From f4b169322d5e00141acddc53f15452ca31dfcce3 Mon Sep 17 00:00:00 2001
From: Megvii Engine Team <megengine@megvii.com>
Date: Sat, 10 Oct 2020 11:15:50 +0800
Subject: [PATCH] test(mgb/imperative): add adaptive pooling pytest

GitOrigin-RevId: c4dfed1f8047b3c6c6cca428e9790b5a2cff0b4a
---
 .../test/integration/test_correctness.py      | 61 ++++++++++++++++---
 1 file changed, 54 insertions(+), 7 deletions(-)

diff --git a/imperative/python/test/integration/test_correctness.py b/imperative/python/test/integration/test_correctness.py
index d33bde89e..41db93425 100644
--- a/imperative/python/test/integration/test_correctness.py
+++ b/imperative/python/test/integration/test_correctness.py
@@ -19,9 +19,17 @@ import megengine.autodiff as ad
 import megengine.functional as F
 from megengine import jit
 from megengine.core._trace_option import set_tensor_shape
+from megengine.core.tensor.utils import make_shape_tuple
 from megengine.functional.debug_param import set_conv_execution_strategy
 from megengine.jit import SublinearMemoryConfig
-from megengine.module import AvgPool2d, BatchNorm2d, Conv2d, Linear, Module
+from megengine.module import (
+    AdaptiveAvgPool2d,
+    AvgPool2d,
+    BatchNorm2d,
+    Conv2d,
+    Linear,
+    Module,
+)
 from megengine.optimizer import SGD
 from megengine.tensor import Tensor
 
@@ -57,10 +65,13 @@ def get_xpu_name():
 
 
 class MnistNet(Module):
-    def __init__(self, has_bn=False):
+    def __init__(self, has_bn=False, use_adaptive_pooling=False):
         super().__init__()
         self.conv0 = Conv2d(1, 20, kernel_size=5, bias=True)
-        self.pool0 = AvgPool2d(2)
+        if use_adaptive_pooling:
+            self.pool0 = AdaptiveAvgPool2d(12)
+        else:
+            self.pool0 = AvgPool2d(2)
         self.conv1 = Conv2d(20, 20, kernel_size=5, bias=True)
         self.pool1 = AvgPool2d(2)
         self.fc0 = Linear(20 * 4 * 4, 500, bias=True)
@@ -134,7 +145,12 @@ def update_model(model_path):
 
 
 def run_train(
-    model_path, use_jit, use_symbolic, sublinear_memory_config=None, max_err=None,
+    model_path,
+    use_jit,
+    use_symbolic,
+    sublinear_memory_config=None,
+    max_err=None,
+    use_adaptive_pooling=False,
 ):
 
     """
@@ -146,7 +162,7 @@ def run_train(
     Please think twice before you do so.
 
     """
-    net = MnistNet(has_bn=True)
+    net = MnistNet(has_bn=True, use_adaptive_pooling=use_adaptive_pooling)
     checkpoint = mge.load(model_path)
     net.load_state_dict(checkpoint["net_init"])
     lr = checkpoint["sgd_lr"]
@@ -181,7 +197,11 @@ def run_train(
 
 
 def run_eval(
-    model_path, use_symbolic, sublinear_memory_config=None, max_err=None,
+    model_path,
+    use_symbolic,
+    sublinear_memory_config=None,
+    max_err=None,
+    use_adaptive_pooling=False,
 ):
 
     """
@@ -193,7 +213,7 @@ def run_eval(
     Please think twice before you do so.
 
     """
-    net = MnistNet(has_bn=True)
+    net = MnistNet(has_bn=True, use_adaptive_pooling=use_adaptive_pooling)
     checkpoint = mge.load(model_path)
     net.load_state_dict(checkpoint["net_init"])
 
@@ -231,3 +251,30 @@ def test_correctness():
 
     run_eval(model_path, False, max_err=1e-7)
     run_eval(model_path, True, max_err=1e-7)
+
+
+def test_correctness_use_adaptive_pooling():
+    if mge.is_cuda_available():
+        model_name = "mnist_model_with_test.mge"
+    else:
+        model_name = "mnist_model_with_test_cpu.mge"
+    model_path = os.path.join(os.path.dirname(__file__), model_name)
+    set_conv_execution_strategy("HEURISTIC_REPRODUCIBLE")
+
+    run_train(model_path, False, False, max_err=1e-5, use_adaptive_pooling=True)
+    run_train(model_path, True, False, max_err=1e-5, use_adaptive_pooling=True)
+    run_train(model_path, True, True, max_err=1e-5, use_adaptive_pooling=True)
+
+    # sublinear
+    config = SublinearMemoryConfig(genetic_nr_iter=10)
+    run_train(
+        model_path,
+        True,
+        True,
+        sublinear_memory_config=config,
+        max_err=1e-5,
+        use_adaptive_pooling=True,
+    )
+
+    run_eval(model_path, False, max_err=1e-7, use_adaptive_pooling=True)
+    run_eval(model_path, True, max_err=1e-7, use_adaptive_pooling=True)
-- 
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