add resnet50 test for post trainint quantization, test=develop (#21272)

python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

@@ -99,7 +99,7 @@ class PostTrainingQuantization(object):
             params_filename = None
             save_model_path = path/to/save_model_path
             # prepare the sample generator according to the model, and the 
-            # sample generator must return a simple every time. The reference
+            # sample generator must return a sample every time. The reference
             # document: https://www.paddlepaddle.org.cn/documentation/docs/zh
             # /user_guides/howto/prepare_data/use_py_reader.html
             sample_generator = your_sample_generator

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

@@ -48,7 +48,8 @@ endfunction()
 
 if(WIN32)
     list(REMOVE_ITEM TEST_OPS test_light_nas)
-    list(REMOVE_ITEM TEST_OPS test_post_training_quantization)
+    list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
+    list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
 endif()
 
 # int8 image classification python api test

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py

@@ -110,10 +110,9 @@ class TestPostTrainingQuantization(unittest.TestCase):
         self.int8_download = 'int8/download'
         self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
                                                self.int8_download)
-
+        self.data_cache_folder = ''
         data_urls = []
         data_md5s = []
-        self.data_cache_folder = ''
         if os.environ.get('DATASET') == 'full':
             data_urls.append(
                 'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partaa'
@@ -145,7 +144,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
             'DATASET') == 'full' else 1
 
         self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
-        self.int8_model = ''
+        self.int8_model = os.path.join(os.getcwd(),
+                                       "post_training_" + self.timestamp)
 
     def tearDown(self):
         try:
@@ -191,14 +191,14 @@ class TestPostTrainingQuantization(unittest.TestCase):
     def download_model(self):
         pass
 
-    def run_program(self, model_path):
+    def run_program(self, model_path, batch_size, infer_iterations):
         image_shape = [3, 224, 224]
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
         [infer_program, feed_dict, fetch_targets] = \
             fluid.io.load_inference_model(model_path, exe)
-        val_reader = paddle.batch(val(), self.batch_size)
-        iterations = self.infer_iterations
+        val_reader = paddle.batch(val(), batch_size)
+        iterations = infer_iterations
 
         test_info = []
         cnt = 0
@@ -237,8 +237,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
                                  model_path,
                                  algo="KL",
                                  is_full_quantize=False):
-        self.int8_model = os.path.join(os.getcwd(),
-                                       "post_training_" + self.timestamp)
         try:
             os.system("mkdir " + self.int8_model)
         except Exception as e:
@@ -264,52 +262,50 @@ class TestPostTrainingQuantization(unittest.TestCase):
         ptq.quantize()
         ptq.save_quantized_model(self.int8_model)
 
+    def run_test(self, model, algo, data_urls, data_md5s):
+        infer_iterations = self.infer_iterations
+        batch_size = self.batch_size
+        sample_iterations = self.sample_iterations
 
-class TestPostTrainingForMobilenetv1(TestPostTrainingQuantization):
-    def download_model(self):
-        # mobilenetv1 fp32 data
-        data_urls = [
-            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
-        ]
-        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
-        self.model_cache_folder = self.download_data(data_urls, data_md5s,
-                                                     "mobilenetv1_fp32")
-        self.model = "MobileNet-V1"
-        self.algo = "KL"
-
-    def test_post_training_mobilenetv1(self):
-        self.download_model()
+        model_cache_folder = self.download_data(data_urls, data_md5s, model)
 
         print("Start FP32 inference for {0} on {1} images ...".format(
-            self.model, self.infer_iterations * self.batch_size))
-        (fp32_throughput, fp32_latency,
-         fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
+            model, infer_iterations * batch_size))
+        (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program(
+            model_cache_folder + "/model", batch_size, infer_iterations)
 
         print("Start INT8 post training quantization for {0} on {1} images ...".
-              format(self.model, self.sample_iterations * self.batch_size))
+              format(model, sample_iterations * batch_size))
         self.generate_quantized_model(
-            self.model_cache_folder + "/model",
-            algo=self.algo,
-            is_full_quantize=True)
+            model_cache_folder + "/model", algo=algo, is_full_quantize=True)
 
         print("Start INT8 inference for {0} on {1} images ...".format(
-            self.model, self.infer_iterations * self.batch_size))
-        (int8_throughput, int8_latency,
-         int8_acc1) = self.run_program(self.int8_model)
+            model, infer_iterations * batch_size))
+        (int8_throughput, int8_latency, int8_acc1) = self.run_program(
+            self.int8_model, batch_size, infer_iterations)
 
         print(
             "FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
-            format(self.model, self.batch_size, fp32_throughput, fp32_latency,
-                   fp32_acc1))
+            format(model, batch_size, fp32_throughput, fp32_latency, fp32_acc1))
         print(
             "INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
-            format(self.model, self.batch_size, int8_throughput, int8_latency,
-                   int8_acc1))
+            format(model, batch_size, int8_throughput, int8_latency, int8_acc1))
         sys.stdout.flush()
 
         delta_value = fp32_acc1 - int8_acc1
         self.assertLess(delta_value, 0.025)
 
 
+class TestPostTrainingForMobilenetv1(TestPostTrainingQuantization):
+    def test_post_training_mobilenetv1(self):
+        model = "MobileNet-V1"
+        algo = "KL"
+        data_urls = [
+            'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
+        ]
+        data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        self.run_test(model, algo, data_urls, data_md5s)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_resnet50.py

@@ -0,0 +1,32 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import unittest
+from test_post_training_quantization_mobilenetv1 import TestPostTrainingQuantization
+
+
+class TestPostTrainingForResnet50(TestPostTrainingQuantization):
+    def test_post_training_resnet50(self):
+        model = "ResNet-50"
+        algo = "direct"
+        data_urls = [
+            'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz'
+        ]
+        data_md5s = ['4a5194524823d9b76da6e738e1367881']
+        self.run_test(model, algo, data_urls, data_md5s)
+
+
+if __name__ == '__main__':
+    unittest.main()