def testBuildNonExistingLayerLargeModel(self):
"""Tests that the model is built correctly without unnecessary layers."""
inputs = tf.random_uniform((5, 331, 331, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
pnasnet.build_pnasnet_large(inputs, 1000)
vars_names = [x.op.name for x in tf.trainable_variables()]
self.assertIn('cell_stem_0/1x1/weights', vars_names)
self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names)
def testBuildNonExistingLayerLargeModel(self):
"""Tests that the model is built correctly without unnecessary layers."""
inputs = tf.random_uniform((5, 331, 331, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
pnasnet.build_pnasnet_large(inputs, 1000)
vars_names = [x.op.name for x in tf.trainable_variables()]
self.assertIn('cell_stem_0/1x1/weights', vars_names)
self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names)
def pnasnetfunrotate(input_imgs,reuse ):
rotatedarr = []
for i in range(batchsize):
rotated = tf.contrib.image.rotate(input_imgs,
tf.random_uniform((), minval=-np.pi/4, maxval=np.pi/4))
rotatedarr.append(tf.reshape(rotated,[1,image_size,image_size,3]))
inputarr = tf.concat(rotatedarr,axis = 0)
print(inputarr.get_shape())
preprocessed = tf.subtract(tf.multiply(inputarr, 2.0), 1.0)#2 *( input_imgs / 255.0)-1.0
arg_scope = pnasnet.pnasnet_large_arg_scope() #获得模型命名空间
with slim.arg_scope(arg_scope):
with slim.arg_scope([slim.conv2d,
slim.batch_norm, slim.fully_connected,
slim.separable_conv2d],reuse=reuse):
rotated_logits, end_points = pnasnet.build_pnasnet_large(preprocessed,num_classes = 1001, is_training=False)
prob = end_points['Predictions']
return rotated_logits, prob
def testBuildPreLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
net, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
self.assertFalse('AuxLogits' in end_points)
self.assertFalse('Predictions' in end_points)
self.assertTrue(net.op.name.startswith('final_layer/Mean'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 4320])
def testBuildPreLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
net, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
self.assertFalse('AuxLogits' in end_points)
self.assertFalse('Predictions' in end_points)
self.assertTrue(net.op.name.startswith('final_layer/Mean'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 4320])
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
Extracts features using the first half of the PNASNet network.
We construct the network in `align_feature_maps=True` mode, which means
that all VALID paddings in the network are changed to SAME padding so that
the feature maps are aligned.
Args:
preprocessed_inputs: A [batch, height, width, channels] float32 tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
end_points: A dictionary mapping feature extractor tensor names to tensors
Raises:
ValueError: If the created network is missing the required activation.
"""
del scope
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError(
'`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
with slim.arg_scope(
pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._train_batch_norm)):
with arg_scope(
[slim.conv2d, slim.batch_norm, slim.separable_conv2d],
reuse=self._reuse_weights):
_, end_points = pnasnet.build_pnasnet_large(
preprocessed_inputs,
num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_7')
# Note that both 'Cell_6' and 'Cell_7' have equal depth = 2160.
# Cell_7 is the last cell before second reduction.
rpn_feature_map = tf.concat(
[end_points['Cell_6'], end_points['Cell_7']], 3)
# pnasnet.py does not maintain the batch size in the first dimension.
# This work around permits us retaining the batch for below.
batch = preprocessed_inputs.get_shape().as_list()[0]
shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
rpn_feature_map_shape = [batch] + shape_without_batch
rpn_feature_map.set_shape(rpn_feature_map_shape)
return rpn_feature_map, end_points
def testOverrideHParamsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('data_format', 'NCHW')
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(
inputs, num_classes, config=config)
self.assertListEqual(
end_points['Stem'].shape.as_list(), [batch_size, 540, 42, 42])
def testOverrideHParamsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('data_format', 'NCHW')
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(
inputs, num_classes, config=config)
self.assertListEqual(
end_points['Stem'].shape.as_list(), [batch_size, 540, 42, 42])
def testNoAuxHeadLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
for use_aux_head in (True, False):
tf.reset_default_graph()
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('use_aux_head', int(use_aux_head))
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes,
config=config)
self.assertEqual('AuxLogits' in end_points, use_aux_head)
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
Extracts features using the first half of the PNASNet network.
We construct the network in `align_feature_maps=True` mode, which means
that all VALID paddings in the network are changed to SAME padding so that
the feature maps are aligned.
Args:
preprocessed_inputs: A [batch, height, width, channels] float32 tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
end_points: A dictionary mapping feature extractor tensor names to tensors
Raises:
ValueError: If the created network is missing the required activation.
