def get_coco_dataset():
"""A dummy COCO dataset that includes only the 'classes' field."""
ds = AttrDict()
classes = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',
'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def test_merge_cfg_from_cfg(self):
# Test: merge from deepcopy
s = 'dummy0'
cfg2 = copy.deepcopy(cfg)
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge from yaml
s = 'dummy1'
cfg2 = core_config.load_cfg(envu.yaml_dump(cfg))
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with a valid key
s = 'dummy2'
cfg2 = AttrDict()
cfg2.MODEL = AttrDict()
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with an invalid key
s = 'dummy3'
cfg2 = AttrDict()
cfg2.FOO = AttrDict()
cfg2.FOO.BAR = s
with self.assertRaises(KeyError):
core_config.merge_cfg_from_cfg(cfg2)
# Test: merge with converted type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = [1]
core_config.merge_cfg_from_cfg(cfg2)
assert type(cfg.TRAIN.SCALES) is tuple
assert cfg.TRAIN.SCALES[0] == 1
# Test: merge with invalid type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = 1
with self.assertRaises(ValueError):
core_config.merge_cfg_from_cfg(cfg2)
def get_ivoc_dataset():
"""A dummy COCO dataset that includes only the 'classes' field."""
ds = AttrDict()
classes = ['__background__', 'people', 'bicycle', 'electric bicycle']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def inference(self, image_list):
"""Do an inference on the model with a set of inputs.
# Arguments:
image_list: The input image list
Return the result of the inference.
"""
image = image_list[0]
image = self.linear_to_srgb(image)*255.
imcpy = image.copy()
# Initialize the model out of the configuration and weights files
if not hasattr(self, 'model'):
workspace.ResetWorkspace()
# Reset to default config
merge_cfg_from_cfg(self.default_cfg)
# Load mask rcnn configuration file
merge_cfg_from_file(self.cfg_file)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
# Save mask rcnn full configuration file
self.mrcnn_cfg = copy.deepcopy(AttrDict(cfg)) # cfg from detectron.core.config
else:
# There is a global config file for all detectron models (Densepose, Mask RCNN..)
# Check if current global config file is correct for mask rcnn
if not dict_equal(self.mrcnn_cfg, cfg):
# Free memory of previous workspace
workspace.ResetWorkspace()
# Load mask rcnn configuration file
merge_cfg_from_cfg(self.mrcnn_cfg)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, _ = infer_engine.im_detect_all(
self.model, image[:, :, ::-1], None
)
if self.binary_masks:
res = vis_utils.vis_one_image_binary(
imcpy,
cls_boxes,
cls_segms,
thresh=self.thresh
)
else:
res = vis_utils.vis_one_image_opencv(
imcpy,
cls_boxes,
cls_segms,
cls_keyps,
thresh=self.thresh,
show_box=self.show_box,
show_class=self.show_class,
dataset=self.dummy_coco_dataset,
alpha=self.alpha,
show_border=self.show_border,
border_thick=self.border_thick,
bbox_thick=self.bbox_thick,
font_scale=self.font_scale
)
res = self.srgb_to_linear(res.astype(np.float32) / 255.)
return [res]
def test_merge_cfg_from_cfg(self):
# Test: merge from deepcopy
s = 'dummy0'
cfg2 = copy.deepcopy(cfg)
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge from yaml
s = 'dummy1'
cfg2 = core_config.load_cfg(yaml.dump(cfg))
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with a valid key
s = 'dummy2'
cfg2 = AttrDict()
cfg2.MODEL = AttrDict()
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with an invalid key
s = 'dummy3'
cfg2 = AttrDict()
cfg2.FOO = AttrDict()
cfg2.FOO.BAR = s
with self.assertRaises(KeyError):
core_config.merge_cfg_from_cfg(cfg2)
# Test: merge with converted type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = [1]
core_config.merge_cfg_from_cfg(cfg2)
assert type(cfg.TRAIN.SCALES) is tuple
assert cfg.TRAIN.SCALES[0] == 1
# Test: merge with invalid type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = 1
with self.assertRaises(ValueError):
core_config.merge_cfg_from_cfg(cfg2)
def test_immutability(self):
# Top level immutable
a = AttrDict()
a.foo = 0
a.immutable(True)
with self.assertRaises(AttributeError):
a.foo = 1
a.bar = 1
assert a.is_immutable()
assert a.foo == 0
a.immutable(False)
assert not a.is_immutable()
a.foo = 1
assert a.foo == 1
# Recursively immutable
a.level1 = AttrDict()
a.level1.foo = 0
a.level1.level2 = AttrDict()
a.level1.level2.foo = 0
a.immutable(True)
assert a.is_immutable()
with self.assertRaises(AttributeError):
a.level1.level2.foo = 1
a.level1.bar = 1
assert a.level1.level2.foo == 0
# Serialize immutability state
a.immutable(True)
a2 = core_config.load_cfg(yaml.dump(a))
assert a.is_immutable()
assert a2.is_immutable()
def get_hanzi_dataset():
ds = AttrDict()
classes = ['__background__', 'hanzi']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def inference(self, image_list):
"""Do an inference of the DensePose model with a set of image inputs.
