use develop PaddleDetection create_feed interface (#3767)

slim/distillation/compress.py

@@ -143,7 +143,7 @@ def main():
 
     # build program
     model = create(main_arch)
-    train_loader, train_feed_vars = create_feed(train_feed, iterable=True)
+    _, train_feed_vars = create_feed(train_feed, False)
     train_fetches = model.train(train_feed_vars)
     loss = train_fetches['loss']
     lr = lr_builder()
@@ -155,7 +155,6 @@ def main():
 
     cfg.max_iters = 258
     train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-    train_loader.set_sample_list_generator(train_reader, place)
 
     exe.run(fluid.default_startup_program())
 
@@ -174,7 +173,7 @@ def main():
     with fluid.program_guard(eval_prog, startup_prog):
         with fluid.unique_name.guard():
             model = create(main_arch)
-            _, test_feed_vars = create_feed(eval_feed, iterable=True)
+            _, test_feed_vars = create_feed(eval_feed, False)
             fetches = model.eval(test_feed_vars)
     eval_prog = eval_prog.clone(True)
 

slim/eval.py

@@ -32,11 +32,13 @@ from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
 from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
 from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
 
+
 def set_paddle_flags(**kwargs):
     for key, value in kwargs.items():
         if os.environ.get(key, None) is None:
             os.environ[key] = str(value)
 
+
 # NOTE(paddle-dev): All of these flags should be set before
 # `import paddle`. Otherwise, it would not take any effect.
 set_paddle_flags(
@@ -59,6 +61,8 @@ import logging
 FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=FORMAT)
 logger = logging.getLogger(__name__)
+
+
 def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
     """
     Run evaluation program, return program outputs.
@@ -71,8 +75,7 @@ def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
     has_bbox = 'bbox' in keys
     for data in reader():
         data = test_feed.feed(data)
-        feed_data = {'image': data['image'],
-                     'im_size': data['im_size']}
+        feed_data = {'image': data['image'], 'im_size': data['im_size']}
         outs = exe.run(compile_program,
                        feed=feed_data,
                        fetch_list=values[0],
@@ -123,7 +126,6 @@ def main():
         devices_num = int(
             os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
 
-
     if 'eval_feed' not in cfg:
         eval_feed = create(main_arch + 'EvalFeed')
     else:
@@ -132,42 +134,40 @@ def main():
     place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
-    _, test_feed_vars = create_feed(eval_feed, iterable=True)
+    _, test_feed_vars = create_feed(eval_feed, False)
 
     eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
     #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
     test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
 
-
     assert os.path.exists(FLAGS.model_path)
     infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
-            dirname=FLAGS.model_path, executor=exe,
-            model_filename=FLAGS.model_name,
-            params_filename=FLAGS.params_name)
+        dirname=FLAGS.model_path,
+        executor=exe,
+        model_filename=FLAGS.model_name,
+        params_filename=FLAGS.params_name)
 
     eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
-    eval_values = ['multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult']
+    eval_values = [
+        'multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult'
+    ]
     eval_cls = []
     eval_values[0] = fetch_targets[0]
 
-    results = eval_run(exe, infer_prog, eval_reader,
-                        eval_keys, eval_values, eval_cls, test_data_feed)
+    results = eval_run(exe, infer_prog, eval_reader, eval_keys, eval_values,
+                       eval_cls, test_data_feed)
 
     resolution = None
     if 'mask' in results[0]:
         resolution = model.mask_head.resolution
-    eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
-            resolution, False, FLAGS.output_eval)
+    eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
+                 False, FLAGS.output_eval)
 
 
 if __name__ == '__main__':
     parser = ArgsParser()
     parser.add_argument(
-        "-m",
-        "--model_path",
-        default=None,
-        type=str,
-        help="path of checkpoint")
+        "-m", "--model_path", default=None, type=str, help="path of checkpoint")
     parser.add_argument(
         "--output_eval",
         default=None,

slim/prune/compress.py

@@ -143,7 +143,7 @@ def main():
     with fluid.program_guard(train_prog, startup_prog):
         with fluid.unique_name.guard():
             model = create(main_arch)
-            train_loader, feed_vars = create_feed(train_feed, iterable=True)
+            _, feed_vars = create_feed(train_feed, False)
             train_fetches = model.train(feed_vars)
             loss = train_fetches['loss']
             lr = lr_builder()
@@ -151,7 +151,6 @@ def main():
             optimizer.minimize(loss)
 
     train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-    train_loader.set_sample_list_generator(train_reader, place)
 
     # parse train fetches
     train_keys, train_values, _ = parse_fetches(train_fetches)
@@ -166,13 +165,12 @@ def main():
     with fluid.program_guard(eval_prog, startup_prog):
         with fluid.unique_name.guard():
             model = create(main_arch)
-            _, test_feed_vars = create_feed(eval_feed, iterable=True)
+            _, test_feed_vars = create_feed(eval_feed, False)
             fetches = model.eval(test_feed_vars)
 
     eval_prog = eval_prog.clone(True)
 
     eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
-    #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
     test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
 
     # parse eval fetches

slim/quantization/compress.py

@@ -151,7 +151,7 @@ def main():
     with fluid.program_guard(train_prog, startup_prog):
         with fluid.unique_name.guard():
             model = create(main_arch)
-            train_loader, feed_vars = create_feed(train_feed, iterable=True)
+            _, feed_vars = create_feed(train_feed, False)
             train_fetches = model.train(feed_vars)
             loss = train_fetches['loss']
             lr = lr_builder()
@@ -159,7 +159,6 @@ def main():
             optimizer.minimize(loss)
 
     train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-    train_loader.set_sample_list_generator(train_reader, place)
 
     # parse train fetches
     train_keys, train_values, _ = parse_fetches(train_fetches)
@@ -174,7 +173,7 @@ def main():
     with fluid.program_guard(eval_prog, startup_prog):
         with fluid.unique_name.guard():
             model = create(main_arch)
-            _, test_feed_vars = create_feed(eval_feed, iterable=True)
+            _, test_feed_vars = create_feed(eval_feed, False)
             fetches = model.eval(test_feed_vars)
     eval_prog = eval_prog.clone(True)
 

slim/quantization/freeze.py

@@ -134,7 +134,7 @@ def main():
     place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
-    _, test_feed_vars = create_feed(eval_feed, iterable=True)
+    _, test_feed_vars = create_feed(eval_feed, False)
 
     eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
     #eval_pyreader.decorate_sample_list_generator(eval_reader, place)