Models and examples built with TensorFlow
This report summarizes scanner evidence for tensorflow/models, a Python project with 77,651 GitHub stars. SynthScan examined 799,748 lines of code across 3477 source files, recording 14879 pattern matches distributed across 22 syntactic categories. The legacy adjusted pattern signal is 31.6 (High pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 9721 finding(s).
Are imports declared and package/API names verified? 2983 finding(s).
Are wrappers, guards, and repeated structures necessary? 7317 finding(s).
Which changes need focused human review? 14879 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 14879 distinct pattern matches across 22 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| HIGH | …rch/object_detection/metrics/calibration_evaluation.py | 0 | clears the state to prepare for a fresh evaluation. | STRING |
| HIGH | research/object_detection/metrics/coco_evaluation.py | 0 | clears the state to prepare for a fresh evaluation. | STRING |
| HIGH | research/object_detection/metrics/lvis_evaluation.py | 0 | clears the state to prepare for a fresh evaluation. | STRING |
| HIGH | …/object_detection/utils/object_detection_evaluation.py | 0 | clears the state to prepare for a fresh evaluation. | STRING |
| HIGH | research/object_detection/utils/vrd_evaluation.py | 0 | clears the state to prepare for a fresh evaluation. | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| HIGH | …earch/object_detection/metrics/lvis_evaluation_test.py | 0 | tests that map is calculated correctly on gt and detections. | STRING |
| HIGH | …earch/object_detection/metrics/coco_evaluation_test.py | 0 | tests that map is calculated correctly on gt and detections. | STRING |
| HIGH | …t_detection/metrics/coco_evaluation_all_frames_test.py | 0 | tests that map is calculated correctly on gt and detections. | STRING |
| HIGH | …_detection/meta_architectures/faster_rcnn_meta_arch.py | 0 | extracts second stage box classifier features. args: proposal_feature_maps: a 4-d float tensor with shape [batch_size * | STRING |
| HIGH | …tion/models/faster_rcnn_resnet_v1_feature_extractor.py | 0 | extracts second stage box classifier features. args: proposal_feature_maps: a 4-d float tensor with shape [batch_size * | STRING |
| HIGH | …n/models/faster_rcnn_inception_v2_feature_extractor.py | 0 | extracts second stage box classifier features. args: proposal_feature_maps: a 4-d float tensor with shape [batch_size * | STRING |
| HIGH | …n/models/faster_rcnn_mobilenet_v1_feature_extractor.py | 0 | extracts second stage box classifier features. args: proposal_feature_maps: a 4-d float tensor with shape [batch_size * | STRING |
| HIGH | …_detection/meta_architectures/faster_rcnn_meta_arch.py | 0 | returns a list of regularization losses for this model. returns a list of regularization losses for this model that the | STRING |
| HIGH | …/object_detection/meta_architectures/rfcn_meta_arch.py | 0 | returns a list of regularization losses for this model. returns a list of regularization losses for this model that the | STRING |
| HIGH | …h/object_detection/meta_architectures/ssd_meta_arch.py | 0 | returns a list of regularization losses for this model. returns a list of regularization losses for this model that the | STRING |
| HIGH | research/object_detection/core/model.py | 0 | returns a list of regularization losses for this model. returns a list of regularization losses for this model that the | STRING |
| HIGH | research/object_detection/legacy/trainer_tf1_test.py | 0 | returns a list of regularization losses for this model. returns a list of regularization losses for this model that the | STRING |
| HIGH | …_detection/meta_architectures/faster_rcnn_meta_arch.py | 0 | returns a list of update operators for this model. returns a list of update operators for this model that must be execut | STRING |
| HIGH | …/object_detection/meta_architectures/rfcn_meta_arch.py | 0 | returns a list of update operators for this model. returns a list of update operators for this model that must be execut | STRING |
| HIGH | …h/object_detection/meta_architectures/ssd_meta_arch.py | 0 | returns a list of update operators for this model. returns a list of update operators for this model that must be execut | STRING |
| HIGH | research/object_detection/core/model.py | 0 | returns a list of update operators for this model. returns a list of update operators for this model that must be execut | STRING |
| HIGH | research/object_detection/legacy/trainer_tf1_test.py | 0 | returns a list of update operators for this model. returns a list of update operators for this model that must be execut | STRING |
| HIGH | …t_detection/meta_architectures/center_net_meta_arch.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …ction/models/center_net_hourglass_feature_extractor.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …ect_detection/models/keras_models/hourglass_network.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …s/deepmac_maskrcnn/modeling/heads/hourglass_network.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …odels/center_net_mobilenet_v2_fpn_feature_extractor.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …on/models/center_net_mobilenet_v2_feature_extractor.py | 0 | the number of feature outputs returned by the feature extractor. | STRING |
| HIGH | …h/object_detection/meta_architectures/ssd_meta_arch.py | 0 | interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training m | STRING |
| HIGH | …detection/models/ssd_inception_v3_feature_extractor.py | 0 | interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training m | STRING |
| HIGH | …detection/models/ssd_inception_v2_feature_extractor.py | 0 | interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training m | STRING |
| HIGH | …etection/models/ssd_resnet_v1_ppn_feature_extractor.py | 0 | interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training m | STRING |
| HIGH | …lstm_ssd_interleaved_mobilenet_v2_feature_extractor.py | 0 | interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training m | STRING |
| HIGH | research/object_detection/core/minibatch_sampler.py | 0 | subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all but | STRING |
| HIGH | …ial/vision/utils/object_detection/minibatch_sampler.py | 0 | subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all but | STRING |
| HIGH | official/vision/ops/sampling_ops.py | 0 | subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all but | STRING |
| HIGH | research/object_detection/core/preprocessor.py | 0 | resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if t | STRING |
| HIGH | official/projects/centernet/ops/preprocess_ops.py | 0 | resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if t | STRING |
| HIGH | official/vision/utils/object_detection/preprocessor.py | 0 | resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if t | STRING |
| HIGH | research/object_detection/core/box_list_ops.py | 0 | enum class for sort order. attributes: ascend: ascend order. descend: descend order. | STRING |
| HIGH | research/object_detection/utils/np_box_list_ops.py | 0 | enum class for sort order. attributes: ascend: ascend order. descend: descend order. | STRING |
| HIGH | official/vision/utils/object_detection/box_list_ops.py | 0 | enum class for sort order. attributes: ascend: ascend order. descend: descend order. | STRING |
| HIGH | research/object_detection/core/box_list_ops.py | 0 | computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas. | STRING |
| HIGH | official/vision/utils/object_detection/box_list_ops.py | 0 | computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas. | STRING |
| HIGH | …utils/object_detection/region_similarity_calculator.py | 0 | computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas. | STRING |
| HIGH | official/projects/centernet/ops/box_list_ops.py | 0 | computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas. | STRING |
| HIGH | research/object_detection/core/box_list_ops.py | 0 | computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with sha | STRING |
| HIGH | official/vision/utils/object_detection/box_list_ops.py | 0 | computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with sha | STRING |
| HIGH | official/projects/centernet/ops/box_list_ops.py | 0 | computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with sha | STRING |
| HIGH | research/object_detection/core/box_list_ops.py | 0 | scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_sca | STRING |
| HIGH | official/vision/utils/object_detection/preprocessor.py | 0 | scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_sca | STRING |
| HIGH | official/vision/utils/object_detection/box_list_ops.py | 0 | scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_sca | STRING |
| HIGH | official/projects/centernet/ops/box_list_ops.py | 0 | scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_sca | STRING |
| 909 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/efficient-hrl/cond_fn.py | 134 | def failed_reset_after_n_episodes(agent, | CODE |
| LOW | research/efficient-hrl/train_utils.py | 165 | def gen_debug_batch_summaries(batch): | CODE |
| LOW | research/efficient-hrl/eval.py | 48 | def get_evaluate_checkpoint_fn(master, output_dir, eval_step_fns, | STRING |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 95 | def relative_context_transition_fn( | CODE |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 111 | def relative_context_multi_transition_fn( | CODE |
