feat(mge/module): add __repr__ method for qat and quantized module

GitOrigin-RevId: 0d78cbab9a93801c62f0c5d079ec20ccc705ff17

imperative/python/megengine/module/qat/module.py

@@ -39,6 +39,9 @@ class QATModule(Module):
         self.weight_fake_quant = None  # type: FakeQuantize
         self.act_fake_quant = None  # type: FakeQuantize
 
+    def __repr__(self):
+        return "QAT." + super().__repr__()
+
     def set_qconfig(self, qconfig: QConfig):
         r"""
         Set quantization related configs with ``qconfig``, including

imperative/python/megengine/module/quantized/module.py

@@ -22,6 +22,9 @@ class QuantizedModule(Module):
             raise ValueError("quantized module only support inference.")
         return super().__call__(*inputs, **kwargs)
 
+    def __repr__(self):
+        return "Quantized." + super().__repr__()
+
     @classmethod
     @abstractmethod
     def from_qat_module(cls, qat_module: QATModule):

imperative/python/test/unit/quantization/test_module.py

+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
 from functools import partial
 
 import numpy as np

imperative/python/test/unit/quantization/test_observer.py

+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
 import platform
 
 import numpy as np

imperative/python/test/unit/quantization/test_op.py

+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
 import numpy as np
 import pytest
 

imperative/python/test/unit/quantization/test_repr.py

+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+import megengine.module as M
+from megengine.quantization import quantize, quantize_qat
+
+
+def test_repr():
+    class Net(M.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv_bn = M.ConvBnRelu2d(3, 3, 3)
+            self.linear = M.Linear(3, 3)
+
+        def forward(self, x):
+            return x
+
+    net = Net()
+    ground_truth = (
+        "Net(\n"
+        "  (conv_bn): ConvBnRelu2d(\n"
+        "    (conv): Conv2d(3, 3, kernel_size=(3, 3))\n"
+        "    (bn): BatchNorm2d(3, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)\n"
+        "  )\n"
+        "  (linear): Linear(in_features=3, out_features=3, bias=True)\n"
+        ")"
+    )
+    assert net.__repr__() == ground_truth
+    quantize_qat(net)
+    ground_truth = (
+        "Net(\n"
+        "  (conv_bn): QAT.ConvBnRelu2d(\n"
+        "    (conv): Conv2d(3, 3, kernel_size=(3, 3))\n"
+        "    (bn): BatchNorm2d(3, eps=1e-05, momentum=0.9, affine=True, track_running_stats=True)\n"
+        "    (act_observer): ExponentialMovingAverageObserver()\n"
+        "    (act_fake_quant): FakeQuantize()\n"
+        "    (weight_observer): MinMaxObserver()\n"
+        "    (weight_fake_quant): FakeQuantize()\n"
+        "  )\n"
+        "  (linear): QAT.Linear(\n"
+        "    in_features=3, out_features=3, bias=True\n"
+        "    (act_observer): ExponentialMovingAverageObserver()\n"
+        "    (act_fake_quant): FakeQuantize()\n"
+        "    (weight_observer): MinMaxObserver()\n"
+        "    (weight_fake_quant): FakeQuantize()\n"
+        "  )\n"
+        ")"
+    )
+    assert net.__repr__() == ground_truth
+    quantize(net)
+    ground_truth = (
+        "Net(\n"
+        "  (conv_bn): Quantized.ConvBnRelu2d(3, 3, kernel_size=(3, 3))\n"
+        "  (linear): Quantized.Linear()\n"
+        ")"
+    )
+    assert net.__repr__() == ground_truth