fix gpu num check bug (#4406)

PaddleCV/metric_learning/train_elem.py

@@ -63,15 +63,15 @@ add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job
 
 model_list = [m for m in dir(models) if "__" not in m]
 
+
 def optimizer_setting(params):
     ls = params["learning_strategy"]
     assert ls["name"] == "piecewise_decay", \
-           "learning rate strategy must be {}, \
-           but got {}".format("piecewise_decay", lr["name"])
+           "learning rate strategy must be {}, but got {}".format("piecewise_decay", lr["name"])
 
     bd = [int(e) for e in ls["lr_steps"].split(',')]
     base_lr = params["lr"]
-    lr = [base_lr * (0.1 ** i) for i in range(len(bd) + 1)]
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
     optimizer = fluid.optimizer.Momentum(
         learning_rate=fluid.layers.piecewise_decay(
             boundaries=bd, values=lr),
@@ -81,30 +81,28 @@ def optimizer_setting(params):
 
 
 def net_config(image, label, model, args, is_train):
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
 
     out = model.net(input=image, embedding_size=args.embedding_size)
     if not is_train:
         return None, None, None, out
 
     if args.loss_name == "softmax":
-        metricloss = SoftmaxLoss(
-                class_dim=args.class_dim,
-        )
+        metricloss = SoftmaxLoss(class_dim=args.class_dim, )
     elif args.loss_name == "arcmargin":
         metricloss = ArcMarginLoss(
-                class_dim = args.class_dim,
-                margin = args.arc_margin,
-                scale = args.arc_scale,
-                easy_margin = args.arc_easy_margin,
-        )
+            class_dim=args.class_dim,
+            margin=args.arc_margin,
+            scale=args.arc_scale,
+            easy_margin=args.arc_easy_margin, )
     cost, logit = metricloss.loss(out, label)
     avg_cost = fluid.layers.mean(x=cost)
     acc_top1 = fluid.layers.accuracy(input=logit, label=label, k=1)
     acc_top5 = fluid.layers.accuracy(input=logit, label=label, k=5)
     return avg_cost, acc_top1, acc_top5, out
 
+
 def build_program(is_train, main_prog, startup_prog, args):
     image_shape = [int(m) for m in args.image_shape.split(",")]
     model = models.__dict__[args.model]()
@@ -119,11 +117,13 @@ def build_program(is_train, main_prog, startup_prog, args):
                 use_double_buffer=True)
             image, label = fluid.layers.read_file(py_reader)
         else:
-            image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+            image = fluid.layers.data(
+                name='image', shape=image_shape, dtype='float32')
             label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
         with fluid.unique_name.guard():
-            avg_cost, acc_top1, acc_top5, out = net_config(image, label, model, args, is_train)
+            avg_cost, acc_top1, acc_top5, out = net_config(image, label, model,
+                                                           args, is_train)
             if is_train:
                 params = model.params
                 params["lr"] = args.lr
@@ -175,7 +175,9 @@ def train_async(args):
         args=args)
     test_prog = tmp_prog.clone(for_test=True)
 
-    train_fetch_list = [global_lr.name, train_cost.name, train_acc1.name, train_acc5.name]
+    train_fetch_list = [
+        global_lr.name, train_cost.name, train_acc1.name, train_acc5.name
+    ]
     test_fetch_list = [test_feas.name]
 
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
@@ -196,13 +198,18 @@ def train_async(args):
         fluid.io.load_vars(
             exe, pretrained_model, main_program=train_prog, predicate=if_exist)
 
+    if args.use_gpu:
         devicenum = get_gpu_num()
+    else:
+        devicenum = int(os.environ.get('CPU_NUM', 1))
     assert (args.train_batch_size % devicenum) == 0
     train_batch_size = args.train_batch_size // devicenum
     test_batch_size = args.test_batch_size
 
-    train_reader = paddle.batch(reader.train(args), batch_size=train_batch_size, drop_last=True)
-    test_reader = paddle.batch(reader.test(args), batch_size=test_batch_size, drop_last=False)
+    train_reader = paddle.batch(
+        reader.train(args), batch_size=train_batch_size, drop_last=True)
+    test_reader = paddle.batch(
+        reader.test(args), batch_size=test_batch_size, drop_last=False)
     test_feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
     train_py_reader.decorate_paddle_reader(train_reader)
 
@@ -244,7 +251,9 @@ def train_async(args):
             f, l = [], []
             for batch_id, data in enumerate(test_reader()):
                 t1 = time.time()
-                [feas] = exe.run(test_prog, fetch_list = test_fetch_list, feed=test_feeder.feed(data))
+                [feas] = exe.run(test_prog,
+                                 fetch_list=test_fetch_list,
+                                 feed=test_feeder.feed(data))
                 label = np.asarray([x[1] for x in data])
                 f.append(feas)
                 l.append(label)
@@ -285,10 +294,10 @@ def initlogging():
     logging.basicConfig(
         level=loglevel,
         # logger.BASIC_FORMAT,
-        format=
-        "%(levelname)s:%(filename)s[%(lineno)s] %(name)s:%(funcName)s->%(message)s",
+        format="%(levelname)s:%(filename)s[%(lineno)s] %(name)s:%(funcName)s->%(message)s",
         datefmt='%a, %d %b %Y %H:%M:%S')
 
+
 def main():
     args = parser.parse_args()
     print_arguments(args)

PaddleCV/metric_learning/train_pair.py

@@ -64,15 +64,15 @@ add_arg('npairs_reg_lambda', float, 0.01, "npairs reg lambda.")
 
