Repository Analysis

tensorflow/models

Models and examples built with TensorFlow

29.9 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of tensorflow/models, a Python project with 77,671 GitHub stars. SynthScan v2.0 examined 771,042 lines of code across 3406 source files, recording 13782 pattern matches distributed across 21 syntactic categories. The overall adjusted score of 29.9 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

29.9
Adjusted Score
29.9
Raw Score
100%
Time Factor
2026-06-19
Last Push
77.7K
Stars
Python
Language
771.0K
Lines of Code
3.4K
Files
13.8K
Pattern Hits
2026-07-14
Scan Date
0.44
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 19HIGH 1496MEDIUM 1549LOW 10718

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 13782 distinct pattern matches across 21 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Cross-File Repetition950 hits · 4750 pts
SeverityFileLineSnippetContext
HIGH…search/object_detection/export_tflite_ssd_graph_lib.py0exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] contSTRING
HIGH…search/object_detection/export_tflite_graph_lib_tf2.py0exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] contSTRING
HIGH…/lstm_object_detection/export_tflite_lstd_graph_lib.py0exports center-size encoded anchors as a constant tensor. args: anchors: a float32 tensor of shape [num_anchors, 4] contSTRING
HIGH…rch/object_detection/metrics/calibration_evaluation.py0clears the state to prepare for a fresh evaluation.STRING
HIGHresearch/object_detection/metrics/coco_evaluation.py0clears the state to prepare for a fresh evaluation.STRING
HIGHresearch/object_detection/metrics/lvis_evaluation.py0clears the state to prepare for a fresh evaluation.STRING
HIGH…/object_detection/utils/object_detection_evaluation.py0clears the state to prepare for a fresh evaluation.STRING
HIGHresearch/object_detection/utils/vrd_evaluation.py0clears the state to prepare for a fresh evaluation.STRING
HIGHresearch/object_detection/metrics/coco_evaluation.py0observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observeSTRING
HIGHresearch/object_detection/metrics/lvis_evaluation.py0observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observeSTRING
HIGH…/object_detection/utils/object_detection_evaluation.py0observes an evaluation result dict for a single example. when executing eagerly, once all observations have been observeSTRING
HIGH…earch/object_detection/metrics/lvis_evaluation_test.py0tests that map is calculated correctly on gt and detections.STRING
HIGH…earch/object_detection/metrics/coco_evaluation_test.py0tests that map is calculated correctly on gt and detections.STRING
HIGH…t_detection/metrics/coco_evaluation_all_frames_test.py0tests that map is calculated correctly on gt and detections.STRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py0extracts 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.py0extracts 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.py0extracts 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.py0extracts 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.py0returns 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.py0returns 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.py0returns a list of regularization losses for this model. returns a list of regularization losses for this model that the STRING
HIGHresearch/object_detection/core/model.py0returns a list of regularization losses for this model. returns a list of regularization losses for this model that the STRING
HIGHresearch/object_detection/legacy/trainer_tf1_test.py0returns 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.py0returns a list of update operators for this model. returns a list of update operators for this model that must be executSTRING
HIGH…/object_detection/meta_architectures/rfcn_meta_arch.py0returns a list of update operators for this model. returns a list of update operators for this model that must be executSTRING
HIGH…h/object_detection/meta_architectures/ssd_meta_arch.py0returns a list of update operators for this model. returns a list of update operators for this model that must be executSTRING
HIGHresearch/object_detection/core/model.py0returns a list of update operators for this model. returns a list of update operators for this model that must be executSTRING
HIGHresearch/object_detection/legacy/trainer_tf1_test.py0returns a list of update operators for this model. returns a list of update operators for this model that must be executSTRING
HIGH…t_detection/meta_architectures/center_net_meta_arch.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…on/meta_architectures/center_net_meta_arch_tf2_test.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…ction/models/center_net_hourglass_feature_extractor.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…ect_detection/models/keras_models/hourglass_network.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…s/deepmac_maskrcnn/modeling/heads/hourglass_network.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…odels/center_net_mobilenet_v2_fpn_feature_extractor.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…on/models/center_net_mobilenet_v2_feature_extractor.py0the number of feature outputs returned by the feature extractor.STRING
HIGH…h/object_detection/meta_architectures/ssd_meta_arch.py0interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training mSTRING
HIGH…detection/models/ssd_inception_v3_feature_extractor.py0interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training mSTRING
HIGH…detection/models/ssd_inception_v2_feature_extractor.py0interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training mSTRING
HIGH…etection/models/ssd_resnet_v1_ppn_feature_extractor.py0interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training mSTRING
HIGH…lstm_ssd_interleaved_mobilenet_v2_feature_extractor.py0interleaved feature extractor for lstd models with mobilenet v2. args: is_training: whether the network is in training mSTRING
HIGHresearch/object_detection/core/minibatch_sampler.py0subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all butSTRING
HIGH…ial/vision/utils/object_detection/minibatch_sampler.py0subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all butSTRING
HIGHofficial/vision/ops/sampling_ops.py0subsample indicator vector. given a boolean indicator vector with m elements set to `true`, the function assigns all butSTRING
HIGHresearch/object_detection/core/preprocessor.py0resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if tSTRING
HIGHofficial/projects/centernet/ops/preprocess_ops.py0resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if tSTRING
HIGHofficial/vision/utils/object_detection/preprocessor.py0resizes an image so its dimensions are within the provided value. the output size can be described by two cases: 1. if tSTRING
HIGHresearch/object_detection/core/box_list_ops.py0enum class for sort order. attributes: ascend: ascend order. descend: descend order.STRING
HIGHresearch/object_detection/utils/np_box_list_ops.py0enum class for sort order. attributes: ascend: ascend order. descend: descend order.STRING
HIGHofficial/vision/utils/object_detection/box_list_ops.py0enum class for sort order. attributes: ascend: ascend order. descend: descend order.STRING
HIGHresearch/object_detection/core/box_list_ops.py0computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas.STRING
HIGHofficial/vision/utils/object_detection/box_list_ops.py0computes 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.py0computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas.STRING
HIGHofficial/projects/centernet/ops/box_list_ops.py0computes area of boxes. args: boxlist: boxlist holding n boxes returns: a tensor with shape [n] representing box areas.STRING
HIGHresearch/object_detection/core/box_list_ops.py0computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with shaSTRING
HIGHofficial/vision/utils/object_detection/box_list_ops.py0computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with shaSTRING
HIGHofficial/projects/centernet/ops/box_list_ops.py0computes height and width of boxes in boxlist. args: boxlist: boxlist holding n boxes returns: height: a tensor with shaSTRING
HIGHresearch/object_detection/core/box_list_ops.py0scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_scaSTRING
HIGHofficial/vision/utils/object_detection/preprocessor.py0scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_scaSTRING
HIGHofficial/vision/utils/object_detection/box_list_ops.py0scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_scaSTRING
HIGHofficial/projects/centernet/ops/box_list_ops.py0scale box coordinates in x and y dimensions. args: boxlist: boxlist holding n boxes y_scale: (float) scalar tensor x_scaSTRING
890 more matches not shown…
Hyper-Verbose Identifiers3973 hits · 3749 pts
SeverityFileLineSnippetContext
LOWresearch/efficient-hrl/cond_fn.py134def failed_reset_after_n_episodes(agent,CODE
LOWresearch/efficient-hrl/train_utils.py165def gen_debug_batch_summaries(batch):CODE
LOWresearch/efficient-hrl/eval.py48def get_evaluate_checkpoint_fn(master, output_dir, eval_step_fns,STRING
