tensorflow/models

Pattern Findings

13082 matches across 17 categories. Click a row to expand file-level details.

Cross-File Repetition907 hits · 4535 pts

Severity	File	Line	Snippet
HIGH	research/efficient-hrl/agent.py	0	returns the next action for the state. args: state: a [num_state_dims] tensor representing a state. context: a list of [
HIGH	research/efficient-hrl/agent.py	0	returns the next action for the state. args: state: a [num_state_dims] tensor representing a state. context: a list of [
HIGH	research/efficient-hrl/agent.py	0	returns the next action for the state. args: state: a [num_state_dims] tensor representing a state. context: a list of [
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/efficient-hrl/context/samplers.py	0	sample a batch of context. args: batch_size: batch size. returns: two [batch_size, num_context_dims] tensors.
HIGH	research/rebar/rebar.py	0	returns sampled random variables parameterized by log_alpha.
HIGH	research/rebar/rebar.py	0	returns sampled random variables parameterized by log_alpha.
HIGH	research/rebar/rebar.py	0	returns sampled random variables parameterized by log_alpha.
HIGH	research/rebar/rebar.py	0	returns sampled random variables parameterized by log_alpha.
HIGH	research/lfads/distributions.py	0	compute the log-likelihood under the distribution. args: z (optional): value to compute likelihood for, if none, use sam
HIGH	research/lfads/distributions.py	0	compute the log-likelihood under the distribution. args: z (optional): value to compute likelihood for, if none, use sam
HIGH	research/lfads/distributions.py	0	compute the log-likelihood under the distribution. args: z (optional): value to compute likelihood for, if none, use sam
HIGH	research/vid2depth/dataset/dataset_loader.py	0	checks whether we can find a valid sequence around this frame.
HIGH	research/vid2depth/dataset/dataset_loader.py	0	checks whether we can find a valid sequence around this frame.
HIGH	research/vid2depth/dataset/dataset_loader.py	0	checks whether we can find a valid sequence around this frame.
HIGH	research/vid2depth/dataset/dataset_loader.py	0	checks whether we can find a valid sequence around this frame.
HIGH	…search/object_detection/export_tflite_ssd_graph_lib.py	0	exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] cont
HIGH	…search/object_detection/export_tflite_graph_lib_tf2.py	0	exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] cont
HIGH	…/lstm_object_detection/export_tflite_lstd_graph_lib.py	0	exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] cont
HIGH	research/object_detection/exporter_lib_v2.py	0	initializes a module for detection. args: detection_model: the detection model to use for inference. use_side_inputs: wh
HIGH	research/object_detection/exporter_lib_v2.py	0	initializes a module for detection. args: detection_model: the detection model to use for inference. use_side_inputs: wh
HIGH	research/object_detection/exporter_lib_v2.py	0	initializes a module for detection. args: detection_model: the detection model to use for inference. use_side_inputs: wh
HIGH	research/object_detection/model_lib_tf2_test.py	0	tests that estimator and input function are constructed correctly.
HIGH	research/object_detection/model_lib_tf2_test.py	0	tests that estimator and input function are constructed correctly.
HIGH	research/object_detection/model_lib_tf1_test.py	0	tests that estimator and input function are constructed correctly.
HIGH	research/object_detection/model_lib_tf1_test.py	0	tests that estimator and input function are constructed correctly.
HIGH	research/object_detection/inputs_test.py	0	tests the training input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the training input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the training input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the training input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the eval input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the eval input function for fasterrcnnresnet50.
HIGH	research/object_detection/inputs_test.py	0	tests the eval input function for fasterrcnnresnet50.
HIGH	…rch/object_detection/metrics/calibration_evaluation.py	0	returns a dictionary of eval metric ops. note that once value_op is called, the detections and groundtruth added via upd
HIGH	research/object_detection/metrics/coco_evaluation.py	0	returns a dictionary of eval metric ops. note that once value_op is called, the detections and groundtruth added via upd
HIGH	research/object_detection/metrics/coco_evaluation.py	0	returns a dictionary of eval metric ops. note that once value_op is called, the detections and groundtruth added via upd
HIGH	…rch/object_detection/metrics/calibration_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/metrics/coco_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/metrics/coco_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/metrics/coco_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/metrics/lvis_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	…/object_detection/utils/object_detection_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	…/object_detection/utils/object_detection_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/utils/vrd_evaluation.py	0	clears the state to prepare for a fresh evaluation.
HIGH	research/object_detection/metrics/coco_evaluation.py	0	saves the detections into json_output_path in the format used by ms coco. args: json_output_path: string containing the
HIGH	research/object_detection/metrics/coco_evaluation.py	0	saves the detections into json_output_path in the format used by ms coco. args: json_output_path: string containing the
HIGH	research/object_detection/metrics/lvis_evaluation.py	0	saves the detections into json_output_path in the format used by ms coco. args: json_output_path: string containing the
HIGH	research/object_detection/metrics/coco_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	research/object_detection/metrics/coco_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	research/object_detection/metrics/coco_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	research/object_detection/metrics/lvis_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	…/object_detection/utils/object_detection_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	…/object_detection/utils/object_detection_evaluation.py	0	observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observe
HIGH	…earch/object_detection/metrics/lvis_evaluation_test.py	0	tests that map is calculated correctly on gt and detections.
HIGH	…earch/object_detection/metrics/coco_evaluation_test.py	0	tests that map is calculated correctly on gt and detections.
847 more matches not shown…

