def custom_relu_static(func,
device,
dtype,
np_x,
use_func=True,
test_infer=False):
paddle.enable_static()
paddle.set_device(device)
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name='X', shape=[None, 8], dtype=dtype)
x.stop_gradient = False
out = func(x) if use_func else paddle.nn.functional.relu(x)
static.append_backward(out)
exe = static.Executor()
exe.run(static.default_startup_program())
# in static mode, x data has been covered by out
out_v = exe.run(static.default_main_program(),
feed={'X': np_x},
fetch_list=[out.name])
paddle.disable_static()
return out_v
def check_static_result(self, place):
from paddle.distributed.fleet.meta_parallel.parallel_layers.random import dropout
with static.program_guard(static.Program(), static.Program()):
input = static.data(name="input", shape=[40, 40], dtype="float32")
res1 = dropout(
input,
p=0.3,
training=True,
mode='upscale_in_train',
rng_name='seed0')
res2 = dropout(
input,
p=0.3,
training=True,
mode='upscale_in_train',
rng_name='seed1')
res3 = dropout(input, p=0.3)
in_np = np.random.random([40, 40]).astype("float32")
exe = static.Executor(place)
res_list = [res1, res2]
for i in range(2):
out1, out2 = exe.run(static.default_main_program(),
feed={"input": in_np},
fetch_list=res_list)
self.assertTrue(np.allclose(out1, out2))
def custom_relu_static_pe(func, device, dtype, np_x, use_func=True):
paddle.enable_static()
paddle.set_device(device)
places = static.cpu_places() if device is 'cpu' else static.cuda_places()
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name='X', shape=[None, 8], dtype=dtype)
x.stop_gradient = False
out = func(x) if use_func else paddle.nn.functional.relu(x)
static.append_backward(out)
exe = static.Executor()
exe.run(static.default_startup_program())
# in static mode, x data has been covered by out
compiled_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(
loss_name=out.name, places=places)
out_v = exe.run(compiled_prog,
feed={'X': np_x},
fetch_list=[out.name])
paddle.disable_static()
return out_v
def linear_static(func, device, dtype, np_x, np_weight, np_bias):
paddle.enable_static()
paddle.set_device(device)
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name="x", shape=[None, np_x.shape[1]], dtype=dtype)
weight = static.data(name="weight",
shape=np_weight.shape,
dtype=dtype)
bias = static.data(name="bias", shape=np_bias.shape, dtype=dtype)
x.stop_gradient = False
weight.stop_gradient = False
bias.stop_gradient = False
out = func(x, weight, bias)
mean_out = paddle.mean(out)
static.append_backward(mean_out)
exe = static.Executor()
exe.run(static.default_startup_program())
out_v, x_grad_v, weight_grad_v, bias_grad_v = exe.run(
static.default_main_program(),
feed={
"x": np_x.astype(dtype),
"weight": np_weight.astype(dtype),
"bias": np_bias.astype(dtype)
},
fetch_list=[
out.name, x.name + "@GRAD", weight.name + "@GRAD",
bias.name + "@GRAD"
])
paddle.disable_static()
return out_v, x_grad_v, weight_grad_v, bias_grad_v
def run_prog(self, a, b):
main_program, out = self.get_prog()
place = paddle.set_device('npu')
exe = static.Executor(place)
out_ = exe.run(main_program, feed={"a": a, "b": b}, fetch_list=[out])
return out_
def test_static(self):
mp, sp = static.Program(), static.Program()
with static.program_guard(mp, sp):
x = static.data("x", shape=[10, 10, 2], dtype="float64")
out = paddle.as_complex(x)
exe = static.Executor()
exe.run(sp)
[out_np] = exe.run(mp, feed={"x": self.x}, fetch_list=[out])
self.assertTrue(np.allclose(self.out, out_np))
def check_static_result(self, place):
import paddle.distributed.fleet.meta_parallel.parallel_layers.random as random
with static.program_guard(static.Program(), static.Program()):
res1 = random.determinate_seed('seed0')
exe = static.Executor(place)