LOW…/efficient-hrl/context/context_transition_functions.py95def relative_context_transition_fn(CODE
LOW…/efficient-hrl/context/context_transition_functions.py111def relative_context_multi_transition_fn(CODE
LOWresearch/efficient-hrl/context/context.py345 def context_multi_transition_fn(self, contexts, **kwargs):CODE
LOWresearch/efficient-hrl/agents/ddpg_agent.py439 def get_trainable_critic_vars(self):CODE
LOWresearch/efficient-hrl/agents/ddpg_agent.py585 def get_trainable_critic_vars(self):CODE
LOWresearch/efficient-hrl/agents/ddpg_agent.py708def gen_debug_td_error_summaries(CODE
LOWresearch/efficient-hrl/utils/eval_utils.py30def evaluate_checkpoint_repeatedly(checkpoint_dir,CODE
LOWresearch/autoaugment/train_cifar.py181 def _calc_num_trainable_params(self):CODE
LOWresearch/autoaugment/train_cifar.py345 def _compute_final_accuracies(self, meval):CODE
LOWresearch/autoaugment/shake_shake.py26def _shake_shake_skip_connection(x, output_filters, stride):CODE
LOWresearch/rebar/rebar.py565 def get_simple_muprop_gradient(self):CODE
LOWresearch/rebar/rebar.py659 def _create_gumbel_control_variate(self, logQHard, temperature=None):CODE
LOWresearch/rebar/rebar.py687 def _create_gumbel_control_variate_quadratic(self, logQHard, temperature=None):CODE
LOWresearch/rebar/rebar.py744 def multiply_by_eta_per_layer(self, h_grads, eta):CODE
LOWresearch/rebar/rebar.py778 def get_dynamic_rebar_gradient(self):CODE
LOWresearch/pcl_rl/env_spec.py134 def convert_env_actions_to_actions(self, actions):CODE
LOWresearch/pcl_rl/controller.py346 def convert_from_batched_episodes(CODE
LOWresearch/pcl_rl/controller.py373 def convert_to_batched_episodes(self, episodes, max_length=None):CODE
LOWresearch/pcl_rl/trust_region.py110 def calc_fisher_vector_product(tangent):CODE
LOWresearch/adversarial_text/train_utils.py99def maybe_restore_pretrained_model(sess, saver_for_restore, model_dir):CODE
LOWresearch/adversarial_text/adversarial_losses.py124def random_perturbation_loss_bidir(embedded, length, loss_fn):CODE
LOWresearch/adversarial_text/adversarial_losses.py145def virtual_adversarial_loss_bidir(logits, embedded, inputs,CODE
LOWresearch/adversarial_text/adversarial_losses.py202def _kl_divergence_with_logits(q_logits, p_logits, weights):CODE
LOWresearch/adversarial_text/inputs.py168def _read_single_sequence_example(file_list, tokens_shape=None):CODE
LOWresearch/adversarial_text/gen_data.py56def build_shuffling_tf_record_writer(fname):CODE
LOWresearch/adversarial_text/graphs.py670def make_restore_average_vars_dict():CODE
LOWresearch/adversarial_text/layers.py368def _summarize_vars_and_grads(grads_and_vars):CODE
LOWresearch/adversarial_text/data/data_utils.py325def write_vocab_and_frequency(ordered_vocab_freqs, output_dir):CODE
LOWresearch/audioset/vggish/vggish_slim.py109def load_vggish_slim_checkpoint(session, checkpoint_path):CODE
LOWresearch/audioset/vggish/mel_features.py114def spectrogram_to_mel_matrix(num_mel_bins=20,CODE
LOWresearch/audioset/yamnet/features.py22def waveform_to_log_mel_spectrogram_patches(waveform, params):CODE
LOWresearch/cvt_text/model/encoder.py72 def _get_unidirectional_reprs(self, word_reprs):CODE
LOWresearch/lfads/run_lfads.py499def build_hyperparameter_dict(flags):CODE
LOWresearch/lfads/distributions.py50def diag_gaussian_log_likelihood(z, mu=0.0, logvar=0.0):CODE
LOWresearch/lfads/distributions.py68def gaussian_pos_log_likelihood(unused_mean, logvar, noise):CODE
LOWresearch/lfads/utils.py303def list_t_bxn_to_tensor_bxtxn(values_t_bxn):CODE
LOWresearch/lfads/utils.py323def tensor_bxtxn_to_list_t_bxn(tensor_bxtxn):CODE
LOWresearch/lfads/lfads.py1091 def example_idxs_mod_batch_size(nexamples, batch_size):CODE
LOWresearch/lfads/lfads.py1120 def randomize_example_idxs_mod_batch_size(nexamples, batch_size):CODE
LOWresearch/lfads/lfads.py1191 def shuffle_and_flatten_datasets(self, datasets, kind='train'):CODE
LOWresearch/lfads/lfads.py1735 def eval_model_runs_avg_epoch(self, data_name, data_extxd,CODE
LOWresearch/lfads/lfads.py1838 def eval_model_runs_push_mean(self, data_name, data_extxd,CODE
LOWresearch/lfads/synth_data/synthetic_data_utils.py233def add_alignment_projections(datasets, npcs, ntime=None, nsamples=None):CODE
LOWresearch/vid2depth/model.py98 def build_inference_for_training(self):CODE
LOWresearch/vid2depth/model.py341 def build_egomotion_test_graph(self):CODE
LOWresearch/vid2depth/reader.py136 def augment_images_scale_crop(cls, im, intrinsics, out_h, out_w):CODE
LOWresearch/vid2depth/reader.py213 def get_multi_scale_intrinsics(cls, intrinsics, num_scales):CODE
LOWresearch/vid2depth/inference.py138def _normalize_depth_for_display(depth,CODE
LOWresearch/vid2depth/ops/icp_test.py70 def _generate_organized_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py95 def test_translate_small_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py107 def test_translate_random_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py133 def test_translate_organized_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py145 def test_rotate_organized_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py167 def test_translate_lidar_cloud(self):CODE
LOWresearch/vid2depth/ops/icp_test.py179 def test_translate_lidar_cloud_ego_motion(self):CODE
LOWresearch/vid2depth/ops/icp_test.py193 def test_rotate_lidar_cloud_ego_motion(self):CODE
LOWresearch/vid2depth/ops/icp_test.py207 def test_no_change_lidar_cloud(self):CODE
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Unused Imports2906 hits · 2852 pts
SeverityFileLineSnippetContext
LOWresearch/efficient-hrl/run_eval.py21CODE
LOWresearch/efficient-hrl/run_eval.py22CODE
LOWresearch/efficient-hrl/run_eval.py23CODE
LOWresearch/efficient-hrl/train_utils.py18CODE
LOWresearch/efficient-hrl/train_utils.py19CODE
LOWresearch/efficient-hrl/train_utils.py20CODE
LOWresearch/efficient-hrl/train_utils.py23CODE
LOWresearch/efficient-hrl/train_utils.py28CODE
LOWresearch/efficient-hrl/run_train.py21CODE
LOWresearch/efficient-hrl/run_train.py22CODE
LOWresearch/efficient-hrl/run_train.py23CODE
LOWresearch/efficient-hrl/agent.py25CODE
LOWresearch/efficient-hrl/train.py21CODE
LOWresearch/efficient-hrl/train.py22CODE
LOWresearch/efficient-hrl/train.py23CODE
LOWresearch/efficient-hrl/train.py33CODE
LOWresearch/efficient-hrl/train.py34CODE
LOWresearch/efficient-hrl/eval.py23CODE
LOWresearch/efficient-hrl/eval.py24CODE
LOWresearch/efficient-hrl/eval.py25CODE
LOWresearch/efficient-hrl/eval.py32CODE
LOWresearch/efficient-hrl/context/gin_utils.py19CODE
LOWresearch/efficient-hrl/context/gin_utils.py20CODE
LOWresearch/efficient-hrl/context/gin_utils.py21CODE
LOW…/efficient-hrl/context/context_transition_functions.py23CODE
LOW…/efficient-hrl/context/context_transition_functions.py24CODE
LOW…/efficient-hrl/context/context_transition_functions.py25CODE
LOWresearch/efficient-hrl/context/gin_imports.py20CODE
LOWresearch/efficient-hrl/context/gin_imports.py21CODE
LOWresearch/efficient-hrl/context/gin_imports.py22CODE
LOWresearch/efficient-hrl/context/gin_imports.py23CODE
LOWresearch/efficient-hrl/context/gin_imports.py24CODE
LOWresearch/efficient-hrl/context/context.py24CODE
LOWresearch/efficient-hrl/context/context.py25CODE
LOWresearch/efficient-hrl/context/context.py26CODE
LOWresearch/efficient-hrl/context/rewards_functions.py25CODE
LOWresearch/efficient-hrl/context/rewards_functions.py26CODE
LOWresearch/efficient-hrl/context/rewards_functions.py27CODE
LOWresearch/efficient-hrl/context/samplers.py21CODE
LOWresearch/efficient-hrl/context/samplers.py22CODE
LOWresearch/efficient-hrl/context/samplers.py23CODE
LOWresearch/efficient-hrl/environments/maze_env_utils.py17CODE
LOWresearch/efficient-hrl/utils/utils.py19CODE
LOWresearch/efficient-hrl/utils/utils.py20CODE
LOWresearch/efficient-hrl/utils/utils.py21CODE
LOWresearch/efficient-hrl/utils/eval_utils.py19CODE
LOWresearch/efficient-hrl/utils/eval_utils.py20CODE
LOWresearch/efficient-hrl/utils/eval_utils.py21CODE
LOWresearch/efficient-hrl/utils/eval_utils.py24CODE
LOWresearch/efficient-hrl/scripts/local_eval.py19CODE
LOWresearch/efficient-hrl/scripts/local_eval.py20CODE
LOWresearch/efficient-hrl/scripts/local_eval.py21CODE
LOWresearch/efficient-hrl/scripts/local_train.py19CODE
LOWresearch/efficient-hrl/scripts/local_train.py20CODE
LOWresearch/efficient-hrl/scripts/local_train.py21CODE
LOWresearch/autoaugment/train_cifar.py19CODE
LOWresearch/autoaugment/train_cifar.py20CODE
LOWresearch/autoaugment/train_cifar.py21CODE
LOWresearch/autoaugment/custom_ops.py21CODE
LOWresearch/autoaugment/custom_ops.py22CODE
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Decorative Section Separators933 hits · 2808 pts
SeverityFileLineSnippetContext
MEDIUMresearch/efficient-hrl/run_eval.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/cond_fn.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/train_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/run_train.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/run_env.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/agent.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/train.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/eval.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/context/gin_utils.py14# ==============================================================================COMMENT
