chore: import upstream snapshot with attribution

test/quantization/test_post_training_quantization_resnet50.py

@@ -0,0 +1,153 @@
+# 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 time
+import unittest
+
+import numpy as np
+from test_post_training_quantization_mobilenetv1 import (
+    TestPostTrainingQuantization,
+    val,
+)
+
+import paddle
+
+paddle.enable_static()
+
+
+class TestPostTrainingForResnet50(TestPostTrainingQuantization):
+    def test_post_training_resnet50(self):
+        model = "ResNet-50"
+        algo = "min_max"
+        round_type = "round"
+        data_urls = [
+            'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model_combined.tar.gz'
+        ]
+        data_md5s = ['db212fd4e9edc83381aef4533107e60c']
+        quantizable_op_type = ["conv2d", "mul"]
+        is_full_quantize = False
+        is_use_cache_file = False
+        is_optimize_model = False
+        diff_threshold = 0.025
+        self.run_test(
+            model,
+            'model.pdmodel',
+            'model.pdiparams',
+            algo,
+            round_type,
+            data_urls,
+            data_md5s,
+            "model",
+            quantizable_op_type,
+            is_full_quantize,
+            is_use_cache_file,
+            is_optimize_model,
+            diff_threshold,
+        )
+
+    def run_program(
+        self,
+        model_path,
+        model_filename,
+        params_filename,
+        batch_size,
+        infer_iterations,
+    ):
+        image_shape = [3, 224, 224]
+        place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        [
+            infer_program,
+            feed_dict,
+            fetch_targets,
+        ] = paddle.static.load_inference_model(
+            model_path,
+            exe,
+            model_filename=model_filename,
+            params_filename=params_filename,
+        )
+        val_reader = paddle.batch(val(), batch_size)
+        iterations = infer_iterations
+
+        test_info = []
+        cnt = 0
+        periods = []
+        for batch_id, data in enumerate(val_reader()):
+            image = np.array([x[0].reshape(image_shape) for x in data]).astype(
+                "float32"
+            )
+            label = np.array([x[1] for x in data]).astype("int64")
+            label = label.reshape([-1, 1])
+
+            t1 = time.time()
+            _, acc1, _ = exe.run(
+                infer_program,
+                feed={feed_dict[0]: image, feed_dict[1]: label},
+                fetch_list=fetch_targets,
+            )
+            t2 = time.time()
+            period = t2 - t1
+            periods.append(period)
+
+            test_info.append(np.mean(acc1) * len(data))
+            cnt += len(data)
+
+            if (batch_id + 1) % 100 == 0:
+                print(f"{batch_id + 1} images,")
+                sys.stdout.flush()
+            if (batch_id + 1) == iterations:
+                break
+
+        throughput = cnt / np.sum(periods)
+        latency = np.average(periods)
+        acc1 = np.sum(test_info) / cnt
+        return (throughput, latency, acc1, feed_dict)
+
+
+class TestPostTrainingForResnet50ONNXFormat(TestPostTrainingForResnet50):
+    def test_post_training_resnet50(self):
+        model = "ResNet-50"
+        algo = "min_max"
+        round_type = "round"
+        data_urls = [
+            'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model_combined.tar.gz'
+        ]
+        data_md5s = ['db212fd4e9edc83381aef4533107e60c']
+        quantizable_op_type = ["conv2d", "mul"]
+        is_full_quantize = False
+        is_use_cache_file = False
+        is_optimize_model = False
+        diff_threshold = 0.025
+        onnx_format = True
+        self.run_test(
+            model,
+            'model.pdmodel',
+            'model.pdiparams',
+            algo,
+            round_type,
+            data_urls,
+            data_md5s,
+            "model",
+            quantizable_op_type,
+            is_full_quantize,
+            is_use_cache_file,
+            is_optimize_model,
+            diff_threshold,
+            onnx_format=onnx_format,
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()