| LOW | research/efficient-hrl/context/context.py | 345 | def context_multi_transition_fn(self, contexts, **kwargs): | CODE |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 439 | def get_trainable_critic_vars(self): | CODE |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 585 | def get_trainable_critic_vars(self): | CODE |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 708 | def gen_debug_td_error_summaries( | CODE |
| LOW | research/efficient-hrl/utils/eval_utils.py | 30 | def evaluate_checkpoint_repeatedly(checkpoint_dir, | CODE |
| LOW | research/autoaugment/train_cifar.py | 181 | def _calc_num_trainable_params(self): | CODE |
| LOW | research/autoaugment/train_cifar.py | 345 | def _compute_final_accuracies(self, meval): | CODE |
| LOW | research/autoaugment/shake_shake.py | 26 | def _shake_shake_skip_connection(x, output_filters, stride): | CODE |
| LOW | research/rebar/rebar.py | 565 | def get_simple_muprop_gradient(self): | CODE |
| LOW | research/rebar/rebar.py | 659 | def _create_gumbel_control_variate(self, logQHard, temperature=None): | CODE |
| LOW | research/rebar/rebar.py | 687 | def _create_gumbel_control_variate_quadratic(self, logQHard, temperature=None): | CODE |
| LOW | research/rebar/rebar.py | 744 | def multiply_by_eta_per_layer(self, h_grads, eta): | CODE |
| LOW | research/rebar/rebar.py | 778 | def get_dynamic_rebar_gradient(self): | CODE |
| LOW | research/pcl_rl/env_spec.py | 134 | def convert_env_actions_to_actions(self, actions): | CODE |
| LOW | research/pcl_rl/controller.py | 346 | def convert_from_batched_episodes( | CODE |
| LOW | research/pcl_rl/controller.py | 373 | def convert_to_batched_episodes(self, episodes, max_length=None): | CODE |
| LOW | research/pcl_rl/trust_region.py | 110 | def calc_fisher_vector_product(tangent): | CODE |
| LOW | research/adversarial_text/train_utils.py | 99 | def maybe_restore_pretrained_model(sess, saver_for_restore, model_dir): | CODE |
| LOW | research/adversarial_text/adversarial_losses.py | 124 | def random_perturbation_loss_bidir(embedded, length, loss_fn): | CODE |
| LOW | research/adversarial_text/adversarial_losses.py | 145 | def virtual_adversarial_loss_bidir(logits, embedded, inputs, | CODE |
| LOW | research/adversarial_text/adversarial_losses.py | 202 | def _kl_divergence_with_logits(q_logits, p_logits, weights): | CODE |
| LOW | research/adversarial_text/inputs.py | 168 | def _read_single_sequence_example(file_list, tokens_shape=None): | CODE |
| LOW | research/adversarial_text/gen_data.py | 56 | def build_shuffling_tf_record_writer(fname): | CODE |
| LOW | research/adversarial_text/graphs.py | 670 | def make_restore_average_vars_dict(): | CODE |
| LOW | research/adversarial_text/layers.py | 368 | def _summarize_vars_and_grads(grads_and_vars): | CODE |
| LOW | research/adversarial_text/data/data_utils.py | 325 | def write_vocab_and_frequency(ordered_vocab_freqs, output_dir): | CODE |
| LOW | research/audioset/vggish/vggish_slim.py | 109 | def load_vggish_slim_checkpoint(session, checkpoint_path): | CODE |
| LOW | research/audioset/vggish/mel_features.py | 114 | def spectrogram_to_mel_matrix(num_mel_bins=20, | CODE |
| LOW | research/audioset/yamnet/features.py | 22 | def waveform_to_log_mel_spectrogram_patches(waveform, params): | CODE |
| LOW | research/cvt_text/model/encoder.py | 72 | def _get_unidirectional_reprs(self, word_reprs): | CODE |
| LOW | research/lfads/run_lfads.py | 499 | def build_hyperparameter_dict(flags): | CODE |
| LOW | research/lfads/distributions.py | 50 | def diag_gaussian_log_likelihood(z, mu=0.0, logvar=0.0): | CODE |
| LOW | research/lfads/distributions.py | 68 | def gaussian_pos_log_likelihood(unused_mean, logvar, noise): | CODE |
| LOW | research/lfads/utils.py | 303 | def list_t_bxn_to_tensor_bxtxn(values_t_bxn): | CODE |
| LOW | research/lfads/utils.py | 323 | def tensor_bxtxn_to_list_t_bxn(tensor_bxtxn): | CODE |
| LOW | research/lfads/lfads.py | 1091 | def example_idxs_mod_batch_size(nexamples, batch_size): | CODE |
| LOW | research/lfads/lfads.py | 1120 | def randomize_example_idxs_mod_batch_size(nexamples, batch_size): | CODE |
| LOW | research/lfads/lfads.py | 1191 | def shuffle_and_flatten_datasets(self, datasets, kind='train'): | CODE |
| LOW | research/lfads/lfads.py | 1735 | def eval_model_runs_avg_epoch(self, data_name, data_extxd, | CODE |
| LOW | research/lfads/lfads.py | 1838 | def eval_model_runs_push_mean(self, data_name, data_extxd, | CODE |
| LOW | research/lfads/synth_data/synthetic_data_utils.py | 233 | def add_alignment_projections(datasets, npcs, ntime=None, nsamples=None): | CODE |
| LOW | research/vid2depth/model.py | 98 | def build_inference_for_training(self): | CODE |
| LOW | research/vid2depth/model.py | 341 | def build_egomotion_test_graph(self): | CODE |
| LOW | research/vid2depth/reader.py | 136 | def augment_images_scale_crop(cls, im, intrinsics, out_h, out_w): | CODE |
| LOW | research/vid2depth/reader.py | 213 | def get_multi_scale_intrinsics(cls, intrinsics, num_scales): | CODE |
| LOW | research/vid2depth/inference.py | 138 | def _normalize_depth_for_display(depth, | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 70 | def _generate_organized_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 95 | def test_translate_small_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 107 | def test_translate_random_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 133 | def test_translate_organized_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 145 | def test_rotate_organized_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 167 | def test_translate_lidar_cloud(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 179 | def test_translate_lidar_cloud_ego_motion(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 193 | def test_rotate_lidar_cloud_ego_motion(self): | CODE |
| LOW | research/vid2depth/ops/icp_test.py | 207 | def test_no_change_lidar_cloud(self): | CODE |
| 4559 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | research/efficient-hrl/run_eval.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/cond_fn.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/train_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/run_train.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/run_env.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/agent.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/train.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/eval.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/context/gin_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | …/efficient-hrl/context/context_transition_functions.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/context/gin_imports.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/context/context.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/context/rewards_functions.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/context/samplers.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/maze_env.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/ant_maze_env.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/create_maze_env.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/ant.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/point_maze_env.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/point.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/environments/maze_env_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/agents/ddpg_agent.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/agents/ddpg_networks.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/agents/circular_buffer.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/utils/utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/utils/eval_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/scripts/local_eval.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/efficient-hrl/scripts/local_train.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/train_cifar.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/custom_ops.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/shake_shake.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/shake_drop.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/data_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/helper_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/wrn.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/augmentation_transforms.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/autoaugment/policies.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/rebar_train.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/config.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/rebar.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/datasets.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/logger.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/download_data.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/rebar/utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/objective.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/env_spec.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/controller.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/baseline.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/policy.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/model.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/optimizers.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/full_episode_objective.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/replay_buffer.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/gym_wrapper.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/trainer.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/trust_region.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/pcl_rl/expert_paths.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/adversarial_text/pretrain.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/adversarial_text/train_utils.py | 14 | # ============================================================================== | COMMENT |