Hyper-Verbose Identifiers3941 hits · 3716 pts

Severity	File	Line	Snippet
LOW	research/efficient-hrl/cond_fn.py	134	def failed_reset_after_n_episodes(agent,
LOW	research/efficient-hrl/train_utils.py	165	def gen_debug_batch_summaries(batch):
LOW	research/efficient-hrl/eval.py	48	def get_evaluate_checkpoint_fn(master, output_dir, eval_step_fns,
LOW	…/efficient-hrl/context/context_transition_functions.py	95	def relative_context_transition_fn(
LOW	…/efficient-hrl/context/context_transition_functions.py	111	def relative_context_multi_transition_fn(
LOW	research/efficient-hrl/context/context.py	345	def context_multi_transition_fn(self, contexts, **kwargs):
LOW	research/efficient-hrl/agents/ddpg_agent.py	439	def get_trainable_critic_vars(self):
LOW	research/efficient-hrl/agents/ddpg_agent.py	585	def get_trainable_critic_vars(self):
LOW	research/efficient-hrl/agents/ddpg_agent.py	708	def gen_debug_td_error_summaries(
LOW	research/efficient-hrl/utils/eval_utils.py	30	def evaluate_checkpoint_repeatedly(checkpoint_dir,
LOW	research/autoaugment/train_cifar.py	181	def _calc_num_trainable_params(self):
LOW	research/autoaugment/train_cifar.py	345	def _compute_final_accuracies(self, meval):
LOW	research/autoaugment/shake_shake.py	26	def _shake_shake_skip_connection(x, output_filters, stride):
LOW	research/rebar/rebar.py	565	def get_simple_muprop_gradient(self):
LOW	research/rebar/rebar.py	659	def _create_gumbel_control_variate(self, logQHard, temperature=None):
LOW	research/rebar/rebar.py	687	def _create_gumbel_control_variate_quadratic(self, logQHard, temperature=None):
LOW	research/rebar/rebar.py	744	def multiply_by_eta_per_layer(self, h_grads, eta):
LOW	research/rebar/rebar.py	778	def get_dynamic_rebar_gradient(self):
LOW	research/pcl_rl/env_spec.py	134	def convert_env_actions_to_actions(self, actions):
LOW	research/pcl_rl/controller.py	346	def convert_from_batched_episodes(
LOW	research/pcl_rl/controller.py	373	def convert_to_batched_episodes(self, episodes, max_length=None):
LOW	research/pcl_rl/trust_region.py	110	def calc_fisher_vector_product(tangent):
LOW	research/adversarial_text/train_utils.py	99	def maybe_restore_pretrained_model(sess, saver_for_restore, model_dir):
LOW	research/adversarial_text/adversarial_losses.py	124	def random_perturbation_loss_bidir(embedded, length, loss_fn):
LOW	research/adversarial_text/adversarial_losses.py	145	def virtual_adversarial_loss_bidir(logits, embedded, inputs,
LOW	research/adversarial_text/adversarial_losses.py	202	def _kl_divergence_with_logits(q_logits, p_logits, weights):
LOW	research/adversarial_text/inputs.py	168	def _read_single_sequence_example(file_list, tokens_shape=None):
LOW	research/adversarial_text/gen_data.py	56	def build_shuffling_tf_record_writer(fname):
LOW	research/adversarial_text/graphs.py	670	def make_restore_average_vars_dict():
LOW	research/adversarial_text/layers.py	368	def _summarize_vars_and_grads(grads_and_vars):
LOW	research/adversarial_text/data/data_utils.py	325	def write_vocab_and_frequency(ordered_vocab_freqs, output_dir):
LOW	research/audioset/vggish/vggish_slim.py	109	def load_vggish_slim_checkpoint(session, checkpoint_path):
LOW	research/audioset/vggish/mel_features.py	114	def spectrogram_to_mel_matrix(num_mel_bins=20,
LOW	research/audioset/yamnet/features.py	22	def waveform_to_log_mel_spectrogram_patches(waveform, params):
LOW	research/cvt_text/model/encoder.py	72	def _get_unidirectional_reprs(self, word_reprs):
LOW	research/lfads/run_lfads.py	499	def build_hyperparameter_dict(flags):
LOW	research/lfads/distributions.py	50	def diag_gaussian_log_likelihood(z, mu=0.0, logvar=0.0):
LOW	research/lfads/distributions.py	68	def gaussian_pos_log_likelihood(unused_mean, logvar, noise):
LOW	research/lfads/utils.py	303	def list_t_bxn_to_tensor_bxtxn(values_t_bxn):
LOW	research/lfads/utils.py	323	def tensor_bxtxn_to_list_t_bxn(tensor_bxtxn):
LOW	research/lfads/lfads.py	1091	def example_idxs_mod_batch_size(nexamples, batch_size):
LOW	research/lfads/lfads.py	1120	def randomize_example_idxs_mod_batch_size(nexamples, batch_size):
LOW	research/lfads/lfads.py	1191	def shuffle_and_flatten_datasets(self, datasets, kind='train'):
LOW	research/lfads/lfads.py	1735	def eval_model_runs_avg_epoch(self, data_name, data_extxd,
LOW	research/lfads/lfads.py	1838	def eval_model_runs_push_mean(self, data_name, data_extxd,
LOW	research/lfads/synth_data/synthetic_data_utils.py	233	def add_alignment_projections(datasets, npcs, ntime=None, nsamples=None):
LOW	research/vid2depth/model.py	98	def build_inference_for_training(self):
LOW	research/vid2depth/model.py	341	def build_egomotion_test_graph(self):
LOW	research/vid2depth/reader.py	136	def augment_images_scale_crop(cls, im, intrinsics, out_h, out_w):
LOW	research/vid2depth/reader.py	213	def get_multi_scale_intrinsics(cls, intrinsics, num_scales):
LOW	research/vid2depth/inference.py	138	def _normalize_depth_for_display(depth,
LOW	research/vid2depth/ops/icp_test.py	70	def _generate_organized_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	95	def test_translate_small_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	107	def test_translate_random_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	133	def test_translate_organized_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	145	def test_rotate_organized_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	167	def test_translate_lidar_cloud(self):
LOW	research/vid2depth/ops/icp_test.py	179	def test_translate_lidar_cloud_ego_motion(self):
LOW	research/vid2depth/ops/icp_test.py	193	def test_rotate_lidar_cloud_ego_motion(self):
LOW	research/vid2depth/ops/icp_test.py	207	def test_no_change_lidar_cloud(self):
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Unused Imports2906 hits · 2852 pts

