def _build(self, image, gt_boxes=None, is_training=False):
if gt_boxes is not None:
gt_boxes = tf.cast(gt_boxes, tf.float32)
image.set_shape((None, None, 3))
conv_feature_map = self.base_network(
tf.expand_dims(image, 0), is_training=is_training
)
# The RPN submodule which generates proposals of objects.
self._rpn = RPN(
self._num_anchors, self._config.model.rpn,
debug=self._debug, seed=self._seed
)
if self._with_rcnn:
self._rcnn = RCNN(
self._num_classes, self._config.model.rcnn,
debug=self._debug, seed=self._seed
)
image_shape = tf.shape(image)[0:2]
variable_summaries(
conv_feature_map, 'conv_feature_map', 'reduced'
)
all_anchors = self._generate_anchors(tf.shape(conv_feature_map))
rpn_prediction = self._rpn(
conv_feature_map, image_shape, all_anchors,
gt_boxes=gt_boxes, is_training=is_training
)
prediction_dict = {
'rpn_prediction': rpn_prediction,
}
if self._debug:
prediction_dict['image'] = image
prediction_dict['image_shape'] = image_shape
prediction_dict['all_anchors'] = all_anchors
prediction_dict['anchor_reference'] = tf.convert_to_tensor(
self._anchor_reference
)
if gt_boxes is not None:
prediction_dict['gt_boxes'] = gt_boxes
prediction_dict['conv_feature_map'] = conv_feature_map
if self._with_rcnn:
proposals = tf.stop_gradient(rpn_prediction['proposals'])
classification_pred = self._rcnn(
conv_feature_map, proposals,
image_shape, self.base_network,
gt_boxes=gt_boxes, is_training=is_training
)
prediction_dict['classification_prediction'] = classification_pred
return prediction_dict
def setUp(self):
tf.reset_default_graph()
self._num_classes = 5
self._num_proposals = 256
self._total_num_gt = 128
self._image_shape = (600, 800)
# The score we'll give to the true labels when testing for perfect
# score generation.
self._high_score = 100
self._equality_delta = 1e-03
self._config = EasyDict({
'enabled': True,
'layer_sizes': [4096, 4096],
'dropout_keep_prop': 1.0,
'activation_function': 'relu6',
'use_mean': False,
'initializer': {
'type': 'variance_scaling_initializer',
'factor': 1.0,
'uniform': True,
'mode': 'FAN_AVG',
},
'l2_regularization_scale': 0.0005,
'roi': {
'pooling_mode': 'crop',
'pooled_width': 7,
'pooled_height': 7,
'padding': 'VALID',
},
'proposals': {
'class_max_detections': 100,
'class_nms_threshold': 0.6,
'total_max_detections': 300,
'min_prob_threshold': 0.0,
},
'target': {
'foreground_fraction': 0.25,
'minibatch_size': 64,
'foreground_threshold': 0.5,
'background_threshold_high': 0.5,
'background_threshold_low': 0.1,
},
})
self._shared_model = RCNN(self._num_classes, self._config)
# Declare placeholders
# We use the '_ph' suffix for placeholders.
self._pretrained_feature_map_shape = (self._num_proposals,
self._config.roi.pooled_width,
self._config.roi.pooled_height,
4)
self._pretrained_feature_map_ph = tf.placeholder(
tf.float32, shape=self._pretrained_feature_map_shape)
self._proposals_shape = (self._num_proposals, 5)
self._proposals_ph = tf.placeholder(tf.float32,
shape=self._proposals_shape)
self._image_shape_shape = (2, )
self._image_shape_ph = tf.placeholder(tf.float32,
shape=self._image_shape_shape)
self._gt_boxes_shape = (self._total_num_gt, 5)
self._gt_boxes_ph = tf.placeholder(tf.float32,
shape=self._gt_boxes_shape)
class RCNNTest(tf.test.TestCase):
def setUp(self):
tf.reset_default_graph()
self._num_classes = 5
self._num_proposals = 256
self._total_num_gt = 128
self._image_shape = (600, 800)
# The score we'll give to the true labels when testing for perfect
# score generation.
self._high_score = 100
self._equality_delta = 1e-03
self._config = EasyDict({
'enabled': True,
'layer_sizes': [4096, 4096],
'dropout_keep_prop': 1.0,
'activation_function': 'relu6',
'use_mean': False,
'initializer': {
'type': 'variance_scaling_initializer',
'factor': 1.0,
'uniform': True,
'mode': 'FAN_AVG',
},
'l2_regularization_scale': 0.0005,
'roi': {
'pooling_mode': 'crop',
'pooled_width': 7,
'pooled_height': 7,
'padding': 'VALID',
},
'proposals': {
'class_max_detections': 100,
'class_nms_threshold': 0.6,
'total_max_detections': 300,
'min_prob_threshold': 0.0,
},
'target': {
'foreground_fraction': 0.25,
'minibatch_size': 64,
'foreground_threshold': 0.5,
'background_threshold_high': 0.5,
'background_threshold_low': 0.1,
},
})
self._shared_model = RCNN(self._num_classes, self._config)
# Declare placeholders
# We use the '_ph' suffix for placeholders.
self._pretrained_feature_map_shape = (self._num_proposals,
self._config.roi.pooled_width,
self._config.roi.pooled_height,
4)
self._pretrained_feature_map_ph = tf.placeholder(
tf.float32, shape=self._pretrained_feature_map_shape)
self._proposals_shape = (self._num_proposals, 5)
self._proposals_ph = tf.placeholder(tf.float32,
shape=self._proposals_shape)
self._image_shape_shape = (2, )
self._image_shape_ph = tf.placeholder(tf.float32,
shape=self._image_shape_shape)
self._gt_boxes_shape = (self._total_num_gt, 5)
self._gt_boxes_ph = tf.placeholder(tf.float32,
shape=self._gt_boxes_shape)
def _run_net_with_feed_dict(self, net, feed_dict):
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
return sess.run(net, feed_dict=feed_dict)
def _check_returning_shapes(self, prediction_dict, training=False):
"""Asserts a prediction_dict has the right shapes.
