def ff_true():
feature_fastrcnn = resnet_conv5(
roi_resized, config.RESNET_NUM_BLOCK[-1]) # nxcx7x7
if config.TRAIN_HEADS_ONLY:
feature_fastrcnn = tf.stop_gradient(feature_fastrcnn)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head(
'fastrcnn', feature_fastrcnn, config.NUM_CLASS)
return feature_fastrcnn, fastrcnn_label_logits, fastrcnn_box_logits
def ff_true():
feature_fastrcnn = resnet_conv5(
roi_resized, config.RESNET_NUM_BLOCK[-1]) # nxcx7x7
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head(
'fastrcnn', feature_fastrcnn, config.NUM_CLASS)
return feature_fastrcnn, fastrcnn_label_logits, fastrcnn_box_logits
def ff_true():
feature_fastrcnn = resnet_conv5(roi_resized, config.RESNET_NUM_BLOCK[-1]) # nxcx7x7
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head('fastrcnn', feature_fastrcnn, config.NUM_CLASS)
return feature_fastrcnn, fastrcnn_label_logits, fastrcnn_box_logits
def _build_graph(self, inputs):
is_training = get_current_tower_context().is_training
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
fm_anchors = self._get_anchors(image)
image = self._preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:]
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors)
featuremap = pretrained_resnet_conv4(image,
config.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, 1024,
config.NUM_ANCHOR)
decoded_boxes = decode_bbox_target(rpn_box_logits,
fm_anchors) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(decoded_boxes, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]), image_shape2d)
if is_training:
# sample proposal boxes in training
rcnn_sampled_boxes, rcnn_labels, fg_inds_wrt_gt = sample_fast_rcnn_targets(
proposal_boxes, gt_boxes, gt_labels)
boxes_on_featuremap = rcnn_sampled_boxes * (1.0 /
config.ANCHOR_STRIDE)
else:
# use all proposal boxes in inference
boxes_on_featuremap = proposal_boxes * (1.0 / config.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(roi_resized,
config.RESNET_NUM_BLOCK[-1]) # nxcx7x7
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head(
'fastrcnn', feature_fastrcnn, config.NUM_CLASS)
if is_training:
# rpn loss
rpn_label_loss, rpn_box_loss = rpn_losses(anchor_labels,
anchor_boxes_encoded,
rpn_label_logits,
rpn_box_logits)
# fastrcnn loss
fg_inds_wrt_sample = tf.reshape(tf.where(rcnn_labels > 0),
[-1]) # fg inds w.r.t all samples
fg_sampled_boxes = tf.gather(rcnn_sampled_boxes,
fg_inds_wrt_sample)
with tf.name_scope('fg_sample_patch_viz'):
fg_sampled_patches = crop_and_resize(
image, fg_sampled_boxes,
tf.zeros_like(fg_inds_wrt_sample, dtype=tf.int32), 300)
fg_sampled_patches = tf.transpose(fg_sampled_patches,
[0, 2, 3, 1])
tf.summary.image('viz', fg_sampled_patches, max_outputs=30)
matched_gt_boxes = tf.gather(gt_boxes, fg_inds_wrt_gt)
encoded_boxes = encode_bbox_target(
matched_gt_boxes, fg_sampled_boxes) * tf.constant(
config.FASTRCNN_BBOX_REG_WEIGHTS)
fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses(
rcnn_labels, fastrcnn_label_logits, encoded_boxes,
tf.gather(fastrcnn_box_logits, fg_inds_wrt_sample))
wd_cost = regularize_cost(
'(?:group1|group2|group3|rpn|fastrcnn)/.*W',
l2_regularizer(1e-4),
name='wd_cost')
self.cost = tf.add_n([
rpn_label_loss, rpn_box_loss, fastrcnn_label_loss,
fastrcnn_box_loss, wd_cost
], 'total_cost')
add_moving_summary(self.cost, wd_cost)
else:
label_probs = tf.nn.softmax(
fastrcnn_label_logits,
name='fastrcnn_all_probs') # #proposal x #Class
anchors = tf.tile(
tf.expand_dims(proposal_boxes, 1),
[1, config.NUM_CLASS - 1, 1]) # #proposal x #Cat x 4
decoded_boxes = decode_bbox_target(
fastrcnn_box_logits /
tf.constant(config.FASTRCNN_BBOX_REG_WEIGHTS), anchors)
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
# indices: Nx2. Each index into (#proposal, #category)
pred_indices, final_probs = fastrcnn_predictions(
decoded_boxes, label_probs)
final_probs = tf.identity(final_probs, 'final_probs')
tf.gather_nd(decoded_boxes, pred_indices, name='final_boxes')
tf.add(pred_indices[:, 1], 1, name='final_labels')
def _build_graph(self, inputs):
is_training = get_current_tower_context().is_training
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
image = tf.expand_dims(image, 0)
# FSxFSxNAx4 (FS=MAX_SIZE//ANCHOR_STRIDE)
with tf.name_scope('anchors'):
all_anchors = tf.constant(get_all_anchors(),
name='all_anchors',
dtype=tf.float32)
fm_anchors = tf.slice(
all_anchors, [0, 0, 0, 0],
tf.stack([
tf.shape(image)[1] // config.ANCHOR_STRIDE,
tf.shape(image)[2] // config.ANCHOR_STRIDE, -1, -1
]),
name='fm_anchors')