Severity	File	Line
LOW	research/efficient-hrl/run_eval.py	21
LOW	research/efficient-hrl/run_eval.py	22
LOW	research/efficient-hrl/run_eval.py	23
LOW	research/efficient-hrl/train_utils.py	18
LOW	research/efficient-hrl/train_utils.py	19
LOW	research/efficient-hrl/train_utils.py	20
LOW	research/efficient-hrl/train_utils.py	23
LOW	research/efficient-hrl/train_utils.py	28
LOW	research/efficient-hrl/run_train.py	21
LOW	research/efficient-hrl/run_train.py	22
LOW	research/efficient-hrl/run_train.py	23
LOW	research/efficient-hrl/agent.py	25
LOW	research/efficient-hrl/train.py	21
LOW	research/efficient-hrl/train.py	22
LOW	research/efficient-hrl/train.py	23
LOW	research/efficient-hrl/train.py	33
LOW	research/efficient-hrl/train.py	34
LOW	research/efficient-hrl/eval.py	23
LOW	research/efficient-hrl/eval.py	24
LOW	research/efficient-hrl/eval.py	25
LOW	research/efficient-hrl/eval.py	32
LOW	research/efficient-hrl/context/gin_utils.py	19
LOW	research/efficient-hrl/context/gin_utils.py	20
LOW	research/efficient-hrl/context/gin_utils.py	21
LOW	…/efficient-hrl/context/context_transition_functions.py	23
LOW	…/efficient-hrl/context/context_transition_functions.py	24
LOW	…/efficient-hrl/context/context_transition_functions.py	25
LOW	research/efficient-hrl/context/gin_imports.py	20
LOW	research/efficient-hrl/context/gin_imports.py	21
LOW	research/efficient-hrl/context/gin_imports.py	22
LOW	research/efficient-hrl/context/gin_imports.py	23
LOW	research/efficient-hrl/context/gin_imports.py	24
LOW	research/efficient-hrl/context/context.py	24
LOW	research/efficient-hrl/context/context.py	25
LOW	research/efficient-hrl/context/context.py	26
LOW	research/efficient-hrl/context/rewards_functions.py	25
LOW	research/efficient-hrl/context/rewards_functions.py	26
LOW	research/efficient-hrl/context/rewards_functions.py	27
LOW	research/efficient-hrl/context/samplers.py	21
LOW	research/efficient-hrl/context/samplers.py	22
LOW	research/efficient-hrl/context/samplers.py	23
LOW	research/efficient-hrl/environments/maze_env_utils.py	17
LOW	research/efficient-hrl/utils/utils.py	19
LOW	research/efficient-hrl/utils/utils.py	20
LOW	research/efficient-hrl/utils/utils.py	21
LOW	research/efficient-hrl/utils/eval_utils.py	19
LOW	research/efficient-hrl/utils/eval_utils.py	20
LOW	research/efficient-hrl/utils/eval_utils.py	21
LOW	research/efficient-hrl/utils/eval_utils.py	24
LOW	research/efficient-hrl/scripts/local_eval.py	19
LOW	research/efficient-hrl/scripts/local_eval.py	20
LOW	research/efficient-hrl/scripts/local_eval.py	21
LOW	research/efficient-hrl/scripts/local_train.py	19
LOW	research/efficient-hrl/scripts/local_train.py	20
LOW	research/efficient-hrl/scripts/local_train.py	21
LOW	research/autoaugment/train_cifar.py	19
LOW	research/autoaugment/train_cifar.py	20
LOW	research/autoaugment/train_cifar.py	21
LOW	research/autoaugment/custom_ops.py	21
LOW	research/autoaugment/custom_ops.py	22
2846 more matches not shown…

Decorative Section Separators925 hits · 2784 pts

Severity	File	Line	Snippet
MEDIUM	research/efficient-hrl/run_eval.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/cond_fn.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/train_utils.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/run_train.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/run_env.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/agent.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/train.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/eval.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/context/gin_utils.py	14	# ==============================================================================
MEDIUM	…/efficient-hrl/context/context_transition_functions.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/context/gin_imports.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/context/context.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/context/rewards_functions.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/context/samplers.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/maze_env.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/ant_maze_env.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/create_maze_env.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/ant.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/point_maze_env.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/point.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/environments/maze_env_utils.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/agents/ddpg_agent.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/agents/ddpg_networks.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/agents/circular_buffer.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/utils/utils.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/utils/eval_utils.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/scripts/local_eval.py	14	# ==============================================================================
MEDIUM	research/efficient-hrl/scripts/local_train.py	14	# ==============================================================================
MEDIUM	research/autoaugment/train_cifar.py	14	# ==============================================================================
MEDIUM	research/autoaugment/custom_ops.py	14	# ==============================================================================
MEDIUM	research/autoaugment/shake_shake.py	14	# ==============================================================================
MEDIUM	research/autoaugment/shake_drop.py	14	# ==============================================================================
MEDIUM	research/autoaugment/data_utils.py	14	# ==============================================================================
MEDIUM	research/autoaugment/helper_utils.py	14	# ==============================================================================
MEDIUM	research/autoaugment/wrn.py	14	# ==============================================================================
MEDIUM	research/autoaugment/augmentation_transforms.py	14	# ==============================================================================
MEDIUM	research/autoaugment/policies.py	14	# ==============================================================================
MEDIUM	research/rebar/rebar_train.py	14	# ==============================================================================
MEDIUM	research/rebar/config.py	14	# ==============================================================================
MEDIUM	research/rebar/rebar.py	14	# ==============================================================================
MEDIUM	research/rebar/datasets.py	14	# ==============================================================================
MEDIUM	research/rebar/logger.py	14	# ==============================================================================
MEDIUM	research/rebar/download_data.py	14	# ==============================================================================
MEDIUM	research/rebar/utils.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/objective.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/env_spec.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/controller.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/baseline.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/policy.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/model.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/optimizers.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/full_episode_objective.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/replay_buffer.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/gym_wrapper.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/trainer.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/trust_region.py	14	# ==============================================================================
MEDIUM	research/pcl_rl/expert_paths.py	14	# ==============================================================================
MEDIUM	research/adversarial_text/pretrain.py	14	# ==============================================================================
MEDIUM	research/adversarial_text/train_utils.py	14	# ==============================================================================
MEDIUM	research/adversarial_text/adversarial_losses.py	14	# ==============================================================================
865 more matches not shown…

