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config.py
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config.py
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# -*- coding: utf-8 -*-
# File: config.py
import numpy as np
import os
import pprint
import six
from tensorpack.utils import logger
from tensorpack.utils.gpu import get_num_gpu
__all__ = ['config', 'finalize_configs']
class AttrDict():
_freezed = False
""" Avoid accidental creation of new hierarchies. """
def __getattr__(self, name):
if self._freezed:
raise AttributeError(name)
if name.startswith('_'):
# Do not mess with internals. Otherwise copy/pickle will fail
raise AttributeError(name)
ret = AttrDict()
setattr(self, name, ret)
return ret
def __setattr__(self, name, value):
if self._freezed and name not in self.__dict__:
raise AttributeError(
"Config was freezed! Unknown config: {}".format(name))
super().__setattr__(name, value)
def __str__(self):
return pprint.pformat(self.to_dict(), indent=1, width=100, compact=True)
__repr__ = __str__
def to_dict(self):
"""Convert to a nested dict. """
return {k: v.to_dict() if isinstance(v, AttrDict) else v
for k, v in self.__dict__.items() if not k.startswith('_')}
def from_dict(self, d):
self.freeze(False)
for k, v in d.items():
self_v = getattr(self, k)
if isinstance(self_v, AttrDict):
self_v.from_dict(v)
else:
setattr(self, k, v)
def update_args(self, args):
"""Update from command line args. """
for cfg in args:
keys, v = cfg.split('=', maxsplit=1)
keylist = keys.split('.')
dic = self
for i, k in enumerate(keylist[:-1]):
assert k in dir(dic), "Unknown config key: {}".format(keys)
dic = getattr(dic, k)
key = keylist[-1]
oldv = getattr(dic, key)
if not isinstance(oldv, str):
v = eval(v)
setattr(dic, key, v)
def freeze(self, freezed=True):
self._freezed = freezed
for v in self.__dict__.values():
if isinstance(v, AttrDict):
v.freeze(freezed)
# avoid silent bugs
def __eq__(self, _):
raise NotImplementedError()
def __ne__(self, _):
raise NotImplementedError()
config = AttrDict()
_C = config # short alias to avoid coding
# mode flags ---------------------
_C.TRAINER = 'replicated' # options: 'horovod', 'replicated'
_C.MODE_MASK = True # Faster R-CNN or Mask R-CNN
_C.MODE_FPN = True
# dataset -----------------------
_C.DATA.BASEDIR = '/path/to/your/DATA/DIR'
# All available dataset names are defined in `dataset/coco.py:register_coco`.
# All TRAIN dataset will be concatenated for training.
_C.DATA.TRAIN = ('coco_train2017',) # i.e. trainval35k
# Each VAL dataset will be evaluated separately (instead of concatenated)
_C.DATA.VAL = ('coco_val2017',) # AKA minival2014
# These two configs will be populated later inside `finalize_configs`.
_C.DATA.NUM_CATEGORY = -1 # without the background class (e.g., 80 for COCO)
_C.DATA.CLASS_NAMES = [] # NUM_CLASS (NUM_CATEGORY+1) strings, the first is "BG".
# whether the coordinates in your registered dataset are
# absolute pixel values in range [0, W or H] or relative values in [0, 1]
_C.DATA.ABSOLUTE_COORD = True
# Number of data loading workers.
# In case of horovod training, this is the number of workers per-GPU (so you may want to use a smaller number).
# Set to 0 to disable parallel data loading
_C.DATA.NUM_WORKERS = 10
# backbone ----------------------
_C.BACKBONE.WEIGHTS = ''