| MEDIUM | research/adversarial_text/adversarial_losses.py | 14 | # ============================================================================== | COMMENT |
| 1064 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| 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 | STRING |
| 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 | STRING |
| 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. | STRING |
| 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. | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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. | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| HIGH | research/adversarial_text/inputs.py | 128 | Returns input filenames for configuration. Args: phase: str, 'train', 'test', or 'valid'. bidir: bool, bidire | STRING |
| HIGH | research/adversarial_text/inputs.py | 195 | Inputs for text model. Args: data_dir: str, directory containing TFRecord files of SequenceExample. fname: st | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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; | STRING |
| HIGH | research/lfads/lfads.py | 1121 | Indices 1:nexamples, randomized, in 2D form of shape = (nexamples / batch_size) x batch_size. The remainder is | STRING |
| 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 | STRING |
| HIGH | research/object_detection/model_lib.py | 765 | Creates `Estimator`, input functions, and steps. Args: run_config: A `RunConfig`. hparams: (optional) A `HPar | STRING |
| HIGH | research/object_detection/eval_util.py | 259 | Evaluates metrics defined in evaluators and returns summaries. This function loads the latest checkpoint in checkpoin | STRING |
| HIGH | research/object_detection/eval_util.py | 433 | Periodically evaluates desired tensors using checkpoint_dirs or restore_fn. This function repeatedly loads a checkpoi | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| HIGH | research/object_detection/inputs.py | 163 | A single function that is responsible for all input data transformations. Data transformation functions are applied i | STRING |
| 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 | STRING |
| HIGH | research/object_detection/inputs.py | 777 | Returns `features` and `labels` tensor dictionaries for training. Args: train_config: A train_pb2.TrainConfig. | STRING |
| HIGH | research/object_detection/inputs.py | 938 | Returns `features` and `labels` tensor dictionaries for evaluation. Args: eval_config: An eval_pb2.EvalConfig. | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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 | STRING |
| 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_ | STRING |
| 563 more matches not shown… | ||||
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|---|---|---|---|---|
| LOW | research/efficient-hrl/run_eval.py | 21 | CODE | |
| LOW | research/efficient-hrl/run_eval.py | 22 | CODE | |
| LOW | research/efficient-hrl/run_eval.py | 23 | CODE | |
| LOW | research/efficient-hrl/train_utils.py | 18 | CODE | |
| LOW | research/efficient-hrl/train_utils.py | 19 | CODE | |
| LOW | research/efficient-hrl/train_utils.py | 20 | CODE | |
| LOW | research/efficient-hrl/train_utils.py | 23 | CODE | |
| LOW | research/efficient-hrl/train_utils.py | 28 | CODE | |
| LOW | research/efficient-hrl/run_train.py | 21 | CODE | |
| LOW | research/efficient-hrl/run_train.py | 22 | CODE | |
| LOW | research/efficient-hrl/run_train.py | 23 | CODE | |
| LOW | research/efficient-hrl/agent.py | 25 | CODE | |
| LOW | research/efficient-hrl/train.py | 21 | CODE | |
| LOW | research/efficient-hrl/train.py | 22 | CODE | |
| LOW | research/efficient-hrl/train.py | 23 | CODE | |
| LOW | research/efficient-hrl/train.py | 33 | CODE | |
| LOW | research/efficient-hrl/train.py | 34 | CODE | |
| LOW | research/efficient-hrl/eval.py | 23 | CODE | |
| LOW | research/efficient-hrl/eval.py | 24 | CODE | |
| LOW | research/efficient-hrl/eval.py | 25 | CODE | |
| LOW | research/efficient-hrl/eval.py | 32 | CODE | |
| LOW | research/efficient-hrl/context/gin_utils.py | 19 | CODE | |
| LOW | research/efficient-hrl/context/gin_utils.py | 20 | CODE | |
| LOW | research/efficient-hrl/context/gin_utils.py | 21 | CODE | |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 23 | CODE | |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 24 | CODE | |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 25 | CODE | |
| LOW | research/efficient-hrl/context/gin_imports.py | 20 | CODE | |
| LOW | research/efficient-hrl/context/gin_imports.py | 21 | CODE | |
| LOW | research/efficient-hrl/context/gin_imports.py | 22 | CODE | |
| LOW | research/efficient-hrl/context/gin_imports.py | 23 | CODE | |
| LOW | research/efficient-hrl/context/gin_imports.py | 24 | CODE | |
| LOW | research/efficient-hrl/context/context.py | 24 | CODE | |
| LOW | research/efficient-hrl/context/context.py | 25 | CODE | |
| LOW | research/efficient-hrl/context/context.py | 26 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 25 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 26 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 27 | CODE | |
| LOW | research/efficient-hrl/context/samplers.py | 21 | CODE | |
| LOW | research/efficient-hrl/context/samplers.py | 22 | CODE | |
| LOW | research/efficient-hrl/context/samplers.py | 23 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env_utils.py | 17 | CODE | |
| LOW | research/efficient-hrl/utils/utils.py | 19 | CODE | |
| LOW | research/efficient-hrl/utils/utils.py | 20 | CODE | |
| LOW | research/efficient-hrl/utils/utils.py | 21 | CODE | |
| LOW | research/efficient-hrl/utils/eval_utils.py | 19 | CODE | |
| LOW | research/efficient-hrl/utils/eval_utils.py | 20 | CODE | |
| LOW | research/efficient-hrl/utils/eval_utils.py | 21 | CODE | |
| LOW | research/efficient-hrl/utils/eval_utils.py | 24 | CODE | |
| LOW | research/efficient-hrl/scripts/local_eval.py | 19 | CODE | |
| LOW | research/efficient-hrl/scripts/local_eval.py | 20 | CODE | |
| LOW | research/efficient-hrl/scripts/local_eval.py | 21 | CODE | |
| LOW | research/efficient-hrl/scripts/local_train.py | 19 | CODE | |
| LOW | research/efficient-hrl/scripts/local_train.py | 20 | CODE | |
| LOW | research/efficient-hrl/scripts/local_train.py | 21 | CODE | |
| LOW | research/autoaugment/train_cifar.py | 19 | CODE | |
| LOW | research/autoaugment/train_cifar.py | 20 | CODE | |
| LOW | research/autoaugment/train_cifar.py | 21 | CODE | |
| LOW | research/autoaugment/custom_ops.py | 21 | CODE | |
| LOW | research/autoaugment/custom_ops.py | 22 | CODE | |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/efficient-hrl/run_eval.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/cond_fn.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/train_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/run_train.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/run_env.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/agent.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/train.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/eval.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/context/gin_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | …/efficient-hrl/context/context_transition_functions.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/context/gin_imports.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/context/context.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/context/rewards_functions.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/context/samplers.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/maze_env.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/ant_maze_env.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/create_maze_env.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/ant.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/point_maze_env.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/point.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/environments/maze_env_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/agents/ddpg_networks.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/agents/circular_buffer.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/utils/utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/utils/eval_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/scripts/local_eval.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/efficient-hrl/scripts/local_train.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/train_cifar.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/custom_ops.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/shake_shake.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/shake_drop.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/data_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/helper_utils.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/wrn.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/augmentation_transforms.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/autoaugment/policies.py | 1 | # Copyright 2018 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/rebar/rebar_train.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/config.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/rebar.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/datasets.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/logger.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/download_data.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/rebar/utils.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/objective.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/env_spec.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/controller.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/baseline.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/policy.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/model.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/optimizers.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/full_episode_objective.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/replay_buffer.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/gym_wrapper.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/trainer.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/trust_region.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/pcl_rl/expert_paths.py | 1 | # Copyright 2017 The TensorFlow Authors All Rights Reserved. | COMMENT |