MEDIUM…/efficient-hrl/context/context_transition_functions.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/context/gin_imports.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/context/context.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/context/rewards_functions.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/context/samplers.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/maze_env.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/ant_maze_env.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/create_maze_env.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/ant.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/point_maze_env.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/point.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/environments/maze_env_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/agents/ddpg_agent.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/agents/ddpg_networks.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/agents/circular_buffer.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/utils/utils.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/utils/eval_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/scripts/local_eval.py14# ==============================================================================COMMENT
MEDIUMresearch/efficient-hrl/scripts/local_train.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/train_cifar.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/custom_ops.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/shake_shake.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/shake_drop.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/data_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/helper_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/wrn.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/augmentation_transforms.py14# ==============================================================================COMMENT
MEDIUMresearch/autoaugment/policies.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/rebar_train.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/config.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/rebar.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/datasets.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/logger.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/download_data.py14# ==============================================================================COMMENT
MEDIUMresearch/rebar/utils.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/objective.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/env_spec.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/controller.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/baseline.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/policy.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/model.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/optimizers.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/full_episode_objective.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/replay_buffer.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/gym_wrapper.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/trainer.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/trust_region.py14# ==============================================================================COMMENT
MEDIUMresearch/pcl_rl/expert_paths.py14# ==============================================================================COMMENT
MEDIUMresearch/adversarial_text/pretrain.py14# ==============================================================================COMMENT
MEDIUMresearch/adversarial_text/train_utils.py14# ==============================================================================COMMENT
MEDIUMresearch/adversarial_text/adversarial_losses.py14# ==============================================================================COMMENT
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Docstring Block Structure542 hits · 2710 pts
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HIGHresearch/efficient-hrl/agents/ddpg_agent.py187Returns the output of the actor network. Args: states: A [batch_size, num_state_dims] tensor representing a bSTRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py206Returns the output of the critic network. Args: states: A [batch_size, num_state_dims] tensor representing a STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py224Returns the output of the target actor network. The target network is used to compute stable targets for training. STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py241Returns the output of the target critic network. The target network is used to compute stable targets for training.STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py289Computes a loss for training the critic network. The loss is the mean squared error between the Q value predictionsSTRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py343Computes a loss for training the actor network. Note that output does not represent an actual loss. It is called a STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py410Performs a soft update of the target network parameters. For each weight w_s in the actor/critic networks, and its STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py597Returns the output of the critic network. Args: states: A [batch_size, num_state_dims] tensor representing a STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py618Returns the output of the target critic network. The target network is used to compute stable targets for training.STRING
HIGHresearch/efficient-hrl/agents/ddpg_agent.py677Performs a soft update of the target network parameters. For each weight w_s in the actor/critic networks, and its STRING
HIGHresearch/efficient-hrl/agents/circular_buffer.py93Adds an element (list/tuple/dict of tensors) to the buffer. Args: tensors: (list/tuple/dict of tensors) to beSTRING
HIGHresearch/efficient-hrl/agents/circular_buffer.py107Adds an element (tensors) to the buffer based on the condition.. Args: tensors: (list/tuple of tensors) to beSTRING
HIGHresearch/efficient-hrl/agents/circular_buffer.py150Samples a batch of tensors from the buffer with replacement. Args: batch_size: (integer) number of elements tSTRING
HIGHresearch/efficient-hrl/agents/circular_buffer.py188Returns elements at the specified indices from the buffer. Args: indices: (list of integers or rank 1 int TenSTRING
HIGHresearch/efficient-hrl/agents/circular_buffer.py221Returns elements at the specified indices from the buffer. Args: num_steps: (integer) length of trajectories STRING
HIGHresearch/autoaugment/helper_utils.py44Evaluates `model` on held out data depending on `mode`. Args: session: TensorFlow session the model will be run wSTRING
HIGHresearch/adversarial_text/inputs.py128Returns input filenames for configuration. Args: phase: str, 'train', 'test', or 'valid'. bidir: bool, bidireSTRING
HIGHresearch/adversarial_text/inputs.py195Inputs for text model. Args: data_dir: str, directory containing TFRecord files of SequenceExample. fname: stSTRING
HIGHresearch/adversarial_text/data/document_generators.py76Generates Documents based on FLAGS.dataset. Args: dataset: str, identifies folder within IMDB data directory, tesSTRING
HIGHresearch/adversarial_text/data/document_generators.py115Given a Document, produces character or word tokens. Tokens can be either characters, or word-level tokens (unigrams STRING
HIGHresearch/adversarial_text/data/document_generators.py161Generates Documents for IMDB dataset. Data from http://ai.stanford.edu/~amaas/data/sentiment/ Args: dataset: sSTRING
HIGHresearch/adversarial_text/data/document_generators.py225Generates Documents for DBpedia dataset. Dataset linked to at https://github.com/zhangxiangxiao/Crepe. Args: dSTRING
HIGHresearch/adversarial_text/data/document_generators.py270Generates Documents for Reuters Corpus (rcv1) dataset. Dataset described at http://www.ai.mit.edu/projects/jmlr/papSTRING
HIGHresearch/adversarial_text/data/document_generators.py321Generates Documents for the Rotten Tomatoes dataset. Dataset available at http://www.cs.cornell.edu/people/pabo/movieSTRING
HIGHresearch/audioset/vggish/mel_features.py119Return a matrix that can post-multiply spectrogram rows to make mel. Returns a np.array matrix A that can be used to STRING
HIGHresearch/lfads/lfads.py1092Given a number of examples, E, and a batch_size, B, generate indices [0, 1, 2, ... B-1; [B, B+1, ... 2*B-1; STRING
HIGHresearch/lfads/lfads.py1121Indices 1:nexamples, randomized, in 2D form of shape = (nexamples / batch_size) x batch_size. The remainder is STRING
HIGHresearch/object_detection/model_lib.py244Unstacks all tensors in `tensor_dict` along 0th dimension. Unstacks tensor from the tensor dict along 0th dimension aSTRING
HIGHresearch/object_detection/model_lib.py765Creates `Estimator`, input functions, and steps. Args: run_config: A `RunConfig`. hparams: (optional) A `HParSTRING