This includes testing that:
- objects, objects_labels and objects_labels_prob have the same
shape in the first dimension. (i.e. the same number of
objects).
- objects has shape (_, 4). objects_labels and objects_labels_prob
have shape (_,).
- cls_score and cls_prob have shape (num_proposals,
num_classes + 1).
- bbox_offsets has shape (num_proposals, num_classes * 4).
And, if training:
- cls_target has shape (num_proposals,).
- bbox_offsets_target has shape (num_proposals, 4).
"""
objects_shape = prediction_dict['objects'].shape
objects_labels_shape = prediction_dict['labels'].shape
objects_labels_prob_shape = prediction_dict['probs'] \
.shape
cls_score_shape = prediction_dict['rcnn']['cls_score'].shape
cls_prob_shape = prediction_dict['rcnn']['cls_prob'].shape
bbox_offsets_shape = prediction_dict['rcnn']['bbox_offsets'].shape
# We choose cls_score as the 'standard' num_proposals which we will
# compare to the other shapes that should include num_proposals. We
# could have chosen a different one.
num_proposals = cls_score_shape[0]
self.assertEqual(objects_shape[0], objects_labels_shape[0])
self.assertEqual(objects_shape[0], objects_labels_prob_shape[0])
self.assertEqual(objects_shape[1], 4)
self.assertEqual(len(objects_labels_shape), 1)
self.assertEqual(len(objects_labels_prob_shape), 1)
self.assertEqual(cls_score_shape, cls_prob_shape)
self.assertEqual(cls_prob_shape,
(num_proposals, self._num_classes + 1))
self.assertEqual(bbox_offsets_shape,
(num_proposals, self._num_classes * 4))
if training:
cls_target_shape = prediction_dict['target']['cls'].shape
self.assertEqual(cls_target_shape, (num_proposals, ))
bbox_offsets_trgt_shape = (
prediction_dict['target']['bbox_offsets'].shape)
self.assertEqual(bbox_offsets_trgt_shape, (num_proposals, 4))
def testReturningShapes(self):
"""Tests we're returning consistent shapes.
We test both the case where we're training and the case where we are
not.
"""
# Prediction session (not training)
rcnn_net_not_training = self._shared_model(
self._pretrained_feature_map_ph, self._proposals_ph,
self._image_shape_ph)
prediction_dict_not_training = self._run_net_with_feed_dict(
rcnn_net_not_training,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
})
# Training session
rcnn_net_training = self._shared_model(self._pretrained_feature_map_ph,
self._proposals_ph,
self._image_shape_ph,
self._gt_boxes_ph)
prediction_dict_training = self._run_net_with_feed_dict(
rcnn_net_training,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
self._gt_boxes_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._gt_boxes_shape,
),
})
# Assertions
self._check_returning_shapes(prediction_dict_not_training)
self._check_returning_shapes(prediction_dict_training, training=True)
def testMinibatchBehaviour(self):
"""Tests we're using minibatch_size correctly when testing.
"""
rcnn_net = self._shared_model(self._pretrained_feature_map_ph,
self._proposals_ph, self._image_shape_ph,
self._gt_boxes_ph)
prediction_dict = self._run_net_with_feed_dict(
rcnn_net,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
self._gt_boxes_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._gt_boxes_shape,
),
})
# Assertions
self.assertLessEqual(
prediction_dict['target']['cls'][
prediction_dict['target']['cls'] >= 0].shape[0],
self._config.target.minibatch_size,
)
def testNumberOfObjects(self):
"""Tests we're not returning more objects than we get proposals.
"""
rcnn_net = self._shared_model(self._pretrained_feature_map_ph,
self._proposals_ph, self._image_shape_ph)
prediction_dict = self._run_net_with_feed_dict(
rcnn_net,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
})
# Assertions
self.assertLessEqual(prediction_dict['objects'].shape[0],
self._num_proposals)
def testLoss(self):
"""Tests we're computing loss correctly.
In particular, we're testing whether computing a perfect score when we
have to.
"""
# Generate placeholders and loss_graph
cls_score_shape = (self._num_proposals, self._num_classes + 1)
cls_score_ph = tf.placeholder(tf.float32, cls_score_shape)
cls_prob_shape = (self._num_proposals, self._num_classes + 1)
cls_prob_ph = tf.placeholder(tf.float32, cls_prob_shape)
cls_target_shape = (self._num_proposals, )
cls_target_ph = tf.placeholder(tf.float32, cls_target_shape)
bbox_offsets_shape = (self._num_proposals, self._num_classes * 4)
bbox_offsets_ph = tf.placeholder(tf.float32, bbox_offsets_shape)
bbox_offsets_target_shape = (self._num_proposals, 4)
bbox_offsets_target_ph = tf.placeholder(tf.float32,
bbox_offsets_target_shape)
loss_graph = self._shared_model.loss({
'rcnn': {
'cls_score': cls_score_ph,
'cls_prob': cls_prob_ph,
'bbox_offsets': bbox_offsets_ph,
},
'target': {
'cls': cls_target_ph,
'bbox_offsets': bbox_offsets_target_ph,
}
})
# Generate values that ensure a perfect score
# We first initialize all our values to zero.
cls_score = np.zeros(cls_score_shape, dtype=np.float32)
cls_prob = np.zeros(cls_prob_shape, dtype=np.float32)
cls_target = np.zeros(cls_target_shape, dtype=np.float32)
bbox_offsets = np.zeros(bbox_offsets_shape, dtype=np.float32)
bbox_offsets_target = np.zeros(bbox_offsets_target_shape,
dtype=np.float32)
for i in range(self._num_proposals):
this_class = np.random.randint(low=1, high=self._num_classes + 1)
cls_score[i][this_class] = self._high_score
cls_prob[i][this_class] = 1.
cls_target[i] = this_class
# Find out where in the axis 1 in bbox_offsets we should
# put the offsets, because the shape is
# (num_proposals, num_classes * 4), and we're using
# 1-indexed classes.
class_place = (this_class - 1) * 4
for j in range(4):
this_coord = np.random.randint(low=0,
high=np.amax(self._image_shape))
bbox_offsets[i][class_place + j] = this_coord
bbox_offsets_target[i][j] = this_coord
# Now get the loss dict using the values we just generated.
loss_dict = self._run_net_with_feed_dict(loss_graph,
feed_dict={
cls_score_ph:
cls_score,
cls_prob_ph:
cls_prob,
cls_target_ph:
cls_target,
bbox_offsets_ph:
bbox_offsets,
bbox_offsets_target_ph:
bbox_offsets_target,
})
# Assertions
self.assertAlmostEqual(loss_dict['rcnn_cls_loss'],
0,
delta=self._equality_delta)
self.assertAlmostEqual(loss_dict['rcnn_reg_loss'],
0,
delta=self._equality_delta)
def _build(self, image, gt_boxes=None, is_training=False):
"""
Returns bounding boxes and classification probabilities.
