def __init__(self, sess):
self.sess = sess
self.dataset_root = 'F:\\Learning\\tensorflow\\detect\\Dataset\\'
if Detection_or_Classifier=='classifier':
self.train_data = DataLoader(root=self.dataset_root+'SmallNORB\\trainImages',batch=batch_size)
self.test_data = DataLoader(root=self.dataset_root+'SmallNORB\\testImages',batch=batch_size)
self.labels = [str(i) for i in range(1,1001)]
print("Building the model...")
self.model = IGCV3FPN(num_classes=len(self.labels),
num_anchors=5,
batch_size = batch_size,
max_box_per_image = max_box_per_image,
max_grid=[max_input_size,max_input_size],
)
print("Model is built successfully\n\n")
elif Detection_or_Classifier=='detection':
train_ints, valid_ints, self.labels = create_training_instances(
self.dataset_root+'VOC2012\\Annotations\\',
self.dataset_root+'VOC2012\\JPEGImages\\',
'data.pkl',
'','','',
['person','head','hand','foot','aeroplane','tvmonitor','train','boat','dog','chair',
'bird','bicycle','bottle','sheep','diningtable','horse','motorbike','sofa','cow',
'car','cat','bus','pottedplant']
)
self.train_data = BatchGenerator(
instances = train_ints,
anchors = anchors,
labels = self.labels,
downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image = max_box_per_image,
batch_size = batch_size,
min_net_size = min_input_size,
max_net_size = max_input_size,
shuffle = True,
jitter = 0.3,
norm = normalize
)
self.test_data = BatchGenerator(
instances = valid_ints,
anchors = anchors,
labels = self.labels,
downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image = max_box_per_image,
batch_size = batch_size,
min_net_size = min_input_size,
max_net_size = max_input_size,
shuffle = True,
jitter = 0.0,
norm = normalize
)
print("Building the model...")
self.model = IGCV3FPN(num_classes=len(self.labels),
num_anchors=5,
batch_size = batch_size,
max_box_per_image = max_box_per_image,
max_grid=[max_input_size,max_input_size],
)
print("Model is built successfully\n\n")
#tf.profiler.profile(tf.get_default_graph(),options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter(), cmd='scope')
num_params = get_num_params()
print('all params:{}'.format(num_params))
var = tf.global_variables()
var_list = [val for val in var]
if Detection_or_Classifier=='detection' and False:
#var_list = [val for val in var if (('zsc_preprocessing' in val.name) or ('zsc_feature' in val.name) or ('zsc_attention' in val.name) or ('zsc_detection' in val.name)) and ('SE' not in val.name)]
var_list = [val for val in var if ('SE' not in val.name)]
self.saver = tf.train.Saver(var_list=var_list,max_to_keep=max_to_keep,
keep_checkpoint_every_n_hours=10)
self.save_checkpoints_path = os.path.join(os.getcwd(),'checkpoints',Detection_or_Classifier)
if not os.path.exists(self.save_checkpoints_path):
os.makedirs(self.save_checkpoints_path)
# Initializing the model
self.init = None
self.__init_model()
# Loading the model checkpoint if exists
self.__load_model()
summary_dir = os.path.join(os.getcwd(),'logs',Detection_or_Classifier)
if not os.path.exists(summary_dir):
os.makedirs(summary_dir)
summary_dir_train = os.path.join(summary_dir,'train')
if not os.path.exists(summary_dir_train):
os.makedirs(summary_dir_train)
summary_dir_test = os.path.join(summary_dir,'test')
if not os.path.exists(summary_dir_test):
os.makedirs(summary_dir_test)
self.train_writer = tf.summary.FileWriter(summary_dir_train,sess.graph)
self.test_writer = tf.summary.FileWriter(summary_dir_test)
def __init__(self, sess):
self.sess = sess
self.dataset_root = 'F:\\Learning\\tensorflow\\detect\\Dataset\\'
if Detection_or_Classifier == 'classifier':
self.labels = list(range(18))
self.train_data = DataLoader(root=os.path.join(
self.dataset_root, 'data', 'train'),
classes=len(self.labels),
batch=batch_size)
print("Building the model...")
self.model = Model(num_classes=len(self.labels))
print("Model is built successfully\n\n")
elif Detection_or_Classifier == 'detection':
train_ints, valid_ints, self.labels = create_training_instances(
os.path.join(self.dataset_root, 'Fish', 'Annotations'),
os.path.join(self.dataset_root, 'Fish', 'JPEGImages'),
'data.pkl', '', '', '', False, [
'heidiao', 'niyu', 'lvqimamiantun', 'hualu', 'heijun',
'dalongliuxian', 'tiaoshiban'
]
#['person','head','hand','foot','aeroplane','tvmonitor','train','boat','dog','chair',
# 'bird','bicycle','bottle','sheep','diningtable','horse','motorbike','sofa','cow',
# 'car','cat','bus','pottedplant']
)
self.train_data = BatchGenerator(train_ints, self.labels,
batch_size)
print("Building the model...")
