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Functions384 in github.com/SudeepDasari/RoboNet

⨍ Functions 384◇ Types & classes 40↳ Endpoints 3
Method__init__
(self, batch_size, dataset_files_or_metadata, hparams=dict())
robonet/datasets/variants/annotation_benchmark_dataset.py:11
Method__init__
(self, dataset, tensor_names)
robonet/datasets/util/tensor_multiplexer.py:29
Method__init__
(self, base_path, meta_data)
robonet/datasets/util/metadata_helper.py:14
Method__init__
Initializes the basic convolutional LSTM cell. Args: input_shape: int tuple, Shape of the input, excluding the batch size.
robonet/video_prediction/rnn_ops.py:37
Method__init__
(self)
robonet/video_prediction/metrics.py:318
Method__init__
(self, web_dir, title, reflesh=0)
robonet/video_prediction/utils/html.py:8
Method__init__
(self)
robonet/video_prediction/utils/tf_utils.py:537
Method__init__
(self, mode, inputs, hparams, reuse=None)
robonet/video_prediction/layers/deterministic_embedding_rnn_cell.py:13
Method__init__
(self, mode, inputs, hparams, reuse=None)
robonet/video_prediction/layers/dnaflow_rnn_cell.py:138
Method__init__
(self, data_loader_hparams, num_gpus, graph_type, tpu_mode=False, graph_scope=None)
robonet/video_prediction/models/base_model.py:8
Method__init__
(self, model_path, test_hparams={}, n_gpus=1, first_gpu=0, sess=None)
robonet/video_prediction/testing/model_evaluation_interface.py:15
Method__init__
(self, conv_filters, kernel_size, out_dim, vgg_path, n_convs=3, padding='same', fc_layer=256)
robonet/inverse_model/models/graphs/lstm_baseline.py:9
Method__init__
(self, model_path, test_hparams={}, n_gpus=1, first_gpu=0, sess=None)
robonet/inverse_model/testing/action_inference_interface.py:14
Method__init__
(self, config)
scripts/examples/create_prediction_gifs.py:14
Method__le__
(self, other)
robonet/datasets/util/metadata_helper.py:68
Method__len__
(self)
robonet/datasets/util/metadata_helper.py:80
Method__lt__
(self, other)
robonet/datasets/util/metadata_helper.py:65
Method__ne__
(self, other)
robonet/datasets/util/metadata_helper.py:62
Method__repr__
(self)
robonet/datasets/util/metadata_helper.py:53
Method__str__
(self)
robonet/datasets/util/metadata_helper.py:56
Method_default_hparams
(self)
robonet/video_prediction/training/finetuning_trainable_interface.py:10
Method_default_hparams
(self)
robonet/video_prediction/training/data_filter.py:7
Method_default_hparams
(self)
robonet/video_prediction/training/data_filter.py:49
Method_default_hparams
(self)
robonet/inverse_model/training/inverse_trainable.py:12
Method_default_scope
(self)
robonet/inverse_model/models/base_inverse_model.py:9
Method_filter_metadata
(self, metadata)
robonet/video_prediction/training/data_filter.py:12
Method_filter_metadata
(self, metadata)
robonet/video_prediction/training/data_filter.py:55
Method_get
(self, key, mode)
robonet/datasets/robonet_dataset.py:99
Method_get
(self, key, mode)
robonet/datasets/record_dataset.py:114
Method_get_default_hparams
()
robonet/datasets/robonet_dataset.py:112
Method_get_default_hparams
()
robonet/datasets/variants/val_filter_dataset_variants.py:61
Method_get_default_hparams
(parent_hparams=None)
robonet/datasets/variants/annotation_benchmark_dataset.py:16
Method_get_dict
(self, *args)
robonet/datasets/robonet_dataset.py:222
Method_get_graph
(self, graph_type)
robonet/inverse_model/models/base_inverse_model.py:6
Method_get_input_targets
(self, DatasetClass, metadata, dataset_hparams)
robonet/inverse_model/training/inverse_trainable.py:26
Method_get_model_class
(self, model_name)