"""
del scope
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
with slim.arg_scope(pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._train_batch_norm)):
with arg_scope([slim.conv2d,
slim.batch_norm,
slim.separable_conv2d],
reuse=self._reuse_weights):
_, end_points = pnasnet.build_pnasnet_large(
preprocessed_inputs, num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_7')
# Note that both 'Cell_6' and 'Cell_7' have equal depth = 2160.
# Cell_7 is the last cell before second reduction.
rpn_feature_map = tf.concat([end_points['Cell_6'],
end_points['Cell_7']], 3)
# pnasnet.py does not maintain the batch size in the first dimension.
# This work around permits us retaining the batch for below.
batch = preprocessed_inputs.get_shape().as_list()[0]
shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
rpn_feature_map_shape = [batch] + shape_without_batch
rpn_feature_map.set_shape(rpn_feature_map_shape)
return rpn_feature_map, end_points
def testNoAuxHeadLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
for use_aux_head in (True, False):
tf.reset_default_graph()
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('use_aux_head', int(use_aux_head))
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes,
config=config)
self.assertEqual('AuxLogits' in end_points, use_aux_head)
def pnasnet_large(inputs, is_training, opts):
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope(
weight_decay=opts.weight_decay,
batch_norm_decay=opts.batch_norm_decay,
batch_norm_epsilon=opts.batch_norm_epsilon)):
config = pnasnet.large_imagenet_config()
config.set_hparam('dense_dropout_keep_prob', opts.dropout_keep_prob)
config.set_hparam('use_aux_head', int(opts.create_aux_logits))
return pnasnet.build_pnasnet_large(
inputs,
num_classes=opts.num_classes,
is_training=is_training,
config=config)
def testBuildLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
auxlogits = end_points['AuxLogits']
predictions = end_points['Predictions']
self.assertListEqual(auxlogits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(predictions.get_shape().as_list(),
[batch_size, num_classes])
def testBuildLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
auxlogits = end_points['AuxLogits']
predictions = end_points['Predictions']
self.assertListEqual(auxlogits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(predictions.get_shape().as_list(),
[batch_size, num_classes])
def extract_features(self, preprocessed_inputs):
"""Extract features from preprocessed inputs.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
"""
feature_map_layout = {
'from_layer': ['Cell_7', 'Cell_11', '', '', '',
''][:self._num_layers],
'layer_depth': [-1, -1, 512, 256, 256, 128][:self._num_layers],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(
pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._is_training)):
with slim.arg_scope(
[slim.conv2d, slim.batch_norm, slim.separable_conv2d],
reuse=self._reuse_weights):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = pnasnet.build_pnasnet_large(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
with tf.compat.v1.variable_scope('SSD_feature_maps',
reuse=self._reuse_weights):
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()
def pnasnetfun(input_imgs, reuse):
preprocessed = tf.subtract(tf.multiply(tf.expand_dims(input_imgs, 0), 2.0),
1.0) #2 *( input_imgs / 255.0)-1.0
arg_scope = pnasnet.pnasnet_large_arg_scope() #获得模型命名空间
with slim.arg_scope(arg_scope):
with slim.arg_scope([
slim.conv2d, slim.batch_norm, slim.fully_connected,
slim.separable_conv2d
],
reuse=reuse):
logits, end_points = pnasnet.build_pnasnet_large(preprocessed,
num_classes=1001,
is_training=False)
prob = end_points['Predictions']
return logits, prob
def extract_features(self, preprocessed_inputs):
"""Extract features from preprocessed inputs.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
"""
feature_map_layout = {
'from_layer': ['Cell_7', 'Cell_11', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(
pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._is_training)):
with slim.arg_scope([slim.conv2d, slim.batch_norm, slim.separable_conv2d],
