定義了一個類:class SolverWrapper
class SolverWrapper(object):
"""A simple wrapper around Caffe's solver.
This wrapper gives us control over he snapshotting process, which we
use to unnormalize the learned bounding-box regression weights.
"""
def __init__(self, solver_prototxt, roidb, output_dir,
pretrained_model=None):
"""Initialize the SolverWrapper."""
self.output_dir = output_dir
#cfg.TRAIN.HAS_RPN: True ---> 只有在訓練rpn是TRAIN.HAS_RPN才爲ture
#cfg.TRAIN.BBOX_REG: False ---> 只有在訓練rpn的時候才爲False
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 = \
#add_bbox_regression_targets主要是爲roidb添加一些有用的信息,比如bbox_targets mean std
rdl_roidb.add_bbox_regression_targets(roidb)
print 'done'
#新建一個sgdsolver示例,此過程會:調用底層SGDSolver的構造函數 --> creating Net --> Init各層 直至“Network initialization done.”,直至“Solver scaffolding done.”
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print ('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
#加載已經訓練好的網絡,這是fine-tuning所需要調用的代碼,同時,solver.net.copy_from會調用底層的Net<Dtype>::CopyTrainedLayersFrom方法
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)
# 調用later.py中的set_roidb方法,爲網絡的第一層(RoIDataLayer)設置roidb同時打亂順序
# 這樣做是必然的,畢竟類imdb或者pascal_voc的實例中的roidb必須要傳到layer中,網絡才能繼續向前傳播;
# 在RoIDataLayer的foward方法中,就是將RoIDataLayer實例的_roidb拷貝給RoIDataLayer的top blob
self.solver.net.layers[0].set_roidb(roidb)
def snapshot(self):
"""Take a snapshot of the network after unnormalizing the learned
bounding-box regression weights. This enables easy use at test-time.
"""
net = self.solver.net
scale_bbox_params = (cfg.TRAIN.BBOX_REG and
cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
net.params.has_key('bbox_pred'))
if scale_bbox_params:
# save original values
orig_0 = net.params['bbox_pred'][0].data.copy()
orig_1 = net.params['bbox_pred'][1].data.copy()
# scale and shift with bbox reg unnormalization; then save snapshot
net.params['bbox_pred'][0].data[...] = \
(net.params['bbox_pred'][0].data *
self.bbox_stds[:, np.newaxis])
net.params['bbox_pred'][1].data[...] = \
(net.params['bbox_pred'][1].data *
self.bbox_stds + self.bbox_means)
infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
filename = (self.solver_param.snapshot_prefix + infix +
'_iter_{:d}'.format(self.solver.iter) + '.caffemodel')
filename = os.path.join(self.output_dir, filename)
net.save(str(filename))
print 'Wrote snapshot to: {:s}'.format(filename)
if scale_bbox_params:
# restore net to original state
net.params['bbox_pred'][0].data[...] = orig_0
net.params['bbox_pred'][1].data[...] = orig_1
return filename
def train_model(self, max_iters):
"""Network training loop."""
last_snapshot_iter = -1
timer = Timer()
model_paths = []
while self.solver.iter < max_iters:
# Make one SGD update
timer.tic()
#step(1)迭代一次,調用底層的Solver::Step()方法
self.solver.step(1)
timer.toc()
if self.solver.iter % (10 * self.solver_param.display) == 0:
print 'speed: {:.3f}s / iter'.format(timer.average_time)
# iter是一個Caffe.Solver的一個屬性,調用底層的Solver<Dtype>::iter方法
if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
last_snapshot_iter = self.solver.iter
model_paths.append(self.snapshot())
if last_snapshot_iter != self.solver.iter:
model_paths.append(self.snapshot())
return model_paths