{} is not in lists: {}".format(args.model,
model_list) model_list)
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') image = paddle.static.data(
name='image', shape=[None] + image_shape, dtype='float32')
if args.use_pact: if args.use_pact:
image.stop_gradient = False image.stop_gradient = False
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
# model definition # model definition
model = models.__dict__[args.model]() model = models.__dict__[args.model]()
out = model.net(input=image, class_dim=class_dim) out = model.net(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
train_prog = fluid.default_main_program() train_prog = paddle.static.default_main_program()
val_program = fluid.default_main_program().clone(for_test=True) val_program = paddle.static.default_main_program().clone(for_test=True)
if not args.analysis: if not args.analysis:
learning_rate, opt = create_optimizer(args) learning_rate, opt = create_optimizer(args)
opt.minimize(avg_cost) opt.minimize(avg_cost)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
train_reader = paddle.fluid.io.batch( train_reader = paddle.batch(
train_reader, batch_size=args.batch_size, drop_last=True) train_reader, batch_size=args.batch_size, drop_last=True)
train_loader = fluid.io.DataLoader.from_generator( train_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label], feed_list=[image, label],
capacity=512, capacity=512,
use_double_buffer=True, use_double_buffer=True,
iterable=True) iterable=True)
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() places = paddle.static.cuda_places(
) if args.use_gpu else paddle.static.cpu_places()
train_loader.set_sample_list_generator(train_reader, places) train_loader.set_sample_list_generator(train_reader, places)
val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size) val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
valid_loader = fluid.io.DataLoader.from_generator( valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label], feed_list=[image, label],
capacity=512, capacity=512,
use_double_buffer=True, use_double_buffer=True,
...@@ -246,21 +248,21 @@ def compress(args): ...@@ -246,21 +248,21 @@ def compress(args):
helper = LayerHelper("pact", **locals()) helper = LayerHelper("pact", **locals())
dtype = 'float32' dtype = 'float32'
init_thres = values[x.name.split('_tmp_input')[0]] init_thres = values[x.name.split('_tmp_input')[0]]
u_param_attr = fluid.ParamAttr( u_param_attr = paddle.ParamAttr(
name=x.name + '_pact', name=x.name + '_pact',
initializer=fluid.initializer.ConstantInitializer(value=init_thres), initializer=paddle.nn.initializer.Constant(value=init_thres),
regularizer=fluid.regularizer.L2Decay(0.0001), regularizer=paddle.regularizer.L2Decay(0.0001),
learning_rate=1) learning_rate=1)
u_param = helper.create_parameter( u_param = helper.create_parameter(
attr=u_param_attr, shape=[1], dtype=dtype) attr=u_param_attr, shape=[1], dtype=dtype)
part_a = fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)) part_a = paddle.nn.functional.relu(x - u_param)
part_b = fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)) part_b = paddle.nn.functional.relu(-u_param - x)
x = x - part_a + part_b x = x - part_a + part_b
return x return x
def get_optimizer(): def get_optimizer():
return fluid.optimizer.MomentumOptimizer(args.lr, 0.9) return paddle.optimizer.Momentum(args.lr, 0.9)
if args.use_pact: if args.use_pact:
act_preprocess_func = pact act_preprocess_func = pact
...@@ -294,11 +296,7 @@ def compress(args): ...@@ -294,11 +296,7 @@ def compress(args):
args.pretrained_model), "pretrained_model doesn't exist" args.pretrained_model), "pretrained_model doesn't exist"
if args.pretrained_model: if args.pretrained_model:
paddle.static.load(train_prog, args.pretrained_model, exe)
def if_exist(var):
return os.path.exists(os.path.join(args.pretrained_model, var.name))
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
def test(epoch, program): def test(epoch, program):
batch_id = 0 batch_id = 0
...@@ -325,44 +323,42 @@ def compress(args): ...@@ -325,44 +323,42 @@ def compress(args):
np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns)))) np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
return np.mean(np.array(acc_top1_ns)) return np.mean(np.array(acc_top1_ns))
def train(epoch, compiled_train_prog): def train(epoch, compiled_train_prog, lr):
batch_id = 0 batch_id = 0
for data in train_loader(): for data in train_loader():
start_time = time.time() start_time = time.time()
lr_n, loss_n, acc_top1_n, acc_top5_n = exe.run( loss_n, acc_top1_n, acc_top5_n = exe.run(
compiled_train_prog, compiled_train_prog,
feed=data, feed=data,
fetch_list=[ fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
learning_rate.name, avg_cost.name, acc_top1.name,
acc_top5.name
])
end_time = time.time() end_time = time.time()
lr_n = np.mean(lr_n)
loss_n = np.mean(loss_n) loss_n = np.mean(loss_n)
acc_top1_n = np.mean(acc_top1_n) acc_top1_n = np.mean(acc_top1_n)
acc_top5_n = np.mean(acc_top5_n) acc_top5_n = np.mean(acc_top5_n)
if batch_id % args.log_period == 0: if batch_id % args.log_period == 0:
_logger.info( _logger.info(
"epoch[{}]-batch[{}] lr: {:.6f} - loss: {:.6f}; acc_top1: {:.6f}; acc_top5: {:.6f}; time: {:.3f}". "epoch[{}]-batch[{}] lr: {:.6f} - loss: {:.6f}; acc_top1: {:.6f}; acc_top5: {:.6f}; time: {:.3f}".