# To train from scratch, set it to empty, and set FREEZE_AT to 0
# To train from ImageNet pre-trained models, use the one that matches your
# architecture from http://models.tensorpack.com under the 'FasterRCNN' section.
# To train from an existing COCO model, use the path to that file, and change
# the other configurations according to that model.
_C.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3] # for resnet50
# RESNET_NUM_BLOCKS = [3, 4, 23, 3] # for resnet101
_C.BACKBONE.FREEZE_AFFINE = False # do not train affine parameters inside norm layers
_C.BACKBONE.NORM = 'FreezeBN' # options: FreezeBN, SyncBN, GN, None
_C.BACKBONE.FREEZE_AT = 2 # options: 0, 1, 2. How many stages in backbone to freeze (not training)
# Use a base model with TF-preferred padding mode,
# which may pad more pixels on right/bottom than top/left.
# See https://github.com/tensorflow/tensorflow/issues/18213
# In tensorpack model zoo, ResNet models with TF_PAD_MODE=False are marked with "-AlignPadding".
# All other models under `ResNet/` in the model zoo are using TF_PAD_MODE=True.
# Using either one should probably give the same performance.
# We use the "AlignPadding" one just to be consistent with caffe2.
_C.BACKBONE.TF_PAD_MODE = False
_C.BACKBONE.STRIDE_1X1 = False # True for MSRA models
# schedule -----------------------
_C.TRAIN.NUM_GPUS = None # by default, will be set from code
_C.TRAIN.WEIGHT_DECAY = 1e-4
_C.TRAIN.BASE_LR = 1e-2 # defined for total batch size=8. Otherwise it will be adjusted automatically
_C.TRAIN.WARMUP = 1000 # in terms of iterations. This is not affected by #GPUs
_C.TRAIN.WARMUP_INIT_LR = 1e-2 * 0.33 # defined for total batch size=8. Otherwise it will be adjusted automatically
_C.TRAIN.STEPS_PER_EPOCH = 500
_C.TRAIN.STARTING_EPOCH = 1 # the first epoch to start with, useful to continue a training
# LR_SCHEDULE means equivalent steps when the total batch size is 8.
# When the total bs!=8, the actual iterations to decrease learning rate, and
# the base learning rate are computed from BASE_LR and LR_SCHEDULE.
# Therefore, there is *no need* to modify the config if you only change the number of GPUs.
_C.TRAIN.LR_SCHEDULE = "1x" # "1x" schedule in detectron
_C.TRAIN.EVAL_PERIOD = 50 # period (epochs) to run evaluation
_C.TRAIN.CHECKPOINT_PERIOD = 20 # period (epochs) to save model
# preprocessing --------------------
# Alternative old (worse & faster) setting: 600
_C.PREPROC.TRAIN_SHORT_EDGE_SIZE = [800, 800] # [min, max] to sample from
_C.PREPROC.TEST_SHORT_EDGE_SIZE = 800
_C.PREPROC.MAX_SIZE = 1333
# mean and std in RGB order.
# Un-scaled version: [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
_C.PREPROC.PIXEL_MEAN = [123.675, 116.28, 103.53]
_C.PREPROC.PIXEL_STD = [58.395, 57.12, 57.375]
# anchors -------------------------
_C.RPN.ANCHOR_STRIDE = 16
_C.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512) # sqrtarea of the anchor box
_C.RPN.ANCHOR_RATIOS = (0.5, 1., 2.)
_C.RPN.POSITIVE_ANCHOR_THRESH = 0.7
_C.RPN.NEGATIVE_ANCHOR_THRESH = 0.3
# rpn training -------------------------
_C.RPN.FG_RATIO = 0.5 # fg ratio among selected RPN anchors
_C.RPN.BATCH_PER_IM = 256 # total (across FPN levels) number of anchors that are marked valid