Args:
image: A tensor with the image.
Its shape should be `(height, width, 3)`.
gt_boxes: A tensor with all the ground truth boxes of that image.
Its shape should be `(num_gt_boxes, 5)`
Where for each gt box we have (x1, y1, x2, y2, label),
in that order.
is_training: A boolean to whether or not it is used for training.
Returns:
classification_prob: A tensor with the softmax probability for
each of the bounding boxes found in the image.
Its shape should be: (num_bboxes, num_categories + 1)
classification_bbox: A tensor with the bounding boxes found.
It's shape should be: (num_bboxes, 4). For each of the bboxes
we have (x1, y1, x2, y2)
"""
if gt_boxes is not None:
gt_boxes = tf.cast(gt_boxes, tf.float32)
# A Tensor with the feature map for the image,
# its shape should be `(feature_height, feature_width, 512)`.
# The shape depends of the pretrained network in use.
# Set rank and last dimension before using base network
# TODO: Why does it loose information when using queue?
image.set_shape((None, None, 3))
conv_feature_map = self.base_network(tf.expand_dims(image, 0),
is_training=is_training)
# The RPN submodule which generates proposals of objects.
self._rpn = RPN(
self._num_anchors,
self._config.model.rpn,
debug=self._debug,
seed=self._seed,
)
if self._with_rcnn:
# The RCNN submodule which classifies RPN's proposals and
# classifies them as background or a specific class.
self._rcnn = RCNN(
self._num_classes,
self._config.model.rcnn,
debug=self._debug,
seed=self._seed,
)
image_shape = tf.shape(image)[0:2]
variable_summaries(conv_feature_map, "conv_feature_map", "reduced")
# Generate anchors for the image based on the anchor reference.
all_anchors = self._generate_anchors(tf.shape(conv_feature_map))
rpn_prediction = self._rpn(
conv_feature_map,
image_shape,
all_anchors,
gt_boxes=gt_boxes,
is_training=is_training,
)
prediction_dict = {
"rpn_prediction": rpn_prediction,
}
if self._debug:
prediction_dict["image"] = image
prediction_dict["image_shape"] = image_shape
prediction_dict["all_anchors"] = all_anchors
prediction_dict["anchor_reference"] = tf.convert_to_tensor(
self._anchor_reference)
if gt_boxes is not None:
prediction_dict["gt_boxes"] = gt_boxes
prediction_dict["conv_feature_map"] = conv_feature_map
if self._with_rcnn:
proposals = tf.stop_gradient(rpn_prediction["proposals"])
classification_pred = self._rcnn(
conv_feature_map,
proposals,
image_shape,
self.base_network,
gt_boxes=gt_boxes,
is_training=is_training,
)
prediction_dict["classification_prediction"] = classification_pred
return prediction_dict
class FasterRCNN(snt.AbstractModule):
"""Faster RCNN Network module
Builds the Faster RCNN network architecture using different submodules.
Calculates the total loss of the model based on the different losses by
each of the submodules.
It is also responsible for building the anchor reference which is used in
graph for generating the dynamic anchors.
"""
def __init__(self, config, name="fasterrcnn"):
super(FasterRCNN, self).__init__(name=name)
# Main configuration object, it holds not only the necessary
# information for this module but also configuration for each of the
# different submodules.
self._config = config
# Total number of classes to classify. If not using RCNN then it is not
# used. TODO: Make it *more* optional.
self._num_classes = config.model.network.num_classes
# Generate network with RCNN thus allowing for classification of
# objects and not just finding them.
self._with_rcnn = config.model.network.with_rcnn
# Turn on debug mode with returns more Tensors which can be used for
# better visualization and (of course) debugging.
self._debug = config.train.debug
self._seed = config.train.seed
# Anchor config, check out the docs of base_config.yml for a better
# understanding of how anchors work.
self._anchor_base_size = config.model.anchors.base_size
self._anchor_scales = np.array(config.model.anchors.scales)
self._anchor_ratios = np.array(config.model.anchors.ratios)
self._anchor_stride = config.model.anchors.stride
# Anchor reference for building dynamic anchors for each image in the
# computation graph.
self._anchor_reference = generate_anchors_reference(
self._anchor_base_size, self._anchor_ratios, self._anchor_scales)
# Total number of anchors per point.
self._num_anchors = self._anchor_reference.shape[0]
# Weights used to sum each of the losses of the submodules
self._rpn_cls_loss_weight = config.model.loss.rpn_cls_loss_weight
self._rpn_reg_loss_weight = config.model.loss.rpn_reg_loss_weights
self._rcnn_cls_loss_weight = config.model.loss.rcnn_cls_loss_weight
self._rcnn_reg_loss_weight = config.model.loss.rcnn_reg_loss_weights
self._losses_collections = ["fastercnn_losses"]
# We want the pretrained model to be outside the FasterRCNN name scope.
self.base_network = TruncatedBaseNetwork(config.model.base_network)
def _build(self, image, gt_boxes=None, is_training=False):
"""
Returns bounding boxes and classification probabilities.
Args:
image: A tensor with the image.
Its shape should be `(height, width, 3)`.
gt_boxes: A tensor with all the ground truth boxes of that image.
Its shape should be `(num_gt_boxes, 5)`
Where for each gt box we have (x1, y1, x2, y2, label),
in that order.
is_training: A boolean to whether or not it is used for training.