format(epoch, batch_id, lr_n, loss_n, acc_top1_n, format(epoch, batch_id,
learning_rate.get_lr(), loss_n, acc_top1_n,
acc_top5_n, end_time - start_time)) acc_top5_n, end_time - start_time))
if args.use_pact and batch_id % 1000 == 0: if args.use_pact and batch_id % 1000 == 0:
threshold = {} threshold = {}
for var in val_program.list_vars(): for var in val_program.list_vars():
if 'pact' in var.name: if 'pact' in var.name:
array = np.array(fluid.global_scope().find_var(var.name) array = np.array(paddle.static.global_scope().find_var(
.get_tensor()) var.name).get_tensor())
threshold[var.name] = array[0] threshold[var.name] = array[0]
_logger.info(threshold) _logger.info(threshold)
batch_id += 1 batch_id += 1
lr.step()
build_strategy = fluid.BuildStrategy() build_strategy = paddle.static.BuildStrategy()
build_strategy.enable_inplace = False build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False build_strategy.fuse_all_reduce_ops = False
exec_strategy = fluid.ExecutionStrategy() exec_strategy = paddle.static.ExecutionStrategy()
compiled_train_prog = compiled_train_prog.with_data_parallel( compiled_train_prog = compiled_train_prog.with_data_parallel(
loss_name=avg_cost.name, loss_name=avg_cost.name,
build_strategy=build_strategy, build_strategy=build_strategy,
...@@ -377,37 +373,38 @@ def compress(args): ...@@ -377,37 +373,38 @@ def compress(args):
ckpt_path = args.checkpoint_dir ckpt_path = args.checkpoint_dir
assert args.checkpoint_epoch is not None, "checkpoint_epoch must be set" assert args.checkpoint_epoch is not None, "checkpoint_epoch must be set"
start_epoch = args.checkpoint_epoch start_epoch = args.checkpoint_epoch
fluid.io.load_persistables( paddle.static.load_vars(
exe, dirname=args.checkpoint_dir, main_program=val_program) exe, dirname=args.checkpoint_dir, main_program=val_program)
start_step = start_epoch * int( start_step = start_epoch * int(
math.ceil(float(args.total_images) / args.batch_size)) math.ceil(float(args.total_images) / args.batch_size))
v = fluid.global_scope().find_var('@LR_DECAY_COUNTER@').get_tensor() v = paddle.static.global_scope().find_var(
'@LR_DECAY_COUNTER@').get_tensor()
v.set(np.array([start_step]).astype(np.float32), place) v.set(np.array([start_step]).astype(np.float32), place)
best_eval_acc1 = 0 best_eval_acc1 = 0
best_acc1_epoch = 0 best_acc1_epoch = 0
for i in range(start_epoch, args.num_epochs): for i in range(start_epoch, args.num_epochs):
train(i, compiled_train_prog) train(i, compiled_train_prog, learning_rate)
acc1 = test(i, val_program) acc1 = test(i, val_program)
if acc1 > best_eval_acc1: if acc1 > best_eval_acc1:
best_eval_acc1 = acc1 best_eval_acc1 = acc1
best_acc1_epoch = i best_acc1_epoch = i
_logger.info("Best Validation Acc1: {:.6f}, at epoch {}".format( _logger.info("Best Validation Acc1: {:.6f}, at epoch {}".format(
best_eval_acc1, best_acc1_epoch)) best_eval_acc1, best_acc1_epoch))
fluid.io.save_persistables( paddle.static.save(
exe, exe,
dirname=os.path.join(args.output_dir, str(i)), dirname=os.path.join(args.output_dir, str(i)),
main_program=val_program) main_program=val_program)
if acc1 > best_acc1: if acc1 > best_acc1:
best_acc1 = acc1 best_acc1 = acc1
best_epoch = i best_epoch = i
fluid.io.save_persistables( paddle.static.save(
exe, exe,
dirname=os.path.join(args.output_dir, 'best_model'), dirname=os.path.join(args.output_dir, 'best_model'),
main_program=val_program) main_program=val_program)
if os.path.exists(os.path.join(args.output_dir, 'best_model')): if os.path.exists(os.path.join(args.output_dir, 'best_model')):
fluid.io.load_persistables( paddle.static.load(
exe, exe,
dirname=os.path.join(args.output_dir, 'best_model'), dirname=os.path.join(args.output_dir, 'best_model'),
main_program=val_program) main_program=val_program)
...@@ -430,7 +427,7 @@ def compress(args): ...@@ -430,7 +427,7 @@ def compress(args):
if not os.path.isdir(model_path): if not os.path.isdir(model_path):
os.makedirs(model_path) os.makedirs(model_path)
fluid.io.save_inference_model( paddle.static.save_inference_model(
dirname=float_path, dirname=float_path,
feeded_var_names=[image.name], feeded_var_names=[image.name],
target_vars=[out], target_vars=[out],
......
demo/quant/quant_aware/train.py
浏览文件 @ bed39157
...@@ -44,25 +44,32 @@ model_list = [m for m in dir(models) if "__" not in m] ...@@ -44,25 +44,32 @@ model_list = [m for m in dir(models) if "__" not in m]
def piecewise_decay(args): def piecewise_decay(args):
step = int(math.ceil(float(args.total_images) / args.batch_size)) places = paddle.static.cuda_places(
) if args.use_gpu else paddle.static.cpu_places()
step = int(
math.ceil(float(args.total_images) / (args.batch_size * len(places))))
bd = [step * e for e in args.step_epochs] bd = [step * e for e in args.step_epochs]
lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)] lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr) learning_rate = paddle.optimizer.lr.PiecewiseDecay(
optimizer = fluid.optimizer.Momentum( boundaries=bd, values=lr, verbose=False)
optimizer = paddle.optimizer.Momentum(
learning_rate=learning_rate, learning_rate=learning_rate,
momentum=args.momentum_rate, momentum=args.momentum_rate,
regularization=fluid.regularizer.L2Decay(args.l2_decay)) weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
return optimizer return optimizer
def cosine_decay(args): def cosine_decay(args):
step = int(math.ceil(float(args.total_images) / args.batch_size)) places = paddle.static.cuda_places(
learning_rate = fluid.layers.cosine_decay( ) if args.use_gpu else paddle.static.cpu_places()
learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs) step = int(
optimizer = fluid.optimizer.Momentum( math.ceil(float(args.total_images) / (args.batch_size * len(places))))
learning_rate = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=args.lr, T_max=step * args.num_epochs, verbose=False)
optimizer = paddle.optimizer.Momentum(
learning_rate=learning_rate, learning_rate=learning_rate,
momentum=args.momentum_rate, momentum=args.momentum_rate,
regularization=fluid.regularizer.L2Decay(args.l2_decay)) weight_decay=paddle.regularizer.L2Decay(args.l2_decay))
return optimizer return optimizer
...@@ -118,20 +125,21 @@ def compress(args): ...@@ -118,20 +125,21 @@ def compress(args):
image_shape = [int(m) for m in image_shape.split(",")] image_shape = [int(m) for m in image_shape.split(",")]
assert args.model in model_list, "{} is not in lists: {}".format(args.model, assert args.model in model_list, "{} is not in lists: {}".format(args.model,
model_list) model_list)
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') image = paddle.static.data(
label = fluid.layers.data(name='label', shape=[1], dtype='int64') name='image', shape=[None] + image_shape, dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
# model definition # model definition
model = models.__dict__[args.model]() model = models.__dict__[args.model]()
out = model.net(input=image, class_dim=class_dim) out = model.net(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
train_prog = fluid.default_main_program() train_prog = paddle.static.default_main_program()
val_program = fluid.default_main_program().clone(for_test=True) val_program = paddle.static.default_main_program().clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
############################################################################################################ ############################################################################################################
# 2. quantization transform programs (training aware) # 2. quantization transform programs (training aware)
# Make some quantization transforms in the graph before training and testing. # Make some quantization transforms in the graph before training and testing.
