def setup_cfg(args):
# load config from file and command-line arguments
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def setup(args):
cfg = get_cfg()
if args.config_file:
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
return cfg
def setup_cfg(args):
# load config from file and command-line arguments
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# Set score_threshold for builtin models
cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
cfg.MODEL.FCOS.INFERENCE_TH_TEST = args.confidence_threshold
cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
cfg.freeze()
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
rank = comm.get_rank()
setup_logger(cfg.OUTPUT_DIR, distributed_rank=rank, name="adet")
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
##############################
# NOTE: pop some unwanterd configs in detectron2
cfg.SOLVER.pop("STEPS", None)
cfg.SOLVER.pop("MAX_ITER", None)
# NOTE: get optimizer from string cfg dict
if cfg.SOLVER.OPTIMIZER_CFG != "":
optim_cfg = eval(cfg.SOLVER.OPTIMIZER_CFG)
iprint("optimizer_cfg:", optim_cfg)
cfg.SOLVER.OPTIMIZER_NAME = optim_cfg['type']
cfg.SOLVER.BASE_LR = optim_cfg['lr']
cfg.SOLVER.MOMENTUM = optim_cfg.get("momentum", 0.9)
cfg.SOLVER.WEIGHT_DECAY = optim_cfg.get("weight_decay", 1e-4)
if cfg.get("DEBUG", False):
iprint("DEBUG")
args.num_gpus = 1
args.num_machines = 1
cfg.DATALOADER.NUM_WORKERS = 0
cfg.TRAIN.PRINT_FREQ = 1
if cfg.TRAIN.get("VERBOSE", False):
cfg.TRAIN.PRINT_FREQ = 1
# register datasets
dataset_names = cfg.DATASETS.TRAIN + cfg.DATASETS.TEST
register_datasets(dataset_names)
cfg.RESUME = args.resume
##########################################
cfg.freeze()
default_setup(cfg, args)
setup_for_distributed(is_master=comm.is_main_process())
rank = comm.get_rank()
setup_my_logger(cfg.OUTPUT_DIR, distributed_rank=rank, name="adet")
setup_my_logger(cfg.OUTPUT_DIR, distributed_rank=rank, name="core")
return cfg
def main():
parser = argparse.ArgumentParser(
description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/FCOS-Detection/R_50_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument('--width', default=0, type=int)
parser.add_argument('--height', default=0, type=int)
parser.add_argument('--level', default=0, type=int)
parser.add_argument(
"--output",
default="output/fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
cfg = get_cfg()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# norm for ONNX: change FrozenBN back to BN
cfg.MODEL.BACKBONE.FREEZE_AT = 0
cfg.MODEL.RESNETS.NORM = "BN"
# turn on the following configuration according to your own convenience
#cfg.MODEL.FCOS.NORM = "BN"
#cfg.MODEL.FCOS.NORM = "NaiveGN"
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
logger = setup_logger(output=output_dir)
logger.info(cfg)
model = build_model(cfg)
model.eval()
model.to(cfg.MODEL.DEVICE)
logger.info("Model:\n{}".format(model))
checkpointer = DetectionCheckpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHTS)
logger.info("load Model:\n{}".format(cfg.MODEL.WEIGHTS))
# patch fcos_head
# step 1. config
fcos_head = model.proposal_generator.fcos_head
norm = None if cfg.MODEL.FCOS.NORM == "none" else cfg.MODEL.FCOS.NORM
head_configs = {
"cls": (cfg.MODEL.FCOS.NUM_CLS_CONVS, cfg.MODEL.FCOS.USE_DEFORMABLE),
"bbox": (cfg.MODEL.FCOS.NUM_BOX_CONVS, cfg.MODEL.FCOS.USE_DEFORMABLE),
"share": (cfg.MODEL.FCOS.NUM_SHARE_CONVS, False)
}
# step 2. seperate module
for l in range(fcos_head.num_levels):
for head in head_configs:
tower = []
num_convs, use_deformable = head_configs[head]
for i in range(num_convs):
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 0]))
if norm in ["GN", "NaiveGN"]:
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 1]))
elif norm in ["BN", "SyncBN"]:
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 1][l]))
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 2]))
fcos_head.add_module('{}_tower{}'.format(head, l),
torch.nn.Sequential(*tower))
# step 3. override forward
def fcos_head_forward(self, x):
logits = []
bbox_reg = []
ctrness = []
bbox_towers = []
for l, feature in enumerate(x):
feature = self.share_tower(feature)
cls_tower = getattr(self, 'cls_tower{}'.format(l))(feature)
bbox_tower = getattr(self, 'bbox_tower{}'.format(l))(feature)
logits.append(self.cls_logits(cls_tower))
ctrness.append(self.ctrness(bbox_tower))
reg = self.bbox_pred(bbox_tower)
if self.scales is not None:
reg = self.scales[l](reg)