HIGHresearch/object_detection/eval_util.py259Evaluates metrics defined in evaluators and returns summaries. This function loads the latest checkpoint in checkpoinSTRING
HIGHresearch/object_detection/eval_util.py433Periodically evaluates desired tensors using checkpoint_dirs or restore_fn. This function repeatedly loads a checkpoiSTRING
HIGHresearch/object_detection/eval_util.py777Merges all detection and groundtruth information for a single example. Note that evaluation tools require classes thaSTRING
HIGHresearch/object_detection/eval_util.py1069Returns the evaluator class according to eval_config, valid for categories. Args: eval_config: An `eval_pb2.EvalCSTRING
HIGHresearch/object_detection/inputs.py119Makes sure boxes have valid sizes (ymax >= ymin, xmax >= xmin). When the hardware supports assertions, the function rSTRING
HIGHresearch/object_detection/inputs.py163A single function that is responsible for all input data transformations. Data transformation functions are applied iSTRING
HIGHresearch/object_detection/inputs.py405Pads input tensors to static shapes. In case num_additional_channels > 0, we assume that the additional channels haSTRING
HIGHresearch/object_detection/inputs.py777Returns `features` and `labels` tensor dictionaries for training. Args: train_config: A train_pb2.TrainConfig. STRING
HIGHresearch/object_detection/inputs.py938Returns `features` and `labels` tensor dictionaries for evaluation. Args: eval_config: An eval_pb2.EvalConfig. STRING
HIGHresearch/object_detection/metrics/coco_tools.py93Load annotations dictionary into COCO datastructure. See http://mscoco.org/dataset/#format for a description of theSTRING
HIGHresearch/object_detection/metrics/coco_tools.py236Computes detection/keypoint metrics. Args: include_metrics_per_category: If True, will include metrics per caSTRING
HIGHresearch/object_detection/metrics/coco_tools.py401Export groundtruth of a single image to COCO format. This function converts groundtruth detection annotations represeSTRING
HIGHresearch/object_detection/metrics/coco_tools.py518Export groundtruth detection annotations in numpy arrays to COCO API. This function converts a set of groundtruth detSTRING
HIGHresearch/object_detection/metrics/coco_tools.py595Export detections of a single image to COCO format. This function converts detections represented as numpy arrays to STRING
HIGHresearch/object_detection/metrics/coco_tools.py687Export detection masks of a single image to COCO format. This function converts detections represented as numpy arraySTRING
HIGHresearch/object_detection/metrics/coco_tools.py746Export detection annotations in numpy arrays to COCO API. This function converts a set of predicted detections represSTRING
HIGHresearch/object_detection/metrics/coco_tools.py810Export segmentation masks in numpy arrays to COCO API. This function converts a set of predicted instance masks repreSTRING
HIGHresearch/object_detection/metrics/coco_tools.py890Exports keypoints in numpy arrays to COCO API. This function converts a set of predicted keypoints represented as nSTRING
HIGHresearch/object_detection/metrics/coco_evaluation.py1744Separate normal and crowd groundtruth class_labels and masks. Args: crowd_gt_indices: None or array of shape STRING
HIGHresearch/object_detection/metrics/coco_evaluation.py1794Match the predicted masks to groundtruths. Args: predicted_masks: array of shape [num_predictions, height, wiSTRING
HIGH…ch/object_detection/metrics/offline_eval_map_corloc.py77Reads pre-computed object detections and groundtruth from tf_record. Args: input_config: input config proto of tySTRING
HIGHresearch/object_detection/metrics/lvis_tools.py124Export groundtruth of a single image to LVIS format. This function converts groundtruth detection annotations represeSTRING
HIGHresearch/object_detection/metrics/lvis_tools.py209Export detection masks of a single image to LVIS format. This function converts detections represented as numpy arraySTRING
HIGH…search/object_detection/metrics/calibration_metrics.py53Calculates Expected Calibration Error (ECE). ECE is a scalar summary statistic of calibration error. It is the sampSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py665Feature-extractor specific preprocessing. See base class. For Faster R-CNN, we perform image resizing in the bSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py733Predicts unpostprocessed tensors from input tensor. This function takes an input batch of images and runs it througSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py1357Adds box predictors to RPN feature map to predict proposals. Note resulting tensors will not have been postprocesseSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py1467Convert prediction tensors to final detections. This function converts raw predictions tensors to final detection rSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py2392Computes scalar box classifier loss tensors. Uses self._detector_target_assigner to obtain regression and classificSTRING
HIGH…_detection/meta_architectures/faster_rcnn_meta_arch.py2821Returns a map of variables to load from a foreign checkpoint. See parent class for details. Args: fine_tSTRING
HIGH…detection/meta_architectures/context_rcnn_meta_arch.py334Overrides the get_side_inputs function in the base class. This function returns context_features and valid_context_STRING
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Over-Commented Block2684 hits · 2666 pts
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LOWresearch/efficient-hrl/run_eval.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/cond_fn.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/train_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/run_train.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/run_env.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/agent.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/train.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/eval.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/context/gin_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOW…/efficient-hrl/context/context_transition_functions.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/context/gin_imports.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/context/context.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/context/rewards_functions.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/context/samplers.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/maze_env.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/ant_maze_env.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/create_maze_env.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/ant.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/point_maze_env.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/point.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/environments/maze_env_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/agents/ddpg_agent.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/agents/ddpg_networks.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/agents/circular_buffer.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/utils/utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/utils/eval_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/scripts/local_eval.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/efficient-hrl/scripts/local_train.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/train_cifar.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/custom_ops.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/shake_shake.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/shake_drop.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/data_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/helper_utils.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/wrn.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/augmentation_transforms.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/autoaugment/policies.py1# Copyright 2018 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/rebar/rebar_train.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/config.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/rebar.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/datasets.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/logger.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/download_data.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/rebar/utils.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/pcl_rl/objective.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/env_spec.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/controller.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/baseline.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/policy.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/model.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/optimizers.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/full_episode_objective.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/replay_buffer.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/gym_wrapper.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/trainer.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/trust_region.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/pcl_rl/expert_paths.py1# Copyright 2017 The TensorFlow Authors All Rights Reserved.COMMENT
