Sign upLogin

tensorflow/models

View on GitHub
Star
research/lstm_object_detection/trainer.py

Summary

Maintainability
D
1 day
Test Coverage

File trainer.py has 311 lines of code (exceeds 250 allowed). Consider refactoring.
Open

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
Severity: Minor
Found in research/lstm_object_detection/trainer.py - About 3 hrs to fix

    Function get_restore_checkpoint_ops has a Cognitive Complexity of 16 (exceeds 5 allowed). Consider refactoring.
    Open

    def get_restore_checkpoint_ops(restore_checkpoints, detection_model,
                               train_config):
  """Restore checkpoint from saved checkpoints.

  Args:
    Severity: Minor
    Found in research/lstm_object_detection/trainer.py - About 2 hrs to fix

    Cognitive Complexity

    Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

    A method's cognitive complexity is based on a few simple rules:

    • Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
    • Code is considered more complex for each "break in the linear flow of the code"
    • Code is considered more complex when "flow breaking structures are nested"

    Further reading

    Function train has 13 arguments (exceeds 4 allowed). Consider refactoring.
    Open

    def train(create_tensor_dict_fn,
    Severity: Major
    Found in research/lstm_object_detection/trainer.py - About 1 hr to fix

      Function train has a Cognitive Complexity of 13 (exceeds 5 allowed). Consider refactoring.
      Open

      def train(create_tensor_dict_fn,
          create_model_fn,
          train_config,
          master,
          task,
      Severity: Minor
      Found in research/lstm_object_detection/trainer.py - About 1 hr to fix

      Cognitive Complexity

      Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

      A method's cognitive complexity is based on a few simple rules:

      • Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
      • Code is considered more complex for each "break in the linear flow of the code"
      • Code is considered more complex when "flow breaking structures are nested"

      Further reading

      Function get_inputs has a Cognitive Complexity of 9 (exceeds 5 allowed). Consider refactoring.
      Open

      def get_inputs(input_queue, num_classes, merge_multiple_label_boxes=False):
  """Dequeues batch and constructs inputs to object detection model.

  Args:
    input_queue: BatchQueue object holding enqueued tensor_dicts.
      Severity: Minor
      Found in research/lstm_object_detection/trainer.py - About 55 mins to fix

      Cognitive Complexity

      Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

      A method's cognitive complexity is based on a few simple rules:

      • Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
      • Code is considered more complex for each "break in the linear flow of the code"
      • Code is considered more complex when "flow breaking structures are nested"

      Further reading

      Identical blocks of code found in 2 locations. Consider refactoring.
      Open

          slim.learning.train(
        train_tensor,
        logdir=train_dir,
        master=master,
        is_chief=is_chief,
      Severity: Major
      Found in research/lstm_object_detection/trainer.py and 1 other location - About 2 hrs to fix
      research/object_detection/legacy/trainer.py on lines 402..412

      Duplicated Code

      Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

      Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

      When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

      Tuning

      This issue has a mass of 57.

      We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

      The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

      If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

      See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

      Refactorings

      Further Reading

      Identical blocks of code found in 2 locations. Consider refactoring.
      Open

        for image in images:
    resized_image, true_image_shape = detection_model.preprocess(image)
    preprocessed_images.append(resized_image)
    true_image_shapes.append(true_image_shape)
      Severity: Major
      Found in research/lstm_object_detection/trainer.py and 1 other location - About 1 hr to fix
      research/object_detection/legacy/trainer.py on lines 184..187

      Duplicated Code

      Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

      Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

      When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

      Tuning

      This issue has a mass of 38.

      We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

      The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

      If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

      See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

      Refactorings

      Further Reading

      Identical blocks of code found in 2 locations. Consider refactoring.
      Open

            if train_config.gradient_clipping_by_norm > 0:
        with tf.name_scope('clip_grads'):
          grads_and_vars = slim.learning.clip_gradient_norms(
              grads_and_vars, train_config.gradient_clipping_by_norm)
      Severity: Major
      Found in research/lstm_object_detection/trainer.py and 1 other location - About 1 hr to fix
      research/object_detection/legacy/trainer.py on lines 337..340

      Duplicated Code

      Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

      Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

      When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

      Tuning

      This issue has a mass of 38.

      We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

      The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

      If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

      See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

      Refactorings

      Further Reading

      Similar blocks of code found in 3 locations. Consider refactoring.
      Open

          for model_var in slim.get_model_variables():
      global_summaries.add(tf.summary.histogram(model_var.op.name, model_var))
      Severity: Minor
      Found in research/lstm_object_detection/trainer.py and 2 other locations - About 30 mins to fix
      research/deeplab/train.py on lines 332..333
      research/slim/train_image_classifier.py on lines 524..525

      Duplicated Code

      Duplicated code can lead to software that is hard to understand and difficult to change. The Don't Repeat Yourself (DRY) principle states:

      Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

      When you violate DRY, bugs and maintenance problems are sure to follow. Duplicated code has a tendency to both continue to replicate and also to diverge (leaving bugs as two similar implementations differ in subtle ways).

      Tuning

      This issue has a mass of 32.

      We set useful threshold defaults for the languages we support but you may want to adjust these settings based on your project guidelines.

      The threshold configuration represents the minimum mass a code block must have to be analyzed for duplication. The lower the threshold, the more fine-grained the comparison.

      If the engine is too easily reporting duplication, try raising the threshold. If you suspect that the engine isn't catching enough duplication, try lowering the threshold. The best setting tends to differ from language to language.

      See codeclimate-duplication's documentation for more information about tuning the mass threshold in your .codeclimate.yml.

      Refactorings

      Further Reading

      There are no issues that match your filters.

      Category
      Status