def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["yaml_path"] = args.config_yaml
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds()
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.net(input_data)
#infer_target_var = model.infer_target_var
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
static_model_class.create_optimizer()
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
auc_num = config.get("runner.auc_num", 1)
train_data_dir = config.get("runner.train_data_dir", None)
epochs = config.get("runner.epochs", None)
print_interval = config.get("runner.print_interval", None)
model_save_path = config.get("runner.model_save_path", "model_output")
model_init_path = config.get("runner.model_init_path", None)
batch_size = config.get("runner.train_batch_size", None)
reader_type = config.get("runner.reader_type", "DataLoader")
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}"
.format(use_gpu, train_data_dir, epochs, print_interval,
model_save_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
last_epoch_id = config.get("last_epoch", -1)
if reader_type == 'QueueDataset':
dataset, file_list = get_reader(input_data, config)
elif reader_type == 'DataLoader':
train_dataloader = create_data_loader(config=config, place=place)
for epoch_id in range(last_epoch_id + 1, epochs):
epoch_begin = time.time()
if use_auc:
reset_auc(auc_num)
if reader_type == 'DataLoader':
fetch_batch_var = dataloader_train(epoch_id, train_dataloader,
input_data_names, fetch_vars,
exe, config)
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() -
epoch_begin))
elif reader_type == 'QueueDataset':
fetch_batch_var = dataset_train(epoch_id, dataset, fetch_vars, exe,
config)
logger.info("epoch: {} done, ".format(epoch_id) +
"epoch time: {:.2f} s".format(time.time() -
epoch_begin))
else:
logger.info("reader type wrong")
save_static_model(paddle.static.default_main_program(),
model_save_path,
epoch_id,
prefix='rec_static')
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}"
.format(use_gpu, test_data_dir, start_epoch, end_epoch, print_interval,
model_load_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
test_dataloader = create_data_loader(config=config,
place=place,
mode="test")
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(paddle.static.default_main_program(),
model_path,
prefix='rec_static')
runner_results = []
epoch_begin = time.time()
interval_begin = time.time()
if use_auc:
reset_auc()
for batch_id, batch_data in enumerate(test_dataloader()):
batch_runner_result = {}
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data)),
fetch_list=[var for _, var in fetch_vars.items()])
for var_idx, var_name in enumerate(fetch_vars):
batch_runner_result[var_name] = np.array(
fetch_batch_var[var_idx]).tolist()
runner_results.append(batch_runner_result)
if batch_id % print_interval == 0:
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(
var_name, fetch_batch_var[var_idx][0])
logger.info(
"epoch: {}, batch_id: {}, ".format(epoch_id, batch_id) +
metric_str +
"speed: {:.2f} ins/s".format(print_interval * batch_size /
(time.time() -
interval_begin)))
interval_begin = time.time()
reader_start = time.time()
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx][0])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
runner_result_save_path = config.get("runner.runner_result_dump_path",
None)
if runner_result_save_path:
logging.info(
"Dump runner result in {}".format(runner_result_save_path))
with open(runner_result_save_path, 'w+') as fout:
json.dump(runner_results, fout)
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["yaml_path"] = args.config_yaml
config["config_abs_dir"] = args.abs_dir
# modify config from command
if args.opt:
for parameter in args.opt:
parameter = parameter.strip()
key, value = parameter.split("=")
config[key] = value
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds()
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.net(input_data)
#infer_target_var = model.infer_target_var
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
static_model_class.create_optimizer()
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
use_visual = config.get("runner.use_visual", False)
use_inference = config.get("runner.use_inference", False)
auc_num = config.get("runner.auc_num", 1)
train_data_dir = config.get("runner.train_data_dir", None)
epochs = config.get("runner.epochs", None)
print_interval = config.get("runner.print_interval", None)
model_save_path = config.get("runner.model_save_path", "model_output")
model_init_path = config.get("runner.model_init_path", None)
batch_size = config.get("runner.train_batch_size", None)
reader_type = config.get("runner.reader_type", "DataLoader")
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, use_visual: {}, train_batch_size: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}"
.format(use_gpu, use_visual, batch_size, train_data_dir, epochs,
print_interval, model_save_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
last_epoch_id = config.get("last_epoch", -1)
# Create a log_visual object and store the data in the path
if use_visual:
from visualdl import LogWriter
log_visual = LogWriter(args.abs_dir + "/visualDL_log/train")
else:
log_visual = None
step_num = 0
if reader_type == 'QueueDataset':
dataset, file_list = get_reader(input_data, config)
elif reader_type == 'DataLoader':
train_dataloader = create_data_loader(config=config, place=place)
for epoch_id in range(last_epoch_id + 1, epochs):
epoch_begin = time.time()
if use_auc:
reset_auc(auc_num)
if reader_type == 'DataLoader':
fetch_batch_var, step_num = dataloader_train(
epoch_id, train_dataloader, input_data_names, fetch_vars, exe,
config, use_visual, log_visual, step_num)
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() -
epoch_begin))
elif reader_type == 'QueueDataset':
fetch_batch_var = dataset_train(epoch_id, dataset, fetch_vars, exe,
config)
logger.info("epoch: {} done, ".format(epoch_id) +
"epoch time: {:.2f} s".format(time.time() -
epoch_begin))
else:
logger.info("reader type wrong")
save_static_model(paddle.static.default_main_program(),
model_save_path,
epoch_id,
prefix='rec_static')
if use_inference:
feed_var_names = config.get("runner.save_inference_feed_varnames",
[])
feedvars = []
fetch_var_names = config.get(
"runner.save_inference_fetch_varnames", [])
fetchvars = []
for var_name in feed_var_names:
if var_name not in paddle.static.default_main_program(
).global_block().vars:
raise ValueError(
"Feed variable: {} not in default_main_program, global block has follow vars: {}"
.format(
var_name,
paddle.static.default_main_program().global_block(
).vars.keys()))
else:
feedvars.append(paddle.static.default_main_program().