...@@ -145,37 +153,42 @@ def compress(args): ...@@ -145,37 +153,42 @@ def compress(args):
opt = create_optimizer(args) opt = create_optimizer(args)
opt.minimize(avg_cost) opt.minimize(avg_cost)
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
assert os.path.exists( assert os.path.exists(
args.pretrained_model), "pretrained_model doesn't exist" args.pretrained_model), "pretrained_model doesn't exist"
if args.pretrained_model: if args.pretrained_model:
paddle.static.load(train_prog, args.pretrained_model, exe)
def if_exist(var): val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
return os.path.exists(os.path.join(args.pretrained_model, var.name)) train_reader = paddle.batch(
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size)
train_reader = paddle.fluid.io.batch(
train_reader, batch_size=args.batch_size, drop_last=True) train_reader, batch_size=args.batch_size, drop_last=True)
places = paddle.static.cuda_places(
train_feeder = feeder = fluid.DataFeeder([image, label], place) ) if args.use_gpu else paddle.static.cpu_places()
val_feeder = feeder = fluid.DataFeeder(
[image, label], place, program=val_program) train_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=512,
use_double_buffer=True,
iterable=True)
valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=512,
use_double_buffer=True,
iterable=True)
train_loader.set_sample_list_generator(train_reader, places)
valid_loader.set_sample_list_generator(val_reader, places[0])
def test(epoch, program): def test(epoch, program):
batch_id = 0 batch_id = 0
acc_top1_ns = [] acc_top1_ns = []
acc_top5_ns = [] acc_top5_ns = []
for data in val_reader(): for data in valid_loader():
start_time = time.time() start_time = time.time()
acc_top1_n, acc_top5_n = exe.run( acc_top1_n, acc_top5_n = exe.run(
program, program, feed=data, fetch_list=[acc_top1.name, acc_top5.name])
feed=train_feeder.feed(data),
fetch_list=[acc_top1.name, acc_top5.name])
end_time = time.time() end_time = time.time()
if batch_id % args.log_period == 0: if batch_id % args.log_period == 0:
_logger.info( _logger.info(
...@@ -195,11 +208,11 @@ def compress(args): ...@@ -195,11 +208,11 @@ def compress(args):
def train(epoch, compiled_train_prog): def train(epoch, compiled_train_prog):
batch_id = 0 batch_id = 0
for data in train_reader(): for data in train_loader():
start_time = time.time() start_time = time.time()
loss_n, acc_top1_n, acc_top5_n = exe.run( loss_n, acc_top1_n, acc_top5_n = exe.run(
compiled_train_prog, compiled_train_prog,
feed=train_feeder.feed(data), feed=data,
fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
end_time = time.time() end_time = time.time()
loss_n = np.mean(loss_n) loss_n = np.mean(loss_n)
...@@ -212,12 +225,12 @@ def compress(args): ...@@ -212,12 +225,12 @@ def compress(args):
end_time - start_time)) end_time - start_time))
batch_id += 1 batch_id += 1
build_strategy = fluid.BuildStrategy() build_strategy = paddle.static.BuildStrategy()
build_strategy.memory_optimize = False build_strategy.memory_optimize = False
build_strategy.enable_inplace = False build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False build_strategy.fuse_all_reduce_ops = False
build_strategy.sync_batch_norm = False build_strategy.sync_batch_norm = False
exec_strategy = fluid.ExecutionStrategy() exec_strategy = paddle.static.ExecutionStrategy()
compiled_train_prog = compiled_train_prog.with_data_parallel( compiled_train_prog = compiled_train_prog.with_data_parallel(
loss_name=avg_cost.name, loss_name=avg_cost.name,
build_strategy=build_strategy, build_strategy=build_strategy,
...@@ -231,19 +244,17 @@ def compress(args): ...@@ -231,19 +244,17 @@ def compress(args):
for i in range(args.num_epochs): for i in range(args.num_epochs):
train(i, compiled_train_prog) train(i, compiled_train_prog)
acc1 = test(i, val_program) acc1 = test(i, val_program)
fluid.io.save_persistables( paddle.static.save(
exe, program=val_program,
dirname=os.path.join(args.checkpoint_dir, str(i)), model_path=os.path.join(args.checkpoint_dir, str(i)))
main_program=val_program)
if acc1 > best_acc1: if acc1 > best_acc1:
best_acc1 = acc1 best_acc1 = acc1
best_epoch = i best_epoch = i
fluid.io.save_persistables( paddle.static.save(
exe, program=val_program,
dirname=os.path.join(args.checkpoint_dir, 'best_model'), model_path=os.path.join(args.checkpoint_dir, 'best_model'))
main_program=val_program)
if os.path.exists(os.path.join(args.checkpoint_dir, 'best_model')): if os.path.exists(os.path.join(args.checkpoint_dir, 'best_model')):
fluid.io.load_persistables( paddle.static.load(
exe, exe,
dirname=os.path.join(args.checkpoint_dir, 'best_model'), dirname=os.path.join(args.checkpoint_dir, 'best_model'),
main_program=val_program) main_program=val_program)
...@@ -264,11 +275,10 @@ def compress(args): ...@@ -264,11 +275,10 @@ def compress(args):
'act_' + quant_config['activation_quantize_type'] 'act_' + quant_config['activation_quantize_type']
+ '_w_' + quant_config['weight_quantize_type']) + '_w_' + quant_config['weight_quantize_type'])
float_path = os.path.join(model_path, 'float') float_path = os.path.join(model_path, 'float')
int8_path = os.path.join(model_path, 'int8')
if not os.path.isdir(model_path): if not os.path.isdir(model_path):
os.makedirs(model_path) os.makedirs(model_path)
fluid.io.save_inference_model( paddle.static.save_inference_model(
dirname=float_path, dirname=float_path,
feeded_var_names=[image.name], feeded_var_names=[image.name],
target_vars=[out], target_vars=[out],
...@@ -277,15 +287,6 @@ def compress(args): ...@@ -277,15 +287,6 @@ def compress(args):
model_filename=float_path + '/model', model_filename=float_path + '/model',
params_filename=float_path + '/params') params_filename=float_path + '/params')
fluid.io.save_inference_model(
dirname=int8_path,
feeded_var_names=[image.name],
target_vars=[out],
executor=exe,
main_program=int8_program,
model_filename=int8_path + '/model',
params_filename=int8_path + '/params')
def main(): def main():
paddle.enable_static() paddle.enable_static()
......