| LOW | research/adversarial_text/pretrain.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/adversarial_text/train_utils.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| LOW | research/adversarial_text/adversarial_losses.py | 1 | # Copyright 2017 Google Inc. All Rights Reserved. | COMMENT |
| 2712 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | research/autoaugment/train_cifar.py | 284 | # Create a new session for this model, initialize | COMMENT |
| MEDIUM | research/rebar/rebar_train.py | 71 | # Create the experiment name from the hparams | COMMENT |
| MEDIUM | research/rebar/rebar.py | 181 | # Create the conditional distribution (output is the logits) | COMMENT |
| MEDIUM | research/rebar/rebar.py | 208 | # Create the conditional distribution (output is the logits) | COMMENT |
| MEDIUM | research/audioset/vggish/vggish_inference_demo.py | 109 | # Define the model in inference mode, load the checkpoint, and | STRING |
| MEDIUM | research/audioset/vggish/vggish_export_tfhub.py | 103 | # Create a TF2 wrapper around VGGish. | COMMENT |
| MEDIUM | research/audioset/vggish/vggish_train_demo.py | 133 | # Define a shallow classification model and associated training ops on top | STRING |
| MEDIUM | research/audioset/vggish/vggish_train_demo.py | 168 | # Initialize all variables in the model, and then load the pre-trained | STRING |
| MEDIUM | research/audioset/yamnet/export.py | 107 | # Create a TF2 Module wrapper around YAMNet. | COMMENT |
| MEDIUM | research/audioset/yamnet/export.py | 144 | # Create a TF-Lite compatible Module wrapper around YAMNet. | COMMENT |
| MEDIUM | research/lfads/lfads.py | 314 | # Define the data placeholder, and deal with all parts of the graph | COMMENT |
| MEDIUM | research/vid2depth/ops/icp_train_demo.py | 117 | # Create the feed_dict for the placeholders filled with the next | COMMENT |
| MEDIUM | research/vid2depth/ops/icp_train_demo.py | 189 | # Create a variable to track the global step. | COMMENT |
| MEDIUM | research/object_detection/model_lib.py | 865 | # Create the input functions for TRAIN/EVAL/PREDICT. | COMMENT |
| MEDIUM | research/object_detection/model_lib_v2.py | 553 | # Create the inputs. | COMMENT |
| MEDIUM | …t_detection/meta_architectures/center_net_meta_arch.py | 780 | # Create the image center location. | COMMENT |
| MEDIUM | …t_detection/meta_architectures/center_net_meta_arch.py | 1039 | # Create the y,x grids: [height, width] | COMMENT |
| MEDIUM | research/object_detection/core/densepose_ops.py | 293 | # Create a list of that maps part index to flipped part index (0-indexed). | COMMENT |
| MEDIUM | research/object_detection/utils/visualization_utils.py | 1176 | # Create a display string (and color) for every box location, group any boxes | COMMENT |
| MEDIUM | research/object_detection/utils/autoaugment_utils.py | 406 | # Create the new bbox tensor by converting pixel integer values to floats. | COMMENT |
| 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. | COMMENT |
| MEDIUM | …arch/object_detection/models/keras_models/resnet_v1.py | 405 | # The following codes are based on the existing keras ResNet model pattern: | COMMENT |
| MEDIUM | …dels/keras_models/base_models/original_mobilenet_v2.py | 212 | # This function is taken from the original tf repo. | COMMENT |
| MEDIUM | …ection/dataset_tools/download_and_preprocess_mscoco.sh | 40 | # Create the output directories. | COMMENT |
| MEDIUM | …_detection/dataset_tools/create_pycocotools_package.sh | 29 | # Create the output directory. | COMMENT |
| MEDIUM | research/slim/train_image_classifier.py | 457 | # Create a dataset provider that loads data from the dataset # | COMMENT |
| MEDIUM | research/slim/train_image_classifier.py | 483 | # Define the model # | COMMENT |
| MEDIUM | research/slim/eval_image_classifier.py | 115 | # Create a dataset provider that loads data from the dataset # | COMMENT |
| MEDIUM | research/slim/eval_image_classifier.py | 145 | # Define the model # | COMMENT |
| MEDIUM | research/slim/eval_image_classifier.py | 164 | # Define the metrics: | COMMENT |
| MEDIUM | research/slim/datasets/download_and_convert_imagenet.sh | 58 | # Create the output and temporary directories. | COMMENT |
| MEDIUM | research/slim/datasets/download_imagenet.sh | 89 | # Create a directory and delete anything there. | COMMENT |
| MEDIUM | research/slim/datasets/build_imagenet_data.py | 140 | # This file is the output of process_bounding_box.py | COMMENT |
| MEDIUM | research/slim/datasets/build_imagenet_data.py | 233 | # Create a single Session to run all image coding calls. | COMMENT |
| MEDIUM | research/slim/datasets/build_imagenet_data.py | 444 | # Create a mechanism for monitoring when all threads are finished. | COMMENT |
| MEDIUM | research/slim/datasets/build_imagenet_data.py | 447 | # Create a generic TensorFlow-based utility for converting all image codings. | COMMENT |
| MEDIUM⚡ | research/slim/deployment/model_deploy.py | 35 | # Create the global step on the device storing the variables. | STRING |
| MEDIUM⚡ | research/slim/deployment/model_deploy.py | 39 | # Define the inputs | STRING |
| MEDIUM⚡ | research/slim/deployment/model_deploy.py | 44 | # Define the optimizer. | STRING |
| MEDIUM⚡ | research/slim/deployment/model_deploy.py | 48 | # Define the model including the loss. | STRING |
| MEDIUM | research/slim/deployment/model_deploy_test.py | 172 | # Create an easy training set: | COMMENT |
| MEDIUM | research/slim/deployment/model_deploy_test.py | 319 | # Create an easy training set: | COMMENT |
| MEDIUM | research/slim/deployment/model_deploy_test.py | 468 | # Create an easy training set: | COMMENT |
| MEDIUM | research/slim/preprocessing/vgg_preprocessing.py | 154 | # Create a random bounding box. | COMMENT |
| MEDIUM | research/deeplab/model.py | 427 | # The following codes employ the DeepLabv3 ASPP module. Note that we | STRING |
| MEDIUM | research/deeplab/train.py | 312 | # Create the global step on the device storing the variables. | COMMENT |
| MEDIUM | research/deeplab/train.py | 316 | # Define the model and create clones. | COMMENT |
| MEDIUM | research/deeplab/eval.py | 158 | # Define the evaluation metric. | COMMENT |
| MEDIUM | research/deeplab/core/preprocess_utils.py | 306 | # Create a random bounding box. | COMMENT |
| MEDIUM | research/deeplab/datasets/remove_gt_colormap.py | 67 | # Create the output directory if not exists. | COMMENT |
| MEDIUM | research/cognitive_planning/visualization_utils.py | 598 | # Create a display string (and color) for every box location, group any boxes | COMMENT |
| MEDIUM | …/cognitive_planning/preprocessing/vgg_preprocessing.py | 155 | # Create a random bounding box. | COMMENT |
| MEDIUM | research/deep_speech/deep_speech.py | 171 | # Create the train_op that groups both minimize_ops and update_ops | COMMENT |
| MEDIUM | …earch/delf/delf/python/training/build_image_dataset.py | 146 | # Create the dictionary (key = image_id, value = {label, file_id}). | COMMENT |
| MEDIUM | …earch/delf/delf/python/training/build_image_dataset.py | 369 | # Create the subset for the current label. | COMMENT |
| MEDIUM⚡ | research/delf/delf/python/training/train.py | 179 | # Create the strategy. | COMMENT |
| MEDIUM | research/delf/delf/python/training/train.py | 210 | # Create the distributed train/validation sets. | COMMENT |
| MEDIUM⚡ | research/delf/delf/python/training/train.py | 231 | # Create a checkpoint directory to store the checkpoints. | COMMENT |
| MEDIUM | …rch/delf/delf/python/training/global_features/train.py | 187 | # Define the loss function. | COMMENT |
| MEDIUM | …rch/delf/delf/python/training/global_features/train.py | 208 | # Define the optimizer. | COMMENT |
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| LOW | research/efficient-hrl/train_utils.py | 59 | CODE | |
| LOW | research/efficient-hrl/train.py | 270 | CODE | |
| LOW | research/efficient-hrl/eval.py | 289 | CODE | |
| LOW | research/efficient-hrl/context/context.py | 39 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 301 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 409 | CODE | |