_C.RPN.MIN_SIZE = 0
_C.RPN.PROPOSAL_NMS_THRESH = 0.7
# Anchors which overlap with a crowd box (IOA larger than threshold) will be ignored.
# Setting this to a value larger than 1.0 will disable the feature.
# It is disabled by default because Detectron does not do this.
_C.RPN.CROWD_OVERLAP_THRESH = 9.99
_C.RPN.HEAD_DIM = 1024 # used in C4 only
# RPN proposal selection -------------------------------
# for C4
_C.RPN.TRAIN_PRE_NMS_TOPK = 12000
_C.RPN.TRAIN_POST_NMS_TOPK = 2000
_C.RPN.TEST_PRE_NMS_TOPK = 6000
_C.RPN.TEST_POST_NMS_TOPK = 1000 # if you encounter OOM in inference, set this to a smaller number
# for FPN, #proposals per-level and #proposals after merging are (for now) the same
# if FPN.PROPOSAL_MODE = 'Joint', these options have no effect
_C.RPN.TRAIN_PER_LEVEL_NMS_TOPK = 2000
_C.RPN.TEST_PER_LEVEL_NMS_TOPK = 1000
# fastrcnn training ---------------------
_C.FRCNN.BATCH_PER_IM = 512
_C.FRCNN.BBOX_REG_WEIGHTS = [10., 10., 5., 5.] # Slightly better setting: 20, 20, 10, 10
_C.FRCNN.FG_THRESH = 0.5
_C.FRCNN.FG_RATIO = 0.25 # fg ratio in a ROI batch
# FPN -------------------------
_C.FPN.ANCHOR_STRIDES = (4, 8, 16, 32, 64) # strides for each FPN level. Must be the same length as ANCHOR_SIZES
_C.FPN.PROPOSAL_MODE = 'Level' # 'Level', 'Joint'
_C.FPN.NUM_CHANNEL = 256
_C.FPN.NORM = 'None' # 'None', 'GN'
# The head option is only used in FPN. For C4 models, the head is C5
_C.FPN.FRCNN_HEAD_FUNC = 'fastrcnn_2fc_head'
# choices: fastrcnn_2fc_head, fastrcnn_4conv1fc_{,gn_}head
_C.FPN.FRCNN_CONV_HEAD_DIM = 256
_C.FPN.FRCNN_FC_HEAD_DIM = 1024
_C.FPN.MRCNN_HEAD_FUNC = 'maskrcnn_up4conv_head' # choices: maskrcnn_up4conv_{,gn_}head
# Mask R-CNN
_C.MRCNN.HEAD_DIM = 256
_C.MRCNN.ACCURATE_PASTE = True # slightly more aligned results, but very slow on numpy
# Cascade R-CNN, only available in FPN mode
_C.FPN.CASCADE = False
_C.CASCADE.IOUS = [0.5, 0.6, 0.7]
_C.CASCADE.BBOX_REG_WEIGHTS = [[10., 10., 5., 5.], [20., 20., 10., 10.], [30., 30., 15., 15.]]
# testing -----------------------
_C.TEST.FRCNN_NMS_THRESH = 0.5
# Smaller threshold value gives significantly better mAP. But we use 0.05 for consistency with Detectron.
# mAP with 1e-4 threshold can be found at https://github.com/tensorpack/tensorpack/commit/26321ae58120af2568bdbf2269f32aa708d425a8#diff-61085c48abee915b584027e1085e1043 # noqa
_C.TEST.RESULT_SCORE_THRESH = 0.05
_C.TEST.RESULT_SCORE_THRESH_VIS = 0.5 # only visualize confident results
_C.TEST.RESULTS_PER_IM = 100
_C.freeze() # avoid typo / wrong config keys
def finalize_configs(is_training):
"""
Run some sanity checks, and populate some configs from others
"""
_C.freeze(False) # populate new keys now
if isinstance(_C.DATA.VAL, six.string_types): # support single string (the typical case) as well
_C.DATA.VAL = (_C.DATA.VAL, )
if isinstance(_C.DATA.TRAIN, six.string_types): # support single string
_C.DATA.TRAIN = (_C.DATA.TRAIN, )