Returns:
classification_prob: A tensor with the softmax probability for
each of the bounding boxes found in the image.
Its shape should be: (num_bboxes, num_categories + 1)
classification_bbox: A tensor with the bounding boxes found.
It's shape should be: (num_bboxes, 4). For each of the bboxes
we have (x1, y1, x2, y2)
"""
if gt_boxes is not None:
gt_boxes = tf.cast(gt_boxes, tf.float32)
# A Tensor with the feature map for the image,
# its shape should be `(feature_height, feature_width, 512)`.
# The shape depends of the pretrained network in use.
# Set rank and last dimension before using base network
# TODO: Why does it loose information when using queue?
image.set_shape((None, None, 3))
conv_feature_map = self.base_network(tf.expand_dims(image, 0),
is_training=is_training)
# The RPN submodule which generates proposals of objects.
self._rpn = RPN(
self._num_anchors,
self._config.model.rpn,
debug=self._debug,
seed=self._seed,
)
if self._with_rcnn:
# The RCNN submodule which classifies RPN's proposals and
# classifies them as background or a specific class.
self._rcnn = RCNN(
self._num_classes,
self._config.model.rcnn,
debug=self._debug,
seed=self._seed,
)
image_shape = tf.shape(image)[0:2]
variable_summaries(conv_feature_map, "conv_feature_map", "reduced")
# Generate anchors for the image based on the anchor reference.
all_anchors = self._generate_anchors(tf.shape(conv_feature_map))
rpn_prediction = self._rpn(
conv_feature_map,
image_shape,
all_anchors,
gt_boxes=gt_boxes,
is_training=is_training,
)
prediction_dict = {
"rpn_prediction": rpn_prediction,
}
if self._debug:
prediction_dict["image"] = image
prediction_dict["image_shape"] = image_shape
prediction_dict["all_anchors"] = all_anchors
prediction_dict["anchor_reference"] = tf.convert_to_tensor(
self._anchor_reference)
if gt_boxes is not None:
prediction_dict["gt_boxes"] = gt_boxes
prediction_dict["conv_feature_map"] = conv_feature_map
if self._with_rcnn:
proposals = tf.stop_gradient(rpn_prediction["proposals"])
classification_pred = self._rcnn(
conv_feature_map,
proposals,
image_shape,
self.base_network,
gt_boxes=gt_boxes,
is_training=is_training,
)
prediction_dict["classification_prediction"] = classification_pred
return prediction_dict
def loss(self, prediction_dict, return_all=False):
"""Compute the joint training loss for Faster RCNN.
Args:
prediction_dict: The output dictionary of the _build method from
which we use two different main keys:
rpn_prediction: A dictionary with the output Tensors from the
RPN.
classification_prediction: A dictionary with the output Tensors
from the RCNN.
Returns:
If `return_all` is False, a tensor for the total loss. If True, a
dict with all the internal losses (RPN's, RCNN's, regularization
and total loss).
"""
with tf.name_scope("losses"):
rpn_loss_dict = self._rpn.loss(prediction_dict["rpn_prediction"])
# Losses have a weight assigned, we multiply by them before saving
# them.
rpn_loss_dict["rpn_cls_loss"] = (rpn_loss_dict["rpn_cls_loss"] *
self._rpn_cls_loss_weight)
rpn_loss_dict["rpn_reg_loss"] = (rpn_loss_dict["rpn_reg_loss"] *
self._rpn_reg_loss_weight)
prediction_dict["rpn_loss_dict"] = rpn_loss_dict
if self._with_rcnn:
rcnn_loss_dict = self._rcnn.loss(
prediction_dict["classification_prediction"])
rcnn_loss_dict["rcnn_cls_loss"] = (
rcnn_loss_dict["rcnn_cls_loss"] *
self._rcnn_cls_loss_weight)
rcnn_loss_dict["rcnn_reg_loss"] = (
rcnn_loss_dict["rcnn_reg_loss"] *
self._rcnn_reg_loss_weight)
prediction_dict["rcnn_loss_dict"] = rcnn_loss_dict
else:
rcnn_loss_dict = {}
all_losses_items = list(rpn_loss_dict.items()) + list(
rcnn_loss_dict.items())
for loss_name, loss_tensor in all_losses_items:
tf.summary.scalar(loss_name,
loss_tensor,
collections=self._losses_collections)
# We add losses to the losses collection instead of manually
# summing them just in case somebody wants to use it in another
# place.
tf.losses.add_loss(loss_tensor)
# Regularization loss is automatically saved by TensorFlow, we log
# it differently so we can visualize it independently.
regularization_loss = tf.losses.get_regularization_loss()
# Total loss without regularization
no_reg_loss = tf.losses.get_total_loss(
add_regularization_losses=False)
total_loss = tf.losses.get_total_loss()
tf.summary.scalar("total_loss",
total_loss,
collections=self._losses_collections)
tf.summary.scalar("no_reg_loss",
no_reg_loss,
collections=self._losses_collections)
tf.summary.scalar(
"regularization_loss",
regularization_loss,
collections=self._losses_collections,
)
if return_all:
loss_dict = {
"total_loss": total_loss,
"no_reg_loss": no_reg_loss,
"regularization_loss": regularization_loss,
}
for loss_name, loss_tensor in all_losses_items:
loss_dict[loss_name] = loss_tensor
return loss_dict
# We return the total loss, which includes:
# - rpn loss
# - rcnn loss (if activated)
# - regularization loss
return total_loss
def _generate_anchors(self, feature_map_shape):
"""Generate anchor for an image.
Using the feature map, the output of the pretrained network for an
image, and the anchor_reference generated using the anchor config
values. We generate a list of anchors.
Anchors are just fixed bounding boxes of different ratios and sizes
that are uniformly generated throught the image.
Args:
feature_map_shape: Shape of the convolutional feature map used as
input for the RPN. Should be (batch, height, width, depth).
Returns:
all_anchors: A flattened Tensor with all the anchors of shape
`(num_anchors_per_points * feature_width * feature_height, 4)`
using the (x1, y1, x2, y2) convention.
