Python mobilenet_v1.mobilenet_v1方法代碼示例

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    net, end_points = mobilenet_v1.mobilenet_v1_base(inputs)
    self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13'))
    self.assertListEqual(net.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Conv2d_0',
                          'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                          'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                          'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                          'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                          'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                          'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                          'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                          'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                          'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                          'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                          'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                          'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                          'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    self.assertItemsEqual(end_points.keys(), expected_endpoints) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_0',
                 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'MobilenetV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildCustomNetworkUsingConvDefs(self):
    batch_size = 5
    height, width = 224, 224
    conv_defs = [
        mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
        mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
    ]

    inputs = tf.random_uniform((batch_size, height, width, 3))
    net, end_points = mobilenet_v1.mobilenet_v1_base(
        inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
    self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
    self.assertListEqual(net.get_shape().as_list(),
                         [batch_size, 56, 56, 512])
    expected_endpoints = ['Conv2d_0',
                          'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                          'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                          'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
    self.assertItemsEqual(end_points.keys(), expected_endpoints) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]

    _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=0.5)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(0.5 * original_depth, new_depth) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_13_pointwise']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes)) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
                                          reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,)) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_0',
                 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'MobilenetV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys()) 

# 需要導入模塊: from nets import mobilenet_v1 [as 別名]
# 或者: from nets.mobilenet_v1 import mobilenet_v1 [as 別名]
def testGlobalPoolUnknownImageShape(self):
    tf.reset_default_graph()
    batch_size = 1
    height, width = 250, 300
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes,
                                                     global_pool=True)
      self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_13_pointwise']
      feed_dict = {inputs: input_np}
      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])