chore: import upstream snapshot with attribution

test/cinn/test_efficientnet.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2021 CINN 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 paddle import base
+from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget
+from paddle.cinn.frontend import Interpreter
+
+enable_gpu = sys.argv.pop()
+model_dir = sys.argv.pop()
+
+
+class TestLoadEfficientNetModel(unittest.TestCase):
+    def setUp(self):
+        if enable_gpu == "ON":
+            self.target = DefaultNVGPUTarget()
+        else:
+            self.target = DefaultHostTarget()
+        self.model_dir = model_dir
+        self.x_shape = [1, 3, 224, 224]
+        self.target_tensor = 'save_infer_model/scale_0'
+        self.input_tensor = 'image'
+
+    def get_paddle_inference_result(self, model_dir, data):
+        config = base.core.AnalysisConfig(
+            model_dir + '/__model__', model_dir + '/params'
+        )
+        config.disable_gpu()
+        config.switch_ir_optim(False)
+        self.paddle_predictor = base.core.create_paddle_predictor(config)
+        data = base.core.PaddleTensor(data)
+        results = self.paddle_predictor.run([data])
+        get_tensor = self.paddle_predictor.get_output_tensor(
+            self.target_tensor
+        ).copy_to_cpu()
+        return get_tensor
+
+    def apply_test(self):
+        start = time.time()
+        x_data = np.random.random(self.x_shape).astype("float32")
+        self.executor = Interpreter([self.input_tensor], [self.x_shape])
+        print("self.mode_dir is:", self.model_dir)
+        # True means load combined model
+        self.executor.load_paddle_model(self.model_dir, self.target, True)
+        end1 = time.time()
+        print("load_paddle_model time is: %.3f sec" % (end1 - start))
+        a_t = self.executor.get_tensor(self.input_tensor)
+        a_t.from_numpy(x_data, self.target)
+        out = self.executor.get_tensor(self.target_tensor)
+        out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
+        for i in range(10):
+            self.executor.run()
+
+        repeat = 10
+        end4 = time.perf_counter()
+        for i in range(repeat):
+            self.executor.run()
+        end5 = time.perf_counter()
+
+        print(
+            f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms"
+        )
+        a_t.from_numpy(x_data, self.target)
+        out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
+        self.executor.run()
+
+        out = out.numpy(self.target)
+        target_result = self.get_paddle_inference_result(self.model_dir, x_data)
+
+        print("result in test_model: \n")
+        out = out.reshape(-1)
+        target_result = target_result.reshape(-1)
+        for i in range(0, min(out.shape[0], 200)):
+            if np.abs(out[i] - target_result[i]) > 1e-3:
+                print(
+                    "Error! ",
+                    i,
+                    "-th data has diff with target data:\n",
+                    out[i],
+                    " vs: ",
+                    target_result[i],
+                    ". Diff is: ",
+                    out[i] - target_result[i],
+                )
+        np.testing.assert_allclose(out, target_result, atol=1e-3)
+
+    def test_model(self):
+        self.apply_test()
+        # self.target.arch = Target.NVGPUArch()
+        # self.apply_test()
+
+
+if __name__ == "__main__":
+    unittest.main()