Docstring Block Structure539 hits · 2695 pts

Severity	File	Line	Snippet
HIGH	research/efficient-hrl/agents/ddpg_agent.py	187	Returns the output of the actor network. Args: states: A [batch_size, num_state_dims] tensor representing a b
HIGH	research/efficient-hrl/agents/ddpg_agent.py	206	Returns the output of the critic network. Args: states: A [batch_size, num_state_dims] tensor representing a
HIGH	research/efficient-hrl/agents/ddpg_agent.py	224	Returns the output of the target actor network. The target network is used to compute stable targets for training.
HIGH	research/efficient-hrl/agents/ddpg_agent.py	241	Returns the output of the target critic network. The target network is used to compute stable targets for training.
HIGH	research/efficient-hrl/agents/ddpg_agent.py	289	Computes a loss for training the critic network. The loss is the mean squared error between the Q value predictions
HIGH	research/efficient-hrl/agents/ddpg_agent.py	343	Computes a loss for training the actor network. Note that output does not represent an actual loss. It is called a
HIGH	research/efficient-hrl/agents/ddpg_agent.py	410	Performs a soft update of the target network parameters. For each weight w_s in the actor/critic networks, and its
HIGH	research/efficient-hrl/agents/ddpg_agent.py	597	Returns the output of the critic network. Args: states: A [batch_size, num_state_dims] tensor representing a
HIGH	research/efficient-hrl/agents/ddpg_agent.py	618	Returns the output of the target critic network. The target network is used to compute stable targets for training.
HIGH	research/efficient-hrl/agents/ddpg_agent.py	677	Performs a soft update of the target network parameters. For each weight w_s in the actor/critic networks, and its
HIGH	research/efficient-hrl/agents/circular_buffer.py	93	Adds an element (list/tuple/dict of tensors) to the buffer. Args: tensors: (list/tuple/dict of tensors) to be
HIGH	research/efficient-hrl/agents/circular_buffer.py	107	Adds an element (tensors) to the buffer based on the condition.. Args: tensors: (list/tuple of tensors) to be
HIGH	research/efficient-hrl/agents/circular_buffer.py	150	Samples a batch of tensors from the buffer with replacement. Args: batch_size: (integer) number of elements t
HIGH	research/efficient-hrl/agents/circular_buffer.py	188	Returns elements at the specified indices from the buffer. Args: indices: (list of integers or rank 1 int Ten
HIGH	research/efficient-hrl/agents/circular_buffer.py	221	Returns elements at the specified indices from the buffer. Args: num_steps: (integer) length of trajectories
HIGH	research/autoaugment/helper_utils.py	44	Evaluates `model` on held out data depending on `mode`. Args: session: TensorFlow session the model will be run w
HIGH	research/adversarial_text/inputs.py	128	Returns input filenames for configuration. Args: phase: str, 'train', 'test', or 'valid'. bidir: bool, bidire
HIGH	research/adversarial_text/inputs.py	195	Inputs for text model. Args: data_dir: str, directory containing TFRecord files of SequenceExample. fname: st
HIGH	research/adversarial_text/data/document_generators.py	76	Generates Documents based on FLAGS.dataset. Args: dataset: str, identifies folder within IMDB data directory, tes
HIGH	research/adversarial_text/data/document_generators.py	115	Given a Document, produces character or word tokens. Tokens can be either characters, or word-level tokens (unigrams
HIGH	research/adversarial_text/data/document_generators.py	161	Generates Documents for IMDB dataset. Data from http://ai.stanford.edu/~amaas/data/sentiment/ Args: dataset: s
HIGH	research/adversarial_text/data/document_generators.py	225	Generates Documents for DBpedia dataset. Dataset linked to at https://github.com/zhangxiangxiao/Crepe. Args: d
HIGH	research/adversarial_text/data/document_generators.py	270	Generates Documents for Reuters Corpus (rcv1) dataset. Dataset described at http://www.ai.mit.edu/projects/jmlr/pap
HIGH	research/adversarial_text/data/document_generators.py	321	Generates Documents for the Rotten Tomatoes dataset. Dataset available at http://www.cs.cornell.edu/people/pabo/movie
HIGH	research/audioset/vggish/mel_features.py	119	Return a matrix that can post-multiply spectrogram rows to make mel. Returns a np.array matrix A that can be used to
HIGH	research/lfads/lfads.py	1092	Given a number of examples, E, and a batch_size, B, generate indices [0, 1, 2, ... B-1; [B, B+1, ... 2*B-1;
HIGH	research/lfads/lfads.py	1121	Indices 1:nexamples, randomized, in 2D form of shape = (nexamples / batch_size) x batch_size. The remainder is
HIGH	research/object_detection/model_lib.py	244	Unstacks all tensors in `tensor_dict` along 0th dimension. Unstacks tensor from the tensor dict along 0th dimension a
HIGH	research/object_detection/model_lib.py	765	Creates `Estimator`, input functions, and steps. Args: run_config: A `RunConfig`. hparams: (optional) A `HPar
HIGH	research/object_detection/eval_util.py	259	Evaluates metrics defined in evaluators and returns summaries. This function loads the latest checkpoint in checkpoin
HIGH	research/object_detection/eval_util.py	433	Periodically evaluates desired tensors using checkpoint_dirs or restore_fn. This function repeatedly loads a checkpoi
HIGH	research/object_detection/eval_util.py	777	Merges all detection and groundtruth information for a single example. Note that evaluation tools require classes tha
HIGH	research/object_detection/eval_util.py	1069	Returns the evaluator class according to eval_config, valid for categories. Args: eval_config: An `eval_pb2.EvalC
HIGH	research/object_detection/inputs.py	119	Makes sure boxes have valid sizes (ymax >= ymin, xmax >= xmin). When the hardware supports assertions, the function r
HIGH	research/object_detection/inputs.py	163	A single function that is responsible for all input data transformations. Data transformation functions are applied i
HIGH	research/object_detection/inputs.py	405	Pads input tensors to static shapes. In case num_additional_channels > 0, we assume that the additional channels ha
HIGH	research/object_detection/inputs.py	777	Returns `features` and `labels` tensor dictionaries for training. Args: train_config: A train_pb2.TrainConfig.
HIGH	research/object_detection/inputs.py	938	Returns `features` and `labels` tensor dictionaries for evaluation. Args: eval_config: An eval_pb2.EvalConfig.
HIGH	research/object_detection/metrics/coco_tools.py	93	Load annotations dictionary into COCO datastructure. See http://mscoco.org/dataset/#format for a description of the
HIGH	research/object_detection/metrics/coco_tools.py	236	Computes detection/keypoint metrics. Args: include_metrics_per_category: If True, will include metrics per ca
HIGH	research/object_detection/metrics/coco_tools.py	401	Export groundtruth of a single image to COCO format. This function converts groundtruth detection annotations represe
HIGH	research/object_detection/metrics/coco_tools.py	518	Export groundtruth detection annotations in numpy arrays to COCO API. This function converts a set of groundtruth det
HIGH	research/object_detection/metrics/coco_tools.py	595	Export detections of a single image to COCO format. This function converts detections represented as numpy arrays to
HIGH	research/object_detection/metrics/coco_tools.py	687	Export detection masks of a single image to COCO format. This function converts detections represented as numpy array
HIGH	research/object_detection/metrics/coco_tools.py	746	Export detection annotations in numpy arrays to COCO API. This function converts a set of predicted detections repres
HIGH	research/object_detection/metrics/coco_tools.py	810	Export segmentation masks in numpy arrays to COCO API. This function converts a set of predicted instance masks repre
HIGH	research/object_detection/metrics/coco_tools.py	890	Exports keypoints in numpy arrays to COCO API. This function converts a set of predicted keypoints represented as n
HIGH	research/object_detection/metrics/coco_evaluation.py	1744	Separate normal and crowd groundtruth class_labels and masks. Args: crowd_gt_indices: None or array of shape
HIGH	research/object_detection/metrics/coco_evaluation.py	1794	Match the predicted masks to groundtruths. Args: predicted_masks: array of shape [num_predictions, height, wi
HIGH	…ch/object_detection/metrics/offline_eval_map_corloc.py	77	Reads pre-computed object detections and groundtruth from tf_record. Args: input_config: input config proto of ty
HIGH	research/object_detection/metrics/lvis_tools.py	124	Export groundtruth of a single image to LVIS format. This function converts groundtruth detection annotations represe
HIGH	research/object_detection/metrics/lvis_tools.py	209	Export detection masks of a single image to LVIS format. This function converts detections represented as numpy array
HIGH	…search/object_detection/metrics/calibration_metrics.py	53	Calculates Expected Calibration Error (ECE). ECE is a scalar summary statistic of calibration error. It is the samp
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	665	Feature-extractor specific preprocessing. See base class. For Faster R-CNN, we perform image resizing in the b
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	733	Predicts unpostprocessed tensors from input tensor. This function takes an input batch of images and runs it throug
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	1357	Adds box predictors to RPN feature map to predict proposals. Note resulting tensors will not have been postprocesse
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	1467	Convert prediction tensors to final detections. This function converts raw predictions tensors to final detection r
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	2392	Computes scalar box classifier loss tensors. Uses self._detector_target_assigner to obtain regression and classific
HIGH	…_detection/meta_architectures/faster_rcnn_meta_arch.py	2821	Returns a map of variables to load from a foreign checkpoint. See parent class for details. Args: fine_t
HIGH	…detection/meta_architectures/context_rcnn_meta_arch.py	334	Overrides the get_side_inputs function in the base class. This function returns context_features and valid_context_
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Over-Commented Block2675 hits · 2658 pts