"""
with tf.variable_scope("generate_anchors"):
grid_width = feature_map_shape[2] # width
grid_height = feature_map_shape[1] # height
shift_x = tf.range(grid_width) * self._anchor_stride
shift_y = tf.range(grid_height) * self._anchor_stride
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
shift_x = tf.reshape(shift_x, [-1])
shift_y = tf.reshape(shift_y, [-1])
shifts = tf.stack([shift_x, shift_y, shift_x, shift_y], axis=0)
shifts = tf.transpose(shifts)
# Shifts now is a (H x W, 4) Tensor
# Expand dims to use broadcasting sum.
all_anchors = np.expand_dims(self._anchor_reference,
axis=0) + tf.expand_dims(shifts,
axis=1)
# Flatten
all_anchors = tf.reshape(all_anchors, (-1, 4))
return all_anchors
@property
def summary(self):
"""
Generate merged summary of all the sub-summaries used inside the
Faster R-CNN network.
"""
summaries = [
tf.summary.merge_all(key="rpn"),
]
summaries.append(tf.summary.merge_all(key=self._losses_collections[0]))
if self._with_rcnn:
summaries.append(tf.summary.merge_all(key="rcnn"))
return tf.summary.merge(summaries)
@property
def vars_summary(self):
return {
key: tf.summary.merge_all(key=collection)
for key, collections in VAR_LOG_LEVELS.items()
for collection in collections
}
def get_trainable_vars(self):
"""Get trainable vars included in the module."""
trainable_vars = snt.get_variables_in_module(self)
if self._config.model.base_network.trainable:
pretrained_trainable_vars = self.base_network.get_trainable_vars()
if len(pretrained_trainable_vars):
tf.logging.info("Training {} vars from pretrained module; "
'from "{}" to "{}".'.format(
len(pretrained_trainable_vars),
pretrained_trainable_vars[0].name,
pretrained_trainable_vars[-1].name,
))
else:
tf.logging.info("No vars from pretrained module to train.")
trainable_vars += pretrained_trainable_vars
else:
tf.logging.info("Not training variables from pretrained module")
return trainable_vars
def get_base_network_checkpoint_vars(self):
return self.base_network.get_base_network_checkpoint_vars()
def get_checkpoint_file(self):
return self.base_network.get_checkpoint_file()
def _build(self, image, gt_boxes=None, is_training=True):
"""
Returns bounding boxes and classification probabilities.
Args:
image: A tensor with the image.
Its shape should be `(1, height, width, 3)`.
gt_boxes: A tensor with all the ground truth boxes of that image.
Its shape should be `(num_gt_boxes, 5)`
Where for each gt box we have (x1, y1, x2, y2, label),
in that order.
is_training: A boolean to whether or not it is used for training.
Returns:
classification_prob: A tensor with the softmax probability for
each of the bounding boxes found in the image.
Its shape should be: (num_bboxes, num_categories + 1)
classification_bbox: A tensor with the bounding boxes found.
It's shape should be: (num_bboxes, 4). For each of the bboxes
we have (x1, y1, x2, y2)
"""
if gt_boxes is not None:
gt_boxes = tf.cast(gt_boxes, tf.float32)
# A Tensor with the feature map for the image,
# its shape should be `(feature_height, feature_width, 512)`.
# The shape depends of the pretrained network in use.
conv_feature_map = self.base_network(image, is_training=is_training)
# The RPN submodule which generates proposals of objects.
self._rpn = RPN(self._num_anchors,
self._config.rpn,
debug=self._debug,
seed=self._seed)
if self._with_rcnn:
# The RCNN submodule which classifies RPN's proposals and
# classifies them as background or a specific class.
self._rcnn = RCNN(self._num_classes,
self._config.rcnn,
debug=self._debug,
seed=self._seed)
image_shape = tf.shape(image)[1:3]
variable_summaries(conv_feature_map, 'conv_feature_map', ['rpn'])
# Generate anchors for the image based on the anchor reference.
all_anchors = self._generate_anchors(tf.shape(conv_feature_map))
rpn_prediction = self._rpn(conv_feature_map,
image_shape,
all_anchors,
gt_boxes=gt_boxes)
prediction_dict = {
'rpn_prediction': rpn_prediction,
}
if self._debug:
prediction_dict['image'] = image
prediction_dict['image_shape'] = image_shape
prediction_dict['all_anchors'] = all_anchors
prediction_dict['anchor_reference'] = tf.convert_to_tensor(
self._anchor_reference)
prediction_dict['gt_boxes'] = gt_boxes
prediction_dict['conv_feature_map'] = conv_feature_map
if self._with_rcnn:
classification_pred = self._rcnn(conv_feature_map,
rpn_prediction['proposals'],
image_shape,
gt_boxes=gt_boxes,
is_training=is_training)
prediction_dict['classification_prediction'] = classification_pred
return prediction_dict
class FasterRCNN(snt.AbstractModule):
def __init__(self, config, name='fasterrcnn'):
super(FasterRCNN, self).__init__(name=name)
self._config = config
self._num_classes = config.model.network.num_classes
# Generate network with RCNN
self._with_rcnn = config.model.network.with_rcnn
self._debug = config.train.debug
self._seed = config.train.seed
self._anchor_base_size = config.model.anchors.base_size
self._anchor_scales = np.array(config.model.anchors.scales)
self._anchor_ratios = np.array(config.model.anchors.ratios)
self._anchor_stride = config.model.anchors.stride
self._anchor_reference = generate_anchors_reference(
self._anchor_base_size, self._anchor_ratios, self._anchor_scales
)
self._num_anchors = self._anchor_reference.shape[0]
# Weights used to sum each of the losses of the submodules
self._rpn_cls_loss_weight = config.model.loss.rpn_cls_loss_weight
self._rpn_reg_loss_weight = config.model.loss.rpn_reg_loss_weights
self._rcnn_cls_loss_weight = config.model.loss.rcnn_cls_loss_weight
self._rcnn_reg_loss_weight = config.model.loss.rcnn_reg_loss_weights
self._losses_collections = ['fastercnn_losses']
self.base_network = TruncatedBaseNetwork(config.model.base_network)
def _build(self, image, gt_boxes=None, is_training=False):
if gt_boxes is not None:
gt_boxes = tf.cast(gt_boxes, tf.float32)
image.set_shape((None, None, 3))
conv_feature_map = self.base_network(
tf.expand_dims(image, 0), is_training=is_training
)