LOWresearch/adversarial_text/pretrain.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/adversarial_text/train_utils.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
LOWresearch/adversarial_text/adversarial_losses.py1# Copyright 2017 Google Inc. All Rights Reserved.COMMENT
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Self-Referential Comments608 hits · 2111 pts
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MEDIUMresearch/autoaugment/train_cifar.py284 # Create a new session for this model, initializeCOMMENT
MEDIUMresearch/rebar/rebar_train.py71 # Create the experiment name from the hparamsCOMMENT
MEDIUMresearch/rebar/rebar.py181 # Create the conditional distribution (output is the logits)COMMENT
MEDIUMresearch/rebar/rebar.py208 # Create the conditional distribution (output is the logits)COMMENT
MEDIUMresearch/audioset/vggish/vggish_inference_demo.py109 # Define the model in inference mode, load the checkpoint, andSTRING
MEDIUMresearch/audioset/vggish/vggish_export_tfhub.py103 # Create a TF2 wrapper around VGGish.COMMENT
MEDIUMresearch/audioset/vggish/vggish_train_demo.py133 # Define a shallow classification model and associated training ops on topSTRING
MEDIUMresearch/audioset/vggish/vggish_train_demo.py168 # Initialize all variables in the model, and then load the pre-trainedSTRING
MEDIUMresearch/audioset/yamnet/export.py107 # Create a TF2 Module wrapper around YAMNet.COMMENT
MEDIUMresearch/audioset/yamnet/export.py144 # Create a TF-Lite compatible Module wrapper around YAMNet.COMMENT
MEDIUMresearch/lfads/lfads.py314 # Define the data placeholder, and deal with all parts of the graphCOMMENT
MEDIUMresearch/vid2depth/ops/icp_train_demo.py117 # Create the feed_dict for the placeholders filled with the nextCOMMENT
MEDIUMresearch/vid2depth/ops/icp_train_demo.py189 # Create a variable to track the global step.COMMENT
MEDIUMresearch/object_detection/model_lib.py865 # Create the input functions for TRAIN/EVAL/PREDICT.COMMENT
MEDIUMresearch/object_detection/model_lib_v2.py553 # Create the inputs.COMMENT
MEDIUM…t_detection/meta_architectures/center_net_meta_arch.py780 # Create the image center location.COMMENT
MEDIUM…t_detection/meta_architectures/center_net_meta_arch.py1039 # Create the y,x grids: [height, width]COMMENT
MEDIUMresearch/object_detection/core/densepose_ops.py293 # Create a list of that maps part index to flipped part index (0-indexed).COMMENT
MEDIUMresearch/object_detection/utils/visualization_utils.py1176 # Create a display string (and color) for every box location, group any boxesCOMMENT
MEDIUMresearch/object_detection/utils/autoaugment_utils.py406 # Create the new bbox tensor by converting pixel integer values to floats.COMMENT
MEDIUMresearch/object_detection/utils/autoaugment_utils.py555 # 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.py405# The following codes are based on the existing keras ResNet model pattern:COMMENT
MEDIUM…dels/keras_models/base_models/original_mobilenet_v2.py212# This function is taken from the original tf repo.COMMENT
MEDIUM…ection/dataset_tools/download_and_preprocess_mscoco.sh40# Create the output directories.COMMENT
MEDIUM…_detection/dataset_tools/create_pycocotools_package.sh29# Create the output directory.COMMENT
MEDIUMresearch/slim/train_image_classifier.py457 # Create a dataset provider that loads data from the dataset #COMMENT
MEDIUMresearch/slim/train_image_classifier.py483 # Define the model #COMMENT
MEDIUMresearch/slim/eval_image_classifier.py115 # Create a dataset provider that loads data from the dataset #COMMENT
MEDIUMresearch/slim/eval_image_classifier.py145 # Define the model #COMMENT
MEDIUMresearch/slim/eval_image_classifier.py164 # Define the metrics:COMMENT
MEDIUMresearch/slim/datasets/download_and_convert_imagenet.sh58# Create the output and temporary directories.COMMENT
MEDIUMresearch/slim/datasets/download_imagenet.sh89 # Create a directory and delete anything there.COMMENT
MEDIUMresearch/slim/datasets/build_imagenet_data.py140# This file is the output of process_bounding_box.pyCOMMENT
MEDIUMresearch/slim/datasets/build_imagenet_data.py233 # Create a single Session to run all image coding calls.COMMENT
MEDIUMresearch/slim/datasets/build_imagenet_data.py444 # Create a mechanism for monitoring when all threads are finished.COMMENT
MEDIUMresearch/slim/datasets/build_imagenet_data.py447 # Create a generic TensorFlow-based utility for converting all image codings.COMMENT
MEDIUMresearch/slim/deployment/model_deploy.py35 # Create the global step on the device storing the variables.STRING
MEDIUMresearch/slim/deployment/model_deploy.py39 # Define the inputsSTRING
MEDIUMresearch/slim/deployment/model_deploy.py44 # Define the optimizer.STRING
MEDIUMresearch/slim/deployment/model_deploy.py48 # Define the model including the loss.STRING
MEDIUMresearch/slim/deployment/model_deploy_test.py172 # Create an easy training set:COMMENT
MEDIUMresearch/slim/deployment/model_deploy_test.py319 # Create an easy training set:COMMENT
MEDIUMresearch/slim/deployment/model_deploy_test.py468 # Create an easy training set:COMMENT
MEDIUMresearch/slim/preprocessing/vgg_preprocessing.py154 # Create a random bounding box.COMMENT
MEDIUMresearch/deeplab/model.py427 # The following codes employ the DeepLabv3 ASPP module. Note that weSTRING
MEDIUMresearch/deeplab/train.py312 # Create the global step on the device storing the variables.COMMENT
MEDIUMresearch/deeplab/train.py316 # Define the model and create clones.COMMENT
MEDIUMresearch/deeplab/eval.py158 # Define the evaluation metric.COMMENT
MEDIUMresearch/deeplab/core/preprocess_utils.py306 # Create a random bounding box.COMMENT
MEDIUMresearch/deeplab/datasets/remove_gt_colormap.py67 # Create the output directory if not exists.COMMENT
MEDIUMresearch/cognitive_planning/visualization_utils.py598 # Create a display string (and color) for every box location, group any boxesCOMMENT
MEDIUM…/cognitive_planning/preprocessing/vgg_preprocessing.py155 # Create a random bounding box.COMMENT
MEDIUMresearch/deep_speech/deep_speech.py171 # Create the train_op that groups both minimize_ops and update_opsCOMMENT
MEDIUM…earch/delf/delf/python/training/build_image_dataset.py146 # Create the dictionary (key = image_id, value = {label, file_id}).COMMENT
MEDIUM…earch/delf/delf/python/training/build_image_dataset.py369 # Create the subset for the current label.COMMENT
MEDIUMresearch/delf/delf/python/training/train.py179 # Create the strategy.COMMENT
MEDIUMresearch/delf/delf/python/training/train.py210 # Create the distributed train/validation sets.COMMENT
MEDIUMresearch/delf/delf/python/training/train.py231 # Create a checkpoint directory to store the checkpoints.COMMENT
MEDIUM…rch/delf/delf/python/training/global_features/train.py187 # Define the loss function.COMMENT
MEDIUM…rch/delf/delf/python/training/global_features/train.py208 # Define the optimizer.COMMENT
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AI Structural Patterns523 hits · 500 pts
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LOWresearch/efficient-hrl/train_utils.py59CODE
LOWresearch/efficient-hrl/train.py270CODE
LOWresearch/efficient-hrl/eval.py289CODE
LOWresearch/efficient-hrl/context/context.py39CODE
LOWresearch/efficient-hrl/context/rewards_functions.py301CODE
LOWresearch/efficient-hrl/context/rewards_functions.py409CODE
LOWresearch/efficient-hrl/context/rewards_functions.py484CODE
LOWresearch/efficient-hrl/environments/maze_env.py37CODE
LOWresearch/efficient-hrl/agents/ddpg_agent.py84CODE
LOWresearch/efficient-hrl/agents/ddpg_agent.py517CODE
LOWresearch/efficient-hrl/agents/ddpg_networks.py32CODE
LOWresearch/rebar/rebar_train.py61CODE
LOWresearch/pcl_rl/objective.py102CODE
LOWresearch/pcl_rl/controller.py68CODE
LOWresearch/pcl_rl/model.py308CODE
LOWresearch/adversarial_text/inputs.py249CODE
LOWresearch/audioset/yamnet/yamnet.py48CODE
LOWresearch/audioset/yamnet/yamnet.py72CODE
LOWresearch/lfads/utils.py85CODE
LOWresearch/lfads/plot_lfads.py155CODE
LOWresearch/vid2depth/model.py40CODE
LOWresearch/vid2depth/dataset/dataset_loader.py449CODE
LOWresearch/object_detection/model_lib.py748CODE
LOWresearch/object_detection/exporter.py488CODE
LOWresearch/object_detection/exporter.py576CODE
LOWresearch/object_detection/eval_util.py102CODE
LOWresearch/object_detection/eval_util.py246CODE
LOWresearch/object_detection/eval_util.py416CODE
LOWresearch/object_detection/inputs.py151CODE
LOWresearch/object_detection/model_lib_v2.py444CODE
LOWresearch/object_detection/model_lib_v2.py1022CODE
LOW…_detection/meta_architectures/faster_rcnn_meta_arch.py267CODE
LOW…tion/meta_architectures/context_rcnn_meta_arch_test.py206CODE
LOW…detection/meta_architectures/context_rcnn_meta_arch.py45CODE