Severity	File	Line	Snippet
LOW	research/efficient-hrl/run_eval.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/cond_fn.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/train_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/run_train.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/run_env.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/agent.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/train.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/eval.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/context/gin_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	…/efficient-hrl/context/context_transition_functions.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/context/gin_imports.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/context/context.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/context/rewards_functions.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/context/samplers.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/maze_env.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/ant_maze_env.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/create_maze_env.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/ant.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/point_maze_env.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/point.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/environments/maze_env_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/agents/ddpg_agent.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/agents/ddpg_networks.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/agents/circular_buffer.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/utils/utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/utils/eval_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/scripts/local_eval.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/efficient-hrl/scripts/local_train.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/train_cifar.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/custom_ops.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/shake_shake.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/shake_drop.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/data_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/helper_utils.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/wrn.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/augmentation_transforms.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/autoaugment/policies.py	1	# Copyright 2018 The TensorFlow Authors All Rights Reserved.
LOW	research/rebar/rebar_train.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/config.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/rebar.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/datasets.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/logger.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/download_data.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/rebar/utils.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/pcl_rl/objective.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/env_spec.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/controller.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/baseline.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/policy.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/model.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/optimizers.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/full_episode_objective.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/replay_buffer.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/gym_wrapper.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/trainer.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/trust_region.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/pcl_rl/expert_paths.py	1	# Copyright 2017 The TensorFlow Authors All Rights Reserved.
LOW	research/adversarial_text/pretrain.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/adversarial_text/train_utils.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
LOW	research/adversarial_text/adversarial_losses.py	1	# Copyright 2017 Google Inc. All Rights Reserved.
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Self-Referential Comments606 hits · 2104 pts

Severity	File	Line	Snippet
MEDIUM	research/autoaugment/train_cifar.py	284	# Create a new session for this model, initialize
MEDIUM	research/rebar/rebar_train.py	71	# Create the experiment name from the hparams
MEDIUM	research/rebar/rebar.py	181	# Create the conditional distribution (output is the logits)
MEDIUM	research/rebar/rebar.py	208	# Create the conditional distribution (output is the logits)
MEDIUM	research/audioset/vggish/vggish_inference_demo.py	109	# Define the model in inference mode, load the checkpoint, and
MEDIUM	research/audioset/vggish/vggish_export_tfhub.py	103	# Create a TF2 wrapper around VGGish.
MEDIUM	research/audioset/vggish/vggish_train_demo.py	133	# Define a shallow classification model and associated training ops on top
MEDIUM	research/audioset/vggish/vggish_train_demo.py	168	# Initialize all variables in the model, and then load the pre-trained
MEDIUM	research/audioset/yamnet/export.py	107	# Create a TF2 Module wrapper around YAMNet.
MEDIUM	research/audioset/yamnet/export.py	144	# Create a TF-Lite compatible Module wrapper around YAMNet.
MEDIUM	research/lfads/lfads.py	314	# Define the data placeholder, and deal with all parts of the graph
MEDIUM	research/vid2depth/ops/icp_train_demo.py	117	# Create the feed_dict for the placeholders filled with the next
MEDIUM	research/vid2depth/ops/icp_train_demo.py	189	# Create a variable to track the global step.
MEDIUM	research/object_detection/model_lib.py	865	# Create the input functions for TRAIN/EVAL/PREDICT.
MEDIUM	research/object_detection/model_lib_v2.py	553	# Create the inputs.
MEDIUM	…t_detection/meta_architectures/center_net_meta_arch.py	780	# Create the image center location.
MEDIUM	…t_detection/meta_architectures/center_net_meta_arch.py	1039	# Create the y,x grids: [height, width]
MEDIUM	research/object_detection/core/densepose_ops.py	293	# Create a list of that maps part index to flipped part index (0-indexed).
MEDIUM	research/object_detection/utils/visualization_utils.py	1176	# Create a display string (and color) for every box location, group any boxes
MEDIUM	research/object_detection/utils/autoaugment_utils.py	406	# Create the new bbox tensor by converting pixel integer values to floats.
MEDIUM	research/object_detection/utils/autoaugment_utils.py	555	# Create a mask that will be used to zero out a part of the original image.
MEDIUM	…arch/object_detection/models/keras_models/resnet_v1.py	405	# The following codes are based on the existing keras ResNet model pattern:
MEDIUM	…dels/keras_models/base_models/original_mobilenet_v2.py	212	# This function is taken from the original tf repo.
MEDIUM	…ection/dataset_tools/download_and_preprocess_mscoco.sh	40	# Create the output directories.
MEDIUM	…_detection/dataset_tools/create_pycocotools_package.sh	29	# Create the output directory.
MEDIUM	research/slim/train_image_classifier.py	457	# Create a dataset provider that loads data from the dataset #
MEDIUM	research/slim/train_image_classifier.py	483	# Define the model #
MEDIUM	research/slim/eval_image_classifier.py	115	# Create a dataset provider that loads data from the dataset #
MEDIUM	research/slim/eval_image_classifier.py	145	# Define the model #
MEDIUM	research/slim/eval_image_classifier.py	164	# Define the metrics:
MEDIUM	research/slim/datasets/download_and_convert_imagenet.sh	58	# Create the output and temporary directories.
MEDIUM	research/slim/datasets/download_imagenet.sh	89	# Create a directory and delete anything there.
MEDIUM	research/slim/datasets/build_imagenet_data.py	140	# This file is the output of process_bounding_box.py
MEDIUM	research/slim/datasets/build_imagenet_data.py	233	# Create a single Session to run all image coding calls.
MEDIUM	research/slim/datasets/build_imagenet_data.py	444	# Create a mechanism for monitoring when all threads are finished.
MEDIUM	research/slim/datasets/build_imagenet_data.py	447	# Create a generic TensorFlow-based utility for converting all image codings.
MEDIUM	research/slim/deployment/model_deploy.py	35	# Create the global step on the device storing the variables.
MEDIUM	research/slim/deployment/model_deploy.py	39	# Define the inputs
MEDIUM	research/slim/deployment/model_deploy.py	44	# Define the optimizer.
MEDIUM	research/slim/deployment/model_deploy.py	48	# Define the model including the loss.
MEDIUM	research/slim/deployment/model_deploy_test.py	172	# Create an easy training set:
MEDIUM	research/slim/deployment/model_deploy_test.py	319	# Create an easy training set:
MEDIUM	research/slim/deployment/model_deploy_test.py	468	# Create an easy training set:
MEDIUM	research/slim/preprocessing/vgg_preprocessing.py	154	# Create a random bounding box.
MEDIUM	research/deeplab/model.py	427	# The following codes employ the DeepLabv3 ASPP module. Note that we
MEDIUM	research/deeplab/train.py	312	# Create the global step on the device storing the variables.
MEDIUM	research/deeplab/train.py	316	# Define the model and create clones.
MEDIUM	research/deeplab/eval.py	158	# Define the evaluation metric.
MEDIUM	research/deeplab/core/preprocess_utils.py	306	# Create a random bounding box.
MEDIUM	research/deeplab/datasets/remove_gt_colormap.py	67	# Create the output directory if not exists.
MEDIUM	research/cognitive_planning/visualization_utils.py	598	# Create a display string (and color) for every box location, group any boxes
MEDIUM	…/cognitive_planning/preprocessing/vgg_preprocessing.py	155	# Create a random bounding box.
MEDIUM	research/deep_speech/deep_speech.py	171	# Create the train_op that groups both minimize_ops and update_ops
MEDIUM	…earch/delf/delf/python/training/build_image_dataset.py	146	# Create the dictionary (key = image_id, value = {label, file_id}).
MEDIUM	…earch/delf/delf/python/training/build_image_dataset.py	369	# Create the subset for the current label.
MEDIUM	research/delf/delf/python/training/train.py	179	# Create the strategy.
MEDIUM	research/delf/delf/python/training/train.py	210	# Create the distributed train/validation sets.
MEDIUM	research/delf/delf/python/training/train.py	231	# Create a checkpoint directory to store the checkpoints.
MEDIUM	…rch/delf/delf/python/training/global_features/train.py	187	# Define the loss function.
MEDIUM	…rch/delf/delf/python/training/global_features/train.py	208	# Define the optimizer.
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Deep Nesting456 hits · 442 pts

