TensorFlowプリトレーニングモデルのインポート
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TensorFlowプリトレーニングモデルのインポート
モデルダウンロードアドレス
Pre-trained ModelsおよびIndex of/models/tf/は、VGG、ResNet、各バージョンのInceptionなどを含む、より新しい、パフォーマンスの良いネットワーク事前トレーニングモデルの大部分を提供する.AlexNetモデルをテストするには、AlexNet implementation+weights in TensorFlowに対応するものをダウンロードします.npyファイルは、提供する.pyファイルはネットワークを再構築し、このネットワークにはフィーチャーパケットトレーニングが必要であり、入力画素値範囲は[0255][0,255]であることに注意します.プロバイダの説明によれば、モデルは実際にcaffemodelによって変換される.
【注】tensorflowはcaffemodelを直接インポートすることはできません.caffemodelをtensorflowで読み取ることができるフォーマットに変換する必要があります.具体的にはGitHubプロジェクトcaffe-tensorflowを参照してください.
方法
異なる接尾辞名のモデルでは、異なるロード方法が必要です.
alex_path = '...\\bvlc_alexnet.npy'
alex_data = np.load(open(alex_path, "rb"), encoding="latin1").item()
tf.reset_default_graph()
x = tf.placeholder(tf.float32,shape = [None,227,227,3],name = 'im')
mean = tf.constant([[[[ 123.68 , 116.779, 103.939]]]],name = 'im_mean') #[0,255]
x = tf.subtract(x,mean)
x = tf.reverse(x,axis = [3]) # RGB to BGR
with tf.variable_scope('conv1'):
w0,b0 = alex_data['conv1']
w = tf.get_variable('w',initializer = tf.constant(w0)) # use tensorflow variable rather than constant
b = tf.get_variable('b',initializer = tf.constant(b0))
conv = tf.nn.conv2d(x,w,[1,4,4,1],'SAME')
conv = tf.add(conv,b)
conv = tf.nn.relu(conv)
conv = tf.nn.local_response_normalization(conv,depth_radius = 2,alpha = 2e-5,beta = 0.75,bias = 1.0)
pass
with tf.variable_scope('pool1'):
pool = tf.nn.max_pool(conv,[1,3,3,1],[1,2,2,1],'VALID')
pass
with tf.variable_scope('conv2'):
w0,b0 = alex_data['conv2']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
group = 2
inputs = tf.split(pool,group,3)
kernels = tf.split(w,group,3)
# cannot use tf.constant(kernel0) because kernel0 is a tensorflow Tensor yet
outputs = [tf.nn.conv2d(input0,kernel0,[1,1,1,1],'SAME') for input0,kernel0 in zip(inputs,kernels)]
output = tf.concat(outputs,3)
conv = tf.add(output,b)
conv = tf.nn.relu(conv)
conv = tf.nn.local_response_normalization(conv,depth_radius = 2,alpha = 2e-5,beta = 0.75,bias = 1.0)
pass
with tf.variable_scope('pool2'):
pool = tf.nn.max_pool(conv,[1,3,3,1],[1,2,2,1],'VALID')
pass
with tf.variable_scope('conv3'):
w0,b0 = alex_data['conv3']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
conv = tf.nn.conv2d(pool,w,[1,1,1,1],'SAME')
conv = tf.add(conv,b)
conv = tf.nn.relu(conv)
pass
with tf.variable_scope('conv4'):
w0,b0 = alex_data['conv4']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
group = 2
inputs = tf.split(conv,group,3)
kernels = tf.split(w,group,3)
outputs = [tf.nn.conv2d(input0,kernel0,[1,1,1,1],'SAME') for input0,kernel0 in zip(inputs,kernels)]
output = tf.concat(outputs,3)
conv = tf.add(output,b)
conv = tf.nn.relu(conv)
pass
with tf.variable_scope('conv5'):
w0,b0 = alex_data['conv5']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
group = 2
inputs = tf.split(conv,group,3)
kernels = tf.split(w,group,3)
outputs = [tf.nn.conv2d(input0,kernel0,[1,1,1,1],'SAME') for input0,kernel0 in zip(inputs,kernels)]
output = tf.concat(outputs,3)
conv = tf.add(output,b)
conv = tf.nn.relu(conv)
pass
with tf.variable_scope('pool5'):
pool = tf.nn.max_pool(conv,[1,3,3,1],[1,2,2,1],'VALID')
pass
with tf.variable_scope('fc6'):
w0,b0 = alex_data['fc6']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
pool = tf.layers.flatten(pool) # flatten the convolutional feature for dense connection
fc = tf.matmul(pool,w)
fc = tf.add(fc,b)
fc = tf.nn.relu(fc)
pass
with tf.variable_scope('fc7'):
w0,b0 = alex_data['fc7']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
fc = tf.matmul(fc,w)
fc = tf.add(fc,b)
fc = tf.nn.relu(fc)
pass
with tf.variable_scope('fc8'):
w0,b0 = alex_data['fc8']
w = tf.get_variable('w',initializer = tf.constant(w0))
b = tf.get_variable('b',initializer = tf.constant(b0))
fc = tf.matmul(fc,w)
fc = tf.add(fc,b)
pass
with tf.variable_scope('prob'):
prob = tf.nn.softmax(fc)
pass
y = tf.argmax(prob,axis = 1)model_fn = r'...\resnet_v1_152.pb'
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
pass
g = tf.Graph()
sess = tf.InteractiveSession(graph = g)
x = tf.placeholder(tf.float32,shape = (None,224,224,3),name = 'im')
mean = tf.constant([[[[ 123.68 , 116.779, 103.939]]]],name = 'im_mean') # [0,255]
x1 = tf.subtract(x,mean)
tf.import_graph_def(graph_def,{'InputImage':x1}) # match the input of VGG19 to the placeholder 'x1'
layer = 'import/resnet_v1_152/predictions/Softmax:0'
y = tf.argmax(g.get_tensor_by_name(layer),axis = 1) # get output前のコードに比べて、ここはずっと簡潔です.3. .ckptファイルckptファイルのインポートは何度も試みたが成功しなかったが、問題を見てonly publish pre-trained model’s checkpoint files are useless.#6954でやっと分かる
.ckpt files are useless on their own, but are intended to be used by a script that builds a model, sets up saver, and loads them. pre-trained checkpoint files are useless by themselves, you need to use the code that created the model that generated it I'm guessing those .ckpt files are read by some .py script that sets up the model, and loads the checkpoint. They are not meant to be used directly.だから、光がある.ckptファイルは足りないので、tensorflow/modelsにセットする必要があります.pyファイルインポート.例えばVGG 19import sys
nets_path = r'...
ets'
sys.path.insert(0,nets_path)
from vgg import vgg_19
ckpt_path = r'...\vgg_19_2016_08_28\vgg_19.ckpt'
tf.reset_default_graph()
x = tf.placeholder(tf.float32,shape = [None,224,224,3],name = 'im')
mean = tf.constant([[[[ 123.68 , 116.779, 103.939]]]],name = 'im_mean',dtype = tf.float32) # [0,255]
x = tf.subtract(x,mean)
net,endpoints = vgg_19(x)
saver = tf.train.Saver()
sess = tf.InteractiveSession()
saver.restore(sess,ckpt_path)
# layer = 'vgg_19/fc8/squeezed:0'#'vgg_19/fc8/BiasAdd:0'
# fc8 = sess.graph.get_tensor_by_name(layer)
prob = tf.nn.softmax(net,axis = 1)
y = tf.argmax(prob,axis = 1)しかし、このようにNasNetを導入すると、報告が間違っていて、なぜか...