update infer.py

deploy/python/infer.py

@@ -33,6 +33,7 @@ gflags.DEFINE_boolean("use_pr", default=False, help="Use optimized model")
 gflags.DEFINE_string("trt_mode", default="", help="Use optimized model")
 gflags.FLAGS = gflags.FLAGS
 
+
 # Generate ColorMap for visualization
 def generate_colormap(num_classes):
     color_map = num_classes * [0, 0, 0]
@@ -45,9 +46,10 @@ def generate_colormap(num_classes):
             color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
             j += 1
             lab >>= 3
-    color_map = [color_map[i:i+3] for i in range(0, len(color_map), 3)]
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
     return color_map
 
+
 # Paddle-TRT Precision Map
 trt_precision_map = {
     "int8": fluid.core.AnalysisConfig.Precision.Int8,
@@ -55,6 +57,7 @@ trt_precision_map = {
     "fp16": fluid.core.AnalysisConfig.Precision.Half
 }
 
+
 # scan a directory and get all images with support extensions
 def get_images_from_dir(img_dir, support_ext=".jpg|.jpeg"):
     if (not os.path.exists(img_dir) or not os.path.isdir(img_dir)):
@@ -67,6 +70,7 @@ def get_images_from_dir(img_dir, support_ext=".jpg|.jpeg"):
             imgs.append(item_path)
     return imgs
 
+
 # Deploy Configuration File Parser
 class DeployConfig:
     def __init__(self, conf_file):
@@ -77,7 +81,8 @@ class DeployConfig:
             configs = yaml.load(fp, Loader=yaml.FullLoader)
             deploy_conf = configs["DEPLOY"]
             # 1. get eval_crop_size
-            self.eval_crop_size = ast.literal_eval(deploy_conf["EVAL_CROP_SIZE"])
+            self.eval_crop_size = ast.literal_eval(
+                deploy_conf["EVAL_CROP_SIZE"])
             # 2. get mean
             self.mean = deploy_conf["MEAN"]
             # 3. get std
@@ -85,10 +90,10 @@ class DeployConfig:
             # 4. get class_num
             self.class_num = deploy_conf["NUM_CLASSES"]
             # 5. get paddle model and params file path
-            self.model_file = os.path.join(
-                deploy_conf["MODEL_PATH"], deploy_conf["MODEL_FILENAME"])
-            self.param_file = os.path.join(
-                deploy_conf["MODEL_PATH"], deploy_conf["PARAMS_FILENAME"])
+            self.model_file = os.path.join(deploy_conf["MODEL_PATH"],
+                                           deploy_conf["MODEL_FILENAME"])
+            self.param_file = os.path.join(deploy_conf["MODEL_PATH"],
+                                           deploy_conf["PARAMS_FILENAME"])
             # 6. use_gpu
             self.use_gpu = deploy_conf["USE_GPU"]
             # 7. predictor_mode
@@ -98,6 +103,7 @@ class DeployConfig:
             # 9. channels
             self.channels = deploy_conf["CHANNELS"]
 
+
 class ImageReader:
     def __init__(self, configs):
         self.config = configs
@@ -133,7 +139,7 @@ class ImageReader:
             im = im[:, :, :].astype('float32') / 255.0
             im -= im_mean
             im /= im_std
-        im = im[np.newaxis,:,:,:]
+        im = im[np.newaxis, :, :, :]
         info = [image_path, im, (ori_w, ori_h)]
         return info
 
@@ -141,12 +147,15 @@ class ImageReader:
     def process(self, imgs, use_pr=False):
         imgs_data = []
         with ThreadPoolExecutor(max_workers=self.config.batch_size) as exec:
-            tasks = [exec.submit(self.process_worker, imgs, idx, use_pr)
-                        for idx in range(len(imgs))]
+            tasks = [
+                exec.submit(self.process_worker, imgs, idx, use_pr)
+                for idx in range(len(imgs))
+            ]
         for task in as_completed(tasks):
             imgs_data.append(task.result())
         return imgs_data
 