Severity	File	Line
LOW	research/efficient-hrl/train.py	270
LOW	research/efficient-hrl/eval.py	48
LOW	research/efficient-hrl/eval.py	80
LOW	research/efficient-hrl/environments/maze_env.py	37
LOW	research/efficient-hrl/environments/maze_env.py	323
LOW	research/efficient-hrl/environments/maze_env.py	472
LOW	research/efficient-hrl/environments/create_maze_env.py	26
LOW	research/efficient-hrl/environments/maze_env_utils.py	54
LOW	research/autoaugment/train_cifar.py	79
LOW	research/autoaugment/train_cifar.py	404
LOW	research/rebar/rebar_train.py	63
LOW	research/rebar/rebar.py	151
LOW	research/rebar/rebar.py	220
LOW	research/rebar/rebar.py	759
LOW	research/pcl_rl/env_spec.py	47
LOW	research/pcl_rl/env_spec.py	105
LOW	research/pcl_rl/baseline.py	47
LOW	research/pcl_rl/policy.py	62
LOW	research/pcl_rl/model.py	123
LOW	research/pcl_rl/expert_paths.py	47
LOW	research/adversarial_text/data/document_generators.py	73
LOW	research/adversarial_text/data/document_generators.py	114
LOW	research/adversarial_text/data/document_generators.py	266
LOW	research/adversarial_text/data/document_generators.py	317
LOW	research/cvt_text/cvt.py	37
LOW	research/cvt_text/preprocessing.py	34
LOW	…h/cvt_text/task_specific/word_level/word_level_data.py	57
LOW	…h/cvt_text/task_specific/word_level/word_level_data.py	83
LOW	research/cvt_text/corpus_processing/unlabeled_data.py	59
LOW	research/cvt_text/base/embeddings.py	150
LOW	research/cvt_text/base/embeddings.py	113
LOW	research/lfads/run_lfads.py	760
LOW	research/lfads/utils.py	170
LOW	research/lfads/lfads.py	280
LOW	research/lfads/lfads.py	2090
LOW	research/vid2depth/util.py	35
LOW	research/vid2depth/model.py	130
LOW	research/vid2depth/inference.py	73
LOW	research/vid2depth/dataset/dataset_loader.py	204
LOW	research/vid2depth/dataset/gen_data.py	69
LOW	research/object_detection/model_lib.py	432
LOW	research/object_detection/model_lib.py	455
LOW	research/object_detection/exporter.py	363
LOW	research/object_detection/eval_util.py	246
LOW	research/object_detection/eval_util.py	416
LOW	research/object_detection/eval_util.py	1068
LOW	research/object_detection/eval_util.py	1159
LOW	research/object_detection/inputs.py	1134
LOW	research/object_detection/model_lib_v2.py	444
LOW	research/object_detection/model_lib_v2.py	833
LOW	research/object_detection/model_lib_tf1_test.py	131
LOW	research/object_detection/metrics/coco_tools.py	391
LOW	research/object_detection/metrics/coco_tools.py	588
LOW	research/object_detection/metrics/coco_tools.py	232
LOW	research/object_detection/metrics/coco_evaluation.py	1786
LOW	…ch/object_detection/metrics/offline_eval_map_corloc.py	76
LOW	…t_detection/meta_architectures/center_net_meta_arch.py	1239
LOW	…t_detection/meta_architectures/center_net_meta_arch.py	4785
LOW	…ject_detection/meta_architectures/deepmac_meta_arch.py	79
LOW	…ject_detection/meta_architectures/deepmac_meta_arch.py	131
396 more matches not shown…

