def __init__(self,
n_fg_class=None,
pretrained_model=None,
min_size=600,
max_size=1000,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
vgg_initialW=None,
rpn_initialW=None,
loc_initialW=None,
score_initialW=None,
proposal_creator_params={}):
param, path = utils.prepare_pretrained_model(
{'n_fg_class': n_fg_class}, pretrained_model, self._models)
if loc_initialW is None:
loc_initialW = chainer.initializers.Normal(0.001)
if score_initialW is None:
score_initialW = chainer.initializers.Normal(0.01)
if rpn_initialW is None:
rpn_initialW = chainer.initializers.Normal(0.01)
if vgg_initialW is None and pretrained_model:
vgg_initialW = chainer.initializers.constant.Zero()
extractor = VGG16(initialW=vgg_initialW)
extractor.pick = 'conv5_3'
# Delete all layers after conv5_3.
extractor.remove_unused()
rpn = RegionProposalNetwork(
512,
512,
ratios=ratios,
anchor_scales=anchor_scales,
feat_stride=self.feat_stride,
initialW=rpn_initialW,
proposal_creator_params=proposal_creator_params,
)
head = VGG16RoIHead(param['n_fg_class'] + 1,
roi_size=7,
spatial_scale=1. / self.feat_stride,
vgg_initialW=vgg_initialW,
loc_initialW=loc_initialW,
score_initialW=score_initialW)
super(FasterRCNNVGG16,
self).__init__(extractor,
rpn,
head,
mean=np.array([122.7717, 115.9465, 102.9801],
dtype=np.float32)[:, None, None],
min_size=min_size,
max_size=max_size)
if path == 'imagenet':
self._copy_imagenet_pretrained_vgg16()
elif path:
chainer.serializers.load_npz(path, self)
def _copy_imagenet_pretrained_vgg16(self):
pretrained_model = VGG16(pretrained_model='imagenet')
self.extractor.conv1_1.copyparams(pretrained_model.conv1_1)
self.extractor.conv1_2.copyparams(pretrained_model.conv1_2)
self.extractor.conv2_1.copyparams(pretrained_model.conv2_1)
self.extractor.conv2_2.copyparams(pretrained_model.conv2_2)
self.extractor.conv3_1.copyparams(pretrained_model.conv3_1)
self.extractor.conv3_2.copyparams(pretrained_model.conv3_2)
self.extractor.conv3_3.copyparams(pretrained_model.conv3_3)
self.extractor.conv4_1.copyparams(pretrained_model.conv4_1)
self.extractor.conv4_2.copyparams(pretrained_model.conv4_2)
self.extractor.conv4_3.copyparams(pretrained_model.conv4_3)
self.extractor.conv5_1.copyparams(pretrained_model.conv5_1)
self.extractor.conv5_2.copyparams(pretrained_model.conv5_2)
self.extractor.conv5_3.copyparams(pretrained_model.conv5_3)
self.head.fc6.copyparams(pretrained_model.fc6)
self.head.fc7.copyparams(pretrained_model.fc7)
def __init__(
self,
n_fg_class=None,
pretrained_model=None,
min_size=600,
max_size=1000,
ratios=(0.5, 1, 2),
anchor_scales=(8, 16, 32),
vgg_initialW=None,
rpn_initialW=None,
loc_initialW=None,
score_initialW=None,
mask_initialW=None,
proposal_creator_params=None,
pooling_func=functions.roi_align_2d,
roi_size=7,
):
if n_fg_class is None:
if pretrained_model not in self._models:
raise ValueError(
'The n_fg_class needs to be supplied as an argument')
n_fg_class = self._models[pretrained_model]['n_fg_class']
if loc_initialW is None:
loc_initialW = chainer.initializers.Normal(0.001)
if score_initialW is None:
score_initialW = chainer.initializers.Normal(0.01)
if mask_initialW is None:
mask_initialW = chainer.initializers.Normal(0.01)
if rpn_initialW is None:
rpn_initialW = chainer.initializers.Normal(0.01)
if vgg_initialW is None and pretrained_model:
vgg_initialW = chainer.initializers.constant.Zero()
if proposal_creator_params is None:
proposal_creator_params = dict(
n_train_pre_nms=12000,
n_train_post_nms=2000,
n_test_pre_nms=6000,
n_test_post_nms=1000,
min_size=0,
)
extractor = VGG16(initialW=vgg_initialW)
extractor.pick = 'conv5_3'
# Delete all layers after conv5_3.
extractor.remove_unused()
rpn = RegionProposalNetwork(
512,
512,
ratios=ratios,
anchor_scales=anchor_scales,
feat_stride=self.feat_stride,
initialW=rpn_initialW,
proposal_creator_params=proposal_creator_params,
)
head = VGG16RoIHead(
n_fg_class + 1,
roi_size=roi_size,
spatial_scale=1. / self.feat_stride,
vgg_initialW=vgg_initialW,
loc_initialW=loc_initialW,
score_initialW=score_initialW,
mask_initialW=mask_initialW,
pooling_func=pooling_func,
)
super(MaskRCNNVGG16,
self).__init__(extractor,
rpn,
head,
mean=np.array([122.7717, 115.9465, 102.9801],
dtype=np.float32)[:, None, None],
min_size=min_size,
max_size=max_size)
if pretrained_model in self._models:
path = download_model(self._models[pretrained_model]['url'])
chainer.serializers.load_npz(path, self)
elif pretrained_model == 'imagenet':
self._copy_imagenet_pretrained_vgg16()
elif pretrained_model:
chainer.serializers.load_npz(pretrained_model, self)