LOW…detection/meta_architectures/ssd_meta_arch_test_lib.py126CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py1239CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py2223CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py2401CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py2671CODE
LOW…n/meta_architectures/faster_rcnn_meta_arch_test_lib.py286CODE
LOW…ject_detection/meta_architectures/deepmac_meta_arch.py550CODE
LOW…ect_detection/meta_architectures/ssd_meta_arch_test.py48CODE
LOW…/object_detection/meta_architectures/rfcn_meta_arch.py51CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py1769CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py1840CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py3382CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py3399CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py3415CODE
LOW…on/meta_architectures/center_net_meta_arch_tf2_test.py3433CODE
LOW…h/object_detection/meta_architectures/ssd_meta_arch.py261CODE
LOWresearch/object_detection/core/preprocessor.py568CODE
LOWresearch/object_detection/core/preprocessor.py1413CODE
LOWresearch/object_detection/core/preprocessor.py1664CODE
LOWresearch/object_detection/core/preprocessor.py1840CODE
LOWresearch/object_detection/core/preprocessor.py2012CODE
LOWresearch/object_detection/core/preprocessor.py2086CODE
LOWresearch/object_detection/core/preprocessor.py2223CODE
LOWresearch/object_detection/core/preprocessor.py3665CODE
LOWresearch/object_detection/core/preprocessor.py3529CODE
LOWresearch/object_detection/core/preprocessor.py3668CODE
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Deep Nesting456 hits · 440 pts
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LOWresearch/efficient-hrl/train.py270CODE
LOWresearch/efficient-hrl/eval.py48CODE
LOWresearch/efficient-hrl/eval.py80CODE
LOWresearch/efficient-hrl/environments/maze_env.py37CODE
LOWresearch/efficient-hrl/environments/maze_env.py323CODE
LOWresearch/efficient-hrl/environments/maze_env.py472CODE
LOWresearch/efficient-hrl/environments/create_maze_env.py26CODE
LOWresearch/efficient-hrl/environments/maze_env_utils.py54CODE
LOWresearch/autoaugment/train_cifar.py79CODE
LOWresearch/autoaugment/train_cifar.py404CODE
LOWresearch/rebar/rebar_train.py63CODE
LOWresearch/rebar/rebar.py151CODE
LOWresearch/rebar/rebar.py220CODE
LOWresearch/rebar/rebar.py759CODE
LOWresearch/pcl_rl/env_spec.py47CODE
LOWresearch/pcl_rl/env_spec.py105CODE
LOWresearch/pcl_rl/baseline.py47CODE
LOWresearch/pcl_rl/policy.py62CODE
LOWresearch/pcl_rl/model.py123CODE
LOWresearch/pcl_rl/expert_paths.py47CODE
LOWresearch/adversarial_text/data/document_generators.py73CODE
LOWresearch/adversarial_text/data/document_generators.py114CODE
LOWresearch/adversarial_text/data/document_generators.py266CODE
LOWresearch/adversarial_text/data/document_generators.py317CODE
LOWresearch/cvt_text/cvt.py37CODE
LOWresearch/cvt_text/preprocessing.py34CODE
LOW…h/cvt_text/task_specific/word_level/word_level_data.py57CODE
LOW…h/cvt_text/task_specific/word_level/word_level_data.py83CODE
LOWresearch/cvt_text/corpus_processing/unlabeled_data.py59CODE
LOWresearch/cvt_text/base/embeddings.py150CODE
LOWresearch/cvt_text/base/embeddings.py113CODE
LOWresearch/lfads/run_lfads.py760CODE
LOWresearch/lfads/utils.py170CODE
LOWresearch/lfads/lfads.py280CODE
LOWresearch/lfads/lfads.py2090CODE
LOWresearch/vid2depth/util.py35CODE
LOWresearch/vid2depth/model.py130CODE
LOWresearch/vid2depth/inference.py73CODE
LOWresearch/vid2depth/dataset/dataset_loader.py204CODE
LOWresearch/vid2depth/dataset/gen_data.py69CODE
LOWresearch/object_detection/model_lib.py432CODE
LOWresearch/object_detection/model_lib.py455CODE
LOWresearch/object_detection/exporter.py363CODE
LOWresearch/object_detection/eval_util.py246CODE
LOWresearch/object_detection/eval_util.py416CODE
LOWresearch/object_detection/eval_util.py1068CODE
LOWresearch/object_detection/eval_util.py1159CODE
LOWresearch/object_detection/inputs.py1134CODE
LOWresearch/object_detection/model_lib_v2.py444CODE
LOWresearch/object_detection/model_lib_v2.py833CODE
LOWresearch/object_detection/model_lib_tf1_test.py131CODE
LOWresearch/object_detection/metrics/coco_tools.py391CODE
LOWresearch/object_detection/metrics/coco_tools.py588CODE
LOWresearch/object_detection/metrics/coco_tools.py232CODE
LOWresearch/object_detection/metrics/coco_evaluation.py1786CODE
LOW…ch/object_detection/metrics/offline_eval_map_corloc.py76CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py1239CODE
LOW…t_detection/meta_architectures/center_net_meta_arch.py4785CODE
LOW…ject_detection/meta_architectures/deepmac_meta_arch.py79CODE
LOW…ject_detection/meta_architectures/deepmac_meta_arch.py131CODE
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Hallucination Indicators19 hits · 180 pts
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CRITICALresearch/object_detection/model_lib_v2.py528 tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')CODE
CRITICALresearch/object_detection/model_lib_v2.py1114 tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')CODE
CRITICALresearch/attention_ocr/python/model_export_test.py133 graph_def = tf.compat.v1.saved_model.loader.load(CODE
CRITICALresearch/attention_ocr/python/model_export_lib.py106 k: tf.compat.v1.saved_model.utils.build_tensor_info(t)CODE
CRITICALresearch/attention_ocr/python/model_export.py168 signature_def = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(STRING
CRITICALresearch/attention_ocr/python/model_export.py172 builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)STRING
CRITICALresearch/attention_ocr/python/sequence_layers.py250 lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(CODE
CRITICALresearch/deeplab/convert_to_tflite.py56 converter = tf.compat.v1.lite.TFLiteConverter.from_session(CODE
CRITICAL…elf/python/detect_to_retrieve/cluster_delf_features.py150 kmeans = tf.compat.v1.estimator.experimental.KMeans(CODE
CRITICALofficial/recommendation/ncf_keras_main.py275 tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(CODE
CRITICALofficial/core/train_utils.py573 opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()CODE
CRITICALofficial/projects/qat/vision/quantization/schemes.py65 return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer(CODE
CRITICALofficial/projects/qat/nlp/quantization/schemes.py197 return tfmot.quantization.keras.graph_transformations.model_transformer.ModelTransformer(CODE
CRITICAL…al/projects/pixel/utils/convert_numpy_weights_to_tf.py39 vit_encoder.encoder.encoder._pos_embed.pos_embedding.assign(CODE
CRITICALofficial/legacy/transformer/transformer_main_test.py70 tf.compat.v2.keras.mixed_precision.global_policy())CODE
CRITICALofficial/legacy/transformer/transformer_main_test.py73 tf.compat.v2.keras.mixed_precision.set_global_policy(self.orig_policy)CODE
CRITICALofficial/legacy/detection/main.py73 tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')CODE
CRITICALofficial/legacy/detection/modeling/base_model.py62 tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')CODE
CRITICALtensorflow_models/tensorflow_models_test.py45 _ = tfm.uplift.layers.encoders.concat_features.ConcatFeatures(['feature'])CODE
Redundant / Tautological Comments45 hits · 70 pts
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LOW…t_detection/core/balanced_positive_negative_sampler.py136 # Check if indicator and labels have a static size.COMMENT
LOWresearch/object_detection/utils/vrd_evaluation.py205 # Verify if one of the labels is negative (this is sure FP)COMMENT
LOWresearch/object_detection/utils/vrd_evaluation.py208 # Verify if all labels are verifiedCOMMENT
LOWresearch/slim/datasets/download_and_convert_flowers.py140 # Read the filename:COMMENT
LOWresearch/slim/nets/nasnet/nasnet_utils.py355 # Add hiddenstate to the list of hiddenstates we can choose fromCOMMENT
LOWresearch/slim/nets/nasnet/nasnet_utils.py379 # Check if a stride is needed, then use a strided 1x1 hereCOMMENT
LOWresearch/lstm_object_detection/lstm/lstm_cells.py391 # Set nodes to be under raw_inputs/ name scope for tfmini export.COMMENT
LOWresearch/lstm_object_detection/lstm/lstm_cells.py619 # Set nodes to be under raw_outputs/ name scope for tfmini export.COMMENT
LOWresearch/delf/delf/python/training/train.py238 # Set reduction to `none` so we can do the reduction afterwards and divideCOMMENT
LOW…rch/delf/delf/python/training/global_features/train.py136 # Check if train dataset is downloaded and download it if not found.COMMENT
LOW…elf/python/detect_to_retrieve/cluster_delf_features.py89 # Loop over list of index images and collect DELF features.COMMENT
LOW…configs/experiments/imagenet_simclr_multitask_tpu.yaml49 input_set_label_to_zero: true # Set labels to zeros to double confirm that no label is used during pretrainCODE
LOW…lumetric_models/tasks/semantic_segmentation_3d_test.py73 # Check if training loss is produced.COMMENT
LOW…lumetric_models/tasks/semantic_segmentation_3d_test.py86 # Check if validation loss is produced.COMMENT
LOW…lumetric_models/tasks/semantic_segmentation_3d_test.py89 # Check if state is updated.COMMENT
LOW…lumetric_models/tasks/semantic_segmentation_3d_test.py94 # Check if all metrics are produced.COMMENT
LOW…metric_models/serving/semantic_segmentation_3d_test.py86 # Check if model is successfully exported.COMMENT
