def __init__(self,
cfg,
weight_decay=0.0001,
data_format='NHWC',
reuse=None,
images_ph=None,
lbls_ph=None):
self.cfg = cfg
num_classes = cfg.num_classes
batch_size = None
if lbls_ph is not None:
self.gt_lbls = tf.reshape(lbls_ph, [-1, num_classes])
else:
self.gt_lbls = tf.placeholder(tf.int32,
shape=(batch_size, num_classes),
name='class_lbls')
self.do_augmentation = tf.placeholder(tf.bool, name='do_augmentation')
self.loss_class_weight = tf.placeholder(tf.float32,
shape=(num_classes,
num_classes),
name='weights')
if cfg.db_name == 'honda':
self.input = tf.placeholder(tf.float32,
shape=(batch_size, const.frame_height,
const.frame_width,
const.context_channels),
name='context_input')
else:
self.input = tf.placeholder(
tf.float32,
shape=(batch_size, const.max_frame_size, const.max_frame_size,
const.frame_channels),
name='context_input')
# if is_training:
if images_ph is not None:
self.input = images_ph
_, w, h, c = self.input.shape
aug_imgs = tf.reshape(self.input, [-1, w, h, 3])
print('No nnutils Augmentation')
else:
if cfg.db_name == 'honda':
aug_imgs = self.input
else:
aug_imgs = tf.cond(
self.do_augmentation,
lambda: batch_augment.augment(self.input,
cfg.preprocess_func,
horizontal_flip=True,
vertical_flip=False,
rotate=0,
crop_probability=0,
color_aug_probability=0),
lambda: batch_augment.center_crop(self.input, cfg.
preprocess_func))
# aug_imgs = self.input ## Already augmented
# else:
# if images_ph is not None:
# self.input = images_ph
# _,w,h,c = self.input.shape
# aug_imgs = tf.reshape(self.input, [-1, w, h, 3])
# print('No nnutils Augmentation')
# else:
# # self.input = tf.placeholder(tf.float32, shape=(batch_size, const.frame_height, const.frame_width,
# # const.frame_channels), name='context_input')
# aug_imgs = tf.cond(self.is_training,
# lambda: nn_utils.augment(self.input, horizontal_flip=True, vertical_flip=False,
# rotate=0, crop_probability=0, color_aug_probability=0)
# , lambda: nn_utils.center_crop(self.input))
with tf.contrib.slim.arg_scope(
densenet_arg_scope(weight_decay=weight_decay,
data_format=data_format)):
nets, train_end_points = densenet(
aug_imgs,
num_classes=num_classes,
reduction=0.5,
growth_rate=48,
num_filters=96,
num_layers=[6, 12, 36, 24],
data_format=data_format,
is_training=True, ## Set is always to false
reuse=None,
scope='densenet161')
val_nets, val_end_points = densenet(
aug_imgs,
num_classes=num_classes,
reduction=0.5,
growth_rate=48,
num_filters=96,
num_layers=[6, 12, 36, 24],
data_format=data_format,
is_training=False, ## Set is always to false
reuse=True,
scope='densenet161')
def cal_metrics(end_points):
gt = tf.argmax(self.gt_lbls, 1)
logits = tf.reshape(end_points['densenet161/logits'],
[-1, num_classes])
pre_logits = end_points['densenet161/dense_block4']
center_supervised_cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.gt_lbls, logits=logits, name='xentropy_center')
loss = tf.reduce_mean(center_supervised_cross_entropy,
name='xentropy_mean')
predictions = tf.reshape(end_points['predictions'],
[-1, num_classes])
class_prediction = tf.argmax(predictions, 1)
supervised_correct_prediction = tf.equal(gt, class_prediction)
supervised_correct_prediction_cast = tf.cast(
supervised_correct_prediction, tf.float32)
accuracy = tf.reduce_mean(supervised_correct_prediction_cast)
confusion_mat = tf.confusion_matrix(gt,
class_prediction,
num_classes=num_classes)
_, accumulated_accuracy = tf.metrics.accuracy(gt, class_prediction)
_, per_class_acc_acc = tf.metrics.mean_per_class_accuracy(
gt, class_prediction, num_classes=num_classes)
per_class_acc_acc = tf.reduce_mean(per_class_acc_acc)
return loss, pre_logits, accuracy, confusion_mat, accumulated_accuracy, per_class_acc_acc