Hallucination Indicators19 hits · 180 pts

Severity	File	Line	Snippet
CRITICAL	research/object_detection/model_lib_v2.py	528	tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')
CRITICAL	research/object_detection/model_lib_v2.py	1114	tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')
CRITICAL	research/attention_ocr/python/model_export_test.py	133	graph_def = tf.compat.v1.saved_model.loader.load(
CRITICAL	research/attention_ocr/python/model_export_lib.py	106	k: tf.compat.v1.saved_model.utils.build_tensor_info(t)
CRITICAL	research/attention_ocr/python/model_export.py	168	signature_def = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
CRITICAL	research/attention_ocr/python/model_export.py	172	builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
CRITICAL	research/attention_ocr/python/sequence_layers.py	250	lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
CRITICAL	research/deeplab/convert_to_tflite.py	56	converter = tf.compat.v1.lite.TFLiteConverter.from_session(
CRITICAL	…elf/python/detect_to_retrieve/cluster_delf_features.py	150	kmeans = tf.compat.v1.estimator.experimental.KMeans(
CRITICAL	official/recommendation/ncf_keras_main.py	275	tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(
CRITICAL	official/core/train_utils.py	573	opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()
CRITICAL	official/projects/qat/vision/quantization/schemes.py	65	return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer(
CRITICAL	official/projects/qat/nlp/quantization/schemes.py	197	return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer(
CRITICAL	…al/projects/pixel/utils/convert_numpy_weights_to_tf.py	39	vit_encoder.encoder.encoder._pos_embed.pos_embedding.assign(
CRITICAL	official/legacy/transformer/transformer_main_test.py	70	tf.compat.v2.keras.mixed_precision.global_policy())
CRITICAL	official/legacy/transformer/transformer_main_test.py	73	tf.compat.v2.keras.mixed_precision.set_global_policy(self.orig_policy)
CRITICAL	official/legacy/detection/main.py	73	tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')
CRITICAL	official/legacy/detection/modeling/base_model.py	62	tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')
CRITICAL	tensorflow_models/tensorflow_models_test.py	45	_ = tfm.uplift.layers.encoders.concat_features.ConcatFeatures(['feature'])

Redundant / Tautological Comments44 hits · 69 pts

Severity	File	Line	Snippet
LOW	…t_detection/core/balanced_positive_negative_sampler.py	136	# Check if indicator and labels have a static size.
LOW	research/object_detection/utils/vrd_evaluation.py	205	# Verify if one of the labels is negative (this is sure FP)
LOW	research/object_detection/utils/vrd_evaluation.py	208	# Verify if all labels are verified
LOW	research/slim/datasets/download_and_convert_flowers.py	140	# Read the filename:
LOW	research/slim/nets/nasnet/nasnet_utils.py	355	# Add hiddenstate to the list of hiddenstates we can choose from
LOW	research/slim/nets/nasnet/nasnet_utils.py	379	# Check if a stride is needed, then use a strided 1x1 here
LOW	research/lstm_object_detection/lstm/lstm_cells.py	391	# Set nodes to be under raw_inputs/ name scope for tfmini export.
LOW	research/lstm_object_detection/lstm/lstm_cells.py	619	# Set nodes to be under raw_outputs/ name scope for tfmini export.
LOW	research/delf/delf/python/training/train.py	238	# Set reduction to `none` so we can do the reduction afterwards and divide
LOW	…rch/delf/delf/python/training/global_features/train.py	136	# Check if train dataset is downloaded and download it if not found.
LOW	…elf/python/detect_to_retrieve/cluster_delf_features.py	89	# Loop over list of index images and collect DELF features.
LOW	…configs/experiments/imagenet_simclr_multitask_tpu.yaml	49	input_set_label_to_zero: true # Set labels to zeros to double confirm that no label is used during pretrain
LOW	…lumetric_models/tasks/semantic_segmentation_3d_test.py	73	# Check if training loss is produced.
LOW	…lumetric_models/tasks/semantic_segmentation_3d_test.py	86	# Check if validation loss is produced.
LOW	…lumetric_models/tasks/semantic_segmentation_3d_test.py	89	# Check if state is updated.
LOW	…lumetric_models/tasks/semantic_segmentation_3d_test.py	94	# Check if all metrics are produced.
LOW	…metric_models/serving/semantic_segmentation_3d_test.py	86	# Check if model is successfully exported.
LOW	…_TF_Cloud_Deployment/client/feature_extraction_test.py	89	# Check if the DataFrames are equal
LOW	…on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh	18	# Check if Docker is installed
LOW	…on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh	52	# Check if the 'models' directory exists before cloning.
LOW	…ion_ml/Triton_TF_Cloud_Deployment/client/run_images.sh	44	# Check if the virtual environment is activated
LOW	…_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py	65	# Check if the dataset already exists
LOW	…_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py	75	# Check if the table already exists
LOW	…riton_TF_Cloud_Deployment/client/inference_pipeline.py	118	# Check if the input and output directories are valid.
LOW	…riton_TF_Cloud_Deployment/client/inference_pipeline.py	146	# Read files from a folder.
LOW	…n_ml/Deploy/detr_cloud_deployment/client/run_images.sh	32	# Check if the virtual environment is activated
LOW	…loy/detr_cloud_deployment/client/inference_pipeline.py	94	# Check if the input and output directories are valid.
LOW	…plications/milk_pouch_detection/src/extract_objects.py	113	# Check if COCO output should be created
LOW	…_applications/milk_pouch_detection/src/models_utils.py	130	# Check if the input data is valid
LOW	…te_identification_ml/model_inference/postprocessing.py	293	# Check if the masks have the same dimensions.
LOW	…l/docker_solution/prediction_pipeline/biq_query_ops.py	50	# Check if the dataset already exists
LOW	…l/docker_solution/prediction_pipeline/biq_query_ops.py	59	# Check if the table already exists
LOW	…jects/waste_identification_ml/data_generation/utils.py	204	# Check if the masks have the same dimensions.
LOW	official/projects/detr/tasks/detection.py	157	# Set pads to large constant
LOW	official/legacy/transformer/data_pipeline.py	235	# Read files and interleave results. When training, the order of the examples
LOW	official/legacy/transformer/data_download.py	193	# Check if extracted files already exist in path
LOW	official/legacy/transformer/utils/tokenizer.py	339	# Check if the matched strings are '\u' or '\\'.
LOW	…object_detection/balanced_positive_negative_sampler.py	142	# Check if indicator and labels have a static size.
LOW	official/vision/ops/augment.py	2804	# Set coordinates to (0, 0, 0, 0) for filtered boxes
LOW	official/vision/ops/sampling_ops.py	248	# Check if indicator and labels have a static size.
LOW	official/modeling/hyperparams/params_dict.py	416	# Add the value to the array.
LOW	official/nlp/modeling/layers/gaussian_process_test.py	183	# Check if linear kernel leads to identity mapping.
LOW	official/nlp/data/create_pretraining_data.py	543	# Check if this would add too many tokens.
LOW	official/nlp/data/create_pretraining_data.py	547	# Check if any of the tokens in this gram have already been masked.

