def test_pit_model(self):
cfg = CN()
cfg.MODEL = CN()
add_pit_backbone_config(cfg)
build_model = BACKBONE_REGISTRY.get("pit_d2go_model_wrapper")
pit_models = {
"pit_ti_ours": 160,
"pit_ti": 224,
"pit_s_ours_v1": 256,
"pit_s": 224,
}
pit_model_weights = {
"pit_ti_ours":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210515/deit_[model]pit_scalable_distilled_[bs]128_[mcfg]pit_ti_ours_.HImkjNCpJI/checkpoint_best.pth",
"pit_ti":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210515/deit_[model]pit_scalable_distilled_[bs]128_[mcfg]pit_ti_.QJeFNUfYOD/checkpoint_best.pth",
"pit_s_ours_v1":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210515/deit_[model]pit_scalable_distilled_[bs]64_[mcfg]pit_s_ours_v1_.LXdwyBDaNY/checkpoint_best.pth",
"pit_s":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210515/deit_[model]pit_scalable_distilled_[bs]128_[mcfg]pit_s_.zReQLPOuJe/checkpoint_best.pth",
}
for model_name, org_size in pit_models.items():
print("model_name", model_name)
cfg.MODEL.PIT.MODEL_CONFIG = f"manifold://mobile_vision_workflows/tree/workflows/wbc/deit/model_cfgs/{model_name}.json"
cfg.MODEL.PIT.WEIGHTS = pit_model_weights[model_name]
cfg.MODEL.PIT.DILATED = True
model = build_model(cfg, None)
model.eval()
for input_size_h in [org_size, 192, 224, 256, 320]:
for input_size_w in [org_size, 192, 224, 256, 320]:
x = torch.rand(1, 3, input_size_h, input_size_w)
y = model(x)
print(f"x.shape: {x.shape}, y.shape: {y.shape}")
def test_modeling_hook_cfg(self):
"""Create model with modeling hook using build_model"""
cfg = CfgNode()
cfg.MODEL = CfgNode()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "TestArch"
cfg.MODEL.MODELING_HOOKS = ["PlusOneHook", "TimesTwoHook"]
model = build_model(cfg)
self.assertEqual(model(2), 10)
self.assertTrue(hasattr(model, "_modeling_hooks"))
self.assertTrue(hasattr(model, "unapply_modeling_hooks"))
orig_model = model.unapply_modeling_hooks()
self.assertIsInstance(orig_model, TestArch)
self.assertEqual(orig_model(2), 4)
def _get_default_config():
cfg = CfgNode()
cfg.INPUT = CfgNode()
cfg.INPUT.CROP = CfgNode()
cfg.INPUT.CROP.ENABLED = False
cfg.INPUT.CROP.SIZE = (0.9, 0.9)
cfg.INPUT.CROP.TYPE = "relative_range"
cfg.MODEL = CfgNode()
cfg.MODEL.MIN_DIM_SIZE = 360
cfg.INFERENCE_SDK = CfgNode()
cfg.INFERENCE_SDK.MODEL = CfgNode()
cfg.INFERENCE_SDK.MODEL.SCORE_THRESHOLD = 0.8
cfg.INFERENCE_SDK.IOU_TRACKER = CfgNode()
cfg.INFERENCE_SDK.IOU_TRACKER.IOU_THRESHOLD = 0.15
cfg.INFERENCE_SDK.ENABLE_ID_TRACKING = True
return cfg
def create_cfg_from_cli_args(args, default_cfg):
"""
Instead of loading from defaults.py, this binary only includes necessary
configs building from scratch, and overrides them from args. There're two
levels of config:
_C: the config system used by this binary, which is a sub-set of training
config, override by configurable_cfg. It can also be override by
args.opts for convinience.
configurable_cfg: common configs that user should explicitly specify
in the args.
