def setup_record(self): filename_queue = tf.train.string_input_producer( [self.record], num_epochs=10) self.image, self.annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue) self.resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(self.image, self.annotation, (self.size, self.size)) self.resized_annotation = tf.squeeze(resized_annotation)
def setup_val(self, tfname): self.restore = glob(os.path.join(self.checkpoint8, "FCN__*", "*.data*" ))[0].split(".data")[0] filename_queue = tf.train.string_input_producer( [tfname], num_epochs=10) self.image_queue, self.annotation_queue = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue) self.image = tf.placeholder_with_default(self.image, shape=[None, None, 3]) self.annotation = tf.placeholder_with_default(self.annotation_queue, shape=[None, None, 1]) self.resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(self.image, self.annotation, (self.size, self.size)) self.resized_annotation = tf.squeeze(resized_annotation) image_batch_tensor = tf.expand_dims(self.image, axis=0) annotation_batch_tensor = tf.expand_dims(self.annotation, axis=0) # Be careful: after adaptation, network returns final labels # and not logits FCN_8s_bis = adapt_network_for_any_size_input(FCN_8s, 32) self.pred, fcn_16s_variables_mapping = FCN_8s_bis(image_batch_tensor=image_batch_tensor, number_of_classes=self.num_labels, is_training=False) self.prob = [h for h in [s for s in [t for t in self.pred.op.inputs][0].op.inputs][0].op.inputs][0] initializer = tf.local_variables_initializer() self.saver = tf.train.Saver() with tf.Session() as sess: sess.run(initializer) self.saver.restore(sess, self.restore)
def setup_record(self): """ Setup record reading. """ filename_queue = tf.train.string_input_producer([self.record], num_epochs=10) self.image, self.annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors( filename_queue) self.resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output( self.image, self.annotation, (self.size, self.size)) self.resized_annotation = tf.squeeze(resized_annotation)
def setup_val(self, tfname): """ Setups the model in case we need to validate. """ self.restore = glob(os.path.join(self.checkpoint8, "FCN__*", "*.data*"))[0].split(".data")[0] filename_queue = tf.train.string_input_producer([tfname], num_epochs=10) self.image_queue, self.annotation_queue = read_tfrecord_and_decode_into_image_annotation_pair_tensors( filename_queue) self.image = tf.placeholder_with_default(self.image, shape=[None, None, 3]) self.annotation = tf.placeholder_with_default(self.annotation_queue, shape=[None, None, 1]) self.resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output( self.image, self.annotation, (self.size, self.size)) self.resized_annotation = tf.squeeze(resized_annotation) image_batch_tensor = tf.expand_dims(self.image, axis=0) annotation_batch_tensor = tf.expand_dims(self.annotation, axis=0) # Be careful: after adaptation, network returns final labels # and not logits FCN_8s_bis = adapt_network_for_any_size_input(FCN_8s, 32) self.pred, fcn_16s_variables_mapping = FCN_8s_bis( image_batch_tensor=image_batch_tensor, number_of_classes=self.num_labels, is_training=False) self.prob = [ h for h in [s for s in [t for t in self.pred.op.inputs][0].op.inputs][0].op.inputs ][0] initializer = tf.local_variables_initializer() self.saver = tf.train.Saver() with tf.Session() as sess: sess.run(initializer) self.saver.restore(sess, self.restore)