res_list = [res1]
for i in range(2):
out1, = exe.run(static.default_main_program(),
fetch_list=res_list)
self.assertEqual(out1, np.cast['int32'](self.rng1.random()))
def test_static_assert_true(self, x_list, p_list):
for p in p_list:
for x in x_list:
with static.program_guard(static.Program(), static.Program()):
input_data = static.data("X", shape=x.shape, dtype=x.dtype)
output = paddle.linalg.cond(input_data, p)
exe = static.Executor()
result = exe.run(feed={"X": x}, fetch_list=[output])
expected_output = np.linalg.cond(x, p)
np.testing.assert_allclose(result[0],
expected_output,
rtol=5e-5)
def run_inference(drop_last):
loader = paddle.io.DataLoader.from_generator(feed_list=[x],
capacity=8, drop_last=drop_last)
loader.set_batch_generator(batch_generator, static.cpu_places())
exe = static.Executor(paddle.CPUPlace())
prog = static.CompiledProgram(static.default_main_program())
prog = prog.with_data_parallel()
result = []
for data in loader():
each_ret, = exe.run(prog, feed=data, fetch_list=[y])
result.extend(each_ret)
return result
def test_in_static_mode(self):
def init_input_output(dtype):
input = np.random.random(self._shape).astype(
dtype) + 1j * np.random.random(self._shape).astype(dtype)
return {'x': input}, numpy_apis[self.api](input)
for dtype in self.dtypes:
input_dict, np_res = init_input_output(dtype)
for place in self.places:
with static.program_guard(static.Program()):
x = static.data(name="x", shape=self._shape, dtype=dtype)
out = paddle_apis[self.api](x)
exe = static.Executor(place)
out_value = exe.run(feed=input_dict, fetch_list=[out.name])
self.assertTrue(np.array_equal(np_res, out_value[0]))
def test_in_static_mode(self):
def init_input_output(dtype):
input = np.random.random(self._shape).astype(dtype)
return {'x': input}, psi(input)
for dtype in self.dtypes:
input_dict, sc_res = init_input_output(dtype)
for place in self.places:
with static.program_guard(static.Program()):
x = static.data(name="x", shape=self._shape, dtype=dtype)
out = paddle.digamma(x)
exe = static.Executor(place)
out_value = exe.run(feed=input_dict, fetch_list=[out.name])
self.assertEqual(
np.allclose(out_value[0], sc_res, rtol=1e-5), True)
def custom_relu_static_inference(func, device, np_data, np_label, path_prefix):
paddle.set_device(device)
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
# simple module
data = static.data(name='data',
shape=[None, 1, 28, 28],
dtype='float32')
label = static.data(name='label', shape=[None, 1], dtype='int64')
hidden = static.nn.fc(data, size=128)
hidden = func(hidden)
hidden = static.nn.fc(hidden, size=128)
predict = static.nn.fc(hidden, size=10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(input=hidden,
label=label)
avg_loss = paddle.mean(loss)
opt = paddle.optimizer.SGD(learning_rate=0.1)
opt.minimize(avg_loss)
# run start up model
exe = static.Executor()
exe.run(static.default_startup_program())
# train
for i in range(4):
avg_loss_v = exe.run(static.default_main_program(),
feed={
'data': np_data,
'label': np_label
},
fetch_list=[avg_loss])
# save inference model
static.save_inference_model(path_prefix, [data], [predict], exe)
# get train predict value
predict_v = exe.run(static.default_main_program(),
feed={
'data': np_data,
'label': np_label
},
fetch_list=[predict])
return predict_v
def concat_static(func, dtype, np_inputs, axis_v, with_attr=False):
paddle.enable_static()
paddle.set_device("cpu")
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x1 = static.data(name="x1", shape=[2, 3], dtype=dtype)
x2 = static.data(name="x2", shape=[2, 3], dtype=dtype)
if with_attr:
axis = axis_v
else:
axis = paddle.full(shape=[1], dtype='int64', fill_value=axis_v)
x1.stop_gradient = False
x2.stop_gradient = False
total_time = 0