reuse=self._reuse_weights):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = pnasnet.build_pnasnet_large(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
with tf.variable_scope('SSD_feature_maps', reuse=self._reuse_weights):
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()
def testAllEndPointsShapesLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random.uniform((batch_size, height, width, 3))
tf.compat.v1.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
endpoints_shapes = {
'Stem': [batch_size, 42, 42, 540],
'Cell_0': [batch_size, 42, 42, 1080],
'Cell_1': [batch_size, 42, 42, 1080],
'Cell_2': [batch_size, 42, 42, 1080],
'Cell_3': [batch_size, 42, 42, 1080],
'Cell_4': [batch_size, 21, 21, 2160],
'Cell_5': [batch_size, 21, 21, 2160],
'Cell_6': [batch_size, 21, 21, 2160],
'Cell_7': [batch_size, 21, 21, 2160],
'Cell_8': [batch_size, 11, 11, 4320],
'Cell_9': [batch_size, 11, 11, 4320],
'Cell_10': [batch_size, 11, 11, 4320],
'Cell_11': [batch_size, 11, 11, 4320],
'global_pool': [batch_size, 4320],
# Logits and predictions
'AuxLogits': [batch_size, 1000],
'Predictions': [batch_size, 1000],
'Logits': [batch_size, 1000],
}
self.assertEqual(len(end_points), 17)
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
tf.compat.v1.logging.info(
'Endpoint name: {}'.format(endpoint_name))
expected_shape = endpoints_shapes[endpoint_name]
self.assertIn(endpoint_name, end_points)
self.assertListEqual(
end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def testAllEndPointsShapesLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
endpoints_shapes = {'Stem': [batch_size, 42, 42, 540],
'Cell_0': [batch_size, 42, 42, 1080],
'Cell_1': [batch_size, 42, 42, 1080],
'Cell_2': [batch_size, 42, 42, 1080],
'Cell_3': [batch_size, 42, 42, 1080],
'Cell_4': [batch_size, 21, 21, 2160],
'Cell_5': [batch_size, 21, 21, 2160],
'Cell_6': [batch_size, 21, 21, 2160],
'Cell_7': [batch_size, 21, 21, 2160],
'Cell_8': [batch_size, 11, 11, 4320],
'Cell_9': [batch_size, 11, 11, 4320],
'Cell_10': [batch_size, 11, 11, 4320],
'Cell_11': [batch_size, 11, 11, 4320],
'global_pool': [batch_size, 4320],
# Logits and predictions
'AuxLogits': [batch_size, 1000],
'Predictions': [batch_size, 1000],
'Logits': [batch_size, 1000],
}
self.assertEqual(len(end_points), 17)
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
tf.logging.info('Endpoint name: {}'.format(endpoint_name))
expected_shape = endpoints_shapes[endpoint_name]
self.assertIn(endpoint_name, end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
def main():
slim = tf.contrib.slim
tf.reset_default_graph()
image_size = pnasnet.build_pnasnet_large.default_image_size # 331
labels = imagenet.create_readable_names_for_imagenet_labels()
sample_images = ['hy.jpg', 'mac.jpg', 'filename3.jpg', '72.jpg', 'ps.jpg']
input_imgs = tf.placeholder(tf.float32, [None, image_size, image_size, 3])
x1 = 2 * (input_imgs / 255.0) - 1.0
arg_scope = pnasnet.pnasnet_large_arg_scope()
with slim.arg_scope(arg_scope):
logit, end_points = pnasnet.build_pnasnet_large(x1,
num_classes=1001,
is_training=False)
print(end_points)
prob = end_points['Predictions']
y = tf.argmax(prob, axis=1)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, checkepoint_file)
def preimg(img):
ch = 3
print(img.mode)
if img.mode == 'RGBA':
ch = 4
imgnp = np.asarray(img.resize((image_size, image_size)),
dtype=np.float32).reshape(
image_size, image_size, ch)
return imgnp[:, :, :3]
batchImg = [
preimg(Image.open(imgfilename)) for imgfilename in sample_images
]
orgImg = [Image.open(imgfilename) for imgfilename in sample_images]
yv, img_norm = sess.run([y, x1], feed_dict={input_imgs: batchImg})
# print(yv, np.shape(yv))
def showresult(yy, img_norm, img_org):
plt.figure()
p1 = plt.subplot(121)
p2 = plt.subplot(122)
p1.imshow(img_org)
p1.axis('off')
p1.set_title("organization image")
p2.imshow((img_norm * 255).astype(np.uint8))
p2.axis('off')
p2.set_title("input image")
plt.show()
# print(yy)
# print(labels[yy])
for yy, img1, img2 in zip(yv, batchImg, orgImg):
showresult(yy, img1, img2)
with open('中文标签.csv', 'r+') as f: #打开文件
labels = list(map(getone, list(f)))
print(len(labels), type(labels), labels[:5]) #显示输出中文标签
sample_images = ['hy.jpg', 'ps.jpg', '72.jpg'] #定义待测试图片路径
