def test_compute_gt(self):
level = 3
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (l, bbs) = next(iter(ds.take(1)))
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), len(voc.IDX_2_LABEL))
nearest_anchors = anchors[gt_reg[0, :, -1] == 1].numpy()
im_random = unnormalize_image(im)
im_random = visualizer.draw_boxes(im_random, nearest_anchors)
im_random = visualizer.draw_boxes(im_random, bbs, colors=[0, 0, 255])
for label in l:
print(voc.IDX_2_LABEL[int(label)])
plt.imshow(im_random)
plt.show(block=True)
print('GT shapes:', gt_labels.shape, gt_reg.shape)
print('Found any overlapping anchor?', np.any(gt_labels[:, :,
-1] == 1.))
def test_regress_boxes(self):
print('Regress anchors test')
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (l, bbs) = next(iter(ds.take(1)))
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), len(voc.IDX_2_LABEL))
near_mask = gt_reg[0, :, -1] == 1
nearest_regressors = tf.expand_dims(
tf.boolean_mask(gt_reg[0], near_mask)[:, :-1], 0)
nearest_anchors = tf.expand_dims(anchors[near_mask], 0)
# apply regression to boxes
regressed_boxes = utils.bndbox.regress_bndboxes(
nearest_anchors, nearest_regressors)
im_random = unnormalize_image(im)
im_random = visualizer.draw_boxes(im_random, regressed_boxes[0])
plt.imshow(im_random)
plt.show(block=True)
def test_regress_boxes(self):
print('Regress anchors test')
level = 3
ds = voc.build_dataset('test/data/VOC2007',
im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(),
512)
for im, (l, bbs) in ds.take(1):
gt_reg, gt_labels = \
utils.anchors.anchor_targets_bbox(anchors.numpy(),
im.numpy(),
bbs.numpy(), l.numpy(),
len(voc.IDX_2_LABEL))
near_mask = gt_reg[0, :, -1] == 1
nearest_regressors = tf.expand_dims(gt_reg[0, near_mask][:, :-1], 0)
nearest_anchors = tf.expand_dims(anchors[near_mask], 0)
# apply regression to boxes
regressed_boxes = utils.bndbox.regress_bndboxes(nearest_anchors,
nearest_regressors)
im_random = im[0].numpy()
for box in regressed_boxes[0].numpy():
box = box.astype('int32')
cv2.rectangle(im_random,
(box[0], box[1]),
(box[2], box[3]), (0, 255, 0), 1)
cv2.imshow('', im_random)
cv2.waitKey()
def test_compute_gt(self):
level = 3
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
for im, (l, bbs) in ds.take(1):
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, im, bbs, l, len(voc.IDX_2_LABEL))
nearest_anchors = anchors[gt_reg[0, :, -1] == 1].numpy()
im_random = im[0].numpy()
for box in nearest_anchors:
box = box.astype('int32')
cv2.rectangle(im_random, (box[0], box[1]), (box[2], box[3]),
(0, 255, 0), 1)
for box in bbs.numpy()[0]:
box = box.astype('int32')
cv2.rectangle(im_random, (box[0], box[1]), (box[2], box[3]),
(0, 0, 255), 3)
for label in l[0]:
print(voc.IDX_2_LABEL[int(label)])
plt.imshow(im_random)
plt.show(block=True)
print('GT shapes:', gt_labels.shape, gt_reg.shape)
print('Found any overlapping anchor?',
np.any(gt_labels[:, :, -1] == 1.))
def test_compute_gt(self):
ds = voc.build_dataset('test/data/VOC2007',
im_input_size=(512, 512),
shuffle=False)
ds = ds.skip(1).batch(1)
wrapped_ds = utils.training.wrap_detection_dataset(ds, (512, 512), 20)
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (regressors, l) = next(iter(wrapped_ds.take(1)))
im = unnormalize_image(im[0])
near_mask = regressors[0, :, -1] == 1
nearest_regressors = tf.expand_dims(
tf.boolean_mask(regressors[0], near_mask)[:, :-1], 0)
nearest_anchors = tf.expand_dims(anchors[near_mask], 0)
# apply regression to boxes
regressed_boxes = utils.bndbox.regress_bndboxes(
nearest_anchors, nearest_regressors)
im = utils.visualizer.draw_boxes(im,
nearest_anchors[0],
colors=[(255, 255, 0)])
im = utils.visualizer.draw_boxes(im,
regressed_boxes[0],
colors=[(0, 255, 255)])
plt.imshow(im)
plt.axis('off')
plt.show(block=True)
print('GT shapes:', l.shape, regressors.shape)
print('Found any overlapping anchor?',
tf.reduce_any(tf.equal(l[:, :, -1], 1.)))
def test_nms(self):
n_classes = len(voc.LABEL_2_IDX)
anchors_config = config.AnchorsConfig()
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors_gen = [
utils.anchors.AnchorGenerator(size=anchors_config.sizes[i - 3],
aspect_ratios=anchors_config.ratios,
stride=anchors_config.strides[i - 3])
for i in range(3, 8)
]
sizes = (80, 40, 20, 10, 5)
im, (l, bbs) = next(iter(ds.take(1)))
anchors = [
anchor_gen((size, size, 3))
for anchor_gen, size in zip(anchors_gen, sizes)
]
anchors = tf.concat(anchors, axis=0)
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), n_classes)
box_score = gt_labels[0]
true_idx = tf.reshape(tf.where(box_score[:, -1] == 1), [-1])
box_score = tf.gather(box_score, true_idx)
anchors = tf.gather(anchors, true_idx)
before_nms_shape = anchors.shape
anchors = tf.expand_dims(anchors, 0)
box_score = tf.expand_dims(box_score[:, :-1], 0)
boxes, labels, scores = bb_utils.nms(anchors, box_score)
after_nms_shape = boxes[0].shape
if anchors.shape[0] != 0:
self.assertTrue(after_nms_shape[0] < before_nms_shape[0],
'After nms boxes should be reduced')
else:
print('No ground truth anchors')
im_random = utils.visualizer.draw_boxes(im, boxes[0])
plt.imshow(im_random)
plt.axis('off')
plt.show(block=True)
def test_forward_inference(self):
batch_size = 2
num_classes = len(voc.IDX_2_LABEL)
model = models.EfficientDet(num_classes=num_classes, D=0, weights=None)
input_size = model.config.input_size
ds = voc.build_dataset('test/data/VOC2007', im_input_size=input_size)
images, annotations = next(iter(ds.take(1)))
boxes, labels, scores = model(tf.expand_dims(images, 0),
training=False)
# TODO: migrate to tensors when output is padded
for i in range(len(boxes)):
self._compare_shapes(boxes[i].shape, [None, 4])
self._compare_shapes(labels[i].shape, [None])