# Arguments:
image_list: The input image list
Return the result of the inference.
"""
# Directly return image when no inference options
if not (self.show_human_index or self.show_uv or self.show_border
or self.show_grid):
return [image_list[0]]
image = image_list[0]
image = self.linear_to_srgb(image) * 255.
imcpy = image.copy()
# Initialize the model out of the configuration and weights files
if not hasattr(self, 'model'):
workspace.ResetWorkspace()
# Reset to default config
merge_cfg_from_cfg(self.default_cfg)
# Load densepose configuration file
merge_cfg_from_file(self.cfg_file)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
# Save densepose full configuration file
self.densepose_cfg = copy.deepcopy(
AttrDict(cfg)) #cfg from detectron.core.config
else:
# There is a global config file for all detectron models (Densepose, Mask RCNN..)
# Check if current global config file is correct for densepose
if not dict_equal(self.densepose_cfg, cfg):
# Free memory of previous workspace
workspace.ResetWorkspace()
# Load densepose configuration file
merge_cfg_from_cfg(self.densepose_cfg)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(
self.weights)
# Compute the image inference
with c2_utils.NamedCudaScope(0):
# image in BGR format for inference
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
self.model, image[:, :, ::-1], None)
res = vis_utils.vis_densepose(
imcpy, # image in RGB format for visualization
cls_boxes,
cls_bodys,
show_human_index=self.show_human_index,
show_uv=self.show_uv,
show_grid=self.show_grid,
show_border=self.show_border,
border_thick=self.border_thick,
alpha=self.alpha)
res = self.srgb_to_linear(res.astype(np.float32) / 255.)
return [res]
def get_nucleus_dataset():
ds = AttrDict()
classes = ['__background__', 'diploidn']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def get_ava_dataset():
"""A dummy COCO dataset that includes only the 'classes' field."""
ds = AttrDict()
classes = ['__background__', 'actor']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def get_cones_dataset():
"""A cones dataset that includes only the 'classes' field."""
ds = AttrDict()
classes = ['__background__', 'Yellow', 'Blue', 'Orange']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def get_theyes_dataset():
ds = AttrDict()
classes = ['__background__', 'bottom', 'shoe', 'bag', 'one_piece', 'tops']
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
def get_coco_dataset():
"""A dummy COCO dataset that includes only the 'classes' field."""
ds = AttrDict()
# classes = [
# '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
# 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
# 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
# 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
# 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
# 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
# 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',
# 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
# 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
# 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
# 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
# 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
# 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
# ]
# classes = ['__background__', 'lane']
#
base_classes = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',
'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet',
'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
classes = [
'__background__',
'guard rail',
# 'car',
'dashed',
'solid',
'solid solid',
'dashed dashed',
'dashed-solid',
'solid-dashed',
'yellow dashed',
'yellow solid',
'yellow solid solid',
'yellow dashed dashed',
'yellow dashed-solid',
'yellow solid-dashed',
'boundary',
'fork_line',
'fork_edge',
'arrow_s',
'arrow_r',
'arrow_l',
'arrow_lr',
'arrow_inclined_r',
'arrow_r_s',
'arrow_l_s',
'sidewalk',
'handrail'
]
base_classes.extend(classes[1:])
classes = base_classes
ds.classes = {i: name for i, name in enumerate(classes)}
return ds
