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Avoid deeply nested control flow statements.
Open

          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
Severity: Major
Found in research/slim/nets/inception_v1.py - About 45 mins to fix

    Avoid deeply nested control flow statements.
    Open

              with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
    Severity: Major
    Found in research/slim/nets/inception_v1.py - About 45 mins to fix

      Avoid deeply nested control flow statements.
      Open

                if final_endpoint == end_point:
            return net, end_points
          end_point = 'MaxPool_2a_3x3'
      Severity: Major
      Found in research/slim/nets/inception_v1.py - About 45 mins to fix

        Avoid deeply nested control flow statements.
        Open

                  if final_endpoint == end_point:
            return net, end_points
          end_point = 'Conv2d_2b_1x1'
        Severity: Major
        Found in research/slim/nets/inception_v1.py - About 45 mins to fix

          Avoid deeply nested control flow statements.
          Open

                    with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
          Severity: Major
          Found in research/slim/nets/inception_v1.py - About 45 mins to fix

            Avoid deeply nested control flow statements.
            Open

                      with slim.arg_scope([slim.conv2d],
                              activation_fn=None, normalizer_fn=None):
            net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
            Severity: Major
            Found in research/slim/nets/resnet_v2.py - About 45 mins to fix

              Avoid deeply nested control flow statements.
              Open

                        with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
              Severity: Major
              Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                Avoid deeply nested control flow statements.
                Open

                          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
                Severity: Major
                Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                  Avoid deeply nested control flow statements.
                  Open

                            with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
                  Severity: Major
                  Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                    Avoid deeply nested control flow statements.
                    Open

                              if output_stride is not None:
            if output_stride % 4 != 0:
              raise ValueError('The output_stride needs to be a multiple of 4.')
            output_stride /= 4
          net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
                    Severity: Major
                    Found in research/slim/nets/resnet_v1.py - About 45 mins to fix

                      Avoid deeply nested control flow statements.
                      Open

                                with tf.variable_scope('block_{}'.format(block_id)):
            res_net = tf.pad(tensor=net, paddings=paddings, mode='REFLECT')
            res_net = slim.conv2d(res_net, num_filters * 4)
            res_net = tf.pad(tensor=res_net, paddings=paddings, mode='REFLECT')
            res_net = slim.conv2d(res_net, num_filters * 4, activation_fn=None)
                      Severity: Major
                      Found in research/slim/nets/cyclegan.py - About 45 mins to fix

                        Avoid deeply nested control flow statements.
                        Open

                                  with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
                        Severity: Major
                        Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                          Avoid deeply nested control flow statements.
                          Open

                                    with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
                          Severity: Major
                          Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                            Avoid deeply nested control flow statements.
                            Open

                                      if output_stride is not None:
            if output_stride % 4 != 0:
              raise ValueError('The output_stride needs to be a multiple of 4.')
            output_stride /= 4
          # We do not include batch normalization or activation functions in
                            Severity: Major
                            Found in research/slim/nets/resnet_v2.py - About 45 mins to fix

                              Avoid deeply nested control flow statements.
                              Open

                                        with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
                              Severity: Major
                              Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                                Avoid deeply nested control flow statements.
                                Open

                                          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
                                Severity: Major
                                Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                                  Avoid deeply nested control flow statements.
                                  Open

                                            with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_3'):
                                  Severity: Major
                                  Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                                    Avoid deeply nested control flow statements.
                                    Open

                                              with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
                                    Severity: Major
                                    Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                                      Avoid deeply nested control flow statements.
                                      Open

                                                with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
                                      Severity: Major
                                      Found in research/slim/nets/inception_v1.py - About 45 mins to fix

                                        Avoid deeply nested control flow statements.
                                        Open

                                                  with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
                                        Severity: Major
                                        Found in research/slim/nets/inception_v1.py - About 45 mins to fix
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