AI Slop Vocabulary16 hits · 26 pts

Severity	File	Line	Snippet
LOW	…earch/object_detection/builders/hyperparams_builder.py	394	# It is insufficient to just set distribution to `normal` from the
LOW	research/object_detection/utils/autoaugment_utils.py	1608	# If no bounding boxes were specified, then just return the images.
LOW	…detection/models/ssd_spaghettinet_feature_extractor.py	99	# If we are building an eval graph just use the values in the
MEDIUM	…h/object_detection/data_decoders/tf_example_decoder.py	455	# If the label_map_proto is provided, try to use it in conjunction with
LOW	research/lstm_object_detection/lstm/utils.py	76	# If we are building an eval graph just use the values in the variables.
LOW	…arch/delf/delf/python/feature_aggregation_extractor.py	108	# Feature visual words are unused in the case of VLAD, so just return
LOW	…ts/unified_detector/data_loaders/tf_example_decoder.py	67	# To add new features, just add entries here.
LOW	official/projects/triviaqa/inputs.py	63	# are statically known. Otherwise, just use -1.
MEDIUM	official/projects/yolo/configs/yolo.py	205	"""Distribute them in order to each level.
MEDIUM	official/legacy/bert/model_training_utils.py	232	# One can't fully utilize a TPU with steps_per_loop=1, so in this case
LOW	official/legacy/xlnet/squad_utils.py	382	# just create a nonce prediction in this case to avoid failure.
MEDIUM	official/vision/serving/export_tflite_lib.py	15	"""Library to facilitate TFLite model conversion."""
LOW	official/nlp/data/squad_lib.py	501	# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
LOW	official/nlp/data/squad_lib.py	731	# just create a nonce prediction in this case to avoid failure.
LOW	official/nlp/data/squad_lib.py	819	# can fail in certain cases in which case we just return `orig_text`.
LOW	official/nlp/data/squad_lib_sp.py	787	# just create a nonce prediction in this case to avoid failure.

Example Usage Blocks15 hits · 22 pts

Severity	File	Line	Snippet
LOW	research/vid2depth/train.py	18	# Example usage:
LOW	research/vid2depth/inference.py	18	# Example usage:
LOW	research/vid2depth/dataset/gen_data.py	18	# Example usage:
LOW	…earch/slim/scripts/finetune_inception_v1_on_flowers.sh	22	# Usage:
LOW	…earch/slim/scripts/finetune_inception_v3_on_flowers.sh	22	# Usage:
LOW	…lim/scripts/finetune_inception_resnet_v2_on_flowers.sh	22	# Usage:
LOW	…earch/slim/scripts/finetune_resnet_v1_50_on_flowers.sh	22	# Usage:
LOW	research/slim/scripts/train_lenet_on_mnist.sh	22	# Usage:
LOW	research/slim/scripts/train_cifarnet_on_cifar10.sh	22	# Usage:
LOW	research/deeplab/local_test_mobilenetv2.sh	20	# Usage:
LOW	research/deeplab/local_test.sh	20	# Usage:
LOW	…earch/deeplab/datasets/download_and_convert_voc2012.sh	19	# Usage:
LOW	…search/deeplab/datasets/download_and_convert_ade20k.sh	19	# Usage:
LOW	research/deeplab/datasets/convert_cityscapes.sh	23	# Usage:
LOW	…ion_ml/llm_applications/milk_pouch_detection/deploy.sh	19	# Usage:

Cross-Language Confusion4 hits · 22 pts

Severity	File	Line	Snippet
HIGH	research/object_detection/utils/colab_utils.py	226	var elem = null;
HIGH	research/object_detection/utils/colab_utils.py	274	boundingBoxes.push(box);
HIGH	…_TF_Cloud_Deployment/client/feature_extraction_test.py	90	self.assertTrue(features_df.equals(pd.DataFrame(COMPARISON_DATA)))
HIGH	official/projects/bigbird/recompute_grad.py	46	return _context_stack.push(self)

Excessive Try-Catch Wrapping13 hits · 15 pts

Severity	File	Line	Snippet
LOW	research/vid2depth/ops/icp_op.py	28	except Exception: # pylint: disable=broad-except
LOW	research/object_detection/model_lib_v2.py	941	except Exception as exc: # pylint:disable=broad-except
LOW	…/context_rcnn/create_cococameratraps_tfexample_main.py	113	except Exception: # pylint: disable=broad-except
LOW	…/dataset_tools/context_rcnn/generate_embedding_data.py	188	except Exception: # pylint: disable=broad-except
LOW	research/slim/datasets/process_bounding_boxes.py	123	except Exception:
LOW	research/slim/nets/mobilenet/mobilenet.py	286	except Exception:
LOW	…rch/delf/delf/python/training/global_features/train.py	356	except Exception as ex:
LOW	official/recommendation/data_pipeline.py	507	except Exception as e:
MEDIUM	official/recommendation/data_pipeline.py	504	def run(self):
LOW	official/core/train_utils.py	581	except Exception as e: # pylint: disable=broad-except
LOW	…ion_ml/Triton_TF_Cloud_Deployment/client/ffmpeg_ops.py	105	except Exception as e: # pylint: disable=broad-exception-caught
MEDIUM	…on_ml/docker_solution/prediction_pipeline/predictor.py	66	print(f'An error occurred: {e}')
LOW	official/legacy/detection/modeling/checkpoint_utils.py	94	except Exception as e:

Verbosity Indicators10 hits · 15 pts

Severity	File	Line	Snippet
LOW	research/deep_speech/run_deep_speech.sh	3	# Step 1: download the LibriSpeech dataset.
LOW	research/deep_speech/run_deep_speech.sh	16	# Step 2: generate train dataset and evaluation dataset
LOW	research/deep_speech/run_deep_speech.sh	33	# Step 3: filter out the audio files that exceed max time duration.
LOW	research/deep_speech/run_deep_speech.sh	41	# Step 4: run the training and evaluation loop in background, and save the running info to a log file
LOW	official/projects/labse/export_tfhub.py	21	# Step 1: export the core LaBSE model.
LOW	official/projects/labse/export_tfhub.py	27	# Step 2: export matching preprocessing (be sure to use same flags).
LOW	official/vision/evaluation/instance_metrics.py	604	# Step 1: Computes IoUs between the detections and the non-crowd ground
LOW	official/vision/evaluation/instance_metrics.py	648	# Step 2: counts true positives grouped by IoU thresholds, classes and
LOW	official/vision/evaluation/instance_metrics.py	671	# Step 3: Counts false positives grouped by IoU thresholds, classes and
LOW	official/vision/evaluation/instance_metrics.py	698	# Step 4: Counts non-crowd groundtruths grouped by classes.

Slop Phrases5 hits · 7 pts

Severity	File	Line	Snippet
LOW	…/object_detection/utils/object_detection_evaluation.py	979	# is inserted first we make sure to break the code if is it not the case.
LOW	research/object_detection/utils/vrd_evaluation.py	190	# is inserted first we make sure to break the code if is it not the case.
LOW	…rnet-docs/themes/hugo-theme-techdoc/layouts/index.html	8	<p>The site is working. Don't forget to customize this homepage with your own. You typically have 2 choices :</p>
LOW	official/projects/const_cl/losses/losses.py	316	# NOTE: make sure to use xla.replica_id() here and in
LOW	official/nlp/modeling/layers/gaussian_process.py	373	# If use this option, make sure to pass through data only once.

Fake / Example Data1 hit · 1 pts

Severity	File	Line	Snippet
LOW	…t-docs/themes/hugo-theme-techdoc/archetypes/default.md	10	Lorem Ipsum.

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