self.model = Model(num_classes=23)
print("Model is built successfully\n\n")
#tf.profiler.profile(tf.get_default_graph(),options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter(), cmd='scope')
num_params = get_num_params()
print('all params:{}'.format(num_params))
self.use_classifier_pretrain = True
if not self.use_classifier_pretrain:
var = tf.global_variables()
else:
var = tf.trainable_variables()
var_list = [val for val in var]
if Detection_or_Classifier == 'detection' and self.use_classifier_pretrain:
var_list = [val for val in var if 'zsc_detection' not in val.name]
if Detection_or_Classifier == 'classifier':
var = tf.global_variables()
var_list = [val for val in var if 'Logits/bias' not in val.name]
self.saver = tf.train.Saver(var_list=var_list, max_to_keep=max_to_keep)
self.save_checkpoints_path = os.path.join(os.getcwd(), 'checkpoints',
Detection_or_Classifier)
if not os.path.exists(self.save_checkpoints_path):
os.makedirs(self.save_checkpoints_path)
# Initializing the model
self.init = None
self.__init_model()
# Loading the model checkpoint if exists
self.__load_model()
def __init__(self, sess):
self.sess = sess
self.dataset_root = '/home/b101/anaconda2/ZSCWork/Dataset/'
if Detection_or_Classifier == 'classifier':
self.train_data = DataLoader(root=self.dataset_root +
'SmallNORB/trainImages',
batch=batch_size)
self.test_data = DataLoader(root=self.dataset_root +
'SmallNORB/testImages',
batch=batch_size)
self.labels = ['1', '2', '3', '4', '5']
print("Building the model...")
self.model = CliqueFPN(
num_classes=len(self.labels),
num_anchors=3,
batch_size=batch_size,
max_box_per_image=max_box_per_image,
max_grid=[max_input_size, max_input_size],
)
print("Model is built successfully\n\n")
elif Detection_or_Classifier == 'detection':
train_ints, valid_ints, self.labels = create_training_instances(
self.dataset_root + 'Fish/Annotations/',
self.dataset_root + 'Fish/JPEGImages/', 'data.pkl', '', '', '',
[
'heidiao', 'niyu', 'lvqimamiantun', 'hualu', 'heijun',
'dalongliuxian', 'tiaoshiban'
]
#['person','head','hand','foot','aeroplane','tvmonitor','train','boat','dog','chair',
# 'bird','bicycle','bottle','sheep','diningtable','horse','motorbike','sofa','cow',
# 'car','cat','bus','pottedplant']
)
self.train_data = BatchGenerator(
instances=train_ints,
anchors=anchors,
labels=self.labels,
downsample=
32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image=max_box_per_image,
batch_size=batch_size,
min_net_size=min_input_size,
max_net_size=max_input_size,
shuffle=True,
jitter=0.3,
norm=normalize)
self.test_data = BatchGenerator(
instances=valid_ints,
anchors=anchors,
labels=self.labels,
downsample=
32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image=max_box_per_image,
batch_size=batch_size,
min_net_size=min_input_size,
max_net_size=max_input_size,
shuffle=True,
jitter=0.0,
norm=normalize)
print("Building the model...")