for i in range(TEST_TIME):
start = time.time()
out = func([x1, x2], axis)
total_time += time.time() - start
print("- static mode concat time cost: {} s".format(total_time /
TEST_TIME))
# mean only support float, so here use sum
sum_out = paddle.sum(out)
static.append_backward(sum_out)
exe = static.Executor()
exe.run(static.default_startup_program())
if with_attr:
feed_dict = {
"x1": np_inputs[0].astype(dtype),
"x2": np_inputs[1].astype(dtype)
}
else:
feed_dict = {
"x1": np_inputs[0].astype(dtype),
"x2": np_inputs[1].astype(dtype),
"axis": axis
}
out_v, x1_grad_v, x2_grad_v = exe.run(
static.default_main_program(),
feed=feed_dict,
fetch_list=[out.name, x1.name + "@GRAD", x2.name + "@GRAD"])
paddle.disable_static()
return out_v, x1_grad_v, x2_grad_v
def test_conj_static_mode(self):
def init_input_output(dtype):
input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
[2, 20, 2, 3]).astype(dtype)
return {'x': input}, np.conj(input)
for dtype in self._dtypes:
input_dict, np_res = init_input_output(dtype)
for place in self._places:
with static.program_guard(static.Program()):
x_dtype = np.complex64 if dtype == "float32" else np.complex128
x = static.data(
name="x", shape=[2, 20, 2, 3], dtype=x_dtype)
out = paddle.conj(x)
exe = static.Executor(place)
out_value = exe.run(feed=input_dict, fetch_list=[out.name])
self.assertTrue(np.array_equal(np_res, out_value[0]))
def conj_static(func, shape, dtype, np_input):
paddle.enable_static()
paddle.set_device("cpu")
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name="x", shape=shape, dtype=dtype)
x.stop_gradient = False
out = func(x)
sum_out = paddle.sum(out)
static.append_backward(sum_out)
exe = static.Executor()
exe.run(static.default_startup_program())
out_v, x_grad_v = exe.run(static.default_main_program(),
feed={"x": np_input},
fetch_list=[out.name, x.name + "@GRAD"])
paddle.disable_static()
return out_v, x_grad_v
def train(self, place, restore):
exe = static.Executor(place)
exe.run(self._startup_prog)
params = []
for pass_id in range(2):
for batch_id in range(3):
exe.run(program=self._train_program, feed={'x': gen_data()})
tmp_param = np.array(static.global_scope().find_var(
self._param_name).get_tensor())
params.append(tmp_param)
with self._ema.apply(exe, restore):
final_ema = np.array(static.global_scope().find_var(
self._param_name).get_tensor())
exe.run(program=self._test_program, feed={'x': gen_data()})
if not restore:
self._ema.restore(exe)
return params, final_ema
def test_relu2_static(device, dtype):
paddle.enable_static()
paddle.set_device(device)
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name='X', shape=[None, 8], dtype=dtype)
x.stop_gradient = False
out = librelu2_op.relu2(x)
static.append_backward(out)
print(static.default_main_program())
exe = static.Executor()
exe.run(static.default_startup_program())
x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
out, = exe.run(static.default_main_program(),
feed={'X': x},
fetch_list=[out.name])
print(out)
def test_static_save_and_load_inference_model(self):
paddle.enable_static()
np_data = np.random.random((1, 1, 28, 28)).astype("float32")
np_label = np.random.random((1, 1)).astype("int64")
path_prefix = "custom_op_inference/custom_relu"
for device in self.devices:
predict = custom_relu_static_inference(
self.custom_ops[0], device, np_data, np_label, path_prefix)
# load inference model
with static.scope_guard(static.Scope()):
exe = static.Executor()
[inference_program, feed_target_names,
fetch_targets] = static.load_inference_model(path_prefix, exe)
predict_infer = exe.run(inference_program,
feed={feed_target_names[0]: np_data},
fetch_list=fetch_targets)
self.assertTrue(
np.array_equal(predict, predict_infer),
"custom op predict: {},\n custom op infer predict: {}".
