indices[:, 0],
[7, 7], method="bilinear")
print(new_rois.shape)
start = 0
for j in range(proposals.shape[0]):
count = tf.where(tf.equal(indices[:, 0], j)).shape[0]
rois_by_images[j].extend(new_rois[start : start + count])
start = count
print("kraj")
rois = tf.ragged.constant(rois_by_images)
print(rois.shape)
return
if __name__ == "__main__":
#np.random.seed(100)
tf.random.set_seed(110)
anchors = anchor_utils.get_all_anchors((512, 512), [64, 128, 256, 512, 1024], [(1, 1), (1, 2), (2, 1)])
rpn_model = rpn.RPN(Resnet34_FPN(), 3)
#weights_path = "weights.ckpt"
#rpn_model.load_weights(weights_path)
num_classes = len(config.CLASSES)
model = Mask_RCNN(rpn_model, anchors, num_classes)
#ds = dataset_util.VOC2012_Dataset("DATASET/VOC2012/VOC2012", "/train_list.txt", 2)
ds = dataset_util.VOC2012_Dataset("dataset/VOC2012", "/train_list.txt", 2)
data1, data2, data3, data5, d4 = ds.next_batch()
data2, data3 = anchor_utils.get_rpn_classes_and_bbox_deltas(len(data1), anchors, data2)
a, b, c, d, e = model([data1, d4], training=True)
import rpn
import backbone
import dataset_util
import anchor_utils
import tensorflow as tf
import config
import mask_rcnn
import numpy as np
import image_util
import metrics
ds = dataset_util.VOC2012_Dataset("dataset/VOC2012", "/valid_list.txt", 2)
#ds = dataset_util.VOC2012_Dataset("dataset/TEST", "/test_list.txt", 2)
anchors = anchor_utils.get_all_anchors(config.IMAGE_SIZE, config.ANCHOR_SCALES,
config.ANCHOR_RATIOS)
backbone2 = backbone.Resnet34_FPN()
rpn2 = rpn.RPN(backbone2, 3)
model = mask_rcnn.Mask_RCNN(rpn2, anchors, len(config.CLASSES))
checkpoint = tf.train.Checkpoint(net=model, step=tf.Variable(1))
manager = tf.train.CheckpointManager(checkpoint,
config.WEIGHTS_DIR,
max_to_keep=4)
if manager.latest_checkpoint:
print("Restoring...", manager.latest_checkpoint)
model([
np.random.rand(1, config.IMAGE_SIZE[0], config.IMAGE_SIZE[1], 3),
np.array([[500, 500]])
],
training=False)