"""
_C = CN()
_C.INPUT = default_cfg.INPUT
_C.DATASETS = default_cfg.DATASETS
_C.DATALOADER = default_cfg.DATALOADER
_C.TEST = default_cfg.TEST
if hasattr(default_cfg, "D2GO_DATA"):
_C.D2GO_DATA = default_cfg.D2GO_DATA
if hasattr(default_cfg, "TENSORBOARD"):
_C.TENSORBOARD = default_cfg.TENSORBOARD
# NOTE configs below might not be necessary, but must add to make code work
_C.MODEL = CN()
_C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
_C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
_C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
_C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
assert _C.MODEL.LOAD_PROPOSALS is False, "caffe2 model doesn't support"
_C.OUTPUT_DIR = args.output_dir
configurable_cfg = [
"DATASETS.TEST",
args.datasets,
"INPUT.MIN_SIZE_TEST",
args.min_size,
"INPUT.MAX_SIZE_TEST",
args.max_size,
]
cfg = _C.clone()
cfg.merge_from_list(configurable_cfg)
cfg.merge_from_list(args.opts)
return cfg
def test_modeling_hook_copy(self):
"""Create model with modeling hook, the model could be copied"""
cfg = CfgNode()
cfg.MODEL = CfgNode()
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.META_ARCHITECTURE = "TestArch"
cfg.MODEL.MODELING_HOOKS = ["PlusOneHook", "TimesTwoHook"]
model = build_model(cfg)
self.assertEqual(model(2), 10)
model_copy = copy.deepcopy(model)
orig_model = model.unapply_modeling_hooks()
self.assertIsInstance(orig_model, TestArch)
self.assertEqual(orig_model(2), 4)
orig_model_copy = model_copy.unapply_modeling_hooks()
self.assertEqual(orig_model_copy(2), 4)
def test_deit_model(self):
cfg = CN()
cfg.MODEL = CN()
add_deit_backbone_config(cfg)
build_model = BACKBONE_REGISTRY.get("deit_d2go_model_wrapper")
deit_models = {
"8X-7-RM_4": 170,
"DeiT-Tiny": 224,
"DeiT-Small": 224,
"32X-1-RM_2": 221,
"8X-7": 160,
"32X-1": 256,
}
deit_model_weights = {
"8X-7-RM_4":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210511/deit_[model]deit_scaling_distill_[bs]128_[mcfg]8X-7-RM_4_.OIXarYpbZw/checkpoint_best.pth",
"DeiT-Tiny":
"manifold://mobile_vision_workflows/tree/workflows/cl114/DeiT-official-ckpt/deit_tiny_distilled_patch16_224-b40b3cf7.pth",
"DeiT-Small":
"manifold://mobile_vision_workflows/tree/workflows/cl114/DeiT-official-ckpt/deit_small_distilled_patch16_224-649709d9.pth",
"32X-1-RM_2":
"manifold://mobile_vision_workflows/tree/workflows/kyungminkim/20210511/deit_[model]deit_scaling_distill_[bs]64_[mcfg]32X-1-RM_2_.xusuFyNMdD/checkpoint_best.pth",
"8X-7":
"manifold://mobile_vision_workflows/tree/workflows/cl114/scaled_best/8X-7.pth",
"32X-1":
"manifold://mobile_vision_workflows/tree/workflows/cl114/scaled_best/32X-1.pth",
}
for model_name, org_size in deit_models.items():
print("model_name", model_name)
cfg.MODEL.DEIT.MODEL_CONFIG = f"manifold://mobile_vision_workflows/tree/workflows/wbc/deit/model_cfgs/{model_name}.json"
cfg.MODEL.DEIT.WEIGHTS = deit_model_weights[model_name]
model = build_model(cfg, None)
model.eval()
for input_size_h in [org_size, 192, 224, 256, 320]:
for input_size_w in [org_size, 192, 224, 256, 320]:
x = torch.rand(1, 3, input_size_h, input_size_w)
y = model(x)
print(f"x.shape: {x.shape}, y.shape: {y.shape}")