format(predict, predict_infer))
paddle.disable_static()
def test_relu2_static(device, dtype, use_custom=True):
paddle.enable_static()
paddle.set_device(device)
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name='X', shape=[None, 8], dtype=dtype)
x.stop_gradient = False
out = custom_relu_op_rf.relu2(
x) if use_custom else paddle.nn.functional.relu(x)
static.append_backward(out)
print(static.default_main_program())
places = static.cuda_places()
print(places)
exe = static.Executor()
compiled_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(
loss_name=out.name, places=static.cuda_places())
x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
out, = exe.run(compiled_prog, feed={'X': x}, fetch_list=[out.name])
print(out)
def linear_static(func, dtype, np_x, np_weight, np_bias):
paddle.enable_static()
paddle.set_device("cpu")
with static.scope_guard(static.Scope()):
with static.program_guard(static.Program()):
x = static.data(name="x", shape=np_x.shape, dtype=dtype)
weight = static.data(
name="weight", shape=np_weight.shape, dtype=dtype)
bias = static.data(name="bias", shape=np_bias.shape, dtype=dtype)
out = func(x, weight, bias)
exe = static.Executor()
exe.run(static.default_startup_program())
out_v, = exe.run(static.default_main_program(),
feed={
"x": np_x.astype(dtype),
"weight": np_weight.astype(dtype),
"bias": np_bias.astype(dtype)
},
fetch_list=[out.name])
paddle.disable_static()
return out_v
def search_mobilenetv2_block(config, args, image_size):
image_shape = [3, image_size, image_size]
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
if args.data == 'cifar10':
train_dataset = paddle.vision.datasets.Cifar10(mode='train',
transform=transform,
backend='cv2')
val_dataset = paddle.vision.datasets.Cifar10(mode='test',
transform=transform,
backend='cv2')
elif args.data == 'imagenet':
train_dataset = imagenet_reader.ImageNetDataset(mode='train')
val_dataset = imagenet_reader.ImageNetDataset(mode='val')
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
if args.is_server:
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
search_steps=args.search_steps,
is_server=True)
else:
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
search_steps=args.search_steps,
is_server=False)
for step in range(args.search_steps):
archs = sa_nas.next_archs()[0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
with static.program_guard(train_program, startup_program):
data_shape = [None] + image_shape
data = static.data(name='data', shape=data_shape, dtype='float32')
label = static.data(name='label', shape=[None, 1], dtype='int64')
if args.data == 'cifar10':
paddle.assign(paddle.reshape(label, [-1, 1]), label)
train_loader = paddle.io.DataLoader(train_dataset,
places=places,
feed_list=[data, label],
drop_last=True,
batch_size=args.batch_size,
return_list=False,
shuffle=True,
use_shared_memory=True,
num_workers=4)
val_loader = paddle.io.DataLoader(val_dataset,
places=place,
feed_list=[data, label],
drop_last=False,
batch_size=args.batch_size,
return_list=False,
shuffle=False)
data = conv_bn_layer(input=data,
num_filters=32,
filter_size=3,
stride=2,
padding='SAME',
act='relu6',
name='mobilenetv2_conv1')
data = archs(data)[0]
data = conv_bn_layer(input=data,
num_filters=1280,
filter_size=1,
stride=1,
padding='SAME',
act='relu6',
name='mobilenetv2_last_conv')
data = F.adaptive_avg_pool2d(data,
output_size=[1, 1],
name='mobilenetv2_last_pool')
output = static.nn.fc(
x=data,
size=args.class_dim,
weight_attr=ParamAttr(name='mobilenetv2_fc_weights'),
bias_attr=ParamAttr(name='mobilenetv2_fc_offset'))
softmax_out = F.softmax(output)
cost = F.cross_entropy(softmax_out, label=label)
avg_cost = paddle.mean(cost)
acc_top1 = paddle.metric.accuracy(input=softmax_out,
label=label,
k=1)
acc_top5 = paddle.metric.accuracy(input=softmax_out,
label=label,
k=5)
test_program = train_program.clone(for_test=True)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1,
momentum=0.9,
weight_decay=paddle.regularizer.L2Decay(1e-4))
optimizer.minimize(avg_cost)
current_flops = flops(train_program)
print('step: {}, current_flops: {}'.format(step, current_flops))
if current_flops > int(321208544):
continue
exe = static.Executor(place)
exe.run(startup_program)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(loss_name=avg_cost.name,
build_strategy=build_strategy)
for epoch_id in range(args.retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'
.format(step, epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(val_loader()):
test_fetches = [avg_cost.name, acc_top1.name, acc_top5.name]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'
.format(step, batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
sa_nas.reward(float(finally_reward[1]))
def search(config, args, image_size, is_server=True):
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
if is_server:
### start a server and a client
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
search_steps=args.search_steps,
is_server=True)
else:
### start a client
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
init_temperature=init_temperature,
is_server=False)
image_shape = [3, image_size, image_size]
for step in range(args.search_steps):
archs = sa_nas.next_archs()[0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
train_fetch_list, _, train_loader = build_program(train_program,
startup_program,
image_shape,
