Merge branch 'dygraph' into dygraph

test/table/eval_table.py → test1/table/eval_table.py

@@ -20,9 +20,9 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 import cv2
 import json
 from tqdm import tqdm
-from test.table.table_metric import TEDS
-from test.table.predict_table import TableSystem
-from test.utility import init_args
+from test1.table.table_metric import TEDS
+from test1.table.predict_table import TableSystem
+from test1.utility import init_args
 from ppocr.utils.logging import get_logger
 
 logger = get_logger()

test/table/predict_structure.py → test1/table/predict_structure.py

@@ -32,7 +32,7 @@ from ppocr.data import create_operators, transform
 from ppocr.postprocess import build_post_process
 from ppocr.utils.logging import get_logger
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
-from test.utility import parse_args
+from test1.utility import parse_args
 
 logger = get_logger()
 

test/table/predict_table.py → test1/table/predict_table.py

@@ -30,9 +30,9 @@ import tools.infer.predict_rec as predict_rec
 import tools.infer.predict_det as predict_det
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
-from test.table.matcher import distance, compute_iou
-from test.utility import parse_args
-import test.table.predict_structure as predict_strture
+from test1.table.matcher import distance, compute_iou
+from test1.utility import parse_args
+import test1.table.predict_structure as predict_strture
 
 logger = get_logger()
 

tools/eval.py

@@ -44,8 +44,15 @@ def main():
     # build model
     # for rec algorithm
     if hasattr(post_process_class, 'character'):
-        config['Architecture']["Head"]['out_channels'] = len(
-            getattr(post_process_class, 'character'))
+        char_num = len(getattr(post_process_class, 'character'))
+        if config['Architecture']["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config['Architecture']["Models"]:
+                config['Architecture']["Models"][key]["Head"][
+                    'out_channels'] = char_num
+        else:  # base rec model
+            config['Architecture']["Head"]['out_channels'] = char_num
+
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
     model_type = config['Architecture']['model_type']

tools/infer/predict_det.py

@@ -31,7 +31,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.data import create_operators, transform
 from ppocr.postprocess import build_post_process
 
-import tools.infer.benchmark_utils as benchmark_utils
+# import tools.infer.benchmark_utils as benchmark_utils
 
 logger = get_logger()
 
@@ -100,8 +100,6 @@ class TextDetector(object):
         self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
             args, 'det', logger)
 
-        self.det_times = utility.Timer()
-
     def order_points_clockwise(self, pts):
         """
         reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
@@ -158,8 +156,8 @@ class TextDetector(object):
     def __call__(self, img):
         ori_im = img.copy()
         data = {'image': img}
-        self.det_times.total_time.start()
-        self.det_times.preprocess_time.start()
+
+        st = time.time()
         data = transform(data, self.preprocess_op)
         img, shape_list = data
         if img is None:
@@ -168,16 +166,12 @@ class TextDetector(object):
         shape_list = np.expand_dims(shape_list, axis=0)
         img = img.copy()
 
-        self.det_times.preprocess_time.end()
-        self.det_times.inference_time.start()
-
         self.input_tensor.copy_from_cpu(img)
         self.predictor.run()
         outputs = []
         for output_tensor in self.output_tensors:
             output = output_tensor.copy_to_cpu()
             outputs.append(output)
-        self.det_times.inference_time.end()
 
         preds = {}
         if self.det_algorithm == "EAST":
@@ -193,8 +187,6 @@ class TextDetector(object):
         else:
             raise NotImplementedError
 
-        self.det_times.postprocess_time.start()
-
         self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
@@ -203,10 +195,8 @@ class TextDetector(object):
         else:
             dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
 
-        self.det_times.postprocess_time.end()
-        self.det_times.total_time.end()
-        self.det_times.img_num += 1
-        return dt_boxes, self.det_times.total_time.value()
+        et = time.time()
+        return dt_boxes, et - st
 
 
 if __name__ == "__main__":
@@ -216,12 +206,13 @@ if __name__ == "__main__":
     count = 0
     total_time = 0
     draw_img_save = "./inference_results"
-    cpu_mem, gpu_mem, gpu_util = 0, 0, 0
 
-    # warmup 10 times
-    fake_img = np.random.uniform(-1, 1, [640, 640, 3]).astype(np.float32)
-    for i in range(10):
-        dt_boxes, _ = text_detector(fake_img)
+    if args.warmup:
+        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
+        for i in range(10):
+            res = text_detector(img)
+
+    cpu_mem, gpu_mem, gpu_util = 0, 0, 0
 
     if not os.path.exists(draw_img_save):
         os.makedirs(draw_img_save)
@@ -239,12 +230,6 @@ if __name__ == "__main__":
             total_time += elapse
         count += 1
 