robonet/inverse_model/training/inverse_trainable.py:9
Method_init
(self)
robonet/video_prediction/training/ray_util/gif_logger.py:11
Method_init_dataset
(self)
robonet/datasets/robonet_dataset.py:28
Function_load_hdf5
(inputs)
robonet/datasets/util/hdf5_2_records.py:34
Function_local_device_chooser
(op)
robonet/video_prediction/utils/tf_utils.py:47
Method_model_default_hparams
(self)
robonet/video_prediction/models/deterministic_generator.py:34
Method_model_default_hparams
(self)
robonet/inverse_model/models/deterministic_inverse_model.py:13
Method_model_default_hparams
(self)
robonet/inverse_model/models/discretized_inverse_model.py:38
Method_model_fn
(self, model_inputs, model_targets, mode)
robonet/video_prediction/models/deterministic_generator.py:50
Method_model_fn
(self, model_inputs, model_targets, mode)
robonet/inverse_model/models/deterministic_inverse_model.py:21
Method_model_fn
(self, model_inputs, model_targets, mode)
robonet/inverse_model/models/discretized_inverse_model.py:55
Method_parse_records
(self, serialized_example, metadata)
robonet/datasets/record_dataset.py:93
Function_plot_buf
(y)
robonet/video_prediction/utils/tf_utils.py:230
Function_reduce_entries
(*entries)
robonet/video_prediction/utils/tf_utils.py:383
Method_save
(self, checkpoint_dir)
robonet/video_prediction/training/trainable_interface.py:257
Method_setup
(self, config)
robonet/video_prediction/training/trainable_interface.py:22
Method_split_files
(self, metadata)
robonet/datasets/variants/val_filter_dataset_variants.py:17
Method_split_files
(self, source_number, metadata)
robonet/datasets/variants/annotation_benchmark_dataset.py:23
Method_train
(self)
robonet/video_prediction/training/trainable_interface.py:183
Method_train
(self)
robonet/inverse_model/training/inverse_trainable.py:49
Functionadd_header
Source: https://stackoverflow.com/questions/34066804/disabling-caching-in-flask Add headers to both force latest IE rendering engine or Chrom
scripts/visualize_dataset.py:58
Methodadd_header1
(self, str)
robonet/video_prediction/utils/html.py:27
Methodadd_header2
(self, str)
robonet/video_prediction/utils/html.py:31
Methodadd_header3
(self, str)
robonet/video_prediction/utils/html.py:35
Functionadd_plot_image_summaries
(metrics, collections=None)
robonet/video_prediction/utils/tf_utils.py:250
Functionadd_plot_summaries
(metrics, x_offset=0, collections=None)
robonet/video_prediction/utils/tf_utils.py:316
Methodadd_row
(self, txts, colspans=None)
robonet/video_prediction/utils/html.py:43
Functionadd_summaries
(outputs, collections=None)
robonet/video_prediction/utils/tf_utils.py:213
Methodadim
(self)
robonet/video_prediction/testing/model_evaluation_interface.py:210
Methodbase_path
(self)
robonet/datasets/util/metadata_helper.py:44
Methodbatch_size
(self)
robonet/datasets/base_dataset.py:84
Functionbatchnorm
(input)
robonet/video_prediction/ops.py:904
Methodbuild_graph
(self, mode, inputs, hparams, n_gpus=1, scope_name='dnaflow_generator')
robonet/video_prediction/models/graphs/deterministic_graph.py:11
Methodbuild_graph
(self, mode, inputs, hparams, n_gpus=1, scope_name='dnaflow_generator')
robonet/video_prediction/models/graphs/dnaflow_graph.py:9
Methodbuild_graph
(self, mode, inputs, hparams, n_gpus=1, scope_name='generator')
robonet/video_prediction/models/graphs/vgg_conv_graph.py:57
Functionbuild_optimizer
(lr, beta1, beta2, decay_steps=(), end_lr=None, global_step=None)
robonet/video_prediction/utils/tf_utils.py:21
Methodcall
2D Convolutional LSTM cell with (optional) normalization and recurrent dropout.