# finalize dataset definitions ...
from dataset import DatasetRegistry
datasets = list(_C.DATA.TRAIN) + list(_C.DATA.VAL)
_C.DATA.CLASS_NAMES = DatasetRegistry.get_metadata(datasets[0], "class_names")
_C.DATA.NUM_CATEGORY = len(_C.DATA.CLASS_NAMES) - 1
assert _C.BACKBONE.NORM in ['FreezeBN', 'SyncBN', 'GN', 'None'], _C.BACKBONE.NORM
if _C.BACKBONE.NORM != 'FreezeBN':
assert not _C.BACKBONE.FREEZE_AFFINE
assert _C.BACKBONE.FREEZE_AT in [0, 1, 2]
_C.RPN.NUM_ANCHOR = len(_C.RPN.ANCHOR_SIZES) * len(_C.RPN.ANCHOR_RATIOS)
assert len(_C.FPN.ANCHOR_STRIDES) == len(_C.RPN.ANCHOR_SIZES)
# image size into the backbone has to be multiple of this number
_C.FPN.RESOLUTION_REQUIREMENT = _C.FPN.ANCHOR_STRIDES[3] # [3] because we build FPN with features r2,r3,r4,r5
if _C.MODE_FPN:
size_mult = _C.FPN.RESOLUTION_REQUIREMENT * 1.
_C.PREPROC.MAX_SIZE = np.ceil(_C.PREPROC.MAX_SIZE / size_mult) * size_mult
assert _C.FPN.PROPOSAL_MODE in ['Level', 'Joint']
assert _C.FPN.FRCNN_HEAD_FUNC.endswith('_head')
assert _C.FPN.MRCNN_HEAD_FUNC.endswith('_head')
assert _C.FPN.NORM in ['None', 'GN']
if _C.FPN.CASCADE:
# the first threshold is the proposal sampling threshold
assert _C.CASCADE.IOUS[0] == _C.FRCNN.FG_THRESH
assert len(_C.CASCADE.BBOX_REG_WEIGHTS) == len(_C.CASCADE.IOUS)
if is_training:
train_scales = _C.PREPROC.TRAIN_SHORT_EDGE_SIZE
if isinstance(train_scales, (list, tuple)) and train_scales[1] - train_scales[0] > 100:
# don't autotune if augmentation is on
os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
os.environ['TF_AUTOTUNE_THRESHOLD'] = '1'
assert _C.TRAINER in ['horovod', 'replicated'], _C.TRAINER
lr = _C.TRAIN.LR_SCHEDULE
if isinstance(lr, six.string_types):
if lr.endswith("x"):
LR_SCHEDULE_KITER = {
"{}x".format(k):
[180 * k - 120, 180 * k - 40, 180 * k]
for k in range(2, 10)}
LR_SCHEDULE_KITER["1x"] = [120, 160, 180]
_C.TRAIN.LR_SCHEDULE = [x * 1000 for x in LR_SCHEDULE_KITER[lr]]
else:
_C.TRAIN.LR_SCHEDULE = eval(lr)
# setup NUM_GPUS
if _C.TRAINER == 'horovod':
import horovod.tensorflow as hvd
ngpu = hvd.size()
logger.info("Horovod Rank={}, Size={}, LocalRank={}".format(
hvd.rank(), hvd.size(), hvd.local_rank()))
else:
assert 'OMPI_COMM_WORLD_SIZE' not in os.environ
ngpu = get_num_gpu()
assert ngpu > 0, "Has to train with GPU!"
assert ngpu % 8 == 0 or 8 % ngpu == 0, "Can only train with 1,2,4 or >=8 GPUs, but found {} GPUs".format(ngpu)
else:
# autotune is too slow for inference
os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
ngpu = get_num_gpu()
if _C.TRAIN.NUM_GPUS is None:
_C.TRAIN.NUM_GPUS = ngpu
else:
if _C.TRAINER == 'horovod':
assert _C.TRAIN.NUM_GPUS == ngpu
else:
assert _C.TRAIN.NUM_GPUS <= ngpu
_C.freeze()
logger.info("Config: ------------------------------------------\n" + str(_C))