# The RPN submodule which generates proposals of objects.
self._rpn = RPN(
self._num_anchors, self._config.model.rpn,
debug=self._debug, seed=self._seed
)
if self._with_rcnn:
self._rcnn = RCNN(
self._num_classes, self._config.model.rcnn,
debug=self._debug, seed=self._seed
)
image_shape = tf.shape(image)[0:2]
variable_summaries(
conv_feature_map, 'conv_feature_map', 'reduced'
)
all_anchors = self._generate_anchors(tf.shape(conv_feature_map))
rpn_prediction = self._rpn(
conv_feature_map, image_shape, all_anchors,
gt_boxes=gt_boxes, is_training=is_training
)
prediction_dict = {
'rpn_prediction': rpn_prediction,
}
if self._debug:
prediction_dict['image'] = image
prediction_dict['image_shape'] = image_shape
prediction_dict['all_anchors'] = all_anchors
prediction_dict['anchor_reference'] = tf.convert_to_tensor(
self._anchor_reference
)
if gt_boxes is not None:
prediction_dict['gt_boxes'] = gt_boxes
prediction_dict['conv_feature_map'] = conv_feature_map
if self._with_rcnn:
proposals = tf.stop_gradient(rpn_prediction['proposals'])
classification_pred = self._rcnn(
conv_feature_map, proposals,
image_shape, self.base_network,
gt_boxes=gt_boxes, is_training=is_training
)
prediction_dict['classification_prediction'] = classification_pred
return prediction_dict
def loss(self, prediction_dict, return_all=False):
with tf.name_scope('losses'):
rpn_loss_dict = self._rpn.loss(
prediction_dict['rpn_prediction']
)
rpn_loss_dict['rpn_cls_loss'] = (
rpn_loss_dict['rpn_cls_loss'] * self._rpn_cls_loss_weight)
rpn_loss_dict['rpn_reg_loss'] = (
rpn_loss_dict['rpn_reg_loss'] * self._rpn_reg_loss_weight)
prediction_dict['rpn_loss_dict'] = rpn_loss_dict
if self._with_rcnn:
rcnn_loss_dict = self._rcnn.loss(
prediction_dict['classification_prediction']
)
rcnn_loss_dict['rcnn_cls_loss'] = (
rcnn_loss_dict['rcnn_cls_loss'] *
self._rcnn_cls_loss_weight
)
rcnn_loss_dict['rcnn_reg_loss'] = (
rcnn_loss_dict['rcnn_reg_loss'] *
self._rcnn_reg_loss_weight
)
prediction_dict['rcnn_loss_dict'] = rcnn_loss_dict
else:
rcnn_loss_dict = {}
all_losses_items = (
list(rpn_loss_dict.items()) + list(rcnn_loss_dict.items()))
for loss_name, loss_tensor in all_losses_items:
tf.summary.scalar(
loss_name, loss_tensor,
collections=self._losses_collections
)
tf.losses.add_loss(loss_tensor)
regularization_loss = tf.losses.get_regularization_loss()
no_reg_loss = tf.losses.get_total_loss(
add_regularization_losses=False
)
total_loss = tf.losses.get_total_loss()
tf.summary.scalar(
'total_loss', total_loss,
collections=self._losses_collections
)
tf.summary.scalar(
'no_reg_loss', no_reg_loss,
collections=self._losses_collections
)
tf.summary.scalar(
'regularization_loss', regularization_loss,
collections=self._losses_collections
)
if return_all:
loss_dict = {
'total_loss': total_loss,
'no_reg_loss': no_reg_loss,
'regularization_loss': regularization_loss,
}
for loss_name, loss_tensor in all_losses_items:
loss_dict[loss_name] = loss_tensor
return loss_dict
return total_loss
def _generate_anchors(self, feature_map_shape):
with tf.variable_scope('generate_anchors'):
grid_width = feature_map_shape[2] # width
grid_height = feature_map_shape[1] # height
shift_x = tf.range(grid_width) * self._anchor_stride
shift_y = tf.range(grid_height) * self._anchor_stride
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
shift_x = tf.reshape(shift_x, [-1])
shift_y = tf.reshape(shift_y, [-1])
shifts = tf.stack(
[shift_x, shift_y, shift_x, shift_y],
axis=0
)
shifts = tf.transpose(shifts)
all_anchors = (
np.expand_dims(self._anchor_reference, axis=0) +
tf.expand_dims(shifts, axis=1)
)
all_anchors = tf.reshape(
all_anchors, (-1, 4)
)
return all_anchors
@property
def summary(self):
summaries = [
tf.summary.merge_all(key='rpn'),
]
summaries.append(
tf.summary.merge_all(key=self._losses_collections[0])
)
if self._with_rcnn:
summaries.append(tf.summary.merge_all(key='rcnn'))
return tf.summary.merge(summaries)
@property
def vars_summary(self):
return {
key: tf.summary.merge_all(key=collection)
for key, collections in VAR_LOG_LEVELS.items()
for collection in collections
}
def get_trainable_vars(self):
trainable_vars = snt.get_variables_in_module(self)
if self._config.model.base_network.trainable:
pretrained_trainable_vars = self.base_network.get_trainable_vars()
if len(pretrained_trainable_vars):
tf.logging.info(
'Training {} vars from pretrained module; '
'from "{}" to "{}".'.format(
len(pretrained_trainable_vars),
pretrained_trainable_vars[0].name,
pretrained_trainable_vars[-1].name,
)
)
else:
tf.logging.info('No vars from pretrained module to train.')