self.train_loss, self.train_pre_logits, self.train_accuracy, self.train_confusion_mat, self.train_accumulated_accuracy, self.train_per_class_acc_acc = cal_metrics(
train_end_points)
self.val_loss, self.val_pre_logits, self.val_accuracy, self.val_confusion_mat, self.val_accumulated_accuracy, self.val_per_class_acc_acc = cal_metrics(
val_end_points)
def dataset_from_files(self,
train_imgs,
train_lbls,
is_training,
cfg,
repeat=True,
shuffle=False):
def _parse_function(filename, label):
print(filename)
image_string = tf.read_file(filename)
#image_string = tf.Print(image_string,[filename,label],'img name ')
image_decoded = tf.image.decode_jpeg(image_string, channels=3)
# print(image_decoded.dtype)
#image_decoded = tf.Print(image_decoded, [tf.shape(image_decoded)], 'shape ::')
# image_resized = tf.image.resize_images(image_decoded, [const.max_frame_size, const.max_frame_size])
# image_decoded = tf.image.resize_images(image_decoded, [const.max_frame_size, const.max_frame_size])
# image_decoded = tf.cast(image_decoded, tf.uint8)
return image_decoded, tf.one_hot(label,
cfg.num_classes,
dtype=tf.int64)
filenames = tf.constant(train_imgs)
labels = tf.constant(train_lbls, dtype=tf.int32)
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
if shuffle:
print('Will shuffle dataset ***')
dataset = dataset.shuffle(len(train_imgs))
if repeat:
# dataset = dataset.shuffle(len(train_imgs))
dataset = dataset.repeat(
None) # Repeat forever. Funny way of stating it.
else:
dataset = dataset.repeat(1) # No Repeat
dataset = dataset.map(_parse_function,
num_parallel_calls=cfg.tuple_loader_queue_size)
# preprocess_mod = locate(config.preprocessing_module)
# func_name = preprocess_mod.preprocess_for_train_simple
# if not is_training:
# func_name = preprocess_mod.preprocess_for_eval_simple
# dataset = dataset.map(lambda im, lbl: (func_name (im,const.frame_height,const.frame_width), lbl))
batch_size = cfg.batch_size
if is_training:
if cfg.aug_style == 'batch':
dataset = dataset.batch(batch_size)
dataset = dataset.map(lambda im_batch, lbl_batch: (
batch_augment.augment(im_batch,
cfg.preprocess_func,
horizontal_flip=True,
vertical_flip=False,
rotate=0,
crop_probability=0,
color_aug_probability=0), lbl_batch))
elif cfg.aug_style == 'img':
dataset = dataset.map(
lambda im, lbl: (img_augment.preprocess_for_train(
im,
cfg.frame_size,
cfg.frame_size,
preprocess_func=cfg.preprocess_func), lbl))
dataset = dataset.batch(batch_size)
else:
if cfg.aug_style == 'batch':
dataset = dataset.batch(batch_size)
dataset = dataset.map(
lambda im_batch, lbl_batch: (batch_augment.center_crop(
im_batch, cfg.preprocess_func), lbl_batch))
elif cfg.aug_style == 'img':
dataset = dataset.map(
lambda im, lbl: (img_augment.preprocess_for_eval(
im,
cfg.frame_size,
cfg.frame_size,
preprocess_func=cfg.preprocess_func), lbl))
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1)
return dataset
def __init__(self,
cfg=None,
is_training=True,
dropout_keep_prob=0.8,
reuse=None,
scope='InceptionV4',
create_aux_logits=True,
images_ph=None,
lbls_ph=None):
self.cfg = cfg
batch_size = None
num_classes = cfg.num_classes
if lbls_ph is not None:
self.gt_lbls = tf.reshape(lbls_ph, [-1, num_classes])
else:
self.gt_lbls = tf.placeholder(tf.int32,
shape=(batch_size, num_classes),
name='class_lbls')
self.do_augmentation = tf.placeholder(tf.bool, name='do_augmentation')
self.loss_class_weight = tf.placeholder(tf.float32,
shape=(num_classes,
num_classes),
name='weights')
if cfg.db_name == 'honda':
self.input = tf.placeholder(tf.float32,
shape=(batch_size, const.frame_height,
const.frame_width,
const.context_channels),
name='context_input')
else:
self.input = tf.placeholder(
tf.float32,
shape=(batch_size, const.max_frame_size, const.max_frame_size,
const.frame_channels),
name='context_input')