demo/quant/quant_post/eval.py
浏览文件 @ bed39157
...@@ -19,7 +19,6 @@ import argparse ...@@ -19,7 +19,6 @@ import argparse
import functools import functools
import paddle import paddle
import paddle.fluid as fluid
sys.path[0] = os.path.join( sys.path[0] = os.path.join(
os.path.dirname("__file__"), os.path.pardir, os.path.pardir) os.path.dirname("__file__"), os.path.pardir, os.path.pardir)
import imagenet_reader as reader import imagenet_reader as reader
...@@ -38,29 +37,33 @@ add_arg('params_name', str, None, "params filename for inference model") ...@@ -38,29 +37,33 @@ add_arg('params_name', str, None, "params filename for inference model")
def eval(args): def eval(args):
# parameters from arguments # parameters from arguments
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
val_program, feed_target_names, fetch_targets = fluid.io.load_inference_model( val_program, feed_target_names, fetch_targets = paddle.static.load_inference_model(
args.model_path, args.model_path,
exe, exe,
model_filename=args.model_name, model_filename=args.model_name,
params_filename=args.params_name) params_filename=args.params_name)
val_reader = paddle.fluid.io.batch(reader.val(), batch_size=128) val_reader = paddle.batch(reader.val(), batch_size=1)
feeder = fluid.DataFeeder(
place=place, feed_list=feed_target_names, program=val_program) image = paddle.static.data(
name='image', shape=[None, 3, 224, 224], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image], capacity=512, use_double_buffer=True, iterable=True)
valid_loader.set_sample_list_generator(val_reader, place)
results = [] results = []
for batch_id, data in enumerate(val_reader()): for batch_id, data in enumerate(val_reader()):
# top1_acc, top5_acc # top1_acc, top5_acc
if len(feed_target_names) == 1: if len(feed_target_names) == 1:
# eval "infer model", which input is image, output is classification probability # eval "infer model", which input is image, output is classification probability
image = [[d[0]] for d in data] image = data[0][0].reshape((1, 3, 224, 224))
label = [[d[1]] for d in data] label = [[d[1]] for d in data]
feed_data = feeder.feed(image)
pred = exe.run(val_program, pred = exe.run(val_program,
feed=feed_data, feed={feed_target_names[0]: image},
fetch_list=fetch_targets) fetch_list=fetch_targets)
pred = np.array(pred[0]) pred = np.array(pred[0])
label = np.array(label) label = np.array(label)
...@@ -76,8 +79,13 @@ def eval(args): ...@@ -76,8 +79,13 @@ def eval(args):
results.append([top_1, top_5]) results.append([top_1, top_5])
else: else:
# eval "eval model", which inputs are image and label, output is top1 and top5 accuracy # eval "eval model", which inputs are image and label, output is top1 and top5 accuracy
image = data[0][0].reshape((1, 3, 224, 224))
label = [[d[1]] for d in data]
result = exe.run(val_program, result = exe.run(val_program,
feed=feeder.feed(data), feed={
feed_target_names[0]: image,
feed_target_names[1]: label
},
fetch_list=fetch_targets) fetch_list=fetch_targets)
result = [np.mean(r) for r in result] result = [np.mean(r) for r in result]
results.append(result) results.append(result)
......
demo/quant/quant_post/export_model.py
浏览文件 @ bed39157
...@@ -7,7 +7,6 @@ import functools ...@@ -7,7 +7,6 @@ import functools
import math import math
import time import time
import numpy as np import numpy as np
import paddle.fluid as fluid
sys.path[0] = os.path.join( sys.path[0] = os.path.join(
os.path.dirname("__file__"), os.path.pardir, os.path.pardir) os.path.dirname("__file__"), os.path.pardir, os.path.pardir)
from paddleslim.common import get_logger from paddleslim.common import get_logger
...@@ -46,28 +45,24 @@ def export_model(args): ...@@ -46,28 +45,24 @@ def export_model(args):
raise ValueError("{} is not supported.".format(args.data)) raise ValueError("{} is not supported.".format(args.data))
image_shape = [int(m) for m in image_shape.split(",")] image_shape = [int(m) for m in image_shape.split(",")]
image = fluid.data( image = paddle.static.data(
name='image', shape=[None] + image_shape, dtype='float32') name='image', shape=[None] + image_shape, dtype='float32')
assert args.model in model_list, "{} is not in lists: {}".format(args.model, assert args.model in model_list, "{} is not in lists: {}".format(args.model,
model_list) model_list)
# model definition # model definition
model = models.__dict__[args.model]() model = models.__dict__[args.model]()
out = model.net(input=image, class_dim=class_dim) out = model.net(input=image, class_dim=class_dim)
val_program = fluid.default_main_program().clone(for_test=True) val_program = paddle.static.default_main_program().clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
if args.pretrained_model: if args.pretrained_model:
paddle.static.load(val_program, args.pretrained_model, exe)
def if_exist(var):
return os.path.exists(os.path.join(args.pretrained_model, var.name))
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
else: else:
assert False, "args.pretrained_model must set" assert False, "args.pretrained_model must set"
fluid.io.save_inference_model( paddle.static.save_inference_model(
'./inference_model/' + args.model, './inference_model/' + args.model,
feeded_var_names=[image.name], feeded_var_names=[image.name],
target_vars=[out], target_vars=[out],
......
demo/quant/quant_post/quant_post.py
浏览文件 @ bed39157
...@@ -7,7 +7,6 @@ import functools ...@@ -7,7 +7,6 @@ import functools
import math import math
import time import time
import numpy as np import numpy as np
import paddle.fluid as fluid
sys.path[0] = os.path.join( sys.path[0] = os.path.join(
os.path.dirname("__file__"), os.path.pardir, os.path.pardir) os.path.dirname("__file__"), os.path.pardir, os.path.pardir)
...@@ -33,12 +32,12 @@ add_arg('params_filename', str, None, "params file name") ...@@ -33,12 +32,12 @@ add_arg('params_filename', str, None, "params file name")
def quantize(args): def quantize(args):
val_reader = reader.train() val_reader = reader.train()
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
assert os.path.exists(args.model_path), "args.model_path doesn't exist" assert os.path.exists(args.model_path), "args.model_path doesn't exist"
assert os.path.isdir(args.model_path), "args.model_path must be a dir" assert os.path.isdir(args.model_path), "args.model_path must be a dir"
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
quant_post( quant_post(
executor=exe, executor=exe,
model_dir=args.model_path, model_dir=args.model_path,
......
paddleslim/quant/quant_embedding.py
浏览文件 @ bed39157
...@@ -21,7 +21,7 @@ import numpy as np ...@@ -21,7 +21,7 @@ import numpy as np
import math import math
from multiprocessing.dummy import Pool as ThreadPool from multiprocessing.dummy import Pool as ThreadPool
import paddle.fluid as fluid import paddle
from paddle.fluid.framework import IrGraph from paddle.fluid.framework import IrGraph
from paddle.fluid import core from paddle.fluid import core
...@@ -102,7 +102,7 @@ def _get_var_tensor(scope, var_name): ...@@ -102,7 +102,7 @@ def _get_var_tensor(scope, var_name):
""" """
get tensor array by name. get tensor array by name.
