fix auto compress bugs (#1293)

* fix demo

* add unittest

* update unittest

example/auto_compression/semantic_segmentation/run.py

@@ -47,7 +47,7 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
     loader = paddle.io.DataLoader(
         eval_dataset,
         batch_sampler=batch_sampler,
-        num_workers=1,
+        num_workers=0,
         return_list=True, )
 
     total_iters = len(loader)
@@ -137,7 +137,7 @@ def main(args):
         train_dataset,
         places=[place],
         batch_sampler=batch_sampler,
-        num_workers=2,
+        num_workers=0,
         return_list=True,
         worker_init_fn=worker_init_fn)
     train_dataloader = reader_wrapper(train_loader)

paddleslim/auto_compression/compressor.py

@@ -143,6 +143,8 @@ class AutoCompression:
                 self.train_config = TrainConfig(**config.pop('TrainConfig'))
             else:
                 self.train_config = None
+        else:
+            self.train_config = None
         self.strategy_config = extract_strategy_config(config)
 
         # prepare dataloader
@@ -596,7 +598,6 @@ class AutoCompression:
             _logger.info(
                 "==> The ACT compression has been completed and the final model is saved in `{}`".
                 format(final_model_path))
-        os._exit(0)
 
     def single_strategy_compress(self, strategy, config, strategy_idx,
                                  train_config):

paddleslim/quant/post_quant_hpo.py

@@ -144,12 +144,7 @@ def standardization(data):
     """standardization numpy array"""
     mu = np.mean(data, axis=0)
     sigma = np.std(data, axis=0)
-    if isinstance(sigma, list) or isinstance(sigma, np.ndarray):
-        for idx, sig in enumerate(sigma):
-            if sig == 0.:
-                sigma[idx] = 1e-13
-    else:
-        sigma = 1e-13 if sigma == 0. else sigma
+    sigma = 1e-13 if sigma == 0. else sigma
     return (data - mu) / sigma
 
 
@@ -246,15 +241,19 @@ def eval_quant_model():
         if have_invalid_num(out_float) or have_invalid_num(out_quant):
             continue
 
-        out_float_list.append(list(out_float))
-        out_quant_list.append(list(out_quant))
+        try:
+            if len(out_float) > 3:
+                out_float = standardization(out_float)
+                out_quant = standardization(out_quant)
+        except:
+            continue
+        out_float_list.append(out_float)
+        out_quant_list.append(out_quant)
         valid_data_num += 1
 
         if valid_data_num >= max_eval_data_num:
             break
 
-    out_float_list = standardization(out_float_list)
-    out_quant_list = standardization(out_quant_list)
     emd_sum = cal_emd_lose(out_float_list, out_quant_list,
                            out_len_sum / float(valid_data_num))
     _logger.info("output diff: {}".format(emd_sum))

tests/act/test_demo.py

+import os
+import sys
+import unittest
+sys.path.append("../../")
+import paddle
+from PIL import Image
+from paddle.vision.datasets import DatasetFolder
+from paddle.vision.transforms import transforms
+from paddleslim.auto_compression import AutoCompression
+paddle.enable_static()
+
+
+class ImageNetDataset(DatasetFolder):
+    def __init__(self, path, image_size=224):
+        super(ImageNetDataset, self).__init__(path)
+        normalize = transforms.Normalize(
+            mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
+        self.transform = transforms.Compose([
+            transforms.Resize(256), transforms.CenterCrop(image_size),
+            transforms.Transpose(), normalize
+        ])
+
+    def __getitem__(self, idx):
+        img_path, _ = self.samples[idx]
+        return self.transform(Image.open(img_path).convert('RGB'))
+
+    def __len__(self):
+        return len(self.samples)
+
+
+class ACTDemo(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        super(ACTDemo, self).__init__(*args, **kwargs)
+        os.system(
+            'wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar'
+        )
+        os.system('tar -xf MobileNetV1_infer.tar')
+        os.system(
+            'wget https://sys-p0.bj.bcebos.com/slim_ci/ILSVRC2012_data_demo.tar.gz'
+        )
+        os.system('tar -xf ILSVRC2012_data_demo.tar.gz')
+
+    def test_demo(self):
+        train_dataset = ImageNetDataset(
+            "./ILSVRC2012_data_demo/ILSVRC2012/train/")
+        image = paddle.static.data(
+            name='inputs', shape=[None] + [3, 224, 224], dtype='float32')
+        train_loader = paddle.io.DataLoader(
+            train_dataset, feed_list=[image], batch_size=32, return_list=False)
+
+        ac = AutoCompression(
+            model_dir="./MobileNetV1_infer",
+            model_filename="inference.pdmodel",
+            params_filename="inference.pdiparams",
+            save_dir="MobileNetV1_quant",
+            config={
+                'Quantization': {},
+                "HyperParameterOptimization": {
+                    'ptq_algo': ['avg'],
+                    'max_quant_count': 3
+                }
+            },
+            train_dataloader=train_loader,
+            eval_dataloader=train_loader)
+        ac.compress()
+
+
+if __name__ == '__main__':
+    unittest.main()