| LOW | research/efficient-hrl/context/rewards_functions.py | 484 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env.py | 37 | CODE | |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 84 | CODE | |
| LOW | research/efficient-hrl/agents/ddpg_agent.py | 517 | CODE | |
| LOW | research/efficient-hrl/agents/ddpg_networks.py | 32 | CODE | |
| LOW | research/rebar/rebar_train.py | 61 | CODE | |
| LOW | research/pcl_rl/objective.py | 102 | CODE | |
| LOW | research/pcl_rl/controller.py | 68 | CODE | |
| LOW | research/pcl_rl/model.py | 308 | CODE | |
| LOW | research/adversarial_text/inputs.py | 249 | CODE | |
| LOW | research/audioset/yamnet/yamnet.py | 48 | CODE | |
| LOW | research/audioset/yamnet/yamnet.py | 72 | CODE | |
| LOW | research/lfads/utils.py | 85 | CODE | |
| LOW | research/lfads/plot_lfads.py | 155 | CODE | |
| LOW | research/vid2depth/model.py | 40 | CODE | |
| LOW | research/vid2depth/dataset/dataset_loader.py | 449 | CODE | |
| LOW | research/object_detection/model_lib.py | 748 | CODE | |
| LOW | research/object_detection/exporter.py | 488 | CODE | |
| LOW | research/object_detection/exporter.py | 576 | CODE | |
| LOW | research/object_detection/eval_util.py | 102 | CODE | |
| LOW | research/object_detection/eval_util.py | 246 | CODE | |
| LOW | research/object_detection/eval_util.py | 416 | CODE | |
| LOW | research/object_detection/inputs.py | 151 | CODE | |
| LOW | research/object_detection/model_lib_v2.py | 444 | CODE | |
| LOW | research/object_detection/model_lib_v2.py | 1022 | CODE | |
| LOW | …_detection/meta_architectures/faster_rcnn_meta_arch.py | 267 | CODE | |
| LOW | …tion/meta_architectures/context_rcnn_meta_arch_test.py | 206 | CODE | |
| LOW | …detection/meta_architectures/context_rcnn_meta_arch.py | 45 | CODE | |
| LOW | …detection/meta_architectures/ssd_meta_arch_test_lib.py | 126 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 1239 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 2223 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 2401 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 2671 | CODE | |
| LOW | …n/meta_architectures/faster_rcnn_meta_arch_test_lib.py | 286 | CODE | |
| LOW | …ject_detection/meta_architectures/deepmac_meta_arch.py | 550 | CODE | |
| LOW | …ect_detection/meta_architectures/ssd_meta_arch_test.py | 48 | CODE | |
| LOW | …/object_detection/meta_architectures/rfcn_meta_arch.py | 51 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 1769 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 1840 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 3382 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 3399 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 3415 | CODE | |
| LOW | …on/meta_architectures/center_net_meta_arch_tf2_test.py | 3433 | CODE | |
| LOW | …h/object_detection/meta_architectures/ssd_meta_arch.py | 261 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 568 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 1413 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 1664 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 1840 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 2012 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 2086 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 2223 | CODE | |
| LOW | research/object_detection/core/preprocessor.py | 3665 | CODE | |
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| LOW | research/object_detection/core/preprocessor.py | 3668 | CODE | |
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| LOW | research/efficient-hrl/train.py | 270 | CODE | |
| LOW | research/efficient-hrl/eval.py | 48 | CODE | |
| LOW | research/efficient-hrl/eval.py | 80 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env.py | 37 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env.py | 323 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env.py | 472 | CODE | |
| LOW | research/efficient-hrl/environments/create_maze_env.py | 26 | CODE | |
| LOW | research/efficient-hrl/environments/maze_env_utils.py | 54 | CODE | |
| LOW | research/autoaugment/train_cifar.py | 79 | CODE | |
| LOW | research/autoaugment/train_cifar.py | 404 | CODE | |
| LOW | research/rebar/rebar_train.py | 63 | CODE | |
| LOW | research/rebar/rebar.py | 151 | CODE | |
| LOW | research/rebar/rebar.py | 220 | CODE | |
| LOW | research/rebar/rebar.py | 759 | CODE | |
| LOW | research/pcl_rl/env_spec.py | 47 | CODE | |
| LOW | research/pcl_rl/env_spec.py | 105 | CODE | |
| LOW | research/pcl_rl/baseline.py | 47 | CODE | |
| LOW | research/pcl_rl/policy.py | 62 | CODE | |
| LOW | research/pcl_rl/model.py | 123 | CODE | |
| LOW | research/pcl_rl/expert_paths.py | 47 | CODE | |
| LOW | research/adversarial_text/data/document_generators.py | 73 | CODE | |
| LOW | research/adversarial_text/data/document_generators.py | 114 | CODE | |
| LOW | research/adversarial_text/data/document_generators.py | 266 | CODE | |
| LOW | research/adversarial_text/data/document_generators.py | 317 | CODE | |
| LOW | research/cvt_text/cvt.py | 37 | CODE | |
| LOW | research/cvt_text/preprocessing.py | 34 | CODE | |
| LOW | …h/cvt_text/task_specific/word_level/word_level_data.py | 57 | CODE | |
| LOW | …h/cvt_text/task_specific/word_level/word_level_data.py | 83 | CODE | |
| LOW | research/cvt_text/corpus_processing/unlabeled_data.py | 59 | CODE | |
| LOW | research/cvt_text/base/embeddings.py | 150 | CODE | |
| LOW | research/cvt_text/base/embeddings.py | 113 | CODE | |
| LOW | research/lfads/run_lfads.py | 760 | CODE | |
| LOW | research/lfads/utils.py | 170 | CODE | |
| LOW | research/lfads/lfads.py | 280 | CODE | |
| LOW | research/lfads/lfads.py | 2090 | CODE | |
| LOW | research/vid2depth/util.py | 35 | CODE | |
| LOW | research/vid2depth/model.py | 130 | CODE | |
| LOW | research/vid2depth/inference.py | 73 | CODE | |
| LOW | research/vid2depth/dataset/dataset_loader.py | 204 | CODE | |
| LOW | research/vid2depth/dataset/gen_data.py | 69 | CODE | |
| LOW | research/object_detection/model_lib.py | 432 | CODE | |
| LOW | research/object_detection/model_lib.py | 455 | CODE | |
| LOW | research/object_detection/exporter.py | 363 | CODE | |
| LOW | research/object_detection/eval_util.py | 246 | CODE | |
| LOW | research/object_detection/eval_util.py | 416 | CODE | |
| LOW | research/object_detection/eval_util.py | 1068 | CODE | |
| LOW | research/object_detection/eval_util.py | 1159 | CODE | |
| LOW | research/object_detection/inputs.py | 1134 | CODE | |
| LOW | research/object_detection/model_lib_v2.py | 444 | CODE | |
| LOW | research/object_detection/model_lib_v2.py | 833 | CODE | |
| LOW | research/object_detection/model_lib_tf1_test.py | 131 | CODE | |
| LOW | research/object_detection/metrics/coco_tools.py | 391 | CODE | |
| LOW | research/object_detection/metrics/coco_tools.py | 588 | CODE | |
| LOW | research/object_detection/metrics/coco_tools.py | 232 | CODE | |
| LOW | research/object_detection/metrics/coco_evaluation.py | 1786 | CODE | |
| LOW | …ch/object_detection/metrics/offline_eval_map_corloc.py | 76 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 1239 | CODE | |
| LOW | …t_detection/meta_architectures/center_net_meta_arch.py | 4785 | CODE | |
| LOW | …ject_detection/meta_architectures/deepmac_meta_arch.py | 79 | CODE | |
| LOW | …ject_detection/meta_architectures/deepmac_meta_arch.py | 131 | CODE | |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | research/vid2depth/dataset/dataset_loader.py | 225 | except ValueError: | CODE |
| HIGH | research/vid2depth/dataset/dataset_loader.py | 334 | except ValueError: | CODE |
| HIGH | research/object_detection/exporter_tf1_test.py | 47 | except ImportError: | CODE |
| HIGH | research/object_detection/model_lib.py | 47 | except ImportError: | CODE |
| HIGH | research/object_detection/exporter.py | 34 | except ImportError: | CODE |
| HIGH | research/object_detection/model_hparams.py | 27 | except ImportError: | CODE |
| HIGH | …detection/meta_architectures/ssd_meta_arch_test_lib.py | 40 | except ImportError: | CODE |
| HIGH | …n/meta_architectures/faster_rcnn_meta_arch_test_lib.py | 46 | except ImportError: | CODE |
| HIGH | …ect_detection/meta_architectures/ssd_meta_arch_test.py | 37 | except ImportError: | CODE |
| HIGH | …h/object_detection/meta_architectures/ssd_meta_arch.py | 39 | except ImportError: | CODE |
| HIGH | …h/object_detection/meta_architectures/ssd_meta_arch.py | 1109 | except AttributeError: | CODE |
| HIGH | research/object_detection/builders/optimizer_builder.py | 30 | except: # pylint: disable=bare-except | CODE |
| HIGH | …ch/object_detection/builders/graph_rewriter_builder.py | 22 | except ImportError: | CODE |
| HIGH | …arch/object_detection/builders/dataset_builder_test.py | 37 | except ImportError: | CODE |
| HIGH | research/object_detection/utils/autoaugment_utils.py | 31 | except ImportError: | CODE |
| HIGH | research/object_detection/utils/config_util_test.py | 40 | except ImportError: | CODE |
| HIGH | …/object_detection/utils/object_detection_evaluation.py | 287 | except TypeError: | CODE |
| HIGH | …/object_detection/utils/object_detection_evaluation.py | 427 | except TypeError: | CODE |
| HIGH | …ject_detection/models/ssd_pnasnet_feature_extractor.py | 31 | except: # pylint: disable=bare-except | CODE |
| HIGH | …detection/models/faster_rcnn_pnas_feature_extractor.py | 35 | except: # pylint: disable=bare-except | CODE |
| HIGH | …ion/models/ssd_mobiledet_feature_extractor_tf1_test.py | 25 | except: # pylint: disable=bare-except | CODE |
| HIGH | …/models/ssd_spaghettinet_feature_extractor_tf1_test.py | 25 | except: # pylint: disable=bare-except | CODE |
