move some of `test_meta_arch_rcnn.py` to oss

Reviewed By: newstzpz

Differential Revision: D27747996

fbshipit-source-id: 6ae3b89c3944098828e246e5a4a89209b8e171a1

configs/mask_rcnn_fbnetv3a_dsmask_C4.yaml

@@ -28,8 +28,7 @@ MODEL:
     POOLER_RESOLUTION: 6
     NORM: "naiveSyncBN"
   ROI_MASK_HEAD:
-    NAME: "MaskRCNNConvUpsampleHead"
-    NUM_CONV: 4
+    NAME: "FBNetV2RoIMaskHead"
     POOLER_RESOLUTION: 14
     NORM: "naiveSyncBN"
 MODEL_EMA:

tests/modeling/test_meta_arch_rcnn.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+
+import copy
+import os
+import unittest
+
+import torch
+from d2go.export.api import convert_and_export_predictor
+from d2go.export.d2_meta_arch import patch_d2_meta_arch
+from d2go.runner import GeneralizedRCNNRunner
+from d2go.utils.testing.data_loader_helper import create_fake_detection_data_loader
+from d2go.utils.testing.rcnn_helper import RCNNBaseTestCases, get_quick_test_config_opts
+from mobile_cv.common.misc.file_utils import make_temp_directory
+
+# Add APIs to D2's meta arch, this is usually called in runner's setup, however in
+# unittest it needs to be called sperarately. (maybe we should apply this by default)
+patch_d2_meta_arch()
+
+
+class TestFBNetV3MaskRCNNNormal(RCNNBaseTestCases.TemplateTestCase):
+    def setup_custom_test(self):
+        self.cfg.merge_from_file("detectron2go://mask_rcnn_fbnetv3a_dsmask_C4.yaml")
+
+    def test_inference(self):
+        self._test_inference()
+
+    def test_export_torchscript_tracing(self):
+        self._test_export("torchscript@tracing", compare_match=True)
+
+    def test_export_torchscript_int8(self):
+        self._test_export("torchscript_int8", compare_match=False)
+
+    def test_export_torchscript_int8_tracing(self):
+        self._test_export("torchscript_int8@tracing", compare_match=False)
+
+
+class TestFBNetV3MaskRCNNQATEager(RCNNBaseTestCases.TemplateTestCase):
+    def setup_custom_test(self):
+        self.cfg.merge_from_file("detectron2go://mask_rcnn_fbnetv3a_dsmask_C4.yaml")
+        # enable QAT
+        self.cfg.merge_from_list(
+            [
+                "QUANTIZATION.BACKEND",
+                "qnnpack",
+                "QUANTIZATION.QAT.ENABLED",
+                "True",
+            ]
+        )
+        # FIXME: NaiveSyncBN is not supported
+        self.cfg.merge_from_list(["MODEL.FBNET_V2.NORM", "bn"])
+
+    def test_inference(self):
+        self._test_inference()
+
+    def test_export_torchscript_int8(self):
+        self._test_export("torchscript_int8", compare_match=False)  # TODO: fix mismatch
+
+
+class TestFBNetV3KeypointRCNNNormal(RCNNBaseTestCases.TemplateTestCase):
+    def setup_custom_test(self):
+        self.cfg.merge_from_file("detectron2go://keypoint_rcnn_fbnetv3a_dsmask_C4.yaml")
+
+        # FIXME: have to use qnnpack due to follow error:
+        # Per Channel Quantization is currently disabled for transposed conv
+        self.cfg.merge_from_list(
+            [
+                "QUANTIZATION.BACKEND",
+                "qnnpack",
+            ]
+        )
+
+    def test_inference(self):
+        self._test_inference()
+
+    def test_export_torchscript_int8(self):
+        self._test_export("torchscript_int8", compare_match=False)
+
+
+class TestTorchVisionExport(unittest.TestCase):
+    def test_export_torchvision_format(self):
+        runner = GeneralizedRCNNRunner()
+        cfg = runner.get_default_cfg()
+        cfg.merge_from_file("detectron2go://mask_rcnn_fbnetv3a_dsmask_C4.yaml")
+        cfg.merge_from_list(get_quick_test_config_opts())
+
+        cfg.merge_from_list(["MODEL.DEVICE", "cpu"])
+        pytorch_model = runner.build_model(cfg, eval_only=True)
+
+        from typing import List, Dict
+
+        class Wrapper(torch.nn.Module):
+            def __init__(self, model):
+                super().__init__()
+                self.model = model
+
+            def forward(self, inputs: List[torch.Tensor]):
+                x = inputs[0].unsqueeze(0) * 255
+                scale = 320.0 / min(x.shape[-2], x.shape[-1])
+                x = torch.nn.functional.interpolate(
+                    x,
+                    scale_factor=scale,
+                    mode="bilinear",
+                    align_corners=True,
+                    recompute_scale_factor=True,
+                )
+                out = self.model(x[0])
+                res: Dict[str, torch.Tensor] = {}
+                res["boxes"] = out[0] / scale
+                res["labels"] = out[2]
+                res["scores"] = out[1]
+                return inputs, [res]
+
+        size_divisibility = max(pytorch_model.backbone.size_divisibility, 10)
+        h, w = size_divisibility, size_divisibility * 2
+        with create_fake_detection_data_loader(h, w, is_train=False) as data_loader:
+            with make_temp_directory("test_export_torchvision_format") as tmp_dir:
+                predictor_path = convert_and_export_predictor(
+                    cfg,
+                    copy.deepcopy(pytorch_model),
+                    "torchscript@tracing",
+                    tmp_dir,
+                    data_loader,
+                )
+
+                orig_model = torch.jit.load(os.path.join(predictor_path, "model.jit"))
+                wrapped_model = Wrapper(orig_model)
+                # optionally do a forward
+                wrapped_model([torch.rand(3, 600, 600)])
+                scripted_model = torch.jit.script(wrapped_model)
+                scripted_model.save(os.path.join(tmp_dir, "new_file.pt"))
+
+
+if __name__ == "__main__":
+    unittest.main()