global_block().vars[var_name])
for var_name in fetch_var_names:
if var_name not in paddle.static.default_main_program(
).global_block().vars:
raise ValueError(
"Fetch variable: {} not in default_main_program, global block has follow vars: {}"
.format(
var_name,
paddle.static.default_main_program().global_block(
).vars.keys()))
else:
fetchvars.append(paddle.static.default_main_program().
global_block().vars[var_name])
save_inference_model(model_save_path, epoch_id, feedvars,
fetchvars, exe)
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
use_visual = config.get("runner.use_visual", False)
auc_num = config.get("runner.auc_num", 1)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, use_visual: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}".
format(use_gpu, use_visual, test_data_dir, start_epoch, end_epoch,
print_interval, model_load_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
test_dataloader = create_data_loader(
config=config, place=place, mode="test")
# Create a log_visual object and store the data in the path
if use_visual:
from visualdl import LogWriter
log_visual = LogWriter(args.abs_dir + "/visualDL_log/infer")
step_num = 0
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(
paddle.static.default_main_program(),
model_path,
prefix='rec_static')
epoch_begin = time.time()
interval_begin = time.time()
if use_auc:
reset_auc(auc_num)
for batch_id, batch_data in enumerate(test_dataloader()):
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data)),
fetch_list=[var for _, var in fetch_vars.items()])
if batch_id % print_interval == 0:
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(
var_name, fetch_batch_var[var_idx][0])
if use_visual:
log_visual.add_scalar(
tag="infer/" + var_name,
step=step_num,
value=fetch_batch_var[var_idx][0])
logger.info("epoch: {}, batch_id: {}, ".format(
epoch_id, batch_id) + metric_str + "speed: {:.2f} ins/s".
format(print_interval * batch_size / (time.time(
) - interval_begin)))
interval_begin = time.time()
reader_start = time.time()
step_num = step_num + 1
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx][0])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
auc_num = config.get("runner.auc_num", 1)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}"
.format(use_gpu, test_data_dir, start_epoch, end_epoch, print_interval,
model_load_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
test_dataloader = create_data_loader(config=config,
place=place,
mode="test")
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(paddle.static.default_main_program(),
model_path,
prefix='rec_static')
epoch_begin = time.time()
interval_begin = time.time()
b = paddle.static.global_scope().find_var("item_emb").get_tensor()
b = np.array(b)
import faiss
if use_gpu:
res = faiss.StandardGpuResources()
flat_config = faiss.GpuIndexFlatConfig()
flat_config.device = 0
faiss_index = faiss.GpuIndexFlatIP(res, b.shape[-1], flat_config)
faiss_index.add(b)
else:
faiss_index = faiss.IndexFlatIP(b.shape[-1])
faiss_index.add(b)
total = 1
total_recall = 0.0
total_ndcg = 0.0
total_hitrate = 0
for batch_id, batch_data in enumerate(test_dataloader()):
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data[:2])),
fetch_list=[var for _, var in fetch_vars.items()])
user_embs = fetch_batch_var[0]
target_items = np.squeeze(np.array(batch_data[-1]), axis=1)
if len(user_embs.shape) == 2:
D, I = faiss_index.search(user_embs, args.top_n)
for i, iid_list in enumerate(target_items):
recall = 0
dcg = 0.0
item_list = set(I[i])
iid_list = list(filter(lambda x: x != 0, list(iid_list)))
for no, iid in enumerate(iid_list):
if iid in item_list:
recall += 1
dcg += 1.0 / math.log(no + 2, 2)
idcg = 0.0
for no in range(recall):
idcg += 1.0 / math.log(no + 2, 2)
total_recall += recall * 1.0 / len(iid_list)
if recall > 0:
total_ndcg += dcg / idcg
total_hitrate += 1
else:
ni = user_embs.shape[1]