trainable_vars += pretrained_trainable_vars
else:
tf.logging.info('Not training variables from pretrained module')
return trainable_vars
def get_base_network_checkpoint_vars(self):
return self.base_network.get_base_network_checkpoint_vars()
def get_checkpoint_file(self):
return self.base_network.get_checkpoint_file()
def setUp(self):
tf.reset_default_graph()
self._num_classes = 5
self._num_proposals = 256
self._total_num_gt = 128
self._image_shape = (600, 800)
# The score we'll give to the true labels when testing for perfect
# score generation.
self._high_score = 100
self._equality_delta = 1e-03
self._config = EasyDict({
"enabled": True,
"layer_sizes": [4096, 4096],
"dropout_keep_prob": 1.0,
"activation_function": "relu6",
"use_mean": False,
"target_normalization_variances": [1.0, 1.0],
"rcnn_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"bbox_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"cls_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"l2_regularization_scale": 0.0005,
"l1_sigma": 3.0,
"loss": {
"type": "cross_entropy",
"weight": [1, 0.01, 0.05, 0.02, 0.1, 0.005],
},
"roi": {
"pooling_mode": "crop",
"pooled_width": 7,
"pooled_height": 7,
"padding": "VALID",
},
"proposals": {
"class_max_detections": 100,
"class_nms_threshold": 0.6,
"total_max_detections": 300,
"min_prob_threshold": 0.0,
},
"target": {
"foreground_fraction": 0.25,
"minibatch_size": 64,
"foreground_threshold": 0.5,
"background_threshold_high": 0.5,
"background_threshold_low": 0.1,
"target_normalization_variances": [1.0, 1.0],
},
})
self._base_network = MockBaseNetwork()
self._shared_model = RCNN(self._num_classes, self._config)
# Declare placeholders
# We use the '_ph' suffix for placeholders.
self._pretrained_feature_map_shape = (
self._num_proposals,
self._config.roi.pooled_width,
self._config.roi.pooled_height,
4,
)
self._pretrained_feature_map_ph = tf.placeholder(
tf.float32, shape=self._pretrained_feature_map_shape)
self._proposals_shape = (self._num_proposals, 4)
self._proposals_ph = tf.placeholder(tf.float32,
shape=self._proposals_shape)
self._image_shape_shape = (2, )
self._image_shape_ph = tf.placeholder(tf.float32,
shape=self._image_shape_shape)
self._gt_boxes_shape = (self._total_num_gt, 5)
self._gt_boxes_ph = tf.placeholder(tf.float32,
shape=self._gt_boxes_shape)
class RCNNTest(tf.test.TestCase):
def setUp(self):
tf.reset_default_graph()
self._num_classes = 5
self._num_proposals = 256
self._total_num_gt = 128
self._image_shape = (600, 800)
# The score we'll give to the true labels when testing for perfect
# score generation.
self._high_score = 100
self._equality_delta = 1e-03
self._config = EasyDict({
"enabled": True,
"layer_sizes": [4096, 4096],
"dropout_keep_prob": 1.0,
"activation_function": "relu6",
"use_mean": False,
"target_normalization_variances": [1.0, 1.0],
"rcnn_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"bbox_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"cls_initializer": {
"type": "variance_scaling_initializer",
"factor": 1.0,
"uniform": True,
"mode": "FAN_AVG",
},
"l2_regularization_scale": 0.0005,
"l1_sigma": 3.0,
"loss": {
"type": "cross_entropy",
"weight": [1, 0.01, 0.05, 0.02, 0.1, 0.005],
},
"roi": {
"pooling_mode": "crop",
"pooled_width": 7,
"pooled_height": 7,
"padding": "VALID",
},
"proposals": {
"class_max_detections": 100,
"class_nms_threshold": 0.6,
"total_max_detections": 300,
"min_prob_threshold": 0.0,
},
"target": {
"foreground_fraction": 0.25,
"minibatch_size": 64,
"foreground_threshold": 0.5,
"background_threshold_high": 0.5,
"background_threshold_low": 0.1,
"target_normalization_variances": [1.0, 1.0],
},
})
self._base_network = MockBaseNetwork()
self._shared_model = RCNN(self._num_classes, self._config)
# Declare placeholders
# We use the '_ph' suffix for placeholders.
self._pretrained_feature_map_shape = (
self._num_proposals,
self._config.roi.pooled_width,
self._config.roi.pooled_height,
4,
)
self._pretrained_feature_map_ph = tf.placeholder(
tf.float32, shape=self._pretrained_feature_map_shape)
self._proposals_shape = (self._num_proposals, 4)
self._proposals_ph = tf.placeholder(tf.float32,
shape=self._proposals_shape)
self._image_shape_shape = (2, )
self._image_shape_ph = tf.placeholder(tf.float32,
shape=self._image_shape_shape)
self._gt_boxes_shape = (self._total_num_gt, 5)
self._gt_boxes_ph = tf.placeholder(tf.float32,
shape=self._gt_boxes_shape)
def _run_net_with_feed_dict(self, net, feed_dict):
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
return sess.run(net, feed_dict=feed_dict)
def _check_returning_shapes(self, prediction_dict, training=False):
"""Asserts a prediction_dict has the right shapes.
This includes testing that:
- objects, objects_labels and objects_labels_prob have the same
shape in the first dimension. (i.e. the same number of
objects).
- objects has shape (_, 4). objects_labels and objects_labels_prob
have shape (_,).
- cls_score and cls_prob have shape (num_proposals,
num_classes + 1).
- bbox_offsets has shape (num_proposals, num_classes * 4).
And, if training:
- cls_target has shape (num_proposals,).
- bbox_offsets_target has shape (num_proposals, 4).