Args: Args:
scope(fluid.Scope): scope to get var scope(paddle.static.Scope): scope to get var
var_name(str): vatiable name var_name(str): vatiable name
Return: Return:
np.array np.array
...@@ -158,8 +158,8 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name, ...@@ -158,8 +158,8 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name,
Args: Args:
graph(IrGraph): graph that includes lookup_table op graph(IrGraph): graph that includes lookup_table op
scope(fluid.Scope): scope scope(paddle.static.Scope): scope
place(fluid.CPUPlace or flud.CUDAPlace): place place(paddle.CPUPlace or paddle.CUDAPlace): place
config(dict): config to quant config(dict): config to quant
""" """
...@@ -250,15 +250,14 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name, ...@@ -250,15 +250,14 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name,
graph.safe_remove_nodes(embedding_node) graph.safe_remove_nodes(embedding_node)
def _quant_embedding_log(graph, scope, place, config, var_name, def _quant_embedding_log(graph, scope, place, config, var_name, embedding_node):
embedding_node):
""" """
quantize embedding using log quantize embedding using log
Args: Args:
graph(IrGraph): graph that includes Embedding Parameter graph(IrGraph): graph that includes Embedding Parameter
scope(fluid.Scope): scope scope(paddle.static.Scope): scope
place(fluid.CPUPlace or flud.CUDAPlace): place to run program place(paddle.CPUPlace or paddle.CUDAPlace): place to run program
config(dict): config to quant Embedding config(dict): config to quant Embedding
""" """
...@@ -424,9 +423,9 @@ def quant_embedding(program, place, config=None, scope=None): ...@@ -424,9 +423,9 @@ def quant_embedding(program, place, config=None, scope=None):
"""quantize lookup_table op parameters """quantize lookup_table op parameters
Args: Args:
program(fluid.Program): infer program program(paddle.static.Program): infer program
scope(fluid.Scope, optional): Scope records the mapping between variable names and variables, similar to brackets in programming languages. Usually users can use `fluid.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_ . When ``None`` will use `fluid.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_. Default : ``None``. scope(paddle.static.Scope, optional): 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``.
place(fluid.CPUPlace or fluid.CUDAPlace): This parameter represents the executor run on which device. place(paddle.CPUPlace or paddle.CUDAPlace): This parameter represents the executor run on which device.
config(dict, optional): config to quantize. The keys are 'quantize_op_types'. For op in quantize_op_types, you can define 'quantize_type', \ config(dict, optional): config to quantize. The keys are 'quantize_op_types'. For op in quantize_op_types, you can define 'quantize_type', \
'quantize_bits', 'dtype', 'threshold'. \ 'quantize_bits', 'dtype', 'threshold'. \
``quantize_type`` is quantize type, supported types are ['abs_max'], default is "abs_max". ``quantize_type`` is quantize type, supported types are ['abs_max'], default is "abs_max".
...@@ -440,7 +439,7 @@ def quant_embedding(program, place, config=None, scope=None): ...@@ -440,7 +439,7 @@ def quant_embedding(program, place, config=None, scope=None):
""" """
config = config or {} config = config or {}
config = _merge_config(copy.deepcopy(_default_config), config) config = _merge_config(copy.deepcopy(_default_config), config)
scope = fluid.global_scope() if scope is None else scope scope = paddle.static.global_scope() if scope is None else scope
graph = IrGraph(core.Graph(program.desc), for_test=True) graph = IrGraph(core.Graph(program.desc), for_test=True)
quantize_params_map = {} quantize_params_map = {}
......
paddleslim/quant/quanter.py
浏览文件 @ bed39157
...@@ -18,7 +18,6 @@ import json ...@@ -18,7 +18,6 @@ import json
import logging import logging
import paddle import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import IrGraph from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
...@@ -191,14 +190,14 @@ def quant_aware(program, ...@@ -191,14 +190,14 @@ def quant_aware(program,
for quantization training or testing. for quantization training or testing.
Args: Args:
program(fluid.Program): training or testing ``program``. program(paddle.static.Program): training or testing ``program``.
place(fluid.CPUPlace or fluid.CUDAPlace): This parameter represents place(paddle.CPUPlace or paddle.CUDAPlace): This parameter represents
the executor run on which device. the executor run on which device.
config(dict, optional): configs for quantization. if None, will use default config. config(dict, optional): configs for quantization. if None, will use default config.
Default: None. Default: None.
scope(fluid.Scope): Scope records the mapping between variable names and variables, scope(paddle.static.Scope): Scope records the mapping between variable names and variables,
similar to brackets in programming languages. Usually users can use similar to brackets in programming languages. Usually users can use
`fluid.global_scope <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_. When ``None`` will use `fluid.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_ . Default: ``None``. `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``. for_test(bool): If the 'program' parameter is a test program, this parameter should be set to ``True``.
Otherwise, set to ``False``.Default: False Otherwise, set to ``False``.Default: False
weight_quantize_func(function): Function that defines how to quantize weight. Using this weight_quantize_func(function): Function that defines how to quantize weight. Using this
...@@ -225,15 +224,15 @@ def quant_aware(program, ...@@ -225,15 +224,15 @@ def quant_aware(program,
Default is None. Default is None.
optimizer_func(function): Fuction return a optimizer. When 'is_test' is False and user want to use self-defined 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. quantization function and preprocess function, this function must be set. Default is None.
exe(Fluid.Executor): If user want to use self-defined quantization function and preprocess function, exe must be set for 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. 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. return_program(bool): If user want return value is a Program rather than Compiled Program, This argument should be set True.
Default is False. Default is False.
Returns: Returns:
fluid.CompiledProgram | fluid.Program: Program with quantization and dequantization ``operators`` paddle.static.CompiledProgram | paddle.static.Program: Program with quantization and dequantization ``operators``
""" """
scope = fluid.global_scope() if not scope else scope scope = paddle.static.global_scope() if not scope else scope
if config is None: if config is None:
config = _quant_config_default config = _quant_config_default
else: else:
...@@ -297,7 +296,7 @@ def quant_aware(program, ...@@ -297,7 +296,7 @@ def quant_aware(program,
if for_test or return_program: if for_test or return_program:
quant_program = main_graph.to_program() quant_program = main_graph.to_program()
else: else:
quant_program = fluid.CompiledProgram(main_graph.graph) quant_program = paddle.static.CompiledProgram(main_graph.graph)
return quant_program return quant_program
...@@ -331,13 +330,13 @@ def quant_post_static( ...@@ -331,13 +330,13 @@ def quant_post_static(
and dequantization operators to obtain the quantized model. and dequantization operators to obtain the quantized model.
Args: Args:
executor(fluid.Executor): The executor to load, run and save the executor(paddle.static.Executor): The executor to load, run and save the
quantized model. quantized model.
model_dir(str): The path of fp32 model that will be quantized, and model_dir(str): The path of fp32 model that will be quantized, and
the model and params that saved by ``fluid.io.save_inference_model`` the model and params that saved by ``paddle.static.io.save_inference_model``
are under the path. are under the path.
quantize_model_path(str): The path to save quantized model using api quantize_model_path(str): The path to save quantized model using api
``fluid.io.save_inference_model``. ``paddle.static.io.save_inference_model``.
batch_generator(Python Generator): The batch generator provides batch_generator(Python Generator): The batch generator provides
calibrate data for DataLoader, and it returns a batch every calibrate data for DataLoader, and it returns a batch every
time. For sample_generator and batch_generator, only one time. For sample_generator and batch_generator, only one
...@@ -357,8 +356,8 @@ def quant_post_static( ...@@ -357,8 +356,8 @@ def quant_post_static(
batch_nums(int, optional): If batch_nums is not None, the number of calibrate batch_nums(int, optional): If batch_nums is not None, the number of calibrate
data is 'batch_size*batch_nums'. If batch_nums is None, use all data data is 'batch_size*batch_nums'. If batch_nums is None, use all data
generated by sample_generator as calibrate data. generated by sample_generator as calibrate data.
scope(fluid.Scope, optional): The scope to run program, use it to load scope(paddle.static.Scope, optional): The scope to run program, use it to load
and save variables. If scope is None, will use fluid.global_scope(). and save variables. If scope is None, will use paddle.static.global_scope().
algo(str, optional): If algo=KL, use KL-divergenc method to algo(str, optional): If algo=KL, use KL-divergenc method to
get the more precise scale factor. If algo='direct', use get the more precise scale factor. If algo='direct', use
abs_max method to get the scale factor. Default: 'KL'. abs_max method to get the scale factor. Default: 'KL'.