# if is_training:
if images_ph is not None:
self.input = images_ph
_, w, h, c = self.input.shape
aug_imgs = tf.reshape(self.input, [-1, w, h, 3])
print('No nnutils Augmentation')
else:
if cfg.db_name == 'honda':
aug_imgs = self.input
else:
aug_imgs = tf.cond(
self.do_augmentation,
lambda: batch_augment.augment(self.input,
cfg.preprocess_func,
horizontal_flip=True,
vertical_flip=False,
rotate=0,
crop_probability=0,
color_aug_probability=0),
lambda: batch_augment.center_crop(self.input, cfg.
preprocess_func))
with slim.arg_scope(inception_v4_arg_scope()):
_, train_end_points = inception_v4(
aug_imgs,
num_classes,
dropout_keep_prob=dropout_keep_prob,
create_aux_logits=create_aux_logits,
is_training=True,
reuse=reuse,
scope=scope)
_, val_end_points = inception_v4(
aug_imgs,
num_classes,
dropout_keep_prob=dropout_keep_prob,
create_aux_logits=create_aux_logits,
is_training=False,
reuse=True,
scope=scope)
def cal_metrics(end_points):
gt = tf.argmax(self.gt_lbls, 1)
logits = tf.reshape(end_points['Logits'], [-1, num_classes])
pre_logits = end_points['Mixed_6h']
center_supervised_cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.gt_lbls, logits=logits, name='xentropy_center')
loss = tf.reduce_mean(center_supervised_cross_entropy,
name='xentropy_mean')
predictions = tf.reshape(end_points['Predictions'],
[-1, num_classes])
class_prediction = tf.argmax(predictions, 1)
supervised_correct_prediction = tf.equal(gt, class_prediction)
supervised_correct_prediction_cast = tf.cast(
supervised_correct_prediction, tf.float32)
accuracy = tf.reduce_mean(supervised_correct_prediction_cast)
confusion_mat = tf.confusion_matrix(gt,
class_prediction,
num_classes=num_classes)
_, accumulated_accuracy = tf.metrics.accuracy(gt, class_prediction)
_, per_class_acc_acc = tf.metrics.mean_per_class_accuracy(
gt, class_prediction, num_classes=num_classes)
per_class_acc_acc = tf.reduce_mean(per_class_acc_acc)
return loss, pre_logits, accuracy, confusion_mat, accumulated_accuracy, per_class_acc_acc, class_prediction
self.train_loss,self.train_pre_logits,self.train_accuracy,self.train_confusion_mat,\
self.train_accumulated_accuracy,self.train_per_class_acc_acc ,self.train_class_prediction = cal_metrics(train_end_points)
self.val_loss,self.val_pre_logits,self.val_accuracy, self.val_confusion_mat,\
self.val_accumulated_accuracy,self.val_per_class_acc_acc ,self.val_class_prediction = cal_metrics(val_end_points)
def __init__(self,
cfg=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope='resnet_v2_50',
images_ph=None,
lbls_ph=None):
self.cfg = cfg
batch_size = None
if lbls_ph is not None:
self.gt_lbls = tf.reshape(lbls_ph, [-1, cfg.num_classes])
else:
self.gt_lbls = tf.placeholder(tf.int32,
shape=(batch_size, cfg.num_classes),
name='class_lbls')
self.do_augmentation = tf.placeholder(tf.bool, name='do_augmentation')
self.loss_class_weight = tf.placeholder(tf.float32,
shape=(cfg.num_classes,
cfg.num_classes),
name='weights')
if 'honda' in cfg.db_name:
self.input = tf.placeholder(tf.float32,
shape=(batch_size, const.frame_height,
const.frame_width,
const.context_channels),
name='context_input')
else:
self.input = tf.placeholder(
tf.float32,
shape=(batch_size, const.max_frame_size, const.max_frame_size,
const.frame_channels),
name='context_input')
# if is_training:
if images_ph is not None:
self.input = images_ph
_, w, h, c = self.input.shape
aug_imgs = tf.reshape(self.input, [-1, w, h, 3])
print('No nnutils Augmentation')
else:
if 'honda' in cfg.db_name:
aug_imgs = self.input
else:
aug_imgs = tf.cond(
self.do_augmentation,
lambda: batch_augment.augment(self.input,
cfg.preprocess_func,
horizontal_flip=True,
vertical_flip=False,
rotate=0,
crop_probability=0,
color_aug_probability=0),
lambda: batch_augment.center_crop(self.input, cfg.
