def testVariablesSetDevice(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) # Force all Variables to reside on the device. with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): inception.inception_v4(inputs, num_classes) with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): inception.inception_v4(inputs, num_classes) for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): self.assertDeviceEqual(v.device, '/cpu:0') for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): self.assertDeviceEqual(v.device, '/gpu:0')
def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 with self.test_session() as sess: train_inputs = tf.random_uniform( (train_batch_size, height, width, 3)) inception.inception_v4(train_inputs, num_classes) eval_inputs = tf.random_uniform( (eval_batch_size, height, width, 3)) logits, _ = inception.inception_v4(eval_inputs, num_classes, is_training=False, reuse=True) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size, ))
def testBuildWithoutAuxLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) logits, endpoints = inception.inception_v4(inputs, num_classes, create_aux_logits=False) self.assertFalse('AuxLogits' in endpoints) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
def testHalfSizeImages(self): batch_size = 5 height, width = 150, 150 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v4(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7d'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 1536])
def testEvaluation(self): batch_size = 2 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = inception.inception_v4(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size, ))
def __call__(self, x_input): """Constructs model and return probabilities for given input.""" reuse = True if self.built else None x_input = image_normalize(x_input, 'default') with slim.arg_scope(inception.inception_v4_arg_scope()): logit, end_points = inception.inception_v4( x_input, num_classes=self.num_classes, is_training=True, reuse=reuse) self.built = True output = end_points['Predictions'] # Strip off the extra reshape op at the output probs = output.op.inputs[0] return logit
def network(self): with slim.arg_scope(inception.inception_v4_arg_scope()): end_points = inception.inception_v4(inputs=self.x, num_classes=1001, is_training=self.is_training, dropout_keep_prob=self.keep) # print(end_points[1]) net = end_points[1]['PreLogitsFlatten'] print(net) self.net = tf.stop_gradient(net) # 这层与之前的层都不进行梯度更新 print(net.shape) with tf.variable_scope('D'): #重新建立两层全连接层 fc1 = slim.fully_connected(self.net, 512, activation_fn=tf.nn.elu, scope='fc1') fc = slim.fully_connected(fc1, 48, activation_fn=tf.nn.sigmoid, scope='coding_layer') self.y = slim.fully_connected(fc, self.num_classes, activation_fn=tf.nn.softmax, scope='output') tvars = tf.trainable_variables() # 获取所有可以更新的变量 d_params = [v for v in tvars if v.name.startswith('D/') ] # 只更新fc1,coding_layer与output的参数,其他的参数不更新 #计算loss值 self.cost = tf.losses.softmax_cross_entropy(onehot_labels=self.y_, logits=self.y) tf.summary.scalar('loss', self.cost) self.global_step = tf.Variable(0, trainable=False) #迭代递减的学习速率 lr = tf.train.exponential_decay( self.config.lr, self.global_step, self.config.total_nums / self.config.batch_size, self.config.decay_rate) #设置优化器 self.train_op = tf.train.GradientDescentOptimizer(lr).minimize( loss=self.cost, global_step=self.global_step, var_list=d_params) #计算正确率 self.correct_prediction = tf.equal(tf.argmax(self.y, 1), tf.argmax(self.y_, 1)) self.accuracy = tf.reduce_mean( tf.cast(self.correct_prediction, tf.float32)) tf.summary.scalar('accuracy', self.accuracy)
def testUnknownBatchSize(self): batch_size = 1 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_v4(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = tf.random_uniform((batch_size, height, width, 3)) sess.run(tf.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes))
def testAllEndPointsShapes(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v4(inputs, num_classes) endpoints_shapes = { 'Conv2d_1a_3x3': [batch_size, 149, 149, 32], 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], 'Mixed_3a': [batch_size, 73, 73, 160], 'Mixed_4a': [batch_size, 71, 71, 192], 'Mixed_5a': [batch_size, 35, 35, 384], # 4 x Inception-A blocks 'Mixed_5b': [batch_size, 35, 35, 384], 'Mixed_5c': [batch_size, 35, 35, 384], 'Mixed_5d': [batch_size, 35, 35, 384], 'Mixed_5e': [batch_size, 35, 35, 384], # Reduction-A block 'Mixed_6a': [batch_size, 17, 17, 1024], # 7 x Inception-B blocks 'Mixed_6b': [batch_size, 17, 17, 1024], 'Mixed_6c': [batch_size, 17, 17, 1024], 'Mixed_6d': [batch_size, 17, 17, 1024], 'Mixed_6e': [batch_size, 17, 17, 1024], 'Mixed_6f': [batch_size, 17, 17, 1024], 'Mixed_6g': [batch_size, 17, 17, 1024], 'Mixed_6h': [batch_size, 17, 17, 1024], # Reduction-A block 'Mixed_7a': [batch_size, 8, 8, 1536], # 3 x Inception-C blocks 'Mixed_7b': [batch_size, 8, 8, 1536], 'Mixed_7c': [batch_size, 8, 8, 1536], 'Mixed_7d': [batch_size, 8, 8, 1536], # Logits and predictions 'AuxLogits': [batch_size, num_classes], 'PreLogitsFlatten': [batch_size, 1536], 'Logits': [batch_size, num_classes], 'Predictions': [batch_size, num_classes] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual( end_points[endpoint_name].get_shape().as_list(), expected_shape)
def testBuildLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v4(inputs, num_classes) auxlogits = end_points['AuxLogits'] predictions = end_points['Predictions'] self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits')) self.assertListEqual(auxlogits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue( predictions.op.name.startswith('InceptionV4/Logits/Predictions')) self.assertListEqual(predictions.get_shape().as_list(), [batch_size, num_classes])