from ast import literal_eval from future.utils import iteritems from past.builtins import basestring import copy import logging import numpy as np import os import os.path as osp import yaml from detectron.utils.collections import AttrDict from detectron.utils.io import cache_url logger = logging.getLogger(__name__) __C = AttrDict() # Consumers can get config by: # from detectron.core.config import cfg cfg = __C # Random note: avoid using '.ON' as a config key since yaml converts it to True; # prefer 'ENABLED' instead # ---------------------------------------------------------------------------- # # Training options # ---------------------------------------------------------------------------- # __C.TRAIN = AttrDict() # Initialize network with weights from this .pkl file __C.TRAIN.WEIGHTS = b''
from detectron.core.config import merge_cfg_from_file from detectron.core.config import merge_cfg_from_list from detectron.core.config import assert_and_infer_cfg import detectron.core.test_engine as infer_engine import detectron.utils.c2 as c2_utils import detectron.utils.io as io_utils import detectron.utils.model_convert_utils as mutils import detectron.datasets.dummy_datasets as dummy_datasets import tools.convert_pkl_to_pb as convert_tools ############################################################# # Supposed to be used for "seg-every-thing models" training # # Set for compatibility but currenlty ignored # from detectron.utils.collections import AttrDict cfg.MRCNN.BBOX2MASK = AttrDict() cfg.MRCNN.BBOX2MASK.BBOX2MASK_ON = False cfg.MRCNN.BBOX2MASK.TYPE = b'' cfg.MRCNN.BBOX2MASK.USE_PRETRAINED_EMBED = False cfg.MRCNN.BBOX2MASK.PRETRAINED_EMBED_NAME = b'' cfg.MRCNN.BBOX2MASK.PRETRAINED_EMBED_DIM = -1 cfg.MRCNN.BBOX2MASK.STOP_DET_W_GRAD = True cfg.MRCNN.BBOX2MASK.INCLUDE_CLS_SCORE = True cfg.MRCNN.BBOX2MASK.INCLUDE_BBOX_PRED = False cfg.MRCNN.BBOX2MASK.USE_LEAKYRELU = True cfg.MRCNN.JOINT_FCN_MLP_HEAD = False cfg.MRCNN.MLP_MASK_BRANCH_TYPE = b'' cfg.TRAIN.TRAIN_MASK_HEAD_ONLY = False cfg.TRAIN.MRCNN_FILTER_LABELS = False cfg.TRAIN.MRCNN_LABELS_TO_KEEP = ()
def merge_cfg_from_file(cfg_filename):
"""Load a yaml config file and merge it into the global config."""
with open(cfg_filename, 'r') as f:
yaml_cfg = AttrDict(load_cfg(f))
_merge_a_into_b(yaml_cfg, __C)
def test_immutability(self):
# Top level immutable
a = AttrDict()
a.foo = 0
a.immutable(True)
with self.assertRaises(AttributeError):
a.foo = 1
a.bar = 1
assert a.is_immutable()
assert a.foo == 0
a.immutable(False)
assert not a.is_immutable()
a.foo = 1
assert a.foo == 1
# Recursively immutable
a.level1 = AttrDict()
a.level1.foo = 0
a.level1.level2 = AttrDict()
a.level1.level2.foo = 0
a.immutable(True)
assert a.is_immutable()
with self.assertRaises(AttributeError):
a.level1.level2.foo = 1
a.level1.bar = 1
assert a.level1.level2.foo == 0
# Serialize immutability state
a.immutable(True)
a2 = core_config.load_cfg(envu.yaml_dump(a))
assert a.is_immutable()
assert a2.is_immutable()
from ast import literal_eval from future.utils import iteritems from past.builtins import basestring import copy import logging import numpy as np import os import os.path as osp import yaml from detectron.utils.collections import AttrDict from detectron.utils.io import cache_url logger = logging.getLogger(__name__) __C = AttrDict() # Consumers can get config by: # from detectron.core.config import cfg cfg = __C # Random note: avoid using '.ON' as a config key since yaml converts it to True; # prefer 'ENABLED' instead # ---------------------------------------------------------------------------- # # Training options # ---------------------------------------------------------------------------- # __C.TRAIN = AttrDict() # Initialize network with weights from this .pkl file __C.TRAIN.WEIGHTS = b''
from __future__ import print_function from __future__ import unicode_literals from ast import literal_eval from future.utils import iteritems import copy, io, logging import numpy as np import os import os.path as osp import six from detectron.utils.collections import AttrDict from detectron.utils.io import cache_url logger = logging.getLogger(__name__) # 日志模块 __C = AttrDict() cfg = __C # Training options __C.TRAIN = AttrDict() __C.TRAIN.WEIGHTS = '' __C.TRAIN.DATASETS = () __C.TRAIN.SCALES = (600, ) __C.TRAIN.MAX_SIZE = 1000 __C.TRAIN.IMS_PER_BATCH = 2