+
 class Predictor:
     def __init__(self, conf_file):
         self.config = DeployConfig(conf_file)
@@ -168,7 +177,7 @@ class Predictor:
                     precision_type = trt_precision_map[gflags.FLAGS.trt_mode]
                     use_calib = (gflags.FLAGS.trt_mode == "int8")
                     predictor_config.enable_tensorrt_engine(
-                        workspace_size=1<<30,
+                        workspace_size=1 << 30,
                         max_batch_size=self.config.batch_size,
                         min_subgraph_size=40,
                         precision_mode=precision_type,
@@ -184,15 +193,15 @@ class Predictor:
         im_tensor = fluid.core.PaddleTensor()
         im_tensor.name = "image"
         if not use_pr:
-            im_tensor.shape = [batch_size,
-                               self.config.channels,
-                               self.config.eval_crop_size[1],
-                               self.config.eval_crop_size[0]]
+            im_tensor.shape = [
+                batch_size, self.config.channels, self.config.eval_crop_size[1],
+                self.config.eval_crop_size[0]
+            ]
         else:
-            im_tensor.shape = [batch_size,
-                               self.config.eval_crop_size[1],
-                               self.config.eval_crop_size[0],
-                               self.config.channels]
+            im_tensor.shape = [
+                batch_size, self.config.eval_crop_size[1],
+                self.config.eval_crop_size[0], self.config.channels
+            ]
         im_tensor.dtype = fluid.core.PaddleDType.FLOAT32
         im_tensor.data = fluid.core.PaddleBuf(inputs.ravel().astype("float32"))
         return [im_tensor]
@@ -225,8 +234,12 @@ class Predictor:
             vis_result_name = img_name_fix + "_result.png"
             result_png = score_png
             # if not use_pr:
-            result_png = cv2.resize(result_png, ori_shape, fx=0, fy=0,
-                                    interpolation=cv2.INTER_CUBIC)
+            result_png = cv2.resize(
+                result_png,
+                ori_shape,
+                fx=0,
+                fy=0,
+                interpolation=cv2.INTER_CUBIC)
             cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1])
             print("save result of [" + img_name + "] done.")
 
@@ -248,7 +261,8 @@ class Predictor:
             if i + batch_size >= len(images):
                 real_batch_size = len(images) - i
             reader_start = time.time()
-            img_datas = self.image_reader.process(images[i: i + real_batch_size])
+            img_datas = self.image_reader.process(images[i:i + real_batch_size],
+                                                  gflags.FLAGS.use_pr)
             input_data = np.concatenate([item[1] for item in img_datas])
             input_data = self.create_tensor(
                 input_data, real_batch_size, use_pr=gflags.FLAGS.use_pr)
@@ -268,15 +282,17 @@ class Predictor:
         total_end = time.time()
         # compute whole processing time
         total_runtime = (total_end - total_start)
-        print("images_num=[%d],preprocessing_time=[%f],infer_time=[%f],postprocessing_time=[%f],total_runtime=[%f]"
-              % (len(images), reader_time, infer_time, post_time, total_runtime))
+        print(
+            "images_num=[%d],preprocessing_time=[%f],infer_time=[%f],postprocessing_time=[%f],total_runtime=[%f]"
+            % (len(images), reader_time, infer_time, post_time, total_runtime))
+
 
 def run(deploy_conf, imgs_dir, support_extensions=".jpg|.jpeg"):
     # 1. scan and get all images with valid extensions in directory imgs_dir
     imgs = get_images_from_dir(imgs_dir)
     if len(imgs) == 0:
-        print("No Image (with extensions : %s) found in [%s]"
-              % (support_extensions, imgs_dir))
+        print("No Image (with extensions : %s) found in [%s]" %
+              (support_extensions, imgs_dir))
         return -1
     # 2. create a predictor
     seg_predictor = Predictor(deploy_conf)
@@ -284,17 +300,19 @@ def run(deploy_conf, imgs_dir, support_extensions=".jpg|.jpeg"):
     seg_predictor.predict(imgs)
     return 0
 
+
 if __name__ == "__main__":
     # 0. parse the arguments
     gflags.FLAGS(sys.argv)
     if (gflags.FLAGS.conf == "" or gflags.FLAGS.input_dir == ""):
-        print("Usage: python infer.py --conf=/config/path/to/your/model "
-              +"--input_dir=/directory/of/your/input/images [--use_pr=True]")
+        print("Usage: python infer.py --conf=/config/path/to/your/model " +
+              "--input_dir=/directory/of/your/input/images [--use_pr=True]")
         exit(-1)
     # set empty to turn off as default
     trt_mode = gflags.FLAGS.trt_mode
     if (trt_mode != "" and trt_mode not in trt_precision_map):
-        print("Invalid trt_mode [%s], only support[int8, fp16, fp32]" % trt_mode)
+        print(
+            "Invalid trt_mode [%s], only support[int8, fp16, fp32]" % trt_mode)
         exit(-1)
     # run inference
     run(gflags.FLAGS.conf, gflags.FLAGS.input_dir)