def init_modules(self):
self.feature_extractor = ResNetFeatureExtractor(
self.feature_extractor_config)
self.rpn_model = RPNModel(self.rpn_config)
if self.pooling_mode == 'align':
self.rcnn_pooling = RoIAlignAvg(self.pooling_size,
self.pooling_size, 1.0 / 16.0)
elif self.pooling_mode == 'ps':
self.rcnn_pooling = PSRoIPool(7, 7, 1.0 / 16, 7, self.n_classes)
elif self.pooling_mode == 'psalign':
raise NotImplementedError('have not implemented yet!')
elif self.pooling_mode == 'deformable_psalign':
raise NotImplementedError('have not implemented yet!')
self.rcnn_cls_pred = nn.Linear(2048, self.n_classes)
if self.reduce:
in_channels = 2048
else:
in_channels = 2048 * 4 * 4
if self.class_agnostic:
self.rcnn_bbox_pred = nn.Linear(in_channels, 4)
else:
self.rcnn_bbox_pred = nn.Linear(in_channels, 4 * self.n_classes)
# loss module
if self.use_focal_loss:
self.rcnn_cls_loss = FocalLoss(2)
else:
self.rcnn_cls_loss = functools.partial(F.cross_entropy,
reduce=False)
self.rcnn_bbox_loss = nn.modules.SmoothL1Loss(reduce=False)
def init_modules(self):
self.feature_extractor = feature_extractors_builder.build(
self.feature_extractor_config)
# self.feature_extractor = ResNetFeatureExtractor(
# self.feature_extractor_config)
# self.feature_extractor = MobileNetFeatureExtractor(
# self.feature_extractor_config)
self.rpn_model = RPNModel(self.rpn_config)
if self.pooling_mode == 'align':
self.rcnn_pooling = RoIAlignAvg(self.pooling_size,
self.pooling_size, 1.0 / 16.0)
elif self.pooling_mode == 'ps':
self.rcnn_pooling = PSRoIPool(7, 7, 1.0 / 16, 7, self.n_classes)
elif self.pooling_mode == 'psalign':
raise NotImplementedError('have not implemented yet!')
elif self.pooling_mode == 'deformable_psalign':
raise NotImplementedError('have not implemented yet!')
self.rcnn_cls_pred = nn.Linear(self.ndin, self.n_classes)
if self.class_agnostic:
self.rcnn_bbox_pred = nn.Linear(self.ndin, 4)
# self.rcnn_bbox_pred = nn.Conv2d(2048,4,3,1,1)
else:
self.rcnn_bbox_pred = nn.Linear(self.ndin, 4 * self.n_classes)
# loss module
if self.use_focal_loss:
self.rcnn_cls_loss = FocalLoss(2,
gamma=2,
alpha=0.2,
auto_alpha=False)
else:
self.rcnn_cls_loss = functools.partial(F.cross_entropy,
reduce=False)
self.rcnn_bbox_loss = nn.modules.SmoothL1Loss(reduce=False)
# attention
if self.use_self_attention:
self.spatial_attention = nn.Conv2d(self.ndin, 1, 3, 1, 1)
self.rcnn_pooling2 = RoIAlignAvg(self.pooling_size, self.pooling_size,
1.0 / 8.0)
self.reduce_pooling = nn.Sequential(nn.Conv2d(512, 1024, 1, 1, 0),
nn.ReLU())
def init_modules(self):
self.feature_extractor = ResNetFeatureExtractor(
self.feature_extractor_config)
self.rpn_model = RPNModel(self.rpn_config)
if self.pooling_mode == 'align':
self.rcnn_pooling = RoIAlignAvg(self.pooling_size,
self.pooling_size, 1.0 / 16.0)
elif self.pooling_mode == 'ps':
self.rcnn_pooling = PSRoIPool(7, 7, 1.0 / 16, 7, self.n_classes)
elif self.pooling_mode == 'psalign':
raise NotImplementedError('have not implemented yet!')
elif self.pooling_mode == 'deformable_psalign':
raise NotImplementedError('have not implemented yet!')
self.rcnn_cls_pred = nn.Linear(2048, 2)
if self.reduce:
in_channels = 2048
else:
in_channels = 2048 * 4 * 4
if self.class_agnostic:
self.rcnn_bbox_pred = nn.Linear(in_channels, 4)
else:
self.rcnn_bbox_pred = nn.Linear(in_channels, 4 * self.n_classes)
if self.multiple_crop:
self.rcnn_pooling2 = RoIAlignAvg(self.pooling_size,
self.pooling_size, 1.0 / 8.0)
# 1x1 fusion
self.pooled_feat_fusion = nn.Conv2d(512, 1024, 1, 1, 0)
# loss module
# if self.enable_cls:
# if self.use_focal_loss:
# self.rcnn_cls_loss = FocalLoss(2, alpha=0.25, gamma=2)
# else:
# self.rcnn_cls_loss = functools.partial(
# F.cross_entropy, reduce=False)
# elif self.enable_iou:
self.rcnn_cls_loss = nn.MSELoss(reduce=False)
self.rcnn_bbox_loss = nn.modules.SmoothL1Loss(reduce=False)