# Note that we use relu, as in the improved FCOS, instead of exp.
bbox_reg.append(F.relu(reg))
return logits, bbox_reg, ctrness, bbox_towers
fcos_head.forward = types.MethodType(fcos_head_forward, fcos_head)
proposal_generator = FCOS(cfg)
onnx_model = torch.nn.Sequential(
OrderedDict([
('backbone', model.backbone),
('proposal_generator', proposal_generator),
('heads', model.proposal_generator.fcos_head),
]))
height, width = 800, 1088
if args.width > 0:
width = args.width
if args.height > 0:
height = args.height
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, height, width)).to(cfg.MODEL.DEVICE)
output_names = []
for item in ["logits", "bbox_reg", "centerness"]:
for l in range(len(cfg.MODEL.FCOS.FPN_STRIDES)):
fpn_name = "P{}".format(3 + l)
output_names.extend([fpn_name + item])
logger.info("Load onnx model from {}.".format(args.output))
sess = rt.InferenceSession(args.output)
for in_blob in sess.get_inputs():
if in_blob.name not in input_names:
print("Input blob name not match that in the mode")
else:
print("Input {}, shape {} and type {}".format(
in_blob.name, in_blob.shape, in_blob.type))
for out_blob in sess.get_outputs():
if out_blob.name not in output_names:
print("Output blob name not match that in the mode")
else:
print("Output {}, shape {} and type {}".format(
out_blob.name, out_blob.shape, out_blob.type))
with torch.no_grad():
torch_output = onnx_model(dummy_input)
logits, bbox_reg, ctrness, bbox_towers = torch_output
lists = logits + bbox_reg + ctrness + bbox_towers
onnx_output = sess.run(None, {input_names[0]: dummy_input.cpu().numpy()})
for i, out in enumerate(onnx_output):
try:
np.testing.assert_allclose(lists[i].cpu().detach().numpy(),
out,
rtol=1e-03,
atol=2e-04)
except AssertionError as e:
print("ouput {} mismatch {}".format(output_names[i], e))
continue
print("ouput {} match".format(output_names[i]))
from detectron2.modeling import GeneralizedRCNNWithTTA
from detectron2.utils.logger import setup_logger
from adet.data.dataset_mapper import DatasetMapperWithBasis
from adet.data.dataset_mapper_depth import DatasetMapperWithDepth
from adet.config import get_cfg
from adet.checkpoint import AdetCheckpointer
from adet.evaluation import TextEvaluator
import matplotlib.pyplot as plt
import numpy as np
from detectron2.engine import DefaultPredictor
import time
from detectron2.data import (
build_detection_test_loader, )
cfg = get_cfg()
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(
r"/home/zqzhou/disk/Code/python/AdelaiDet/configs/BoxData/CondInst/CondInst_R_50_3x_sem.yaml"
)
cfg.MODEL.PROPOSAL_GENERATOR.NAME = 'FCOSM'
cfg.MODEL.META_ARCHITECTURE = 'CondInstM'
cfg.MODEL.FCOSM.USE_BOX_NMS = False
# cfg.MODEL.FCOSM.USE_SINGLE_NMS = False
cfg.MODEL.WEIGHTS = "/home/zqzhou/disk/Code/python/AdelaiDet/output/BoxData/CondInst/condinst_MS_R_50_3x_sem/old_0.0050/model_final.pth"
predictor_old = DefaultPredictor(cfg)
cfg = get_cfg()
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(
r"/home/zqzhou/disk/Code/python/AdelaiDet/configs/BoxData/CondInst/CondInst_R_50_3x_sem.yaml"
def main():
parser = argparse.ArgumentParser(description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/FCOS-Detection/R_50_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument('--width', default=0, type=int)
parser.add_argument('--height', default=0, type=int)
parser.add_argument('--level', default=0, type=int)
parser.add_argument(
"--output",
default="output/fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
cfg = get_cfg()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# norm for ONNX: change FrozenBN back to BN
cfg.MODEL.BACKBONE.FREEZE_AT = 0
cfg.MODEL.RESNETS.NORM = "BN"
cfg.MODEL.BASIS_MODULE.NORM = "BN"
# turn on the following configuration according to your own convenience
#cfg.MODEL.FCOS.NORM = "BN"
#cfg.MODEL.FCOS.NORM = "NaiveGN"
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
logger = setup_logger(output=output_dir)
logger.info(cfg)
model = build_model(cfg)
model.eval()
model.to(cfg.MODEL.DEVICE)
logger.info("Model:\n{}".format(model))
checkpointer = DetectionCheckpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHTS)
logger.info("load Model:\n{}".format(cfg.MODEL.WEIGHTS))
height, width = 800, 1088
if args.width > 0:
width = args.width
if args.height > 0:
height = args.height
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, height, width)).to(cfg.MODEL.DEVICE)
output_names = []
if isinstance(model, BlendMask):