| HIGH | …_detection/models/faster_rcnn_nas_feature_extractor.py | 38 | except: # pylint: disable=bare-except | CODE |
| HIGH | …/dataset_tools/context_rcnn/add_context_to_examples.py | 60 | except ModuleNotFoundError: | CODE |
| HIGH | …/context_rcnn/create_cococameratraps_tfexample_main.py | 48 | except ModuleNotFoundError: | CODE |
| HIGH | …tools/context_rcnn/generate_embedding_data_tf2_test.py | 43 | except ModuleNotFoundError: | CODE |
| HIGH | …text_rcnn/create_cococameratraps_tfexample_tf2_test.py | 36 | except ModuleNotFoundError: | CODE |
| HIGH | …tools/context_rcnn/add_context_to_examples_tf2_test.py | 37 | except ModuleNotFoundError: | CODE |
| HIGH | …tools/context_rcnn/generate_detection_data_tf2_test.py | 45 | except ModuleNotFoundError: | CODE |
| HIGH | …/dataset_tools/context_rcnn/generate_detection_data.py | 55 | except ModuleNotFoundError: | CODE |
| HIGH | …/dataset_tools/context_rcnn/generate_embedding_data.py | 62 | except ModuleNotFoundError: | CODE |
| HIGH | …h/object_detection/data_decoders/tf_example_decoder.py | 40 | except ImportError: | CODE |
| HIGH | …detection/data_decoders/tf_sequence_example_decoder.py | 32 | except ImportError: | CODE |
| HIGH | …l/Deploy/pet_grading_cloud_deployment/big_query_ops.py | 94 | except exceptions.NotFound: | CODE |
| HIGH | …l/Deploy/detr_cloud_deployment/client/big_query_ops.py | 104 | except exceptions.NotFound: | CODE |
| HIGH | …/projects/mobilebert/tf2_model_checkpoint_converter.py | 184 | except tf.errors.OpError: | CODE |
| HIGH | official/legacy/detection/dataloader/input_reader.py | 64 | except AttributeError: | CODE |
| HIGH | official/utils/docs/build_orbit_api_docs.py | 83 | except AttributeError: | CODE |
| HIGH | official/utils/docs/build_tfm_api_docs.py | 128 | except AttributeError: | CODE |
| HIGH | …l/vision/utils/object_detection/visualization_utils.py | 32 | except (ValueError, ImportError): | CODE |
| HIGH | …l/vision/utils/object_detection/visualization_utils.py | 247 | except ValueError: | CODE |
| HIGH | official/modeling/hyperparams/params_dict.py | 491 | except ValueError: | CODE |
| HIGH | official/common/flags.py | 92 | except flags.DuplicateFlagError: | CODE |
| HIGH | official/common/flags.py | 100 | except flags.DuplicateFlagError: | CODE |
| HIGH | …ial/nlp/tools/tf2_bert_encoder_checkpoint_converter.py | 137 | except tf.errors.OpError: | CODE |
| HIGH | …l/nlp/tools/tf2_albert_encoder_checkpoint_converter.py | 153 | except tf.errors.OpError: | CODE |
| HIGH | official/nlp/finetuning/binary_helper.py | 93 | except KeyError: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | research/object_detection/model_lib_v2.py | 528 | tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16') | CODE |
| CRITICAL | research/object_detection/model_lib_v2.py | 1114 | tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16') | CODE |
| CRITICAL | research/attention_ocr/python/model_export_test.py | 133 | graph_def = tf.compat.v1.saved_model.loader.load( | CODE |
| CRITICAL | research/attention_ocr/python/model_export_lib.py | 106 | k: tf.compat.v1.saved_model.utils.build_tensor_info(t) | CODE |
| CRITICAL | research/attention_ocr/python/model_export.py | 168 | signature_def = tf.compat.v1.saved_model.signature_def_utils.build_signature_def( | STRING |
| CRITICAL | research/attention_ocr/python/model_export.py | 172 | builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir) | STRING |
| CRITICAL | research/attention_ocr/python/sequence_layers.py | 250 | lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell( | CODE |
| CRITICAL | research/deeplab/convert_to_tflite.py | 56 | converter = tf.compat.v1.lite.TFLiteConverter.from_session( | CODE |
| CRITICAL | …elf/python/detect_to_retrieve/cluster_delf_features.py | 150 | kmeans = tf.compat.v1.estimator.experimental.KMeans( | CODE |
| CRITICAL | official/recommendation/ncf_keras_main.py | 275 | tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite( | CODE |
| CRITICAL | official/core/train_utils.py | 573 | opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation() | CODE |
| CRITICAL | official/projects/qat/vision/quantization/schemes.py | 65 | return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer( | CODE |
| CRITICAL | official/projects/qat/nlp/quantization/schemes.py | 197 | return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer( | CODE |
| CRITICAL | …al/projects/pixel/utils/convert_numpy_weights_to_tf.py | 39 | vit_encoder.encoder.encoder._pos_embed.pos_embedding.assign( | CODE |
| CRITICAL | official/legacy/transformer/transformer_main_test.py | 70 | tf.compat.v2.keras.mixed_precision.global_policy()) | CODE |
| CRITICAL | official/legacy/transformer/transformer_main_test.py | 73 | tf.compat.v2.keras.mixed_precision.set_global_policy(self.orig_policy) | CODE |
| CRITICAL | official/legacy/detection/main.py | 73 | tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16') | CODE |
| CRITICAL | official/legacy/detection/modeling/base_model.py | 62 | tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16') | CODE |
| CRITICAL | tensorflow_models/tensorflow_models_test.py | 45 | _ = tfm.uplift.layers.encoders.concat_features.ConcatFeatures(['feature']) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …t_detection/core/balanced_positive_negative_sampler.py | 136 | # Check if indicator and labels have a static size. | COMMENT |
| LOW | research/object_detection/utils/vrd_evaluation.py | 205 | # Verify if one of the labels is negative (this is sure FP) | COMMENT |
| LOW | research/object_detection/utils/vrd_evaluation.py | 208 | # Verify if all labels are verified | COMMENT |
| LOW | research/slim/datasets/download_and_convert_flowers.py | 140 | # Read the filename: | COMMENT |
| LOW | research/slim/nets/nasnet/nasnet_utils.py | 355 | # Add hiddenstate to the list of hiddenstates we can choose from | COMMENT |
| LOW | research/slim/nets/nasnet/nasnet_utils.py | 379 | # Check if a stride is needed, then use a strided 1x1 here | COMMENT |
| LOW | research/lstm_object_detection/lstm/lstm_cells.py | 391 | # Set nodes to be under raw_inputs/ name scope for tfmini export. | COMMENT |
| LOW | research/lstm_object_detection/lstm/lstm_cells.py | 619 | # Set nodes to be under raw_outputs/ name scope for tfmini export. | COMMENT |
| LOW⚡ | research/delf/delf/python/training/train.py | 238 | # Set reduction to `none` so we can do the reduction afterwards and divide | COMMENT |
| LOW | …rch/delf/delf/python/training/global_features/train.py | 136 | # Check if train dataset is downloaded and download it if not found. | COMMENT |
| LOW | …elf/python/detect_to_retrieve/cluster_delf_features.py | 89 | # Loop over list of index images and collect DELF features. | COMMENT |
| 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 | CODE |
| LOW | …lumetric_models/tasks/semantic_segmentation_3d_test.py | 73 | # Check if training loss is produced. | COMMENT |
| LOW⚡ | …lumetric_models/tasks/semantic_segmentation_3d_test.py | 86 | # Check if validation loss is produced. | COMMENT |
| LOW⚡ | …lumetric_models/tasks/semantic_segmentation_3d_test.py | 89 | # Check if state is updated. | COMMENT |
| LOW⚡ | …lumetric_models/tasks/semantic_segmentation_3d_test.py | 94 | # Check if all metrics are produced. | COMMENT |
| LOW | …metric_models/serving/semantic_segmentation_3d_test.py | 86 | # Check if model is successfully exported. | COMMENT |
| LOW⚡ | …_TF_Cloud_Deployment/client/feature_extraction_test.py | 89 | # Check if the DataFrames are equal | COMMENT |
| LOW | …on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh | 18 | # Check if Docker is installed | COMMENT |
| LOW | …on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh | 52 | # Check if the 'models' directory exists before cloning. | COMMENT |
| LOW | …ion_ml/Triton_TF_Cloud_Deployment/client/run_images.sh | 44 | # Check if the virtual environment is activated | COMMENT |
| LOW | …_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py | 65 | # Check if the dataset already exists | COMMENT |
| LOW | …_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py | 75 | # Check if the table already exists | COMMENT |
| LOW | …riton_TF_Cloud_Deployment/client/inference_pipeline.py | 118 | # Check if the input and output directories are valid. | COMMENT |
| LOW⚡ | …riton_TF_Cloud_Deployment/client/inference_pipeline.py | 146 | # Read files from a folder. | COMMENT |
| LOW | …n_ml/Deploy/pet_grading_cloud_deployment/run_images.sh | 26 | # Check if the virtual environment is activated | COMMENT |
| LOW | …n_ml/Deploy/detr_cloud_deployment/client/run_images.sh | 32 | # Check if the virtual environment is activated | COMMENT |
| LOW | …loy/detr_cloud_deployment/client/inference_pipeline.py | 94 | # Check if the input and output directories are valid. | COMMENT |
| LOW | …plications/milk_pouch_detection/src/extract_objects.py | 113 | # Check if COCO output should be created | COMMENT |
| LOW | …_applications/milk_pouch_detection/src/models_utils.py | 130 | # Check if the input data is valid | COMMENT |
| LOW | …te_identification_ml/model_inference/postprocessing.py | 293 | # Check if the masks have the same dimensions. | COMMENT |
| LOW | …l/docker_solution/prediction_pipeline/biq_query_ops.py | 50 | # Check if the dataset already exists | COMMENT |
| LOW | …l/docker_solution/prediction_pipeline/biq_query_ops.py | 59 | # Check if the table already exists | COMMENT |
| LOW | …jects/waste_identification_ml/data_generation/utils.py | 204 | # Check if the masks have the same dimensions. | COMMENT |
