def _test_model(self, config_path, device="cpu"):
# requires extra dependencies
from detectron2.export import (
Caffe2Model,
add_export_config,
export_caffe2_model,
)
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(config_path))
cfg = add_export_config(cfg)
cfg.MODEL.DEVICE = device
model = build_model(cfg)
DetectionCheckpointer(model).load(
model_zoo.get_checkpoint_url(config_path))
inputs = [{"image": self._get_test_image()}]
c2_model = export_caffe2_model(cfg, model, copy.deepcopy(inputs))
with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
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"]
def _test_model(self, config_path, device="cpu"):
# requires extra dependencies
from detectron2.export import Caffe2Model, add_export_config, Caffe2Tracer
cfg = model_zoo.get_config(config_path)
add_export_config(cfg)
cfg.MODEL.DEVICE = device
model = model_zoo.get(config_path, trained=True, device=device)
inputs = [{"image": get_sample_coco_image()}]
c2_model = Caffe2Tracer(cfg, model,
copy.deepcopy(inputs)).export_caffe2()
with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
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"]
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"):
# This requires onnx, which is not yet available on public 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"))
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
first_batch = next(iter(data_loader))
#%%
inputs = [{"image": first_batch[0]['image']}]
#%%
tracer = Caffe2Tracer(cfg, torch_model, first_batch)
# convert and save caffe2 model
caffe2_model = tracer.export_caffe2()
caffe2_model.save_protobuf(output)
# draw the caffe2 graph
caffe2_model.save_graph(os.path.join(output, "model.svg"), inputs=first_batch)
#%%
from detectron2.export import Caffe2Model
model = Caffe2Model.load_protobuf(output)
# %%
import torch
torch.cuda.empty_cache()
im = first_batch[0]['image']
inputs = [{"image": im}]
outputs = model(inputs)
# %%