LOW…_TF_Cloud_Deployment/client/feature_extraction_test.py89 # Check if the DataFrames are equalCOMMENT
LOW…on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh18# Check if Docker is installedCOMMENT
LOW…on_ml/Triton_TF_Cloud_Deployment/client/requirement.sh52# Check if the 'models' directory exists before cloning.COMMENT
LOW…ion_ml/Triton_TF_Cloud_Deployment/client/run_images.sh44# Check if the virtual environment is activatedCOMMENT
LOW…_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py65 # Check if the dataset already existsCOMMENT
LOW…_ml/Triton_TF_Cloud_Deployment/client/big_query_ops.py75 # Check if the table already existsCOMMENT
LOW…riton_TF_Cloud_Deployment/client/inference_pipeline.py118 # Check if the input and output directories are valid.COMMENT
LOW…riton_TF_Cloud_Deployment/client/inference_pipeline.py146 # Read files from a folder.COMMENT
LOW…n_ml/Deploy/pet_grading_cloud_deployment/run_images.sh26# Check if the virtual environment is activatedCOMMENT
LOW…n_ml/Deploy/detr_cloud_deployment/client/run_images.sh32# Check if the virtual environment is activatedCOMMENT
LOW…loy/detr_cloud_deployment/client/inference_pipeline.py94 # Check if the input and output directories are valid.COMMENT
LOW…plications/milk_pouch_detection/src/extract_objects.py113 # Check if COCO output should be createdCOMMENT
LOW…_applications/milk_pouch_detection/src/models_utils.py130 # Check if the input data is validCOMMENT
LOW…te_identification_ml/model_inference/postprocessing.py293 # Check if the masks have the same dimensions.COMMENT
LOW…l/docker_solution/prediction_pipeline/biq_query_ops.py50 # Check if the dataset already existsCOMMENT
LOW…l/docker_solution/prediction_pipeline/biq_query_ops.py59 # Check if the table already existsCOMMENT
LOW…jects/waste_identification_ml/data_generation/utils.py204 # Check if the masks have the same dimensions.COMMENT
LOWofficial/projects/detr/tasks/detection.py157 # Set pads to large constantCOMMENT
LOWofficial/legacy/transformer/data_pipeline.py235 # Read files and interleave results. When training, the order of the examplesCOMMENT
LOWofficial/legacy/transformer/data_download.py193 # Check if extracted files already exist in pathCOMMENT
LOWofficial/legacy/transformer/utils/tokenizer.py339 # Check if the matched strings are '\u' or '\\'.STRING
LOW…object_detection/balanced_positive_negative_sampler.py142 # Check if indicator and labels have a static size.COMMENT
LOWofficial/vision/ops/augment.py2804 # Set coordinates to (0, 0, 0, 0) for filtered boxesCOMMENT
LOWofficial/vision/ops/sampling_ops.py248 # Check if indicator and labels have a static size.COMMENT
LOWofficial/modeling/hyperparams/params_dict.py416 # Add the value to the array.COMMENT
LOWofficial/nlp/modeling/layers/gaussian_process_test.py183 # Check if linear kernel leads to identity mapping.COMMENT
LOWofficial/nlp/data/create_pretraining_data.py543 # Check if this would add too many tokens.COMMENT
LOWofficial/nlp/data/create_pretraining_data.py547 # Check if any of the tokens in this gram have already been masked.COMMENT
Fake / Example Data54 hits · 59 pts
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LOW…tools/context_rcnn/generate_embedding_data_tf2_test.py261 output = inference_fn.process(('dummy_key', generated_example))CODE
LOW…tools/context_rcnn/generate_embedding_data_tf2_test.py279 output = inference_fn.process(('dummy_key', generated_example))CODE
LOW…tools/context_rcnn/generate_embedding_data_tf2_test.py297 output = inference_fn.process(('dummy_key', generated_example))CODE
LOW…tools/context_rcnn/add_context_to_examples_tf2_test.py285 ('dummy_key', [tf.train.Example.FromString(CODE
LOW…tools/context_rcnn/add_context_to_examples_tf2_test.py299 ('dummy_key', [tf.train.Example.FromString(CODE
LOWorbit/standard_runner_test.py28 def dummy_data(_):CODE
LOWorbit/standard_runner_test.py34 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/core/base_trainer_test.py84 def dummy_data(_):CODE
LOWofficial/core/base_trainer_test.py90 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/projects/lra/lra_dual_encoder_task.py125 def dummy_data(_):CODE
LOWofficial/projects/lra/lra_dual_encoder_task.py144 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNECODE
LOW…t-docs/themes/hugo-theme-techdoc/archetypes/default.md10Lorem Ipsum.CODE
LOWofficial/projects/mobilebert/distillation.py345 def dummy_data(_):CODE
LOWofficial/projects/mobilebert/distillation.py360 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOW…cts/edgetpu/vision/tasks/semantic_segmentation_test.py32 def dummy_data(_):CODE
LOW…cts/edgetpu/vision/tasks/semantic_segmentation_test.py45 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOW…ects/edgetpu/vision/tasks/image_classification_test.py31 def dummy_data(_):CODE
LOW…ects/edgetpu/vision/tasks/image_classification_test.py38 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOW…rojects/edgetpu/nlp/mobilebert_edgetpu_trainer_test.py26 def dummy_data(_):CODE
LOW…rojects/edgetpu/nlp/mobilebert_edgetpu_trainer_test.py40 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/projects/teams/teams_task.py149 def dummy_data(_):CODE
LOWofficial/projects/teams/teams_task.py163 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/projects/teams/teams_pretrainer.py334 fake_data = self._get_fake_data(inputs, lm_outputs)CODE
LOWofficial/projects/teams/teams_pretrainer.py337 disc_input = fake_data['inputs']CODE
LOWofficial/projects/teams/teams_pretrainer.py338 disc_rtd_label = fake_data['is_fake_tokens']CODE
LOWofficial/projects/teams/teams_pretrainer.py339 disc_mws_candidates = fake_data['candidate_set']CODE
LOW…_classification_example/classification_example_test.py34 def dummy_data(_):CODE
LOW…_classification_example/classification_example_test.py47 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/projects/fffner/fffner_prediction.py123 def dummy_data(_):CODE
LOWofficial/projects/fffner/fffner_prediction.py139 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/projects/perceiver/tasks/pretrain_test.py51 def dummy_data(_):CODE
LOWofficial/projects/perceiver/tasks/pretrain_test.py64 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/legacy/image_classification/mnist_test.py67 dummy_data = (CODE
LOWofficial/legacy/image_classification/mnist_test.py72 tf.data.Dataset.from_tensor_slices(dummy_data),CODE
LOWofficial/legacy/image_classification/mnist_test.py73 tf.data.Dataset.from_tensor_slices(dummy_data),CODE
LOW…ial/modeling/fast_training/progressive/trainer_test.py103 def dummy_data(_):CODE
LOW…ial/modeling/fast_training/progressive/trainer_test.py111 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/question_answering.py206 dummy_data = functools.partial(self._dummy_data, params)CODE
LOWofficial/nlp/tasks/question_answering.py208 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/electra_task.py143 def dummy_data(_):CODE
LOWofficial/nlp/tasks/electra_task.py158 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/masked_lm_determinism_test.py28 def dummy_data(_):CODE
LOWofficial/nlp/tasks/masked_lm_determinism_test.py46 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/dual_encoder.py124 def dummy_data(_):CODE
LOWofficial/nlp/tasks/dual_encoder.py138 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/sentence_prediction.py120 def dummy_data(_):CODE
LOWofficial/nlp/tasks/sentence_prediction.py137 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/tagging.py118 def dummy_data(_):CODE
LOWofficial/nlp/tasks/tagging.py136 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/tasks/masked_lm.py113 def dummy_data(_):CODE
LOWofficial/nlp/tasks/masked_lm.py128 dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)CODE
LOWofficial/nlp/modeling/models/electra_pretrainer.py149 fake_data = self._get_fake_data(inputs, lm_outputs, duplicate=True)CODE
LOWofficial/nlp/modeling/models/electra_pretrainer.py152 disc_input = fake_data['inputs']CODE
LOWofficial/nlp/modeling/models/electra_pretrainer.py153 disc_label = fake_data['is_fake_tokens']CODE
Structural Annotation Overuse17 hits · 28 pts
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LOW…search/object_detection/g3doc/running_on_mobile_tf2.md54### Step 1: Export TFLite inference graphCOMMENT
LOW…search/object_detection/g3doc/running_on_mobile_tf2.md73### Step 2: Convert to TFLiteCOMMENT
LOW…search/object_detection/g3doc/running_on_mobile_tf2.md95### Step 3: add Metadata to the modelCOMMENT
LOWresearch/deep_speech/run_deep_speech.sh3# Step 1: download the LibriSpeech dataset.COMMENT
LOWresearch/deep_speech/run_deep_speech.sh16# Step 2: generate train dataset and evaluation datasetCOMMENT
LOWresearch/deep_speech/run_deep_speech.sh33# Step 3: filter out the audio files that exceed max time duration.COMMENT
LOWresearch/deep_speech/run_deep_speech.sh41# Step 4: run the training and evaluation loop in background, and save the running info to a log fileCOMMENT
LOWofficial/projects/labse/export_tfhub.py21# Step 1: export the core LaBSE model.COMMENT
LOWofficial/projects/labse/export_tfhub.py27# Step 2: export matching preprocessing (be sure to use same flags).COMMENT