-        if args.benchmark:
-            cm, gm, gu = utility.get_current_memory_mb(0)
-            cpu_mem += cm
-            gpu_mem += gm
-            gpu_util += gu
-
         logger.info("Predict time of {}: {}".format(image_file, elapse))
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = os.path.split(image_file)[-1]
@@ -252,36 +237,3 @@ if __name__ == "__main__":
                                 "det_res_{}".format(img_name_pure))
 
         logger.info("The visualized image saved in {}".format(img_path))
-    # print the information about memory and time-spent
-    if args.benchmark:
-        mems = {
-            'cpu_rss_mb': cpu_mem / count,
-            'gpu_rss_mb': gpu_mem / count,
-            'gpu_util': gpu_util * 100 / count
-        }
-    else:
-        mems = None
-    logger.info("The predict time about detection module is as follows: ")
-    det_time_dict = text_detector.det_times.report(average=True)
-    det_model_name = args.det_model_dir
-
-    if args.benchmark:
-        # construct log information
-        model_info = {
-            'model_name': args.det_model_dir.split('/')[-1],
-            'precision': args.precision
-        }
-        data_info = {
-            'batch_size': 1,
-            'shape': 'dynamic_shape',
-            'data_num': det_time_dict['img_num']
-        }
-        perf_info = {
-            'preprocess_time_s': det_time_dict['preprocess_time'],
-            'inference_time_s': det_time_dict['inference_time'],
-            'postprocess_time_s': det_time_dict['postprocess_time'],
-            'total_time_s': det_time_dict['total_time']
-        }
-        benchmark_log = benchmark_utils.PaddleInferBenchmark(
-            text_detector.config, model_info, data_info, perf_info, mems)
-        benchmark_log("Det")

tools/infer/predict_rec.py

@@ -257,13 +257,15 @@ def main(args):
     text_recognizer = TextRecognizer(args)
     valid_image_file_list = []
     img_list = []
-    cpu_mem, gpu_mem, gpu_util = 0, 0, 0
-    count = 0
 
     # warmup 10 times
-    fake_img = np.random.uniform(-1, 1, [1, 32, 320, 3]).astype(np.float32)
-    for i in range(10):
-        dt_boxes, _ = text_recognizer(fake_img)
+    if args.warmup:
+        img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
+        for i in range(10):
+            res = text_recognizer([img])
+
+    cpu_mem, gpu_mem, gpu_util = 0, 0, 0
+    count = 0
 
     for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)

tools/infer/predict_system.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
@@ -93,7 +94,6 @@ class TextSystem(object):
         dt_boxes, elapse = self.text_detector(img)
 
         logger.debug("dt_boxes num : {}, elapse : {}".format(
-
             len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
@@ -147,15 +147,24 @@ def sorted_boxes(dt_boxes):
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list[args.process_id::args.total_process_num]
     text_sys = TextSystem(args)
     is_visualize = True
     font_path = args.vis_font_path
     drop_score = args.drop_score
+
+    # warm up 10 times
+    if args.warmup:
+        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
+        for i in range(10):
+            res = text_sys(img)
+            
     total_time = 0
     cpu_mem, gpu_mem, gpu_util = 0, 0, 0
     _st = time.time()
     count = 0
     for idx, image_file in enumerate(image_file_list):
+
         img, flag = check_and_read_gif(image_file)
         if not flag:
             img = cv2.imread(image_file)
@@ -264,4 +273,18 @@ def main(args):
 
 
 if __name__ == "__main__":
-    main(utility.parse_args())
+    args = utility.parse_args()
+    if args.use_mp:
+        p_list = []
+        total_process_num = args.total_process_num
+        for process_id in range(total_process_num):
+            cmd = [sys.executable, "-u"] + sys.argv + [
+                "--process_id={}".format(process_id),
+                "--use_mp={}".format(False)
+            ]
+            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+            p_list.append(p)
+        for p in p_list:
+            p.wait()
+    else:
+        main(args)

tools/infer/utility.py

@@ -105,7 +105,9 @@ def init_args():
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
     parser.add_argument("--cpu_threads", type=int, default=10)
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
+    parser.add_argument("--warmup", type=str2bool, default=True)
 
+    # multi-process
     parser.add_argument("--use_mp", type=str2bool, default=False)
     parser.add_argument("--total_process_num", type=int, default=1)
     parser.add_argument("--process_id", type=int, default=0)
@@ -113,7 +115,6 @@ def init_args():
     parser.add_argument("--benchmark", type=bool, default=False)
     parser.add_argument("--save_log_path", type=str, default="./log_output/")
 
-
     parser.add_argument("--show_log", type=str2bool, default=True)
     return parser