archs,
args,
is_train=True)
current_params = count_parameters_in_MB(
train_program.global_block().all_parameters(), 'cifar10')
_logger.info('step: {}, current_params: {}M'.format(
step, current_params))
if current_params > float(3.77):
continue
test_fetch_list, _, test_loader = build_program(test_program,
startup_program,
image_shape,
archs,
args,
is_train=False)
test_program = test_program.clone(for_test=True)
exe = static.Executor(place)
exe.run(startup_program)
train_reader = reader.train_valid(batch_size=args.batch_size,
is_train=True,
is_shuffle=True)
test_reader = reader.train_valid(batch_size=args.batch_size,
is_train=False,
is_shuffle=False)
train_loader.set_batch_generator(train_reader, places=place)
test_loader.set_batch_generator(test_reader, places=place)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(
loss_name=train_fetch_list[0].name,
build_strategy=build_strategy)
valid_top1_list = []
for epoch_id in range(args.retain_epoch):
train_top1 = train(train_compiled_program, exe, epoch_id,
train_loader, train_fetch_list, args)
_logger.info("TRAIN: step: {}, Epoch {}, train_acc {:.6f}".format(
step, epoch_id, train_top1))
valid_top1 = valid(test_program, exe, epoch_id, test_loader,
test_fetch_list, args)
_logger.info("TEST: Epoch {}, valid_acc {:.6f}".format(
epoch_id, valid_top1))
valid_top1_list.append(valid_top1)
sa_nas.reward(float(valid_top1_list[-1] + valid_top1_list[-2]) / 2)
def final_test(config, args, image_size, token=None):
assert token != None, "If you want to start a final experiment, you must input a token."
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
is_server=True)
image_shape = [3, image_size, image_size]
archs = sa_nas.tokens2arch(token)[0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
train_fetch_list, (data,
label), train_loader = build_program(train_program,
startup_program,
image_shape,
archs,
args,
is_train=True)
current_params = count_parameters_in_MB(
train_program.global_block().all_parameters(), 'cifar10')
_logger.info('current_params: {}M'.format(current_params))
test_fetch_list, _, test_loader = build_program(test_program,
startup_program,
image_shape,
archs,
args,
is_train=False)
test_program = test_program.clone(for_test=True)
exe = static.Executor(place)
exe.run(startup_program)
train_reader = reader.train_valid(batch_size=args.batch_size,
is_train=True,
is_shuffle=True)
test_reader = reader.train_valid(batch_size=args.batch_size,
is_train=False,
is_shuffle=False)
train_loader.set_batch_generator(train_reader, places=place)
test_loader.set_batch_generator(test_reader, places=place)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(loss_name=train_fetch_list[0].name,
build_strategy=build_strategy)
valid_top1_list = []
for epoch_id in range(args.retain_epoch):
train_top1 = train(train_compiled_program, exe, epoch_id, train_loader,
train_fetch_list, args)
_logger.info("TRAIN: Epoch {}, train_acc {:.6f}".format(
epoch_id, train_top1))
valid_top1 = valid(test_program, exe, epoch_id, test_loader,
test_fetch_list, args)
_logger.info("TEST: Epoch {}, valid_acc {:.6f}".format(
epoch_id, valid_top1))
valid_top1_list.append(valid_top1)
output_dir = os.path.join('darts_output', str(epoch_id))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
static.save_inference_model(output_dir, [data], test_fetch_list, exe)
def test_search_result(tokens, image_size, args, config):
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
sa_nas = SANAS(config,
server_addr=(args.server_address, args.port),
search_steps=args.search_steps,
is_server=True)
image_shape = [3, image_size, image_size]
if args.data == 'cifar10':
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = paddle.vision.datasets.Cifar10(mode='train',
transform=transform,
backend='cv2')
val_dataset = paddle.vision.datasets.Cifar10(mode='test',
transform=transform,
backend='cv2')
elif args.data == 'imagenet':
train_dataset = imagenet_reader.ImageNetDataset(mode='train')
val_dataset = imagenet_reader.ImageNetDataset(mode='val')
archs = sa_nas.tokens2arch(tokens)[0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
train_loader, avg_cost, acc_top1, acc_top5 = build_program(
train_program, startup_program, image_shape, train_dataset, archs,
args, places)
current_flops = flops(train_program)
print('current_flops: {}'.format(current_flops))
test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
test_program,
startup_program,
image_shape,
val_dataset,
archs,
args,
place,
is_test=True)
test_program = test_program.clone(for_test=True)
exe = static.Executor(place)
exe.run(startup_program)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(loss_name=avg_cost.name,
build_strategy=build_strategy)
for epoch_id in range(args.retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
format(epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(test_loader()):
test_fetches = [
test_avg_cost.name, test_acc_top1.name, test_acc_top5.name
]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
format(batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
image = static.data(name='image', shape=[None, 784], dtype='float32')
label = static.data(name='label', shape=[None, 1], dtype='int64')
# Define DataLoader
loader = paddle.io.DataLoader.from_generator(feed_list=[image, label],
capacity=16,
iterable=ITERABLE)
# Define network
loss = simple_net(image, label)
