chore: import upstream snapshot with attribution

test/legacy_test/test_IntermediateLayerGetter.py

@@ -0,0 +1,89 @@
+#   Copyright (c) 2018 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 random
+import unittest
+
+import paddle
+from paddle.vision.models._utils import IntermediateLayerGetter
+
+
+class TestBase:
+    def setUp(self):
+        self.init_model()
+        self.model.eval()
+
+        self.layer_names = [
+            (order, name)
+            for order, (name, _) in enumerate(self.model.named_children())
+        ]
+        # choose two layer children of model randomly
+        self.start, self.end = sorted(
+            random.sample(self.layer_names, 2), key=lambda x: x[0]
+        )
+
+        self.return_layers_dic = {self.start[1]: "feat1", self.end[1]: "feat2"}
+        self.new_model = IntermediateLayerGetter(
+            self.model, self.return_layers_dic
+        )
+
+    def init_model(self):
+        self.model = None
+
+    @paddle.no_grad()
+    def test_inter_result(self):
+        inp = paddle.randn([1, 3, 80, 80])
+        inter_oup = self.new_model(inp)
+
+        for layer_name, layer in self.model.named_children():
+            if (isinstance(layer, paddle.nn.Linear) and inp.ndim == 4) or (
+                len(layer.sublayers()) > 0
+                and isinstance(layer.sublayers()[0], paddle.nn.Linear)
+                and inp.ndim == 4
+            ):
+                inp = paddle.flatten(inp, 1)
+
+            inp = layer(inp)
+            if layer_name in self.return_layers_dic:
+                feat_name = self.return_layers_dic[layer_name]
+                self.assertTrue((inter_oup[feat_name] == inp).all())
+
+
+class TestIntermediateLayerGetterResNet18(TestBase, unittest.TestCase):
+    def init_model(self):
+        self.model = paddle.vision.models.resnet18(pretrained=False)
+
+
+class TestIntermediateLayerGetterDenseNet121(TestBase, unittest.TestCase):
+    def init_model(self):
+        self.model = paddle.vision.models.densenet121(pretrained=False)
+
+
+class TestIntermediateLayerGetterVGG11(TestBase, unittest.TestCase):
+    def init_model(self):
+        self.model = paddle.vision.models.vgg11(pretrained=False)
+
+
+class TestIntermediateLayerGetterMobileNetV3Small(TestBase, unittest.TestCase):
+    def init_model(self):
+        self.model = paddle.vision.models.MobileNetV3Small()
+
+
+class TestIntermediateLayerGetterShuffleNetV2(TestBase, unittest.TestCase):
+    def init_model(self):
+        self.model = paddle.vision.models.shufflenet_v2_x0_25()
+
+
+if __name__ == "__main__":
+    unittest.main()