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|
| import copy |
| import os |
| import tempfile |
| import unittest |
| import torch |
|
|
| from detectron2 import model_zoo |
| from detectron2.export import Caffe2Model, Caffe2Tracer |
| from detectron2.utils.logger import setup_logger |
| from detectron2.utils.testing import get_sample_coco_image |
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| |
| @unittest.skipIf(os.environ.get("CIRCLECI"), "Caffe2 tests crash on CircleCI.") |
| class TestCaffe2Export(unittest.TestCase): |
| def setUp(self): |
| setup_logger() |
|
|
| def _test_model(self, config_path, device="cpu"): |
| cfg = model_zoo.get_config(config_path) |
| cfg.MODEL.DEVICE = device |
| model = model_zoo.get(config_path, trained=True, device=device) |
|
|
| inputs = [{"image": get_sample_coco_image()}] |
| tracer = Caffe2Tracer(cfg, model, copy.deepcopy(inputs)) |
|
|
| with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d: |
| if not os.environ.get("CI"): |
| |
| c2_model = tracer.export_caffe2() |
| c2_model.save_protobuf(d) |
| c2_model.save_graph(os.path.join(d, "test.svg"), inputs=copy.deepcopy(inputs)) |
|
|
| c2_model = Caffe2Model.load_protobuf(d) |
| c2_model(inputs)[0]["instances"] |
|
|
| ts_model = tracer.export_torchscript() |
| ts_model.save(os.path.join(d, "model.ts")) |
|
|
| def testMaskRCNN(self): |
| self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") |
|
|
| @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
| def testMaskRCNNGPU(self): |
| self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", device="cuda") |
|
|
| def testRetinaNet(self): |
| self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml") |
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