fix issue on windows (#2848)

* fix gpu detection issue on windows
1. use the fluid.core.get_cuda_device_count() to decide the GPU number
2. ignore the path missing issue

PaddleCV/image_classification/reader_cv2.py

@@ -207,16 +207,14 @@ def process_image(sample,
         return (img, )
 
 
-def image_mapper(**kwargs):
-    """ image_mapper """
-    return functools.partial(process_image, **kwargs)
-
-
 def process_batch_data(input_data, settings, mode, color_jitter, rotate):
     batch_data = []
     for sample in input_data:
-        batch_data.append(
-            process_image(sample, settings, mode, color_jitter, rotate))
+        if os.path.isfile(sample[0]):
+            batch_data.append(
+                process_image(sample, settings, mode, color_jitter, rotate))
+        else:
+            print("File not exist : %s" % sample[0])
     return batch_data
 
 

PaddleCV/image_classification/train.py

@@ -338,13 +338,9 @@ def build_program(is_train, main_prog, startup_prog, args):
 
 def get_device_num():
     # NOTE(zcd): for multi-processe training, each process use one GPU card.
-    if num_trainers > 1 : return 1
-    visible_device = os.environ.get('CUDA_VISIBLE_DEVICES', None)
-    if visible_device:
-        device_num = len(visible_device.split(','))
-    else:
-        device_num = subprocess.check_output(['nvidia-smi','-L']).decode().count('\n')
-    return device_num
+    if num_trainers > 1:
+        return 1
+    return fluid.core.get_cuda_device_count()
 
 def train(args):
     # parameters from arguments