from matplotlib import pyplot as plt from tf_image_segmentation.utils.pascal_voc import pascal_segmentation_lut from tf_image_segmentation.utils.tf_records import read_tfrecord_and_decode_into_image_annotation_pair_tensors from tf_image_segmentation.utils.inference import adapt_network_for_any_size_input from tf_image_segmentation.utils.visualization import visualize_segmentation_adaptive pascal_voc_lut = pascal_segmentation_lut() tfrecord_filename = 'pascal_augmented_val.tfrecords' number_of_classes = 21 filename_queue = tf.train.string_input_producer([tfrecord_filename], num_epochs=1) image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors( filename_queue) # Fake batch for image and annotation by adding # leading empty axis. image_batch_tensor = tf.expand_dims(image, axis=0) annotation_batch_tensor = tf.expand_dims(annotation, axis=0) # Be careful: after adaptation, network returns final labels # and not logits FCN_8s = adapt_network_for_any_size_input(FCN_8s, 32) pred, fcn_16s_variables_mapping = FCN_8s(image_batch_tensor=image_batch_tensor, number_of_classes=number_of_classes, is_training=False) # Take away the masked out values from evaluation
def read_photo_annotation_depth(batch_size=1): def convert2float(image): """ Transfrom from int image ([0,255]) to float tensor ([-1.,1.]) """ image = tf.image.convert_image_dtype(image, dtype=tf.float32) return (image / 127.5) - 1.0 image_train_size = [256, 256] number_of_classes = 21 # tfrecord_filename = '/home/yaxing/DATA/scene_net_augmented_train_200.tfrecords' tfrecord_filename = '/home/yaxing/data/scene5M/scene_net_augmented_train_16k.tfrecords' pascal_voc_lut = { 0: 'Unknown', 1: 'Bed', 2: 'Books', 3: 'Ceiling', 4: 'Chair', 5: 'Floor', 6: 'Furniture', 7: 'Objects', 8: 'Picture', 9: 'Sofa', 10: 'Table', 11: 'TV', 12: 'Wall', 13: 'Window' } class_labels = pascal_voc_lut.keys() filename_queue = tf.train.string_input_producer([tfrecord_filename], num_epochs=50000) image1, image2, annotation1, depth2, annotation2, depth1 = read_tfrecord_and_decode_into_image_annotation_pair_tensors( filename_queue) # Various data augmentation stages image1, annotation1, depth2, image2, annotation2, depth1 = flip_randomly_left_right_image_with_annotation( image1, annotation1, depth2, image2, annotation2, depth1) # image = distort_randomly_image_color(image) resized_image1, resized_annotation1, resized_depth2, resized_image2, resized_annotation2, resized_depth1 = scale_randomly_image_with_annotation_with_fixed_size_output( image1, annotation1, depth2, image_train_size, img_tensor2=image2, annotation_tensor2=annotation2, depth_tensor1=depth1) resized_annotation1 = tf.squeeze(resized_annotation1) resized_annotation2 = tf.squeeze(resized_annotation2) image_batch1, annotation_batch1, depth_batch2, image_batch2, annotation_batch2, depth_batch1 = tf.train.shuffle_batch( [ resized_image1, resized_annotation1, resized_depth2, resized_image2, resized_annotation2, resized_depth1 ], batch_size=batch_size, capacity=3000, num_threads=2, min_after_dequeue=1000) return tf.to_float(image_batch1) / 127.5 - 1., annotation_batch1, ( tf.exp(depth_batch1) - 1. ) / 1000., tf.to_float(image_batch2) / 127.5 - 1., annotation_batch2, ( tf.exp(depth_batch2) - 1.) / 1000.
restoremodel = options.checkpoint lr = restoremodel.split('__')[1] slim = tf.contrib.slim tfrecord_filename = options.tf_records number_of_classes = options.labels filename_queue = tf.train.string_input_producer( [tfrecord_filename], num_epochs=1) image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue) # Fake batch for image and annotation by adding # leading empty axis. image_batch_tensor = tf.expand_dims(image, axis=0) annotation_batch_tensor = tf.expand_dims(annotation, axis=0) # Be careful: after adaptation, network returns final labels # and not logits FCN_8s = adapt_network_for_any_size_input(FCN_8s, 32) pred, fcn_16s_variables_mapping = FCN_8s(image_batch_tensor=image_batch_tensor, number_of_classes=number_of_classes, is_training=False) # Take away the masked out values from evaluation