user_embs = np.reshape(user_embs, [-1, user_embs.shape[-1]])
D, I = faiss_index.search(user_embs, args.top_n)
for i, iid_list in enumerate(target_items):
recall = 0
dcg = 0.0
item_list_set = set()
item_list = list(
zip(np.reshape(I[i * ni:(i + 1) * ni], -1),
np.reshape(D[i * ni:(i + 1) * ni], -1)))
item_list.sort(key=lambda x: x[1], reverse=True)
for j in range(len(item_list)):
if item_list[j][0] not in item_list_set and item_list[
j][0] != 0:
item_list_set.add(item_list[j][0])
if len(item_list_set) >= args.top_n:
break
iid_list = list(filter(lambda x: x != 0, list(iid_list)))
for no, iid in enumerate(iid_list):
if iid == 0:
break
if iid in item_list_set:
recall += 1
dcg += 1.0 / math.log(no + 2, 2)
idcg = 0.0
for no in range(recall):
idcg += 1.0 / math.log(no + 2, 2)
total_recall += recall * 1.0 / len(iid_list)
if recall > 0:
total_ndcg += dcg / idcg
total_hitrate += 1
total += target_items.shape[0]
if batch_id % print_interval == 0:
recall = total_recall / total
ndcg = total_ndcg / total
hitrate = total_hitrate * 1.0 / total
metric_str = ""
metric_str += "recall@%d: %.5f, " % (args.top_n, recall)
metric_str += "ndcg@%d: %.5f, " % (args.top_n, ndcg)
metric_str += "hitrate@%d: %.5f, " % (args.top_n, hitrate)
logger.info(
"epoch: {}, batch_id: {}, ".format(epoch_id, batch_id) +
metric_str +
"speed: {:.2f} ins/s".format(print_interval * batch_size /
(time.time() -
interval_begin)))
recall = total_recall / total
ndcg = total_ndcg / total
hitrate = total_hitrate * 1.0 / total
metric_str = ""
metric_str += "recall@%d: %.5f, " % (args.top_n, recall)
metric_str += "ndcg@%d: %.5f, " % (args.top_n, ndcg)
metric_str += "hitrate@%d: %.5f, " % (args.top_n, hitrate)
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# modify config from command
if args.opt:
for parameter in args.opt:
parameter = parameter.strip()
key, value = parameter.split("=")
if type(config.get(key)) is int:
value = int(value)
if type(config.get(key)) is float:
value = float(value)
if type(config.get(key)) is bool:
value = (True if value.lower() == "true" else False)
config[key] = value
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_xpu = config.get("runner.use_xpu", False)
use_auc = config.get("runner.use_auc", False)
use_visual = config.get("runner.use_visual", False)
auc_num = config.get("runner.auc_num", 1)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
use_save_data = config.get("runner.use_save_data", False)
reader_type = config.get("runner.reader_type", "DataLoader")
use_fleet = config.get("runner.use_fleet", False)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, use_xpu: {}, use_visual: {}, infer_batch_size: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}"
.format(use_gpu, use_xpu, use_visual, batch_size, test_data_dir,
start_epoch, end_epoch, print_interval, model_load_path))
logger.info("**************common.configs**********")
if use_xpu:
xpu_device = 'xpu:{0}'.format(os.getenv('FLAGS_selected_xpus', 0))
place = paddle.set_device(xpu_device)
else:
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
if reader_type == 'DataLoader':
test_dataloader = create_data_loader(config=config,
place=place,
mode="test")
elif reader_type == "CustomizeDataLoader":
test_dataloader = static_model_class.create_data_loader()
# Create a log_visual object and store the data in the path
if use_visual:
from visualdl import LogWriter
log_visual = LogWriter(args.abs_dir + "/visualDL_log/infer")
step_num = 0
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(paddle.static.default_main_program(),
model_path,
prefix='rec_static')
epoch_begin = time.time()
interval_begin = time.time()
infer_reader_cost = 0.0
infer_run_cost = 0.0
reader_start = time.time()
if use_auc:
reset_auc(use_fleet, auc_num)
#we will drop the last incomplete batch when dataset size is not divisible by the batch size
assert any(
test_dataloader()
), "test_dataloader's size is null, please ensure batch size < dataset size!"