...@@ -424,18 +423,18 @@ def convert(program, place, config=None, scope=None, save_int8=False): ...@@ -424,18 +423,18 @@ def convert(program, place, config=None, scope=None, save_int8=False):
``program``that can be used to save ``inference model``. ``program``that can be used to save ``inference model``.
Args: Args:
program(fluid.Program): quantized and well-trained ``test program``. program(paddle.static.Program): quantized and well-trained ``test program``.
place(fluid.CPUPlace or fluid.CUDAPlace): This parameter represents place(paddle.CPUPlace or paddle.CUDAPlace): This parameter represents
the executor run on which device. the executor run on which device.
config(dict, optional): configs for convert. if set None, will use config(dict, optional): configs for convert. if set None, will use
default config. It must be same with config that used in default config. It must be same with config that used in
'quant_aware'. Default is None. 'quant_aware'. Default is None.
scope(fluid.Scope, optional): Scope records the mapping between scope(paddle.static.Scope, optional): Scope records the mapping between
variable names and variables, similar to brackets in variable names and variables, similar to brackets in
programming languages. Usually users can use programming languages. Usually users can use
`fluid.global_scope <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_. `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 When ``None`` will use
`fluid.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_ `paddle.static.global_scope() <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/executor_cn/global_scope_cn.html>`_
. Default: ``None``. . Default: ``None``.
save_int8: Whether to return ``program`` which model parameters' save_int8: Whether to return ``program`` which model parameters'
dtype is ``int8``. This parameter can only be used to dtype is ``int8``. This parameter can only be used to
...@@ -443,11 +442,11 @@ def convert(program, place, config=None, scope=None, save_int8=False): ...@@ -443,11 +442,11 @@ def convert(program, place, config=None, scope=None, save_int8=False):
Returns: Returns:
Tuple : freezed program which can be used for inference. Tuple : freezed program which can be used for inference.
when ``save_int8`` is False, return ``freezed_program(fluid.Program)``. when ``save_int8`` is False, return ``freezed_program(paddle.static.Program)``.
when ``save_int8`` is True, return ``freezed_program(fluid.Program)`` when ``save_int8`` is True, return ``freezed_program(paddle.static.Program)``
and ``freezed_program_int8(fluid.Program)`` and ``freezed_program_int8(paddle.static.Program)``
""" """
scope = fluid.global_scope() if not scope else scope scope = paddle.static.global_scope() if not scope else scope
if config is None: if config is None:
config = _quant_config_default config = _quant_config_default
......
tests/test_quant_aware.py
浏览文件 @ bed39157
...@@ -15,7 +15,6 @@ import sys ...@@ -15,7 +15,6 @@ import sys
sys.path.append("../") sys.path.append("../")
import unittest import unittest
import paddle import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_aware, convert from paddleslim.quant import quant_aware, convert
from static_case import StaticCase from static_case import StaticCase
sys.path.append("../demo") sys.path.append("../demo")
...@@ -29,15 +28,15 @@ import numpy as np ...@@ -29,15 +28,15 @@ import numpy as np
class TestQuantAwareCase1(StaticCase): class TestQuantAwareCase1(StaticCase):
def get_model(self): def get_model(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
startup_prog = fluid.default_startup_program() startup_prog = paddle.static.default_startup_program()
train_prog = fluid.default_main_program() train_prog = paddle.static.default_main_program()
return startup_prog, train_prog return startup_prog, train_prog
def get_op_number(self, prog): def get_op_number(self, prog):
...@@ -54,9 +53,9 @@ class TestQuantAwareCase1(StaticCase): ...@@ -54,9 +53,9 @@ class TestQuantAwareCase1(StaticCase):
def test_quant_op(self): def test_quant_op(self):
startup_prog, train_prog = self.get_model() startup_prog, train_prog = self.get_model()
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
) else fluid.CPUPlace() ) else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(startup_prog) exe.run(startup_prog)
config_1 = { config_1 = {
'weight_quantize_type': 'channel_wise_abs_max', 'weight_quantize_type': 'channel_wise_abs_max',
...@@ -91,39 +90,48 @@ class TestQuantAwareCase1(StaticCase): ...@@ -91,39 +90,48 @@ class TestQuantAwareCase1(StaticCase):
class TestQuantAwareCase2(StaticCase): class TestQuantAwareCase2(StaticCase):
def test_accuracy(self): def test_accuracy(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
optimizer = fluid.optimizer.Momentum( optimizer = paddle.optimizer.Momentum(
momentum=0.9, momentum=0.9,
learning_rate=0.01, learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5)) weight_decay=paddle.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost) optimizer.minimize(avg_cost)
main_prog = fluid.default_main_program() main_prog = paddle.static.default_main_program()
val_prog = main_prog.clone(for_test=True) val_prog = main_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
) else fluid.CPUPlace() ) else paddle.static.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_loader = paddle.io.DataLoader.from_generator(
train_reader = paddle.fluid.io.batch( feed_list=[image, label],
paddle.dataset.mnist.train(), batch_size=64) capacity=512,
eval_reader = paddle.fluid.io.batch( use_double_buffer=True,
paddle.dataset.mnist.test(), batch_size=64) iterable=True)
valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=512,
use_double_buffer=True,
iterable=True)
train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64)
eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
train_loader.set_sample_list_generator(train_reader, place)
valid_loader.set_sample_list_generator(eval_reader, place)
def train(program): def train(program):
iter = 0 iter = 0
for data in train_reader(): for data in train_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -134,10 +142,10 @@ class TestQuantAwareCase2(StaticCase): ...@@ -134,10 +142,10 @@ class TestQuantAwareCase2(StaticCase):
def test(program): def test(program):
iter = 0 iter = 0
result = [[], [], []] result = [[], [], []]
for data in eval_reader(): for data in valid_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
......