patch_blendmask(cfg, model, output_names)
if isinstance(model, ProposalNetwork):
patch_ProposalNetwork(cfg, model, output_names)
if hasattr(model, 'proposal_generator'):
if isinstance(model.proposal_generator, FCOS):
patch_fcos(cfg, model.proposal_generator)
patch_fcos_head(cfg, model.proposal_generator.fcos_head)
torch.onnx.export(
model,
dummy_input,
args.output,
verbose=True,
input_names=input_names,
output_names=output_names,
keep_initializers_as_inputs=True
)
logger.info("Done. The onnx model is saved into {}.".format(args.output))
def main():
parser = argparse.ArgumentParser(
description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/FCOS-Detection/R_50_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"--output",
default="output/fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
cfg = get_cfg()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# norm for ONNX: change FrozenBN back to BN
cfg.MODEL.BACKBONE.FREEZE_AT = 0
cfg.MODEL.RESNETS.NORM = "BN"
# turn on the following configuration according to your own convenience
#cfg.MODEL.FCOS.NORM = "BN"
#cfg.MODEL.FCOS.NORM = "NaiveGN"
# The onnx model can only be used with DATALOADER.NUM_WORKERS = 0
cfg.DATALOADER.NUM_WORKERS = 0
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
logger = setup_logger(output=output_dir)
logger.info(cfg)
model = build_model(cfg)
model.eval()
model.to(cfg.MODEL.DEVICE)
logger.info("Model:\n{}".format(model))
checkpointer = DetectionCheckpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHTS)
proposal_generator = FCOS(cfg)
onnx_model = torch.nn.Sequential(
OrderedDict([
('backbone', model.backbone),
('proposal_generator', proposal_generator),
('heads', model.proposal_generator.fcos_head),
]))
height, width = 512, 640
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, height, width)).to(cfg.MODEL.DEVICE)
output_names = []
for l in range(len(cfg.MODEL.FCOS.FPN_STRIDES)):
fpn_name = "P{}/".format(3 + l)
output_names.extend([
fpn_name + "logits", fpn_name + "bbox_reg", fpn_name + "centerness"
])
torch.onnx.export(onnx_model,
dummy_input,
args.output,
verbose=True,
input_names=input_names,
output_names=output_names,
keep_initializers_as_inputs=True)
logger.info("Done. The onnx model is saved into {}.".format(args.output))
def main():
parser = argparse.ArgumentParser(description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/FCOS-Detection/R_50_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument('--width', default=0, type=int)
parser.add_argument('--height', default=0, type=int)
parser.add_argument('--level', default=0, type=int)
parser.add_argument(
"--output",
default="output/fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
cfg = get_cfg()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# norm for ONNX: change FrozenBN back to BN
cfg.MODEL.BACKBONE.FREEZE_AT = 0
cfg.MODEL.RESNETS.NORM = "BN"
cfg.MODEL.BASIS_MODULE.NORM = "BN"
# turn on the following configuration according to your own convenience
#cfg.MODEL.FCOS.NORM = "BN"
#cfg.MODEL.FCOS.NORM = "NaiveGN"
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
logger = setup_logger(output=output_dir)
logger.info(cfg)
model = build_model(cfg)
model.eval()
model.to(cfg.MODEL.DEVICE)
logger.info("Model:\n{}".format(model))
checkpointer = DetectionCheckpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHTS)
logger.info("load Model:\n{}".format(cfg.MODEL.WEIGHTS))
height, width = 800, 1088
if args.width > 0:
width = args.width
if args.height > 0:
height = args.height
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, height, width)).to(cfg.MODEL.DEVICE)
output_names = []
if isinstance(model, BlendMask):
patch_blendmask(cfg, model, output_names)
if isinstance(model, ProposalNetwork):
patch_ProposalNetwork(cfg, model, output_names)
if hasattr(model, 'proposal_generator'):
if isinstance(model.proposal_generator, FCOS):
patch_fcos(cfg, model.proposal_generator)
patch_fcos_head(cfg, model.proposal_generator.fcos_head)
logger.info("Load onnx model from {}.".format(args.output))
sess = rt.InferenceSession(args.output)
# check input and output
for in_blob in sess.get_inputs():
if in_blob.name not in input_names:
print("Input blob name not match that in the mode")
else:
print("Input {}, shape {} and type {}".format(in_blob.name, in_blob.shape, in_blob.type))
for out_blob in sess.get_outputs():
if out_blob.name not in output_names:
print("Output blob name not match that in the mode")
else:
print("Output {}, shape {} and type {}".format(out_blob.name, out_blob.shape, out_blob.type))
# run pytorch model
with torch.no_grad():
torch_output = model(dummy_input)
torch_output = to_list(torch_output)
# run onnx by onnxruntime
onnx_output = sess.run(None, {input_names[0]: dummy_input.cpu().numpy()})
# run onnx by tensorrt
logger.info("Load onnx model from {}.".format(args.output))
load_model = onnx.load(args.output)
onnx.checker.check_model(load_model)
onnx_model = backend.prepare(load_model)
tensorrt_output = onnx_model.run(dummy_input.data.numpy())
# compare the result
for i, out in enumerate(onnx_output):
try:
np.testing.assert_allclose(torch_output[i].cpu().detach().numpy(), out, rtol=1e-03, atol=2e-04)
except AssertionError as e:
print("ouput {} mismatch {}".format(output_names[i], e))
continue
print("ouput {} match\n".format(output_names[i]))
# compare the result
for i, out in enumerate(tensorrt_output):
try:
np.testing.assert_allclose(torch_output[i].cpu().detach().numpy(), out, rtol=1e-03, atol=2e-04)
except AssertionError as e:
print("ouput {} mismatch {}".format(output_names[i], e))
continue
print("ouput {} match\n".format(output_names[i]))
def main():
parser = argparse.ArgumentParser(
description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/FCOS-Detection/R_50_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"--output",
default="output/fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
cfg = get_cfg()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# norm for ONNX: change FrozenBN back to BN
cfg.MODEL.BACKBONE.FREEZE_AT = 0
cfg.MODEL.RESNETS.NORM = "BN"
# turn on the following configuration according to your own convenience
#cfg.MODEL.FCOS.NORM = "BN"
#cfg.MODEL.FCOS.NORM = "NaiveGN"
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
logger = setup_logger(output=output_dir)
logger.info(cfg)
model = build_model(cfg)
model.eval()
model.to(cfg.MODEL.DEVICE)
logger.info("Model:\n{}".format(model))
checkpointer = DetectionCheckpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHTS)
logger.info("load Model:\n{}".format(cfg.MODEL.WEIGHTS))
# patch fcos_head
# step 1. config
fcos_head = model.proposal_generator.fcos_head
norm = None if cfg.MODEL.FCOS.NORM == "none" else cfg.MODEL.FCOS.NORM
head_configs = {
"cls": (cfg.MODEL.FCOS.NUM_CLS_CONVS, cfg.MODEL.FCOS.USE_DEFORMABLE),
"bbox": (cfg.MODEL.FCOS.NUM_BOX_CONVS, cfg.MODEL.FCOS.USE_DEFORMABLE),
"share": (cfg.MODEL.FCOS.NUM_SHARE_CONVS, False)
}
# step 2. seperate module
for l in range(fcos_head.num_levels):
for head in head_configs:
tower = []
num_convs, use_deformable = head_configs[head]
for i in range(num_convs):
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 0]))
if norm in ["GN", "NaiveGN"]:
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 1]))
elif norm in ["BN", "SyncBN"]:
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 1][l]))
tower.append(
deepcopy(
getattr(fcos_head,
'{}_tower'.format(head))[i * 3 + 2]))
fcos_head.add_module('{}_tower{}'.format(head, l),
torch.nn.Sequential(*tower))
# step 3. override forward
def fcos_head_forward(self, x):
logits = []
bbox_reg = []
ctrness = []
bbox_towers = []
for l, feature in enumerate(x):
feature = self.share_tower(feature)
cls_tower = getattr(self, 'cls_tower{}'.format(l))(feature)
bbox_tower = getattr(self, 'bbox_tower{}'.format(l))(feature)
logits.append(self.cls_logits(cls_tower))
ctrness.append(self.ctrness(bbox_tower))
reg = self.bbox_pred(bbox_tower)
if self.scales is not None:
reg = self.scales[l](reg)
# Note that we use relu, as in the improved FCOS, instead of exp.
bbox_reg.append(F.relu(reg))
return logits, bbox_reg, ctrness, bbox_towers
fcos_head.forward = types.MethodType(fcos_head_forward, fcos_head)
proposal_generator = FCOS(cfg)
onnx_model = torch.nn.Sequential(
OrderedDict([
('backbone', model.backbone),
('proposal_generator', proposal_generator),
('heads', model.proposal_generator.fcos_head),
]))
height, width = 800, 1088
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, height, width)).to(cfg.MODEL.DEVICE)
output_names = []
for item in ["logits", "bbox_reg", "centerness"]:
for l in range(len(cfg.MODEL.FCOS.FPN_STRIDES)):
fpn_name = "P{}".format(3 + l)
output_names.extend([fpn_name + item])
torch.onnx.export(onnx_model,
dummy_input,
args.output,
verbose=True,
input_names=input_names,
output_names=output_names,
keep_initializers_as_inputs=True)
logger.info("Done. The onnx model is saved into {}.".format(args.output))