# Set data source of DataLoader
#
# If DataLoader is iterable, places must be given and the number of places must be the same with device number.
# - If you are using GPU, call `paddle.static.cuda_places()` to get all GPU places.
# - If you are using CPU, call `paddle.static.cpu_places()` to get all CPU places.
#
# If DataLoader is not iterable, places can be None.
places = static.cuda_places() if USE_GPU else static.cpu_places()
set_data_source(loader, places)
exe = static.Executor(places[0])
exe.run(static.default_startup_program())
prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(loss_name=loss.name)
if loader.iterable:
train_iterable(exe, prog, loss, loader)
else:
train_non_iterable(exe, prog, loss, loader)
def search_mobilenetv2(config, args, image_size, is_server=True):
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
if is_server:
### start a server and a client
rl_nas = RLNAS(key='lstm',
configs=config,
is_sync=False,
server_addr=(args.server_address, args.port),
controller_batch_size=1,
controller_decay_steps=1000,
controller_decay_rate=0.8,
lstm_num_layers=1,
hidden_size=10,
temperature=1.0)
else:
### start a client
rl_nas = RLNAS(key='lstm',
configs=config,
is_sync=False,
server_addr=(args.server_address, args.port),
lstm_num_layers=1,
hidden_size=10,
temperature=1.0,
controller_batch_size=1,
controller_decay_steps=1000,
controller_decay_rate=0.8,
is_server=False)
image_shape = [3, image_size, image_size]
if args.data == 'cifar10':
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = paddle.vision.datasets.Cifar10(mode='train',
transform=transform,
backend='cv2')
val_dataset = paddle.vision.datasets.Cifar10(mode='test',
transform=transform,
backend='cv2')
elif args.data == 'imagenet':
train_dataset = imagenet_reader.ImageNetDataset(mode='train')
val_dataset = imagenet_reader.ImageNetDataset(mode='val')
for step in range(args.search_steps):
archs = rl_nas.next_archs(1)[0][0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
train_loader, avg_cost, acc_top1, acc_top5 = build_program(
train_program, startup_program, image_shape, train_dataset, archs,
args, places)
test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
test_program,
startup_program,
image_shape,
val_dataset,
archs,
args,
place,
is_test=True)
test_program = test_program.clone(for_test=True)
exe = static.Executor(place)
exe.run(startup_program)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(loss_name=avg_cost.name,
build_strategy=build_strategy)
for epoch_id in range(args.retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'
.format(step, epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(test_loader()):
test_fetches = [
test_avg_cost.name, test_acc_top1.name, test_acc_top5.name
]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'
.format(step, batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
rl_nas.reward(np.float32(finally_reward[1]))
# coding=utf-8
import numpy
import paddle
import paddle.static as static
import paddle.nn.functional as F
# 开启静态图模式
paddle.enable_static()
paddle.set_device('cpu')
# 网络结构定义
x = static.data(name='X', shape=[None, 13], dtype='float32')
y = static.data(name='Y', shape=[None, 1], dtype='float32')
predict = static.nn.fc(x=x, size=1)
loss = F.square_error_cost(input=predict, label=y)
avg_loss = paddle.mean(loss)
# 执行环境准备
exe = static.Executor(paddle.CPUPlace())
exe.run(static.default_startup_program())
# 执行网络
x = numpy.random.random(size=(7, 13)).astype('float32')
y = numpy.random.random(size=(8, 1)).astype('float32')
loss_data, = exe.run(static.default_main_program(),
feed={
'X': x,
'Y': y
},
fetch_list=[avg_loss.name])
import paddle import paddle.static as static paddle.enable_static() x = static.data(name="x", shape=[10, 10], dtype='float32') y = static.nn.fc(x, 10) z = static.nn.fc(y, 10) place = paddle.CPUPlace() exe = static.Executor(place) exe.run(static.default_startup_program()) prog = static.default_main_program() static.save(prog, "./temp") static.load(prog, "./temp")
opt = paddle.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