tests/test_quant_aware_user_defined.py
浏览文件 @ bed39157
...@@ -15,90 +15,98 @@ import sys ...@@ -15,90 +15,98 @@ import sys
sys.path.append("../") sys.path.append("../")
import unittest import unittest
import paddle import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_aware, convert from paddleslim.quant import quant_aware, convert
from static_case import StaticCase from static_case import StaticCase
sys.path.append("../demo") sys.path.append("../demo")
from models import MobileNet from models import MobileNet
from layers import conv_bn_layer from layers import conv_bn_layer
import paddle.dataset.mnist as reader import paddle.dataset.mnist as reader
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
import numpy as np import numpy as np
from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layer_helper import LayerHelper
def pact(x, name=None): def pact(x):
helper = LayerHelper("pact", **locals()) helper = LayerHelper("pact", **locals())
dtype = 'float32' dtype = 'float32'
init_thres = 20 init_thres = 20
u_param_attr = fluid.ParamAttr( u_param_attr = paddle.ParamAttr(
name=x.name + '_pact', name=x.name + '_pact',
initializer=fluid.initializer.ConstantInitializer(value=init_thres), initializer=paddle.nn.initializer.Constant(value=init_thres),
regularizer=fluid.regularizer.L2Decay(0.0001), regularizer=paddle.regularizer.L2Decay(0.0001),
learning_rate=1) learning_rate=1)
u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype) u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
x = fluid.layers.elementwise_sub(
x, fluid.layers.relu(fluid.layers.elementwise_sub(x, u_param)))
x = fluid.layers.elementwise_add(
x, fluid.layers.relu(fluid.layers.elementwise_sub(-u_param, x)))
part_a = paddle.nn.functional.relu(x - u_param)
part_b = paddle.nn.functional.relu(-u_param - x)
x = x - part_a + part_b
return x return x
def get_optimizer(): def get_optimizer():
return fluid.optimizer.MomentumOptimizer(0.0001, 0.9) return paddle.optimizer.Momentum(0.0001, 0.9)
class TestQuantAwareCase1(StaticCase): class TestQuantAwareCase1(StaticCase):
def get_model(self): def get_model(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
startup_prog = fluid.default_startup_program() startup_prog = paddle.static.default_startup_program()
train_prog = fluid.default_main_program() train_prog = paddle.static.default_main_program()
return startup_prog, train_prog return startup_prog, train_prog
def test_accuracy(self): def test_accuracy(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
image.stop_gradient = False image.stop_gradient = False
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
optimizer = fluid.optimizer.Momentum( optimizer = paddle.optimizer.Momentum(
momentum=0.9, momentum=0.9,
learning_rate=0.01, learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5)) weight_decay=paddle.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost) optimizer.minimize(avg_cost)
main_prog = fluid.default_main_program() main_prog = paddle.static.default_main_program()
val_prog = main_prog.clone(for_test=True) val_prog = main_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( train_loader = paddle.io.DataLoader.from_generator(
) else fluid.CPUPlace() feed_list=[image, label],
exe = fluid.Executor(place) capacity=512,
exe.run(fluid.default_startup_program()) use_double_buffer=True,
feeder = fluid.DataFeeder([image, label], place, program=main_prog) iterable=True)
train_reader = paddle.fluid.io.batch( valid_loader = paddle.io.DataLoader.from_generator(
paddle.dataset.mnist.train(), batch_size=64) feed_list=[image, label],
eval_reader = paddle.fluid.io.batch( capacity=512,
paddle.dataset.mnist.test(), batch_size=64) use_double_buffer=True,
iterable=True)
place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
) else paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64)
eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
train_loader.set_sample_list_generator(train_reader, place)
valid_loader.set_sample_list_generator(eval_reader, place)
def train(program): def train(program):
iter = 0 iter = 0
for data in train_reader(): for data in train_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -109,10 +117,10 @@ class TestQuantAwareCase1(StaticCase): ...@@ -109,10 +117,10 @@ class TestQuantAwareCase1(StaticCase):
def test(program): def test(program):
iter = 0 iter = 0
result = [[], [], []] result = [[], [], []]
for data in eval_reader(): for data in valid_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
......
tests/test_quant_embedding.py
浏览文件 @ bed39157
...@@ -12,8 +12,8 @@ ...@@ -12,8 +12,8 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import sys import sys
import paddle
sys.path.append("../") sys.path.append("../")
import paddle.fluid as fluid
import paddleslim.quant as quant import paddleslim.quant as quant
import unittest import unittest
...@@ -22,24 +22,25 @@ from static_case import StaticCase ...@@ -22,24 +22,25 @@ from static_case import StaticCase
class TestQuantEmbedding(StaticCase): class TestQuantEmbedding(StaticCase):
def test_quant_embedding(self): def test_quant_embedding(self):
train_program = fluid.Program() train_program = paddle.static.Program()
with fluid.program_guard(train_program): with paddle.static.program_guard(train_program):
input_word = fluid.data( input_word = paddle.static.data(
name="input_word", shape=[None, 1], dtype='int64') name="input_word", shape=[None, 1], dtype='int64')
input_emb = fluid.embedding( param_attr = paddle.ParamAttr(
input=input_word, name='emb',
is_sparse=False, initializer=paddle.nn.initializer.Uniform(-0.005, 0.005))
size=[100, 128], weight = train_program.global_block().create_parameter(
param_attr=fluid.ParamAttr( (100, 128), attr=param_attr, dtype="float32")
name='emb',
initializer=fluid.initializer.Uniform(-0.005, 0.005))) input_emb = paddle.nn.functional.embedding(
x=input_word, weight=weight, sparse=True)
infer_program = train_program.clone(for_test=True) infer_program = train_program.clone(for_test=True)
use_gpu = True use_gpu = True
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
quant_program = quant.quant_embedding(infer_program, place) quant_program = quant.quant_embedding(infer_program, place)
......