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors)
image = image_preprocess(image, bgr=True)
image = tf.transpose(image, [0, 3, 1, 2])
# resnet50
featuremap = pretrained_resnet_conv4(image, [3, 4, 6])
rpn_label_logits, rpn_box_logits = rpn_head(featuremap)
rpn_label_loss, rpn_box_loss = rpn_losses(anchor_labels,
anchor_boxes_encoded,
rpn_label_logits,
rpn_box_logits)
decoded_boxes = decode_bbox_target(
rpn_box_logits, fm_anchors) # (fHxfWxNA)x4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
decoded_boxes, tf.reshape(rpn_label_logits, [-1]),
tf.shape(image)[2:])
if is_training:
rcnn_sampled_boxes, rcnn_encoded_boxes, rcnn_labels = sample_fast_rcnn_targets(
proposal_boxes, gt_boxes, gt_labels)
boxes_on_featuremap = rcnn_sampled_boxes * (1.0 /
config.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(roi_resized) # nxc
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head(
feature_fastrcnn, config.NUM_CLASS)
fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses(
rcnn_labels, rcnn_encoded_boxes, fastrcnn_label_logits,
fastrcnn_box_logits)
wd_cost = regularize_cost(
'(?:group1|group2|group3|rpn|fastrcnn)/.*W',
l2_regularizer(1e-4),
name='wd_cost')
self.cost = tf.add_n([
rpn_label_loss, rpn_box_loss, fastrcnn_label_loss,
fastrcnn_box_loss, wd_cost
], 'total_cost')
for k in self.cost, wd_cost:
add_moving_summary(k)
else:
roi_resized = roi_align(
featuremap, proposal_boxes * (1.0 / config.ANCHOR_STRIDE), 14)
feature_fastrcnn = resnet_conv5(roi_resized) # nxc
label_logits, fastrcnn_box_logits = fastrcnn_head(
feature_fastrcnn, config.NUM_CLASS)
label_probs = tf.nn.softmax(label_logits,
name='fastrcnn_all_probs') # NP,
labels = tf.argmax(label_logits, axis=1)
fg_ind, fg_box_logits = fastrcnn_predict_boxes(
labels, fastrcnn_box_logits)
fg_label_probs = tf.gather(label_probs,
fg_ind,
name='fastrcnn_fg_probs')
fg_boxes = tf.gather(proposal_boxes, fg_ind)
fg_box_logits = fg_box_logits / tf.constant(
config.FASTRCNN_BBOX_REG_WEIGHTS)
decoded_boxes = decode_bbox_target(fg_box_logits,
fg_boxes) # Nfx4, floatbox
decoded_boxes = tf.identity(decoded_boxes,
name='fastrcnn_fg_boxes')
def _build_graph(self, inputs):
is_training = get_current_tower_context().is_training
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
fm_anchors = self._get_anchors(image)
image = self._preprocess(image)
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors)
featuremap = pretrained_resnet_conv4(image,
config.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head(featuremap, 1024,
config.NUM_ANCHOR)
rpn_label_loss, rpn_box_loss = rpn_losses(anchor_labels,
anchor_boxes_encoded,
rpn_label_logits,
rpn_box_logits)
decoded_boxes = decode_bbox_target(
rpn_box_logits, fm_anchors,
config.ANCHOR_STRIDE) # (fHxfWxNA)x4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
decoded_boxes, tf.reshape(rpn_label_logits, [-1]),
tf.shape(image)[2:])
if is_training:
rcnn_sampled_boxes, rcnn_encoded_boxes, rcnn_labels = sample_fast_rcnn_targets(
proposal_boxes, gt_boxes, gt_labels)
boxes_on_featuremap = rcnn_sampled_boxes * (1.0 /
config.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5_gap(
roi_resized, config.RESNET_NUM_BLOCK[-1]) # nxc
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head(
feature_fastrcnn, config.NUM_CLASS)
fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses(
rcnn_labels, rcnn_encoded_boxes, fastrcnn_label_logits,
fastrcnn_box_logits)
wd_cost = regularize_cost(
'(?:group1|group2|group3|rpn|fastrcnn)/.*W',
l2_regularizer(1e-4),
name='wd_cost')
self.cost = tf.add_n([
rpn_label_loss, rpn_box_loss, fastrcnn_label_loss,
fastrcnn_box_loss, wd_cost
], 'total_cost')
for k in self.cost, wd_cost:
add_moving_summary(k)
else:
roi_resized = roi_align(
featuremap, proposal_boxes * (1.0 / config.ANCHOR_STRIDE), 14)
feature_fastrcnn = resnet_conv5_gap(
roi_resized, config.RESNET_NUM_BLOCK[-1]) # nxc
label_logits, fastrcnn_box_logits = fastrcnn_head(
feature_fastrcnn, config.NUM_CLASS)
label_probs = tf.nn.softmax(label_logits,
name='fastrcnn_all_probs') # NP,
labels = tf.argmax(label_logits, axis=1)
fg_ind, fg_box_logits = fastrcnn_predict_boxes(
labels, fastrcnn_box_logits)
fg_label_probs = tf.gather(label_probs,
fg_ind,
name='fastrcnn_fg_probs')
fg_boxes = tf.gather(proposal_boxes, fg_ind)
fg_box_logits = fg_box_logits / tf.constant(
config.FASTRCNN_BBOX_REG_WEIGHTS)
decoded_boxes = decode_bbox_target(
fg_box_logits, fg_boxes,
config.ANCHOR_STRIDE) # Nfx4, floatbox
decoded_boxes = tf.identity(decoded_boxes,
name='fastrcnn_fg_boxes')