# data loader
transform = Compose([Normalize(mean=[127.5], std=[127.5], data_format='CHW')])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
train_loader = paddle.io.DataLoader(train_dataset,
feed_list=[image, label],
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True,
num_workers=2)
# prepare
exe = static.Executor()
exe.run(static.default_startup_program())
places = paddle.static.cuda_places()
compiled_program = static.CompiledProgram(
static.default_main_program()).with_data_parallel(loss_name=loss.name,
places=places)
# train
for epoch_id in range(EPOCH_NUM):
for batch_id, (image_data, label_data) in enumerate(train_loader()):
loss_data = exe.run(compiled_program,
feed={
'image': image_data,
'label': label_data
},
def search_mobilenetv2(config, args, image_size, is_server=True):
places = static.cuda_places() if args.use_gpu else static.cpu_places()
place = places[0]
if is_server:
### start a server and a client
rl_nas = RLNAS(
key='ddpg',
configs=config,
is_sync=False,
obs_dim=26, ### step + length_of_token
server_addr=(args.server_address, args.port))
else:
### start a client
rl_nas = RLNAS(key='ddpg',
configs=config,
is_sync=False,
obs_dim=26,
server_addr=(args.server_address, args.port),
is_server=False)
image_shape = [3, image_size, image_size]
if args.data == 'cifar10':
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = paddle.vision.datasets.Cifar10(mode='train',
transform=transform,
backend='cv2')
val_dataset = paddle.vision.datasets.Cifar10(mode='test',
transform=transform,
backend='cv2')
elif args.data == 'imagenet':
train_dataset = imagenet_reader.ImageNetDataset(mode='train')
val_dataset = imagenet_reader.ImageNetDataset(mode='val')
for step in range(args.search_steps):
if step == 0:
action_prev = [1. for _ in rl_nas.range_tables]
else:
action_prev = rl_nas.tokens[0]
obs = [step]
obs.extend(action_prev)
archs = rl_nas.next_archs(obs=obs)[0][0]
train_program = static.Program()
test_program = static.Program()
startup_program = static.Program()
train_loader, avg_cost, acc_top1, acc_top5 = build_program(
train_program, startup_program, image_shape, train_dataset, archs,
args, places)
test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
test_program,
startup_program,
image_shape,
val_dataset,
archs,
args,
place,
is_test=True)
test_program = test_program.clone(for_test=True)
exe = static.Executor(place)
exe.run(startup_program)
build_strategy = static.BuildStrategy()
train_compiled_program = static.CompiledProgram(
train_program).with_data_parallel(loss_name=avg_cost.name,
build_strategy=build_strategy)
for epoch_id in range(args.retain_epoch):
for batch_id, data in enumerate(train_loader()):
fetches = [avg_cost.name]
s_time = time.time()
outs = exe.run(train_compiled_program,
feed=data,
fetch_list=fetches)[0]
batch_time = time.time() - s_time
if batch_id % 10 == 0:
_logger.info(
'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'
.format(step, epoch_id, batch_id, outs[0], batch_time))
reward = []
for batch_id, data in enumerate(test_loader()):
test_fetches = [
test_avg_cost.name, test_acc_top1.name, test_acc_top5.name
]
batch_reward = exe.run(test_program,
feed=data,
fetch_list=test_fetches)
reward_avg = np.mean(np.array(batch_reward), axis=1)
reward.append(reward_avg)
_logger.info(
'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'
.format(step, batch_id, batch_reward[0], batch_reward[1],
batch_reward[2]))
finally_reward = np.mean(np.array(reward), axis=0)
_logger.info(
'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
finally_reward[0], finally_reward[1], finally_reward[2]))
obs = np.expand_dims(obs, axis=0).astype('float32')
actions = rl_nas.tokens
obs_next = [step + 1]
obs_next.extend(actions[0])
obs_next = np.expand_dims(obs_next, axis=0).astype('float32')
if step == args.search_steps - 1:
terminal = np.expand_dims([True], axis=0).astype(np.bool)
else:
terminal = np.expand_dims([False], axis=0).astype(np.bool)
rl_nas.reward(np.expand_dims(np.float32(finally_reward[1]), axis=0),
obs=obs,
actions=actions.astype('float32'),
obs_next=obs_next,
terminal=terminal)
if step == 2:
sys.exit(0)