| LOW | official/projects/detr/tasks/detection.py | 157 | # Set pads to large constant | COMMENT |
| LOW | official/legacy/transformer/data_pipeline.py | 235 | # Read files and interleave results. When training, the order of the examples | COMMENT |
| LOW | official/legacy/transformer/data_download.py | 193 | # Check if extracted files already exist in path | COMMENT |
| LOW | official/legacy/transformer/utils/tokenizer.py | 339 | # Check if the matched strings are '\u' or '\\'. | STRING |
| LOW | …object_detection/balanced_positive_negative_sampler.py | 142 | # Check if indicator and labels have a static size. | COMMENT |
| LOW | official/vision/ops/augment.py | 2804 | # Set coordinates to (0, 0, 0, 0) for filtered boxes | COMMENT |
| LOW | official/vision/ops/sampling_ops.py | 248 | # Check if indicator and labels have a static size. | COMMENT |
| LOW | official/modeling/hyperparams/params_dict.py | 416 | # Add the value to the array. | COMMENT |
| LOW | official/nlp/modeling/layers/gaussian_process_test.py | 183 | # Check if linear kernel leads to identity mapping. | COMMENT |
| LOW | official/nlp/data/create_pretraining_data.py | 543 | # Check if this would add too many tokens. | COMMENT |
| LOW | official/nlp/data/create_pretraining_data.py | 547 | # Check if any of the tokens in this gram have already been masked. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …tools/context_rcnn/generate_embedding_data_tf2_test.py | 261 | output = inference_fn.process(('dummy_key', generated_example)) | CODE |
| LOW | …tools/context_rcnn/generate_embedding_data_tf2_test.py | 279 | output = inference_fn.process(('dummy_key', generated_example)) | CODE |
| LOW | …tools/context_rcnn/generate_embedding_data_tf2_test.py | 297 | output = inference_fn.process(('dummy_key', generated_example)) | CODE |
| LOW | …tools/context_rcnn/add_context_to_examples_tf2_test.py | 285 | ('dummy_key', [tf.train.Example.FromString( | CODE |
| LOW | …tools/context_rcnn/add_context_to_examples_tf2_test.py | 299 | ('dummy_key', [tf.train.Example.FromString( | CODE |
| LOW | orbit/standard_runner_test.py | 28 | def dummy_data(_): | CODE |
| LOW | orbit/standard_runner_test.py | 34 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/core/base_trainer_test.py | 84 | def dummy_data(_): | CODE |
| LOW | official/core/base_trainer_test.py | 90 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/projects/lra/lra_dual_encoder_task.py | 125 | def dummy_data(_): | CODE |
| LOW | official/projects/lra/lra_dual_encoder_task.py | 144 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE | CODE |
| LOW | …t-docs/themes/hugo-theme-techdoc/archetypes/default.md | 10 | Lorem Ipsum. | CODE |
| LOW | official/projects/mobilebert/distillation.py | 345 | def dummy_data(_): | CODE |
| LOW | official/projects/mobilebert/distillation.py | 360 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | …cts/edgetpu/vision/tasks/semantic_segmentation_test.py | 32 | def dummy_data(_): | CODE |
| LOW | …cts/edgetpu/vision/tasks/semantic_segmentation_test.py | 45 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | …ects/edgetpu/vision/tasks/image_classification_test.py | 31 | def dummy_data(_): | CODE |
| LOW | …ects/edgetpu/vision/tasks/image_classification_test.py | 38 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | …rojects/edgetpu/nlp/mobilebert_edgetpu_trainer_test.py | 26 | def dummy_data(_): | CODE |
| LOW | …rojects/edgetpu/nlp/mobilebert_edgetpu_trainer_test.py | 40 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/projects/teams/teams_task.py | 149 | def dummy_data(_): | CODE |
| LOW | official/projects/teams/teams_task.py | 163 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW⚡ | official/projects/teams/teams_pretrainer.py | 334 | fake_data = self._get_fake_data(inputs, lm_outputs) | CODE |
| LOW⚡ | official/projects/teams/teams_pretrainer.py | 337 | disc_input = fake_data['inputs'] | CODE |
| LOW⚡ | official/projects/teams/teams_pretrainer.py | 338 | disc_rtd_label = fake_data['is_fake_tokens'] | CODE |
| LOW⚡ | official/projects/teams/teams_pretrainer.py | 339 | disc_mws_candidates = fake_data['candidate_set'] | CODE |
| LOW | …_classification_example/classification_example_test.py | 34 | def dummy_data(_): | CODE |
| LOW | …_classification_example/classification_example_test.py | 47 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/projects/fffner/fffner_prediction.py | 123 | def dummy_data(_): | CODE |
| LOW | official/projects/fffner/fffner_prediction.py | 139 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/projects/perceiver/tasks/pretrain_test.py | 51 | def dummy_data(_): | CODE |
| LOW | official/projects/perceiver/tasks/pretrain_test.py | 64 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW⚡ | official/legacy/image_classification/mnist_test.py | 67 | dummy_data = ( | CODE |
| LOW⚡ | official/legacy/image_classification/mnist_test.py | 72 | tf.data.Dataset.from_tensor_slices(dummy_data), | CODE |
| LOW⚡ | official/legacy/image_classification/mnist_test.py | 73 | tf.data.Dataset.from_tensor_slices(dummy_data), | CODE |
| LOW | …ial/modeling/fast_training/progressive/trainer_test.py | 103 | def dummy_data(_): | CODE |
| LOW | …ial/modeling/fast_training/progressive/trainer_test.py | 111 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/question_answering.py | 206 | dummy_data = functools.partial(self._dummy_data, params) | CODE |
| LOW | official/nlp/tasks/question_answering.py | 208 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/electra_task.py | 143 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/electra_task.py | 158 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/masked_lm_determinism_test.py | 28 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/masked_lm_determinism_test.py | 46 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/dual_encoder.py | 124 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/dual_encoder.py | 138 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/sentence_prediction.py | 120 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/sentence_prediction.py | 137 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/tagging.py | 118 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/tagging.py | 136 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW | official/nlp/tasks/masked_lm.py | 113 | def dummy_data(_): | CODE |
| LOW | official/nlp/tasks/masked_lm.py | 128 | dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | CODE |
| LOW⚡ | official/nlp/modeling/models/electra_pretrainer.py | 149 | fake_data = self._get_fake_data(inputs, lm_outputs, duplicate=True) | CODE |
| LOW⚡ | official/nlp/modeling/models/electra_pretrainer.py | 152 | disc_input = fake_data['inputs'] | CODE |
| LOW⚡ | official/nlp/modeling/models/electra_pretrainer.py | 153 | disc_label = fake_data['is_fake_tokens'] | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …search/object_detection/g3doc/running_on_mobile_tf2.md | 54 | ### Step 1: Export TFLite inference graph | COMMENT |
| LOW | …search/object_detection/g3doc/running_on_mobile_tf2.md | 73 | ### Step 2: Convert to TFLite | COMMENT |
| LOW | …search/object_detection/g3doc/running_on_mobile_tf2.md | 95 | ### Step 3: add Metadata to the model | COMMENT |
| LOW | research/deep_speech/run_deep_speech.sh | 3 | # Step 1: download the LibriSpeech dataset. | COMMENT |
| LOW | research/deep_speech/run_deep_speech.sh | 16 | # Step 2: generate train dataset and evaluation dataset | COMMENT |
| LOW⚡ | research/deep_speech/run_deep_speech.sh | 33 | # Step 3: filter out the audio files that exceed max time duration. | COMMENT |
| 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 | COMMENT |
| LOW⚡ | official/projects/labse/export_tfhub.py | 21 | # Step 1: export the core LaBSE model. | COMMENT |
| LOW⚡ | official/projects/labse/export_tfhub.py | 27 | # Step 2: export matching preprocessing (be sure to use same flags). | COMMENT |
| LOW | official/projects/pointpillars/utils/wod_processor.py | 380 | # NOTE: This function might be called by an online data loader in a | COMMENT |
| LOW | official/projects/pointpillars/utils/wod_processor.py | 401 | # NOTE: This function might be called by an online data loader in a | COMMENT |
| LOW | official/projects/text_classification_example/README.md | 19 | #### Step 1: build\_inputs | COMMENT |
| LOW | official/projects/text_classification_example/README.md | 57 | #### Step 2: build\_losses | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 604 | # Step 1: Computes IoUs between the detections and the non-crowd ground | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 648 | # Step 2: counts true positives grouped by IoU thresholds, classes and | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 671 | # Step 3: Counts false positives grouped by IoU thresholds, classes and | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 698 | # Step 4: Counts non-crowd groundtruths grouped by classes. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …earch/object_detection/builders/hyperparams_builder.py | 394 | # It is insufficient to just set distribution to `normal` from the | COMMENT |
| LOW | research/object_detection/utils/autoaugment_utils.py | 1608 | # If no bounding boxes were specified, then just return the images. | COMMENT |
| LOW | …detection/models/ssd_spaghettinet_feature_extractor.py | 99 | # If we are building an eval graph just use the values in the | COMMENT |
| 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 | COMMENT |