robonet/video_prediction/rnn_ops.py:128
Methodcall
(self, inputs, state)
robonet/video_prediction/rnn_ops.py:212
Methodcall
(self, inputs, states)
robonet/video_prediction/layers/deterministic_embedding_rnn_cell.py:16
Methodcall
(self, inputs, states)
robonet/video_prediction/layers/dnaflow_rnn_cell.py:290
Functioncompute
(i, a_flat)
robonet/video_prediction/functional_ops.py:76
Functioncompute_averaged_gradients
(opt, tower_loss, **kwargs)
robonet/video_prediction/utils/tf_utils.py:357
Methodcontext_actions
(self)
robonet/inverse_model/testing/action_inference_interface.py:109
Functionconv1d
(inputs, filters, kernel_size, strides=(1,), padding='SAME', kernel=None, use_bias=True)
robonet/video_prediction/ops.py:45
Functionconv2d_single_fn
(args)
robonet/video_prediction/ops.py:528
Functionconv3d
(inputs, filters, kernel_size, strides=(1, 1), padding='SAME', use_bias=True, use_spectral_norm=False)
robonet/video_prediction/ops.py:762
Functionconv_pool2d
Similar optimization as in upsample_conv2d Example: >>> import numpy as np >>> import tensorflow as tf >>> from robo
robonet/video_prediction/ops.py:793
Functionconv_pool2d_v2
(inputs, filters, kernel_size, strides=(1, 1), padding='SAME', kernel=None, use_bias=True, bias=None, pool_mod
robonet/video_prediction/ops.py:857
Functionconvert_tensor_to_gif_summary
(summ)
robonet/video_prediction/utils/tf_utils.py:323
Functioncosine_distance
Equivalent to: tensor0 = normalize_tensor(tensor0) tensor1 = normalize_tensor(tensor1) return tf.reduce_mean(tf.reduce_su
robonet/video_prediction/metrics.py:265
Functioncosine_distance_np
Equivalent to: tensor0 = normalize_tensor_np(tensor0) tensor1 = normalize_tensor_np(tensor1) return np.mean(np.sum(np.squ
robonet/video_prediction/metrics.py:307
Methoddata_hparams
(self)
robonet/video_prediction/models/base_model.py:59
Functiondataset_fn
(params, DatasetClass, batch_sizes, loader_files, dataset_hparams)
scripts/train_vpred_tpu.py:9
Methoddefault_hparams
()
robonet/video_prediction/models/graphs/deterministic_graph.py:40
Methoddefault_hparams
()
robonet/video_prediction/models/graphs/dnaflow_graph.py:34
Methoddefault_hparams
()
robonet/video_prediction/models/graphs/vgg_conv_graph.py:299
Methoddefault_hparams
()
robonet/inverse_model/models/graphs/base_graph.py:7
Methoddefault_hparams
()
robonet/inverse_model/models/graphs/lstm_baseline.py:123
Functiondepthwise_conv2d
(inputs, channel_multiplier, kernel_size, strides=(1, 1), padding='SAME', kernel=None, use_bias=True)
robonet/video_prediction/ops.py:468
Methoddict
(self)
robonet/datasets/util/tensor_multiplexer.py:44
Functionencode_images
(tensor, fps=4)
robonet/video_prediction/utils/encode_img.py:12
Functionenv_var_constructor
Converts ${VAR}/* from config file to 'os.environ[VAR] + *' Modified from: https://www.programcreek.com/python/example/61563/yaml.add
robonet/yaml_util.py:20
Functionexpected_pixel_distance
(real_dist, pred_dist)
robonet/video_prediction/metrics.py:13
Functionexpected_pixel_distance_np
(true_pix_distrib, pred_pix_distribs, keep_axis=None)
robonet/video_prediction/metrics.py:116
Functionexpected_square_pixel_distance
Calculates E[(p - p_true)^T (p - p_true)]
robonet/video_prediction/metrics.py:25
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