# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Train a Fast R-CNN network."""
import caffe
from fast_rcnn.config import cfg
import roi_data_layer.roidb as rdl_roidb
from utils.timer import Timer
import numpy as np
import os
from caffe.proto import caffe_pb2
import google.protobuf as pb2
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
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 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()
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)
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
def get_training_roidb(imdb):
"""Returns a roidb (Region of Interest database) for use in training."""
if cfg.TRAIN.USE_FLIPPED:
print 'Appending horizontally-flipped training examples...'
imdb.append_flipped_images()
print 'done'
print 'Preparing training data...'
rdl_roidb.prepare_roidb(imdb)
print 'done'
return imdb.roidb
def filter_roidb(roidb):
"""Remove roidb entries that have no usable RoIs."""
def is_valid(entry):
# Valid images have:
# (1) At least one foreground RoI OR
# (2) At least one background RoI
overlaps = entry['max_overlaps']
# find boxes with sufficient overlap
fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
(overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# image is only valid if such boxes exist
valid = len(fg_inds) > 0 or len(bg_inds) > 0
return valid
num = len(roidb)
filtered_roidb = [entry for entry in roidb if is_valid(entry)]
num_after = len(filtered_roidb)
print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
num, num_after)
return filtered_roidb
def train_net(solver_prototxt, roidb, output_dir,
pretrained_model=None, max_iters=40000):
"""Train a Fast R-CNN network."""
roidb = filter_roidb(roidb)
sw = SolverWrapper(solver_prototxt, roidb, output_dir,
pretrained_model=pretrained_model)
print 'Solving...'
model_paths = sw.train_model(max_iters)
print 'done solving'
return model_paths
