def _add_wsl_head(model, add_roi_box_head_func, blob_in, dim_in,
spatial_scale_in):
"""Add a Fast R-CNN head to the model."""
blob_frcn, dim_frcn = add_roi_box_head_func(model, blob_in, dim_in,
spatial_scale_in)
if cfg.WEBLY.WEBLY_ON:
webly_heads.add_wsl_outputs(model, blob_frcn, dim_frcn)
if model.train:
loss_gradients = webly_heads.add_wsl_losses(model)
else:
loss_gradients = None
return loss_gradients
wsl_heads.add_wsl_outputs(model, blob_frcn, dim_frcn)
if model.train:
if isinstance(blob_frcn, list) and False:
loss_gradients = {}
for i in range(len(blob_frcn)):
lg = wsl_heads.add_wsl_losses(model, prefix='_{}_'.format(i))
loss_gradients.update(lg)
else:
loss_gradients = wsl_heads.add_wsl_losses(model)
else:
loss_gradients = None
return loss_gradients
def _add_wsl_head(model, add_roi_box_head_func, blob_in, dim_in,
spatial_scale_in):
"""Add a Fast R-CNN head to the model."""
blob_frcn, dim_frcn = add_roi_box_head_func(model, blob_in, dim_in,
spatial_scale_in)
wsl_heads.add_wsl_outputs(model, blob_frcn, dim_frcn)
if model.train:
loss_gradients = wsl_heads.add_wsl_losses(model)
else:
loss_gradients = None
return loss_gradients
def add_webly_outputs(model, blob_in, dim, prefix=''):
add_wsl_outputs(model, blob_in[0], dim[0], prefix=prefix)
# Box classification layer
model.FC(
blob_in[1],
prefix + 'noisy_fc8c',
dim[1],
model.num_classes - 1,
weight_init=('XavierFill', {}),
# weight_init=('GaussianFill', {
# 'std': 0.0005
# }),
bias_init=const_fill(0.0))
model.FC(
blob_in[1],
prefix + 'noisy_fc8d',
dim[1],
model.num_classes - 1,
# weight_init=('GaussianFill', {
# 'std': 0.0005
# }),
weight_init=('XavierFill', {}),
bias_init=const_fill(0.0))
model.net.Add([prefix + 'fc8c', prefix + 'noisy_fc8c'],
[prefix + 'fc8c_noise'])
model.net.Add([prefix + 'fc8d', prefix + 'noisy_fc8d'],
[prefix + 'fc8d_noise'])
model.Softmax(prefix + 'fc8c_noise', prefix + 'alpha_cls_noise', axis=1)
model.Transpose(prefix + 'fc8d_noise',
prefix + 'fc8d_t_noise',
axes=(1, 0))
model.Softmax(prefix + 'fc8d_t_noise',
prefix + 'alpha_det_t_noise',
axis=1)
model.Transpose(prefix + 'alpha_det_t_noise',
prefix + 'alpha_det_noise',
axes=(1, 0))
model.net.Mul([prefix + 'alpha_cls_noise', prefix + 'alpha_det_noise'],
prefix + 'rois_pred_noise')
def _add_wsl_head(model, add_roi_box_head_func, blob_in, dim_in,
spatial_scale_in):
"""Add a Fast R-CNN head to the model."""
blob_frcn, dim_frcn = add_roi_box_head_func(model, blob_in, dim_in,
spatial_scale_in)
wsl_heads.add_wsl_outputs(model, blob_frcn, dim_frcn)
if model.train and True:
wsl_heads.add_hard_mining(model)
blob_frcn, dim_frcn = get_func(cfg.FAST_RCNN.ROI_BOX_HEAD + '_shared')(
model, "db1_add", dim_in, spatial_scale_in, prefix='mining_')
wsl_heads.add_mining_outputs_shared(model,
blob_frcn,
dim_frcn,
prefix='mining_')
loss_gradients = wsl_heads.add_mining_losses(model, prefix='mining_')
elif model.train:
loss_gradients = wsl_heads.add_wsl_losses(model)
else:
loss_gradients = None
return loss_gradients