"""
objects_shape = prediction_dict["objects"].shape
objects_labels_shape = prediction_dict["labels"].shape
objects_labels_prob_shape = prediction_dict["probs"].shape
cls_score_shape = prediction_dict["rcnn"]["cls_score"].shape
cls_prob_shape = prediction_dict["rcnn"]["cls_prob"].shape
bbox_offsets_shape = prediction_dict["rcnn"]["bbox_offsets"].shape
# We choose cls_score as the 'standard' num_proposals which we will
# compare to the other shapes that should include num_proposals. We
# could have chosen a different one.
num_proposals = cls_score_shape[0]
self.assertEqual(objects_shape[0], objects_labels_shape[0])
self.assertEqual(objects_shape[0], objects_labels_prob_shape[0])
self.assertEqual(objects_shape[1], 4)
self.assertEqual(len(objects_labels_shape), 1)
self.assertEqual(len(objects_labels_prob_shape), 1)
self.assertEqual(cls_score_shape, cls_prob_shape)
self.assertEqual(cls_prob_shape,
(num_proposals, self._num_classes + 1))
self.assertEqual(bbox_offsets_shape,
(num_proposals, self._num_classes * 4))
if training:
cls_target_shape = prediction_dict["target"]["cls"].shape
self.assertEqual(cls_target_shape, (num_proposals, ))
bbox_offsets_trgt_shape = prediction_dict["target"][
"bbox_offsets"].shape
self.assertEqual(bbox_offsets_trgt_shape, (num_proposals, 4))
def testReturningShapes(self):
"""Tests we're returning consistent shapes.
We test both the case where we're training and the case where we are
not.
"""
# Prediction session (not training)
rcnn_net_not_training = self._shared_model(
self._pretrained_feature_map_ph,
self._proposals_ph,
self._image_shape_ph,
self._base_network,
)
prediction_dict_not_training = self._run_net_with_feed_dict(
rcnn_net_not_training,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
},
)
# Training session
rcnn_net_training = self._shared_model(
self._pretrained_feature_map_ph,
self._proposals_ph,
self._image_shape_ph,
self._base_network,
self._gt_boxes_ph,
)
prediction_dict_training = self._run_net_with_feed_dict(
rcnn_net_training,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
self._gt_boxes_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._gt_boxes_shape,
),
},
)
# Assertions
self._check_returning_shapes(prediction_dict_not_training)
self._check_returning_shapes(prediction_dict_training, training=True)
def testMinibatchBehaviour(self):
"""Tests we're using minibatch_size correctly when testing."""
rcnn_net = self._shared_model(
self._pretrained_feature_map_ph,
self._proposals_ph,
self._image_shape_ph,
self._base_network,
self._gt_boxes_ph,
)
prediction_dict = self._run_net_with_feed_dict(
rcnn_net,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
self._gt_boxes_ph:
np.random.randint(
low=0,
high=np.amin(self._image_shape),
size=self._gt_boxes_shape,
),
},
)
# Assertions
self.assertLessEqual(
prediction_dict["target"]["cls"][
prediction_dict["target"]["cls"] >= 0].shape[0],
self._config.target.minibatch_size,
)
def testNumberOfObjects(self):
"""Tests we're not returning too many objects.
The number of objects returned should be lower than the number of
proposals received times the number of classes.
"""
rcnn_net = self._shared_model(
self._pretrained_feature_map_ph,
self._proposals_ph,
self._image_shape_ph,
self._base_network,
)
prediction_dict = self._run_net_with_feed_dict(
rcnn_net,
feed_dict={
self._pretrained_feature_map_ph:
np.random.rand(*self._pretrained_feature_map_shape),
self._proposals_ph:
np.random.randint(
0,
high=np.amin(self._image_shape),
size=self._proposals_shape,
),
self._image_shape_ph:
self._image_shape,
},
)
# Assertions
self.assertLessEqual(prediction_dict["objects"].shape[0],
self._num_proposals * self._num_classes)
def testLoss(self):
"""Tests we're computing loss correctly.
In particular, we're testing whether computing a perfect score when we
have to.
"""
# Generate placeholders and loss_graph
cls_score_shape = (self._num_proposals, self._num_classes + 1)
cls_score_ph = tf.placeholder(tf.float32, cls_score_shape)
cls_prob_shape = (self._num_proposals, self._num_classes + 1)
cls_prob_ph = tf.placeholder(tf.float32, cls_prob_shape)
cls_target_shape = (self._num_proposals, )
cls_target_ph = tf.placeholder(tf.float32, cls_target_shape)
bbox_offsets_shape = (self._num_proposals, self._num_classes * 4)
bbox_offsets_ph = tf.placeholder(tf.float32, bbox_offsets_shape)
bbox_offsets_target_shape = (self._num_proposals, 4)
bbox_offsets_target_ph = tf.placeholder(tf.float32,
bbox_offsets_target_shape)
loss_graph = self._shared_model.loss({
"rcnn": {
"cls_score": cls_score_ph,
"cls_prob": cls_prob_ph,
"bbox_offsets": bbox_offsets_ph,
},
"target": {
"cls": cls_target_ph,
"bbox_offsets": bbox_offsets_target_ph,
},
})
# Generate values that ensure a perfect score
# We first initialize all our values to zero.
cls_score = np.zeros(cls_score_shape, dtype=np.float32)
cls_prob = np.zeros(cls_prob_shape, dtype=np.float32)
cls_target = np.zeros(cls_target_shape, dtype=np.float32)
bbox_offsets = np.zeros(bbox_offsets_shape, dtype=np.float32)
bbox_offsets_target = np.zeros(bbox_offsets_target_shape,
dtype=np.float32)
for i in range(self._num_proposals):
this_class = np.random.randint(low=1, high=self._num_classes + 1)
cls_score[i][this_class] = self._high_score
cls_prob[i][this_class] = 1.0
cls_target[i] = this_class
# Find out where in the axis 1 in bbox_offsets we should
# put the offsets, because the shape is
# (num_proposals, num_classes * 4), and we're using
# 1-indexed classes.
class_place = (this_class - 1) * 4
for j in range(4):
this_coord = np.random.randint(low=0,
high=np.amax(self._image_shape))
bbox_offsets[i][class_place + j] = this_coord
bbox_offsets_target[i][j] = this_coord
# Now get the loss dict using the values we just generated.
loss_dict = self._run_net_with_feed_dict(
loss_graph,
feed_dict={
cls_score_ph: cls_score,
cls_prob_ph: cls_prob,
cls_target_ph: cls_target,
bbox_offsets_ph: bbox_offsets,
bbox_offsets_target_ph: bbox_offsets_target,
},
)
# Assertions
self.assertAlmostEqual(loss_dict["rcnn_cls_loss"],
0,
delta=self._equality_delta)
self.assertAlmostEqual(loss_dict["rcnn_reg_loss"],
0,
delta=self._equality_delta)