for batch_id, batch_data in enumerate(test_dataloader()):
infer_reader_cost += time.time() - reader_start
infer_start = time.time()
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data)),
fetch_list=[var for _, var in fetch_vars.items()])
infer_run_cost += time.time() - infer_start
if batch_id % print_interval == 0:
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(
var_name, fetch_batch_var[var_idx][0])
if use_visual:
log_visual.add_scalar(
tag="infer/" + var_name,
step=step_num,
value=fetch_batch_var[var_idx][0])
logger.info(
"epoch: {}, batch_id: {}, ".format(epoch_id, batch_id) +
metric_str +
"avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.2f} ins/s"
.format(
infer_reader_cost /
print_interval, (infer_reader_cost + infer_run_cost) /
print_interval, batch_size, print_interval *
batch_size / (time.time() - interval_begin)))
interval_begin = time.time()
infer_reader_cost = 0.0
infer_run_cost = 0.0
reader_start = time.time()
step_num = step_num + 1
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx][0])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
if use_save_data:
save_data(fetch_batch_var, model_load_path)
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
sparse_feature_number = config.get(
"hyper_parameters.sparse_feature_number")
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}".
format(use_gpu, test_data_dir, start_epoch, end_epoch, print_interval,
model_load_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
test_dataloader = create_data_loader(
config=config, place=place, mode="test")
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(
paddle.static.default_main_program(),
model_path,
prefix='rec_static')
accum_num_sum = 0
accum_num = 0
epoch_begin = time.time()
interval_begin = time.time()
for batch_id, batch_data in enumerate(test_dataloader()):
#print(np.array(batch_data[0]))
##b_size = len([dat[0] for dat in batch_data])
#print(b_size)
#wa = np.array([dat[0] for dat in batch_data]).astype(
# "int64").reshape(b_size)
#wb = np.array([dat[1] for dat in batch_data]).astype(
# "int64").reshape(b_size)
#wc = np.array([dat[2] for dat in batch_data]).astype(
# "int64").reshape(b_size)
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed={
"analogy_a": np.array(batch_data[0]),
"analogy_b": np.array(batch_data[1]),
"analogy_c": np.array(batch_data[2]),
"all_label": np.arange(sparse_feature_number)
.reshape(sparse_feature_number).astype("int64")
},
fetch_list=[var for _, var in fetch_vars.items()])
pre = np.array(fetch_batch_var[0])
#pre = pred_idx.numpy()
label = np.array(batch_data[3])
inputs_word = np.array(batch_data[4])
for ii in range(len(label)):
top4 = pre[ii][0]
accum_num_sum += 1
for idx in top4:
if int(idx) in inputs_word[ii]:
continue
if int(idx) == int(label[ii][0]):
accum_num += 1
break
if batch_id % print_interval == 0:
logger.info(
"infer epoch: {}, batch_id: {}, acc: {:.6f}, speed: {:.2f} ins/s".
format(epoch_id, batch_id, accum_num * 1.0 / accum_num_sum,
print_interval * batch_size / (time.time() -
interval_begin)))
interval_begin = time.time()
logger.info("infer epoch: {} done, acc: {:.6f}, : epoch time{:.2f} s".
format(epoch_id, accum_num * 1.0 / accum_num_sum,
time.time() - epoch_begin))
epoch_begin = time.time()
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds()
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.net(input_data)
#infer_target_var = model.infer_target_var
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
static_model_class.create_optimizer()
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
train_data_dir = config.get("runner.train_data_dir", None)
epochs = config.get("runner.epochs", None)
print_interval = config.get("runner.print_interval", None)
model_save_path = config.get("runner.model_save_path", "model_output")
model_init_path = config.get("runner.model_init_path", None)
batch_size = config.get("runner.train_batch_size", None)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}".
format(use_gpu, train_data_dir, epochs, print_interval,
model_save_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
last_epoch_id = config.get("last_epoch", -1)
train_dataloader = create_data_loader(config=config, place=place)
for epoch_id in range(last_epoch_id + 1, epochs):
epoch_begin = time.time()
interval_begin = time.time()
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
if use_auc:
reset_auc()
for batch_id, batch_data in enumerate(train_dataloader()):
train_reader_cost += time.time() - reader_start
train_start = time.time()
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data)),
fetch_list=[var for _, var in fetch_vars.items()])
train_run_cost += time.time() - train_start
total_samples += batch_size
if batch_id % print_interval == 0:
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name,
fetch_batch_var[var_idx])
logger.info(
"epoch: {}, batch_id: {}, ".format(epoch_id,
batch_id) + metric_str +
"avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
format(train_reader_cost / print_interval, (
train_reader_cost + train_run_cost) / print_interval,
total_samples / print_interval, total_samples / (
train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
metric_str = ""
for var_idx, var_name in enumerate(fetch_vars):
metric_str += "{}: {}, ".format(var_name, fetch_batch_var[var_idx])
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
save_static_model(
paddle.static.default_main_program(),
model_save_path,
epoch_id,
prefix='rec_static')
