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