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 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_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)
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]))
import os
import paddle
import paddle.static as static
paddle.enable_static()
os.environ['CPU_NUM'] = str(2)
places = static.cpu_places()
data = static.data(name="x", shape=[None, 1], dtype="float32")
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
build_strategy = static.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
program = static.CompiledProgram(static.default_main_program())
program = program.with_data_parallel(loss_name=loss.name,
build_strategy=build_strategy,
places=places)
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
exe = static.Executor(place)
data = static.data(name='X', shape=[None, 1], dtype='float32')
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
test_program = static.default_main_program().clone(for_test=True)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(static.default_startup_program())
compiled_train_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(loss_name=loss.name,
places=parallel_places)
# NOTE: if not set share_vars_from=compiled_train_prog,
# the parameters used in test process are different with
# the parameters used by train process
compiled_test_prog = static.CompiledProgram(test_program).with_data_parallel(
share_vars_from=compiled_train_prog, places=parallel_places)
train_data = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = exe.run(compiled_train_prog,
feed={"X": train_data},
fetch_list=[loss.name])
test_data = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = exe.run(compiled_test_prog,
feed={"X": test_data},
fetch_list=[loss.name])
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)
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]))
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
places = static.cpu_places()
else:
places = static.cuda_places()
data = static.data(name='X', shape=[None, 1], dtype='float32')
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(static.default_startup_program())
build_strategy = static.BuildStrategy()
build_strategy.gradient_scale_strategy = \
static.BuildStrategy.GradientScaleStrategy.Customized
compiled_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy, places=places)
dev_count = len(places)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_grad = numpy.ones((dev_count)).astype("float32") * 0.01
loss_grad_name = loss.name + "@GRAD"
loss_data = exe.run(compiled_prog,
feed={
"X": x,
loss_grad_name: loss_grad
},
fetch_list=[loss.name, loss_grad_name])
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 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 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]))