self.model = CliqueFPN(
num_classes=len(self.labels),
num_anchors=3,
batch_size=batch_size,
max_box_per_image=max_box_per_image,
max_grid=[max_input_size, max_input_size],
)
print("Model is built successfully\n\n")
#tf.profiler.profile(tf.get_default_graph(),options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter(), cmd='scope')
num_params = get_num_params()
print('all params:{}'.format(num_params))
var = tf.global_variables()
var_list = [val for val in var]
if Detection_or_Classifier == 'detection' and False:
var_list = [
val for val in var
if 'PrimaryConv/conv_0/batchnorm' not in val.name
and 'PrimaryConv/conv_1/batchnorm' not in val.name
and 'PrimaryConv/conv_2/batchnorm' not in val.name
and 'PrimaryConv/conv_0/DecoupledOperator' not in val.name
and 'PrimaryConv/conv_1/DecoupledOperator' not in val.name
and 'PrimaryConv/conv_2/DecoupledOperator' not in val.name
and 'PrimaryConv/conv_0/prelu' not in val.name
and 'PrimaryConv/conv_1/prelu' not in val.name
and 'PrimaryConv/conv_2/prelu' not in val.name
and 'SelfAttention/g/batch_norm' not in val.name
and 'SelfAttention/f/batch_norm' not in val.name
and 'SelfAttention/h/batch_norm' not in val.name
and 'SelfAttention/g/prelu' not in val.name
and 'SelfAttention/f/prelu' not in val.name
and 'SelfAttention/h/prelu' not in val.name
and 'SelfAttention/DecoupledOperator' not in val.name
and 'zsc_pred' not in val.name
]
self.saver = tf.train.Saver(var_list=var_list, max_to_keep=max_to_keep)
self.save_checkpoints_path = os.path.join(os.getcwd(), 'checkpoints',
Detection_or_Classifier)
if not os.path.exists(self.save_checkpoints_path):
os.makedirs(self.save_checkpoints_path)
# Initializing the model
self.init = None
self.__init_model()
# Loading the model checkpoint if exists
self.__load_model()
def __init__(self, sess):
self.sess = sess
self.dataset_root = '/home/b101/anaconda2/ZSCWork/Dataset/'
if Detection_or_Classifier=='classifier':
self.train_data = DataLoader(root=self.dataset_root+'SmallNORB/trainImages',batch=batch_size)
self.test_data = DataLoader(root=self.dataset_root+'SmallNORB/testImages',batch=batch_size)
self.labels = ['1','2','3','4','5']
print("Building the model...")
self.model = CliqueFPN(num_classes=len(self.labels),
num_anchors=5,
batch_size = batch_size,
max_box_per_image = max_box_per_image,
max_grid=[max_input_size,max_input_size],
)
print("Model is built successfully\n\n")
elif Detection_or_Classifier=='detection':
train_ints, valid_ints, self.labels = create_training_instances(
self.dataset_root+'VOC2012/Annotations/',
self.dataset_root+'VOC2012/JPEGImages/',
'data.pkl',
'','','',
['person','head','hand','foot','aeroplane','tvmonitor','train','boat','dog','chair',
'bird','bicycle','bottle','sheep','diningtable','horse','motorbike','sofa','cow',
'car','cat','bus','pottedplant']
)
self.train_data = BatchGenerator(
instances = train_ints,
anchors = anchors,
labels = self.labels,
downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image = max_box_per_image,
batch_size = batch_size,
min_net_size = min_input_size,
max_net_size = max_input_size,
shuffle = True,
jitter = 0.3,
norm = normalize
)
self.test_data = BatchGenerator(
instances = valid_ints,
anchors = anchors,
labels = self.labels,
downsample = 32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image = max_box_per_image,
batch_size = batch_size,
min_net_size = min_input_size,
max_net_size = max_input_size,
shuffle = True,
jitter = 0.0,
norm = normalize
)
print("Building the model...")
self.model = CliqueFPN(num_classes=len(self.labels),
num_anchors=5,
batch_size = batch_size,
max_box_per_image = max_box_per_image,
max_grid=[max_input_size,max_input_size],
)
print("Model is built successfully\n\n")
#tf.profiler.profile(tf.get_default_graph(),options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter(), cmd='scope')
num_params = get_num_params()
print('all params:{}'.format(num_params))
var = tf.global_variables()
var_list = [val for val in var]
if Detection_or_Classifier=='detection' and False:
var_list = [val for val in var if 'CliqueBlock_2/loop/deform_conv' not in val.name ]
self.saver = tf.train.Saver(var_list=var_list,max_to_keep=max_to_keep)
self.save_checkpoints_path = os.path.join(os.getcwd(),'checkpoints',Detection_or_Classifier)
if not os.path.exists(self.save_checkpoints_path):
os.makedirs(self.save_checkpoints_path)
# Initializing the model
self.init = None
self.__init_model()
# Loading the model checkpoint if exists
self.__load_model()
summary_dir = os.path.join(os.getcwd(),'logs',Detection_or_Classifier)
if not os.path.exists(summary_dir):
os.makedirs(summary_dir)
summary_dir_train = os.path.join(summary_dir,'train')
if not os.path.exists(summary_dir_train):
os.makedirs(summary_dir_train)
summary_dir_test = os.path.join(summary_dir,'test')
if not os.path.exists(summary_dir_test):
os.makedirs(summary_dir_test)
self.train_writer = tf.summary.FileWriter(summary_dir_train,sess.graph)
self.test_writer = tf.summary.FileWriter(summary_dir_test)