input_imgs = tf.placeholder(tf.float32,
[None, image_size, image_size, 3]) #定义占位符
x1 = 2 * (input_imgs / 255.0) - 1.0 #归一化图片
arg_scope = pnasnet.pnasnet_large_arg_scope() #获得模型命名空间
with slim.arg_scope(arg_scope):
logits, end_points = pnasnet.build_pnasnet_large(x1,
num_classes=1001,
is_training=False)
prob = end_points['Predictions']
y = tf.argmax(prob, axis=1) #获得结果的输出节点
checkpoint_file = r'./pnasnet-5_large_2017_12_13/model.ckpt' #定义模型路径
saver = tf.train.Saver() #定义saver，用于加载模型
with tf.Session() as sess: #建立会话
saver.restore(sess, checkpoint_file) #载入模型
def preimg(img): #定义图片预处理函数
ch = 3
if img.mode == 'RGBA': #兼容RGBA图片
ch = 4
imgnp = np.asarray(img.resize((image_size, image_size)),
使用TF-Slim进行图像分类，还需要安装：TF-slim image models library，这个库不在TF的核心库中。
（1）git clone https://github.com/tensorflow/models
（2）cd models/research/slim
（3）测试是否可用：python -c "from nets import cifarnet; mynet = cifarnet.cifarnet"
注意：models/research/slim这个目录可以取出来放到单独放到一个地方直接使用。
可以查看源码目录中的各种*_test.py学习不同模型的使用
'''
import sys
# 我将slim文件夹放到了/Users/rensike/Files/temp/目录下
sys.path.append("/Users/rensike/Files/temp/slim")
import tensorflow as tf
slim = tf.contrib.slim
'''
获取pnasnet最后的输出层的变量名称
'''
from nets.nasnet import pnasnet
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
auxlogits = end_points['AuxLogits']
predictions = end_points['Predictions']
print(predictions.op.name) # final_layer/predictions
def _build_graph(self, input_tensor):
with tf.name_scope('inputs'):
if input_tensor is None:
input_tensor = tf.placeholder(tf.float32,
shape=self.image_shape,
name='input_img')
else:
assert self.image_shape == tuple(input_tensor.shape.as_list())
self.input_tensor = input_tensor
if self.model_name == 'vgg_16':
self.ckpt_path = slim_models_path + "vgg_16.ckpt"
from nets.vgg import vgg_16, vgg_arg_scope
with slim.arg_scope(vgg_arg_scope()):
self.output, self.outputs = vgg_16(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training,
global_pool=self.global_pool)
if self.model_name == 'resnet_v2_152':
self.ckpt_path = slim_models_path + "resnet_v2_152.ckpt"
from nets.resnet_v2 import resnet_v2_152, resnet_arg_scope
with slim.arg_scope(resnet_arg_scope()):
self.output, self.outputs = resnet_v2_152(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training,
global_pool=self.global_pool)
elif self.model_name == 'resnet_v2_101':
self.ckpt_path = slim_models_path + "resnet_v2_101.ckpt"
from nets.resnet_v2 import resnet_v2_101, resnet_arg_scope
with slim.arg_scope(resnet_arg_scope()):
self.output, self.outputs = resnet_v2_101(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training,
global_pool=self.global_pool)
elif self.model_name == 'resnet_v2_50':
self.ckpt_path = slim_models_path + "resnet_v2_50.ckpt"
from nets.resnet_v2 import resnet_v2_50, resnet_arg_scope
with slim.arg_scope(resnet_arg_scope()):
self.output, self.outputs = resnet_v2_50(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training,
global_pool=self.global_pool)
elif self.model_name == 'InceptionV3':
self.ckpt_path = slim_models_path + "inception_v3.ckpt"
from nets.inception import inception_v3, inception_v3_arg_scope
with slim.arg_scope(inception_v3_arg_scope()):
self.output, self.outputs = inception_v3(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training)
elif self.model_name == 'InceptionV4':
self.ckpt_path = slim_models_path + "inception_v4.ckpt"
from nets.inception import inception_v4, inception_v4_arg_scope
with slim.arg_scope(inception_v4_arg_scope()):
self.output, self.outputs = inception_v4(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training)
elif self.model_name == 'pnasnet_large':
self.ckpt_path = slim_models_path + "pnasnet_large_2.ckpt"
from nets.nasnet.pnasnet import build_pnasnet_large, pnasnet_large_arg_scope
with tf.variable_scope(self.model_name):
with slim.arg_scope(pnasnet_large_arg_scope()):
self.output, self.outputs = build_pnasnet_large(
self.input_tensor,
num_classes=self.num_classes,
is_training=self.is_training)
#collecting all variables related to this model
#self.model_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.model_name+'/model')
self.model_weights = slim.get_model_variables(self.model_name)