tests/test_quant_post.py
浏览文件 @ bed39157
...@@ -15,53 +15,59 @@ import sys ...@@ -15,53 +15,59 @@ import sys
sys.path.append("../") sys.path.append("../")
import unittest import unittest
import paddle import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_post_static from paddleslim.quant import quant_post_static
from static_case import StaticCase from static_case import StaticCase
sys.path.append("../demo") sys.path.append("../demo")
from models import MobileNet from models import MobileNet
from layers import conv_bn_layer from layers import conv_bn_layer
import paddle.dataset.mnist as reader import paddle.dataset.mnist as reader
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
import numpy as np import numpy as np
class TestQuantAwareCase1(StaticCase): class TestQuantAwareCase1(StaticCase):
def test_accuracy(self): def test_accuracy(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
optimizer = fluid.optimizer.Momentum( optimizer = paddle.optimizer.Momentum(
momentum=0.9, momentum=0.9,
learning_rate=0.01, learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5)) weight_decay=paddle.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost) optimizer.minimize(avg_cost)
main_prog = fluid.default_main_program() main_prog = paddle.static.default_main_program()
val_prog = main_prog.clone(for_test=True) val_prog = main_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
) else fluid.CPUPlace() ) else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_loader = paddle.io.DataLoader.from_generator(
train_reader = paddle.fluid.io.batch( feed_list=[image, label],
paddle.dataset.mnist.train(), batch_size=64) capacity=512,
eval_reader = paddle.fluid.io.batch( use_double_buffer=True,
paddle.dataset.mnist.test(), batch_size=64) iterable=True)
valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=512,
use_double_buffer=True,
iterable=True)
train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64)
eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
train_loader.set_sample_list_generator(train_reader, place)
valid_loader.set_sample_list_generator(eval_reader, place)
def train(program): def train(program):
iter = 0 iter = 0
for data in train_reader(): for data in train_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -72,9 +78,9 @@ class TestQuantAwareCase1(StaticCase): ...@@ -72,9 +78,9 @@ class TestQuantAwareCase1(StaticCase):
def test(program, outputs=[avg_cost, acc_top1, acc_top5]): def test(program, outputs=[avg_cost, acc_top1, acc_top5]):
iter = 0 iter = 0
result = [[], [], []] result = [[], [], []]
for data in train_reader(): for data in valid_loader():
cost, top1, top5 = exe.run(program, cost, top1, top5 = exe.run(program,
feed=feeder.feed(data), feed=data,
fetch_list=outputs) fetch_list=outputs)
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -89,7 +95,7 @@ class TestQuantAwareCase1(StaticCase): ...@@ -89,7 +95,7 @@ class TestQuantAwareCase1(StaticCase):
train(main_prog) train(main_prog)
top1_1, top5_1 = test(val_prog) top1_1, top5_1 = test(val_prog)
fluid.io.save_inference_model( paddle.static.save_inference_model(
dirname='./test_quant_post', dirname='./test_quant_post',
feeded_var_names=[image.name, label.name], feeded_var_names=[image.name, label.name],
target_vars=[avg_cost, acc_top1, acc_top5], target_vars=[avg_cost, acc_top1, acc_top5],
...@@ -106,7 +112,7 @@ class TestQuantAwareCase1(StaticCase): ...@@ -106,7 +112,7 @@ class TestQuantAwareCase1(StaticCase):
model_filename='model', model_filename='model',
params_filename='params', params_filename='params',
batch_nums=10) batch_nums=10)
quant_post_prog, feed_target_names, fetch_targets = fluid.io.load_inference_model( quant_post_prog, feed_target_names, fetch_targets = paddle.static.load_inference_model(
dirname='./test_quant_post_inference', dirname='./test_quant_post_inference',
executor=exe, executor=exe,
model_filename='__model__', model_filename='__model__',
......
tests/test_quant_post_only_weight.py
浏览文件 @ bed39157
...@@ -15,53 +15,61 @@ import sys ...@@ -15,53 +15,61 @@ import sys
sys.path.append("../") sys.path.append("../")
import unittest import unittest
import paddle import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_post_dynamic from paddleslim.quant import quant_post_dynamic
from static_case import StaticCase from static_case import StaticCase
sys.path.append("../demo") sys.path.append("../demo")
from models import MobileNet from models import MobileNet
from layers import conv_bn_layer from layers import conv_bn_layer
import paddle.dataset.mnist as reader import paddle.dataset.mnist as reader
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
import numpy as np import numpy as np
class TestQuantPostOnlyWeightCase1(StaticCase): class TestQuantPostOnlyWeightCase1(StaticCase):
def test_accuracy(self): def test_accuracy(self):
image = fluid.layers.data( image = paddle.static.data(
name='image', shape=[1, 28, 28], dtype='float32') name='image', shape=[None, 1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet() model = MobileNet()
out = model.net(input=image, class_dim=10) out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = paddle.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
optimizer = fluid.optimizer.Momentum( optimizer = paddle.optimizer.Momentum(
momentum=0.9, momentum=0.9,
learning_rate=0.01, learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5)) weight_decay=paddle.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost) optimizer.minimize(avg_cost)
main_prog = fluid.default_main_program() main_prog = paddle.static.default_main_program()
val_prog = main_prog.clone(for_test=True) val_prog = main_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
) else fluid.CPUPlace() ) else paddle.CPUPlace()
exe = fluid.Executor(place) exe = paddle.static.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(paddle.static.default_startup_program())
feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_loader = paddle.io.DataLoader.from_generator(
train_reader = paddle.fluid.io.batch( feed_list=[image, label],
paddle.dataset.mnist.train(), batch_size=64) capacity=512,
eval_reader = paddle.fluid.io.batch( use_double_buffer=True,
paddle.dataset.mnist.test(), batch_size=64) iterable=True)
valid_loader = paddle.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=512,
use_double_buffer=True,
iterable=True)
train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64)
eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
train_loader.set_sample_list_generator(train_reader, place)
valid_loader.set_sample_list_generator(eval_reader, place)
def train(program): def train(program):
iter = 0 iter = 0
for data in train_reader(): for data in train_loader():
cost, top1, top5 = exe.run( cost, top1, top5 = exe.run(
program, program,
feed=feeder.feed(data), feed=data,
fetch_list=[avg_cost, acc_top1, acc_top5]) fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -72,9 +80,9 @@ class TestQuantPostOnlyWeightCase1(StaticCase): ...@@ -72,9 +80,9 @@ class TestQuantPostOnlyWeightCase1(StaticCase):
def test(program, outputs=[avg_cost, acc_top1, acc_top5]): def test(program, outputs=[avg_cost, acc_top1, acc_top5]):
iter = 0 iter = 0
result = [[], [], []] result = [[], [], []]
for data in train_reader(): for data in valid_loader():
cost, top1, top5 = exe.run(program, cost, top1, top5 = exe.run(program,
feed=feeder.feed(data), feed=data,
fetch_list=outputs) fetch_list=outputs)
iter += 1 iter += 1
if iter % 100 == 0: if iter % 100 == 0:
...@@ -89,7 +97,7 @@ class TestQuantPostOnlyWeightCase1(StaticCase): ...@@ -89,7 +97,7 @@ class TestQuantPostOnlyWeightCase1(StaticCase):
train(main_prog) train(main_prog)
top1_1, top5_1 = test(val_prog) top1_1, top5_1 = test(val_prog)
fluid.io.save_inference_model( paddle.static.save_inference_model(
dirname='./test_quant_post_dynamic', dirname='./test_quant_post_dynamic',
feeded_var_names=[image.name, label.name], feeded_var_names=[image.name, label.name],
target_vars=[avg_cost, acc_top1, acc_top5], target_vars=[avg_cost, acc_top1, acc_top5],
...@@ -104,7 +112,7 @@ class TestQuantPostOnlyWeightCase1(StaticCase): ...@@ -104,7 +112,7 @@ class TestQuantPostOnlyWeightCase1(StaticCase):
model_filename='model', model_filename='model',
params_filename='params', params_filename='params',
generate_test_model=True) generate_test_model=True)
quant_post_prog, feed_target_names, fetch_targets = fluid.io.load_inference_model( quant_post_prog, feed_target_names, fetch_targets = paddle.static.load_inference_model(
dirname='./test_quant_post_inference/test_model', executor=exe) dirname='./test_quant_post_inference/test_model', executor=exe)
top1_2, top5_2 = test(quant_post_prog, fetch_targets) top1_2, top5_2 = test(quant_post_prog, fetch_targets)
print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1))
......
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