preprocess_func))
with slim.arg_scope(resnet_arg_scope()):
_, train_end_points = resnet_v2_50(aug_imgs,
cfg.num_classes,
is_training=True,
global_pool=global_pool,
output_stride=output_stride,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
_, val_end_points = resnet_v2_50(aug_imgs,
cfg.num_classes,
is_training=False,
global_pool=global_pool,
output_stride=output_stride,
spatial_squeeze=spatial_squeeze,
reuse=True,
scope=scope)
_, val_end_features = resnet_v2_50(aug_imgs,
0,
is_training=False,
global_pool=global_pool,
output_stride=output_stride,
spatial_squeeze=spatial_squeeze,
reuse=True,
scope=scope)
def cal_metrics(end_points):
gt = tf.argmax(self.gt_lbls, 1)
logits = tf.reshape(end_points['resnet_v2_50/logits'],
[-1, cfg.num_classes])
pre_logits = end_points['resnet_v2_50/block4/unit_3/bottleneck_v2']
center_supervised_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=self.gt_lbls, logits=logits, name='xentropy_center')
loss = tf.reduce_mean(center_supervised_cross_entropy,
name='xentropy_mean')
predictions = tf.reshape(end_points['predictions'],
[-1, cfg.num_classes])
class_prediction = tf.argmax(predictions, 1)
supervised_correct_prediction = tf.equal(gt, class_prediction)
supervised_correct_prediction_cast = tf.cast(
supervised_correct_prediction, tf.float32)
accuracy = tf.reduce_mean(supervised_correct_prediction_cast)
confusion_mat = tf.confusion_matrix(gt,
class_prediction,
num_classes=cfg.num_classes)
_, accumulated_accuracy = tf.metrics.accuracy(gt, class_prediction)
_, per_class_acc_acc = tf.metrics.mean_per_class_accuracy(
gt, class_prediction, num_classes=cfg.num_classes)
per_class_acc_acc = tf.reduce_mean(per_class_acc_acc)
#top_3= tf.math.in_top_k(gt,predictions,3)
#supervised_correct_prediction_cast_3 = tf.cast(top_3, tf.float32)
#accuracy_3 = tf.reduce_mean(supervised_correct_prediction_cast_3)
return loss, pre_logits, accuracy, confusion_mat, accumulated_accuracy, per_class_acc_acc, class_prediction, gt, predictions #,accuracy_3
self.train_loss,self.train_pre_logits,self.train_accuracy,self.train_confusion_mat,\
self.train_accumulated_accuracy,self.train_per_class_acc_acc,self.train_class_prediction,self.train_gt,self.train_preds = cal_metrics(train_end_points)
self.val_loss,self.val_pre_logits,self.val_accuracy, self.val_confusion_mat,\
self.val_accumulated_accuracy,self.val_per_class_acc_acc,self.val_class_prediction,self.val_gt,self.val_preds = cal_metrics(val_end_points)
self.val_end_features, self.val_features_labels = val_end_features, tf.argmax(
self.gt_lbls, 1)
def __init__(self,
cfg,
weight_decay=0.0001,
data_format='NHWC',
is_training=False,
reuse=None,
images_ph=None,
lbls_ph=None,
weights_ph=None):
self.cfg = cfg
num_classes = cfg.num_classes
batch_size = None
if lbls_ph is not None:
self.gt_lbls = tf.reshape(lbls_ph, [-1, cfg.num_classes])
else:
self.gt_lbls = tf.placeholder(tf.int32,
shape=(batch_size, cfg.num_classes),
name='class_lbls')