 model_list = [m for m in dir(models) if "__" not in m]
 
+
 def optimizer_setting(params):
     ls = params["learning_strategy"]
     assert ls["name"] == "piecewise_decay", \
-           "learning rate strategy must be {}, \
-           but got {}".format("piecewise_decay", lr["name"])
+           "learning rate strategy must be {}, but got {}".format("piecewise_decay", lr["name"])
 
     bd = [int(e) for e in ls["lr_steps"].split(',')]
     base_lr = params["lr"]
-    lr = [base_lr * (0.1 ** i) for i in range(len(bd) + 1)]
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
     optimizer = fluid.optimizer.Momentum(
         learning_rate=fluid.layers.piecewise_decay(
             boundaries=bd, values=lr),
@@ -82,38 +82,34 @@ def optimizer_setting(params):
 
 
 def net_config(image, label, model, args, is_train):
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
 
     out = model.net(input=image, embedding_size=args.embedding_size)
     if not is_train:
         return None, out
 
     if args.loss_name == "triplet":
-        metricloss = TripletLoss(
-                margin=args.margin,
-        )
+        metricloss = TripletLoss(margin=args.margin, )
     elif args.loss_name == "quadruplet":
         metricloss = QuadrupletLoss(
-                train_batch_size = args.train_batch_size,
-                samples_each_class = args.samples_each_class,
-                margin=args.margin,
-        )
+            train_batch_size=args.train_batch_size,
+            samples_each_class=args.samples_each_class,
+            margin=args.margin, )
     elif args.loss_name == "eml":
         metricloss = EmlLoss(
-                train_batch_size = args.train_batch_size,
-                samples_each_class = args.samples_each_class,
-        )
+            train_batch_size=args.train_batch_size,
+            samples_each_class=args.samples_each_class, )
     elif args.loss_name == "npairs":
         metricloss = NpairsLoss(
-                train_batch_size = args.train_batch_size,
-                samples_each_class = args.samples_each_class,
-                reg_lambda = args.npairs_reg_lambda,
-        )
+            train_batch_size=args.train_batch_size,
+            samples_each_class=args.samples_each_class,
+            reg_lambda=args.npairs_reg_lambda, )
     cost = metricloss.loss(out, label)
     avg_cost = fluid.layers.mean(x=cost)
     return avg_cost, out
 
+
 def build_program(is_train, main_prog, startup_prog, args):
     image_shape = [int(m) for m in args.image_shape.split(",")]
     model = models.__dict__[args.model]()
@@ -128,7 +124,8 @@ def build_program(is_train, main_prog, startup_prog, args):
                 use_double_buffer=True)
             image, label = fluid.layers.read_file(py_reader)
         else:
-            image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+            image = fluid.layers.data(
+                name='image', shape=image_shape, dtype='float32')
             label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
         with fluid.unique_name.guard():
@@ -176,7 +173,9 @@ def train_async(args):
         args=args)
     test_prog = tmp_prog.clone(for_test=True)
 
-    train_fetch_list = [global_lr.name, train_cost.name, train_feas.name, train_label.name]
+    train_fetch_list = [
+        global_lr.name, train_cost.name, train_feas.name, train_label.name
+    ]
     test_fetch_list = [test_feas.name]
 
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
@@ -197,13 +196,18 @@ def train_async(args):
         fluid.io.load_vars(
             exe, pretrained_model, main_program=train_prog, predicate=if_exist)
 
+    if args.use_gpu:
         devicenum = get_gpu_num()
+    else:
+        devicenum = int(os.environ.get('CPU_NUM', 1))
     assert (args.train_batch_size % devicenum) == 0
     train_batch_size = args.train_batch_size / devicenum
     test_batch_size = args.test_batch_size
 
-    train_reader = paddle.batch(reader.train(args), batch_size=train_batch_size, drop_last=True)
-    test_reader = paddle.batch(reader.test(args), batch_size=test_batch_size, drop_last=False)
+    train_reader = paddle.batch(
+        reader.train(args), batch_size=train_batch_size, drop_last=True)
+    test_reader = paddle.batch(
+        reader.test(args), batch_size=test_batch_size, drop_last=False)
     test_feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
     train_py_reader.decorate_paddle_reader(train_reader)
 
@@ -243,7 +247,9 @@ def train_async(args):
             f, l = [], []
             for batch_id, data in enumerate(test_reader()):
                 t1 = time.time()
-                [feas] = exe.run(test_prog, fetch_list = test_fetch_list, feed=test_feeder.feed(data))
+                [feas] = exe.run(test_prog,
+                                 fetch_list=test_fetch_list,
+                                 feed=test_feeder.feed(data))
                 label = np.asarray([x[1] for x in data])
                 f.append(feas)
                 l.append(label)
@@ -270,6 +276,7 @@ def train_async(args):
 
         iter_no += 1
 
+
 def initlogging():
     for handler in logging.root.handlers[:]:
         logging.root.removeHandler(handler)
@@ -277,10 +284,10 @@ def initlogging():
     logging.basicConfig(
         level=loglevel,
         # logger.BASIC_FORMAT,
-        format=
-        "%(levelname)s:%(filename)s[%(lineno)s] %(name)s:%(funcName)s->%(message)s",
+        format="%(levelname)s:%(filename)s[%(lineno)s] %(name)s:%(funcName)s->%(message)s",
         datefmt='%a, %d %b %Y %H:%M:%S')
 
+
 def main():
     args = parser.parse_args()
     print_arguments(args)