LOWofficial/projects/pointpillars/utils/wod_processor.py380 # NOTE: This function might be called by an online data loader in aCOMMENT
LOWofficial/projects/pointpillars/utils/wod_processor.py401 # NOTE: This function might be called by an online data loader in aCOMMENT
LOWofficial/projects/text_classification_example/README.md19#### Step 1: build\_inputsCOMMENT
LOWofficial/projects/text_classification_example/README.md57#### Step 2: build\_lossesCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py604 # Step 1: Computes IoUs between the detections and the non-crowd groundCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py648 # Step 2: counts true positives grouped by IoU thresholds, classes andCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py671 # Step 3: Counts false positives grouped by IoU thresholds, classes andCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py698 # Step 4: Counts non-crowd groundtruths grouped by classes.COMMENT
AI Slop Vocabulary16 hits · 26 pts
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LOW…earch/object_detection/builders/hyperparams_builder.py394 # It is insufficient to just set distribution to `normal` from theCOMMENT
LOWresearch/object_detection/utils/autoaugment_utils.py1608 # If no bounding boxes were specified, then just return the images.COMMENT
LOW…detection/models/ssd_spaghettinet_feature_extractor.py99 # If we are building an eval graph just use the values in theCOMMENT
MEDIUM…h/object_detection/data_decoders/tf_example_decoder.py455 # If the label_map_proto is provided, try to use it in conjunction withCOMMENT
LOWresearch/lstm_object_detection/lstm/utils.py76 # If we are building an eval graph just use the values in the variables.COMMENT
LOW…arch/delf/delf/python/feature_aggregation_extractor.py108 # Feature visual words are unused in the case of VLAD, so just returnCOMMENT
LOW…ts/unified_detector/data_loaders/tf_example_decoder.py67 # To add new features, just add entries here.COMMENT
LOWofficial/projects/triviaqa/inputs.py63 # are statically known. Otherwise, just use -1.COMMENT
MEDIUMofficial/projects/yolo/configs/yolo.py205 """Distribute them in order to each level.STRING
MEDIUMofficial/legacy/bert/model_training_utils.py232 # One can't fully utilize a TPU with steps_per_loop=1, so in this caseCOMMENT
LOWofficial/legacy/xlnet/squad_utils.py382 # just create a nonce prediction in this case to avoid failure.COMMENT
MEDIUMofficial/vision/serving/export_tflite_lib.py15"""Library to facilitate TFLite model conversion."""STRING
LOWofficial/nlp/data/squad_lib.py501 # "Japanese", we just use "Japanese" as the annotation. This is fairly rareCOMMENT
LOWofficial/nlp/data/squad_lib.py731 # just create a nonce prediction in this case to avoid failure.COMMENT
LOWofficial/nlp/data/squad_lib.py819 # can fail in certain cases in which case we just return `orig_text`.COMMENT
LOWofficial/nlp/data/squad_lib_sp.py787 # just create a nonce prediction in this case to avoid failure.COMMENT
Example Usage Blocks15 hits · 22 pts
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LOWresearch/vid2depth/train.py18# Example usage:COMMENT
LOWresearch/vid2depth/inference.py18# Example usage:COMMENT
LOWresearch/vid2depth/dataset/gen_data.py18# Example usage:COMMENT
LOW…earch/slim/scripts/finetune_inception_v1_on_flowers.sh22# Usage:COMMENT
LOW…earch/slim/scripts/finetune_inception_v3_on_flowers.sh22# Usage:COMMENT
LOW…lim/scripts/finetune_inception_resnet_v2_on_flowers.sh22# Usage:COMMENT
LOW…earch/slim/scripts/finetune_resnet_v1_50_on_flowers.sh22# Usage:COMMENT
LOWresearch/slim/scripts/train_lenet_on_mnist.sh22# Usage:COMMENT
LOWresearch/slim/scripts/train_cifarnet_on_cifar10.sh22# Usage:COMMENT
LOWresearch/deeplab/local_test_mobilenetv2.sh20# Usage:COMMENT
LOWresearch/deeplab/local_test.sh20# Usage:COMMENT
LOW…earch/deeplab/datasets/download_and_convert_voc2012.sh19# Usage:COMMENT
LOW…search/deeplab/datasets/download_and_convert_ade20k.sh19# Usage:COMMENT
LOWresearch/deeplab/datasets/convert_cityscapes.sh23# Usage:COMMENT
LOW…ion_ml/llm_applications/milk_pouch_detection/deploy.sh19# Usage:COMMENT
Cross-Language Confusion4 hits · 22 pts
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HIGHresearch/object_detection/utils/colab_utils.py226 var elem = null;CODE
HIGHresearch/object_detection/utils/colab_utils.py274 boundingBoxes.push(box);CODE
HIGH…_TF_Cloud_Deployment/client/feature_extraction_test.py90 self.assertTrue(features_df.equals(pd.DataFrame(COMPARISON_DATA)))CODE
HIGHofficial/projects/bigbird/recompute_grad.py46 return _context_stack.push(self)CODE
Verbosity Indicators10 hits · 18 pts
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LOWresearch/deep_speech/run_deep_speech.sh3# Step 1: download the LibriSpeech dataset.COMMENT
LOWresearch/deep_speech/run_deep_speech.sh16# Step 2: generate train dataset and evaluation datasetCOMMENT
LOWresearch/deep_speech/run_deep_speech.sh33# Step 3: filter out the audio files that exceed max time duration.COMMENT
LOWresearch/deep_speech/run_deep_speech.sh41# Step 4: run the training and evaluation loop in background, and save the running info to a log fileCOMMENT
LOWofficial/projects/labse/export_tfhub.py21# Step 1: export the core LaBSE model.COMMENT
LOWofficial/projects/labse/export_tfhub.py27# Step 2: export matching preprocessing (be sure to use same flags).COMMENT
LOWofficial/vision/evaluation/instance_metrics.py604 # Step 1: Computes IoUs between the detections and the non-crowd groundCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py648 # Step 2: counts true positives grouped by IoU thresholds, classes andCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py671 # Step 3: Counts false positives grouped by IoU thresholds, classes andCOMMENT
LOWofficial/vision/evaluation/instance_metrics.py698 # Step 4: Counts non-crowd groundtruths grouped by classes.COMMENT
Excessive Try-Catch Wrapping13 hits · 15 pts
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LOWresearch/vid2depth/ops/icp_op.py28except Exception: # pylint: disable=broad-exceptCODE
LOWresearch/object_detection/model_lib_v2.py941 except Exception as exc: # pylint:disable=broad-exceptCODE
LOW…/context_rcnn/create_cococameratraps_tfexample_main.py113 except Exception: # pylint: disable=broad-exceptCODE
LOW…/dataset_tools/context_rcnn/generate_embedding_data.py188 except Exception: # pylint: disable=broad-exceptCODE
LOWresearch/slim/datasets/process_bounding_boxes.py123 except Exception:CODE
LOWresearch/slim/nets/mobilenet/mobilenet.py286 except Exception:CODE
LOW…rch/delf/delf/python/training/global_features/train.py356 except Exception as ex:CODE
LOWofficial/recommendation/data_pipeline.py507 except Exception as e:CODE
MEDIUMofficial/recommendation/data_pipeline.py504def run(self):CODE
LOWofficial/core/train_utils.py581 except Exception as e: # pylint: disable=broad-exceptCODE
LOW…ion_ml/Triton_TF_Cloud_Deployment/client/ffmpeg_ops.py105 except Exception as e: # pylint: disable=broad-exception-caughtCODE
MEDIUM…on_ml/docker_solution/prediction_pipeline/predictor.py66 print(f'An error occurred: {e}')CODE
LOWofficial/legacy/detection/modeling/checkpoint_utils.py94 except Exception as e:CODE
Slop Phrases5 hits · 7 pts
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LOW…/object_detection/utils/object_detection_evaluation.py979 # is inserted first we make sure to break the code if is it not the case.COMMENT
LOWresearch/object_detection/utils/vrd_evaluation.py190 # 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.html8<p>The site is working. Don't forget to customize this homepage with your own. You typically have 2 choices :</p>CODE
LOWofficial/projects/const_cl/losses/losses.py316 # NOTE: make sure to use xla.replica_id() here and inCOMMENT
LOWofficial/nlp/modeling/layers/gaussian_process.py373 # If use this option, make sure to pass through data only once.COMMENT
Modern Structural Boilerplate7 hits · 7 pts
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LOWresearch/attention_ocr/python/datasets/__init__.py19__all__ = [fsns, fsns_test]CODE
LOWresearch/slim/nets/mobilenet/mobilenet_v2.py244__all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF']CODE
LOWresearch/slim/nets/mobilenet/mobilenet_v3.py760__all__ = ['training_scope', 'mobilenet', 'V3_LARGE', 'V3_SMALL', 'large',CODE
LOWresearch/slim/deployment/model_deploy.py108__all__ = ['create_clones',CODE
LOWofficial/projects/unified_detector/utils/utilities.py50def set_shape_dim(tensor: tf.Tensor, index: int, size: int) -> None:CODE
LOW…ication_ml/docker_solution/prediction_api/app_utils.py54logger = logging.getLogger(__name__)CODE
LOWofficial/modeling/fast_training/progressive/policies.py152 def update_pt_stage(self, global_step: int, pass_old_model=True) -> None:CODE
Modern AI Meta-Vocabulary2 hits · 6 pts
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MEDIUMofficial/vision/data/process_coco_few_shot.sh3# Processes the COCO few-shot benchmark into TFRecord files. Requires `wget`.COMMENT
MEDIUM…ficial/vision/data/process_coco_few_shot_json_files.py80# https://github.com/ucbdrive/few-shot-object-detection/blob/master/fsdet/evaluation/coco_evaluation.py#L60-L65COMMENT