self.do_augmentation = tf.placeholder(tf.bool, name='do_augmentation')
self.loss_class_weight = tf.placeholder(tf.float32,
shape=(cfg.num_classes,
cfg.num_classes),
name='weights')
if cfg.db_name == 'honda':
self.input = tf.placeholder(tf.float32,
shape=(batch_size, const.frame_height,
const.frame_width,
const.context_channels),
name='context_input')
else:
self.input = tf.placeholder(
tf.float32,
shape=(batch_size, const.max_frame_size, const.max_frame_size,
const.frame_channels),
name='context_input')
# if is_training:
if images_ph is not None:
self.input = images_ph
_, w, h, c = self.input.shape
aug_imgs = tf.reshape(self.input, [-1, w, h, 3])
print('No nnutils Augmentation')
else:
if cfg.db_name == 'honda':
aug_imgs = self.input
else:
aug_imgs = tf.cond(
self.do_augmentation,
lambda: batch_augment.augment(self.input,
cfg.preprocess_func,
horizontal_flip=True,
vertical_flip=False,
rotate=0,
crop_probability=0,
color_aug_probability=0),
lambda: batch_augment.center_crop(self.input, cfg.
preprocess_func))
with tf.contrib.slim.arg_scope(
mobilenet_v1_arg_scope(is_training=is_training,
weight_decay=weight_decay)):
_, train_end_points = mobilenet_v1(
aug_imgs,
num_classes=num_classes,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1',
global_pool=True)
_, val_end_points = mobilenet_v1(
aug_imgs,
num_classes=num_classes,
dropout_keep_prob=0.999,
is_training=False,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=True,
scope='MobilenetV1',
global_pool=True)
def cal_metrics(end_points):
gt = tf.argmax(self.gt_lbls, 1)
logits = tf.reshape(end_points['Logits'], [-1, num_classes])
pre_logits = end_points['Conv2d_13_pointwise']
center_supervised_cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.gt_lbls, logits=logits, name='xentropy_center')
loss = tf.reduce_mean(center_supervised_cross_entropy,
name='xentropy_mean')
predictions = tf.reshape(end_points['Predictions'],
[-1, cfg.num_classes])
class_prediction = tf.argmax(predictions, 1)
supervised_correct_prediction = tf.equal(gt, class_prediction)
supervised_correct_prediction_cast = tf.cast(
supervised_correct_prediction, tf.float32)
accuracy = tf.reduce_mean(supervised_correct_prediction_cast)
confusion_mat = tf.confusion_matrix(gt,
class_prediction,
num_classes=cfg.num_classes)
_, accumulated_accuracy = tf.metrics.accuracy(gt, class_prediction)
_, per_class_acc_acc = tf.metrics.mean_per_class_accuracy(
gt, class_prediction, num_classes=cfg.num_classes)
per_class_acc_acc = tf.reduce_mean(per_class_acc_acc)
return loss, pre_logits, accuracy, confusion_mat, accumulated_accuracy, per_class_acc_acc
self.train_loss,self.train_pre_logits,self.train_accuracy,self.train_confusion_mat,\
self.train_accumulated_accuracy,self.train_per_class_acc_acc = cal_metrics(train_end_points)
self.val_loss,self.val_pre_logits,self.val_accuracy, self.val_confusion_mat,\
self.val_accumulated_accuracy,self.val_per_class_acc_acc = cal_metrics(val_end_points)