def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
python类SGDSolver()的实例源码
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self):
if pa.GPU==True:
caffe.set_device(pa.device)
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
self.solver=caffe.SGDSolver(pa.solver)
if pa.pretrain!="":
self.solver.net.copy_from(pa.pretrain)
self.solver_param=caffe_pb2.SolverParameter()
with open(pa.solver,'rt') as f:
pb2.text_format.Merge(f.read(),self.solver_param)
#self.output_dir=pa.output_dir
self.solver.net.layers[0].set_queue()
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, imdb, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_imdb(imdb)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __get_solver(self, solver_proto_path):
'''
Returns a caffe.SGDSolver for the given protofile path,
ignoring Caffe command line chatter if debug mode is not set
to True.
'''
if not self.debug_mode:
# disable Caffe init chatter when not in debug
with Suppressor():
return caffe.SGDSolver(solver_proto_path)
else:
return caffe.SGDSolver(solver_proto_path)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
self.solver.net.layers[0].set_roidb(roidb)
def __init__(self, solver_prototxt, roidb, maskdb, output_dir, imdb,
pretrained_model=None):
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
if not cfg.CFM_MODE:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = add_bbox_regression_targets(roidb)
print 'done'
else:
# Pre-defined mcg bbox_mean and bbox_std
# We store them on disk to avoid disk level IO
# multiple times (mcg boxes are stored on disk)
mean_cache = './data/cache/mcg_bbox_mean.npy'
std_cache = './data/cache/mcg_bbox_std.npy'
roidb_dir = imdb._roidb_path
if os.path.exists(mean_cache) and os.path.exists(std_cache):
self.bbox_means = np.load(mean_cache)
self.bbox_stds = np.load(std_cache)
else:
self.bbox_means, self.bbox_stds = compute_mcg_mean_std(roidb_dir, imdb.num_classes)
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print 'Loading pretrained model weights from {:s}'.format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
if not cfg.CFM_MODE:
self.solver.net.layers[0].set_roidb(roidb)
if cfg.MNC_MODE:
self.solver.net.layers[0].set_maskdb(maskdb)
else:
self.solver.net.layers[0].set_image_info(imdb, self.bbox_means, self.bbox_stds)
def __init__(self, solver_prototxt, roidb, output_dir,
previous_state=None, pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
# RPN can only use precomputed normalization because there are no
# fixed statistics to compute a priori
assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
if cfg.TRAIN.BBOX_REG:
print 'Computing bounding-box regression targets...'
self.bbox_means, self.bbox_stds = \
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
# Change the snapshot file name
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)
infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX) \
if cfg.TRAIN.SNAPSHOT_INFIX != '' else ''
filename = self.solver_param.snapshot_prefix + infix
filename = os.path.join(self.output_dir, filename)
self.solver_param.snapshot_prefix = filename
print 'Change the snapshot prefix to', self.solver_param.snapshot_prefix
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(str(self.solver_param))
solver_prototxt = f.name
print 'Create temporary solver prototxt at', solver_prototxt
self.solver = caffe.SGDSolver(solver_prototxt)
if previous_state is not None:
print ('Restoring solver state from {:s}').format(previous_state)
self.solver.restore(previous_state)
elif pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver.net.layers[0].set_roidb(roidb)