| LOW | research/lstm_object_detection/lstm/utils.py | 76 | # If we are building an eval graph just use the values in the variables. | COMMENT |
| LOW | …arch/delf/delf/python/feature_aggregation_extractor.py | 108 | # Feature visual words are unused in the case of VLAD, so just return | COMMENT |
| LOW | …ts/unified_detector/data_loaders/tf_example_decoder.py | 67 | # To add new features, just add entries here. | COMMENT |
| LOW | official/projects/triviaqa/inputs.py | 63 | # are statically known. Otherwise, just use -1. | COMMENT |
| MEDIUM | official/projects/yolo/configs/yolo.py | 203 | """Distribute them in order to each level. | STRING |
| 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 | COMMENT |
| LOW | official/legacy/xlnet/squad_utils.py | 382 | # just create a nonce prediction in this case to avoid failure. | COMMENT |
| MEDIUM | official/vision/serving/export_tflite_lib.py | 15 | """Library to facilitate TFLite model conversion.""" | STRING |
| LOW | official/nlp/data/squad_lib.py | 501 | # "Japanese", we just use "Japanese" as the annotation. This is fairly rare | COMMENT |
| LOW | official/nlp/data/squad_lib.py | 731 | # just create a nonce prediction in this case to avoid failure. | COMMENT |
| LOW | official/nlp/data/squad_lib.py | 819 | # can fail in certain cases in which case we just return `orig_text`. | COMMENT |
| LOW | official/nlp/data/squad_lib_sp.py | 787 | # just create a nonce prediction in this case to avoid failure. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/vid2depth/ops/icp_op.py | 28 | except Exception: # pylint: disable=broad-except | CODE |
| LOW | research/object_detection/model_lib_v2.py | 941 | except Exception as exc: # pylint:disable=broad-except | CODE |
| LOW | …/context_rcnn/create_cococameratraps_tfexample_main.py | 113 | except Exception: # pylint: disable=broad-except | CODE |
| LOW | …/dataset_tools/context_rcnn/generate_embedding_data.py | 188 | except Exception: # pylint: disable=broad-except | CODE |
| LOW | research/slim/datasets/process_bounding_boxes.py | 123 | except Exception: | CODE |
| LOW | research/slim/nets/mobilenet/mobilenet.py | 286 | except Exception: | CODE |
| LOW | …rch/delf/delf/python/training/global_features/train.py | 356 | except Exception as ex: | CODE |
| LOW | official/recommendation/data_pipeline.py | 507 | except Exception as e: | CODE |
| MEDIUM | official/recommendation/data_pipeline.py | 504 | def run(self): | CODE |
| LOW | official/core/train_utils.py | 581 | except Exception as e: # pylint: disable=broad-except | CODE |
| LOW | …ion_ml/Triton_TF_Cloud_Deployment/client/ffmpeg_ops.py | 105 | except Exception as e: # pylint: disable=broad-exception-caught | CODE |
| LOW | …racking/sam3_dinov3_tracking_pipeline/config_loader.py | 406 | except Exception as err: | CODE |
| LOW | …inference_with_tracking/rfdetr_dinov3_tracking/main.py | 266 | except Exception: # pylint: disable=broad-exception-caught | CODE |
| MEDIUM | …on_ml/docker_solution/prediction_pipeline/predictor.py | 66 | print(f'An error occurred: {e}') | CODE |
| LOW | …n/auto_labeler_pipeline_rfdetr/filter_sparse_images.py | 346 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …n/auto_labeler_pipeline_rfdetr/filter_sparse_images.py | 359 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …eneration/auto_labeler_pipeline_rfdetr/segmentation.py | 588 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …eneration/auto_labeler_pipeline_rfdetr/segmentation.py | 604 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …eneration/auto_labeler_pipeline_rfdetr/segmentation.py | 685 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …neration/auto_labeler_pipeline/filter_sparse_images.py | 392 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …neration/auto_labeler_pipeline/filter_sparse_images.py | 405 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …/data_generation/auto_labeler_pipeline/segmentation.py | 600 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …/data_generation/auto_labeler_pipeline/segmentation.py | 616 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | …/data_generation/auto_labeler_pipeline/segmentation.py | 705 | except Exception as error: # pylint: disable=broad-exception-caught | CODE |
| LOW | official/legacy/detection/modeling/checkpoint_utils.py | 94 | except Exception as e: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/vid2depth/train.py | 18 | # Example usage: | COMMENT |
| LOW | research/vid2depth/inference.py | 18 | # Example usage: | COMMENT |
| LOW | research/vid2depth/dataset/gen_data.py | 18 | # Example usage: | COMMENT |
| LOW | …earch/slim/scripts/finetune_inception_v1_on_flowers.sh | 22 | # Usage: | COMMENT |
| LOW | …earch/slim/scripts/finetune_inception_v3_on_flowers.sh | 22 | # Usage: | COMMENT |
| LOW | …lim/scripts/finetune_inception_resnet_v2_on_flowers.sh | 22 | # Usage: | COMMENT |
| LOW | …earch/slim/scripts/finetune_resnet_v1_50_on_flowers.sh | 22 | # Usage: | COMMENT |
| LOW | research/slim/scripts/train_lenet_on_mnist.sh | 22 | # Usage: | COMMENT |
| LOW | research/slim/scripts/train_cifarnet_on_cifar10.sh | 22 | # Usage: | COMMENT |
| LOW | research/deeplab/local_test_mobilenetv2.sh | 20 | # Usage: | COMMENT |
| LOW | research/deeplab/local_test.sh | 20 | # Usage: | COMMENT |
| LOW | …earch/deeplab/datasets/download_and_convert_voc2012.sh | 19 | # Usage: | COMMENT |
| LOW | …search/deeplab/datasets/download_and_convert_ade20k.sh | 19 | # Usage: | COMMENT |
| LOW | research/deeplab/datasets/convert_cityscapes.sh | 23 | # Usage: | COMMENT |
| LOW | …ion_ml/llm_applications/milk_pouch_detection/deploy.sh | 19 | # Usage: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | research/object_detection/utils/colab_utils.py | 226 | var elem = null; | CODE |
| HIGH | research/object_detection/utils/colab_utils.py | 274 | boundingBoxes.push(box); | CODE |
| HIGH⚡ | …_TF_Cloud_Deployment/client/feature_extraction_test.py | 90 | self.assertTrue(features_df.equals(pd.DataFrame(COMPARISON_DATA))) | CODE |
| HIGH | official/projects/bigbird/recompute_grad.py | 46 | return _context_stack.push(self) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/deep_speech/run_deep_speech.sh | 3 | # Step 1: download the LibriSpeech dataset. | COMMENT |
| LOW | research/deep_speech/run_deep_speech.sh | 16 | # Step 2: generate train dataset and evaluation dataset | COMMENT |
| LOW⚡ | research/deep_speech/run_deep_speech.sh | 33 | # Step 3: filter out the audio files that exceed max time duration. | COMMENT |
| 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 | COMMENT |
| LOW⚡ | official/projects/labse/export_tfhub.py | 21 | # Step 1: export the core LaBSE model. | COMMENT |
| LOW⚡ | official/projects/labse/export_tfhub.py | 27 | # Step 2: export matching preprocessing (be sure to use same flags). | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 604 | # Step 1: Computes IoUs between the detections and the non-crowd ground | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 648 | # Step 2: counts true positives grouped by IoU thresholds, classes and | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 671 | # Step 3: Counts false positives grouped by IoU thresholds, classes and | COMMENT |
| LOW | official/vision/evaluation/instance_metrics.py | 698 | # Step 4: Counts non-crowd groundtruths grouped by classes. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/attention_ocr/python/datasets/__init__.py | 19 | __all__ = [fsns, fsns_test] | CODE |
| LOW | research/slim/nets/mobilenet/mobilenet_v2.py | 244 | __all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF'] | CODE |
| LOW | research/slim/nets/mobilenet/mobilenet_v3.py | 760 | __all__ = ['training_scope', 'mobilenet', 'V3_LARGE', 'V3_SMALL', 'large', | CODE |
| LOW | research/slim/deployment/model_deploy.py | 108 | __all__ = ['create_clones', | CODE |
| LOW | official/projects/unified_detector/utils/utilities.py | 50 | def set_shape_dim(tensor: tf.Tensor, index: int, size: int) -> None: | CODE |
| LOW | …3_dinov3_tracking_pipeline/visualization_utils_test.py | 544 | def set_postfix_str(self, text: str) -> None: | CODE |
| LOW | …l/fine_tuning/Pytorch_Image_Classifier/vit_training.py | 89 | def set_random_seeds(seed: int = RANDOM_SEED) -> None: | CODE |
| LOW | …ication_ml/docker_solution/prediction_api/app_utils.py | 54 | logger = logging.getLogger(__name__) | CODE |
| LOW | …n/auto_labeler_pipeline_rfdetr/filter_sparse_images.py | 102 | logger = logging.getLogger(__name__) | CODE |
| LOW | official/modeling/fast_training/progressive/policies.py | 152 | def update_pt_stage(self, global_step: int, pass_old_model=True) -> None: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| 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. | COMMENT |
| 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. | COMMENT |
| 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> | CODE |
| LOW | official/projects/const_cl/losses/losses.py | 316 | # NOTE: make sure to use xla.replica_id() here and in | COMMENT |
| LOW | official/nlp/modeling/layers/gaussian_process.py | 373 | # If use this option, make sure to pass through data only once. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | official/vision/data/process_coco_few_shot.sh | 3 | # Processes the COCO few-shot benchmark into TFRecord files. Requires `wget`. | COMMENT |
| MEDIUM | …ficial/vision/data/process_coco_few_shot_json_files.py | 80 | # https://github.com/ucbdrive/few-shot-object-detection/blob/master/fsdet/evaluation/coco_evaluation.py#L60-L65 | COMMENT |