model_lib.py 32.6 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
15
r"""Constructs model, inputs, and training environment."""
16
17
18
19
20

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

21
import copy
22
import functools
23
import os
24
25
26
27

import tensorflow as tf

from object_detection import eval_util
28
from object_detection import exporter as exporter_lib
29
from object_detection import inputs
30
from object_detection.builders import graph_rewriter_builder
31
32
33
34
35
36
37
38
39
from object_detection.builders import model_builder
from object_detection.builders import optimizer_builder
from object_detection.core import standard_fields as fields
from object_detection.utils import config_util
from object_detection.utils import label_map_util
from object_detection.utils import shape_utils
from object_detection.utils import variables_helper
from object_detection.utils import visualization_utils as vis_utils

40
41
42
43
44
45
46
47
# A map of names to methods that help build the model.
MODEL_BUILD_UTIL_MAP = {
    'get_configs_from_pipeline_file':
        config_util.get_configs_from_pipeline_file,
    'create_pipeline_proto_from_configs':
        config_util.create_pipeline_proto_from_configs,
    'merge_external_params_with_configs':
        config_util.merge_external_params_with_configs,
48
49
50
51
52
53
    'create_train_input_fn':
        inputs.create_train_input_fn,
    'create_eval_input_fn':
        inputs.create_eval_input_fn,
    'create_predict_input_fn':
        inputs.create_predict_input_fn,
54
55
56
}


57
58
def _prepare_groundtruth_for_eval(detection_model, class_agnostic):
  """Extracts groundtruth data from detection_model and prepares it for eval.
59
60
61
62
63
64
65
66
67
68
69
70
71

  Args:
    detection_model: A `DetectionModel` object.
    class_agnostic: Whether the detections are class_agnostic.

  Returns:
    A tuple of:
    groundtruth: Dictionary with the following fields:
      'groundtruth_boxes': [num_boxes, 4] float32 tensor of boxes, in
        normalized coordinates.
      'groundtruth_classes': [num_boxes] int64 tensor of 1-indexed classes.
      'groundtruth_masks': 3D float32 tensor of instance masks (if provided in
        groundtruth)
72
73
      'groundtruth_is_crowd': [num_boxes] bool tensor indicating is_crowd
        annotations (if provided in groundtruth).
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
    class_agnostic: Boolean indicating whether detections are class agnostic.
  """
  input_data_fields = fields.InputDataFields()
  groundtruth_boxes = detection_model.groundtruth_lists(
      fields.BoxListFields.boxes)[0]
  # For class-agnostic models, groundtruth one-hot encodings collapse to all
  # ones.
  if class_agnostic:
    groundtruth_boxes_shape = tf.shape(groundtruth_boxes)
    groundtruth_classes_one_hot = tf.ones([groundtruth_boxes_shape[0], 1])
  else:
    groundtruth_classes_one_hot = detection_model.groundtruth_lists(
        fields.BoxListFields.classes)[0]
  label_id_offset = 1  # Applying label id offset (b/63711816)
  groundtruth_classes = (
      tf.argmax(groundtruth_classes_one_hot, axis=1) + label_id_offset)
  groundtruth = {
      input_data_fields.groundtruth_boxes: groundtruth_boxes,
      input_data_fields.groundtruth_classes: groundtruth_classes
  }
  if detection_model.groundtruth_has_field(fields.BoxListFields.masks):
    groundtruth[input_data_fields.groundtruth_instance_masks] = (
        detection_model.groundtruth_lists(fields.BoxListFields.masks)[0])
97
98
99
  if detection_model.groundtruth_has_field(fields.BoxListFields.is_crowd):
    groundtruth[input_data_fields.groundtruth_is_crowd] = (
        detection_model.groundtruth_lists(fields.BoxListFields.is_crowd)[0])
100
101
102
103
104
105
106
  return groundtruth


def unstack_batch(tensor_dict, unpad_groundtruth_tensors=True):
  """Unstacks all tensors in `tensor_dict` along 0th dimension.

  Unstacks tensor from the tensor dict along 0th dimension and returns a
107
  tensor_dict containing values that are lists of unstacked, unpadded tensors.
108
109
110
111
112
113

  Tensors in the `tensor_dict` are expected to be of one of the three shapes:
  1. [batch_size]
  2. [batch_size, height, width, channels]
  3. [batch_size, num_boxes, d1, d2, ... dn]

114
115
  When unpad_groundtruth_tensors is set to true, unstacked tensors of form 3
  above are sliced along the `num_boxes` dimension using the value in tensor
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
  field.InputDataFields.num_groundtruth_boxes.

  Note that this function has a static list of input data fields and has to be
  kept in sync with the InputDataFields defined in core/standard_fields.py

  Args:
    tensor_dict: A dictionary of batched groundtruth tensors.
    unpad_groundtruth_tensors: Whether to remove padding along `num_boxes`
      dimension of the groundtruth tensors.

  Returns:
    A dictionary where the keys are from fields.InputDataFields and values are
    a list of unstacked (optionally unpadded) tensors.

  Raises:
    ValueError: If unpad_tensors is True and `tensor_dict` does not contain
      `num_groundtruth_boxes` tensor.
  """
134
135
136
  unbatched_tensor_dict = {
      key: tf.unstack(tensor) for key, tensor in tensor_dict.items()
  }
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
  if unpad_groundtruth_tensors:
    if (fields.InputDataFields.num_groundtruth_boxes not in
        unbatched_tensor_dict):
      raise ValueError('`num_groundtruth_boxes` not found in tensor_dict. '
                       'Keys available: {}'.format(
                           unbatched_tensor_dict.keys()))
    unbatched_unpadded_tensor_dict = {}
    unpad_keys = set([
        # List of input data fields that are padded along the num_boxes
        # dimension. This list has to be kept in sync with InputDataFields in
        # standard_fields.py.
        fields.InputDataFields.groundtruth_instance_masks,
        fields.InputDataFields.groundtruth_classes,
        fields.InputDataFields.groundtruth_boxes,
        fields.InputDataFields.groundtruth_keypoints,
        fields.InputDataFields.groundtruth_group_of,
        fields.InputDataFields.groundtruth_difficult,
        fields.InputDataFields.groundtruth_is_crowd,
        fields.InputDataFields.groundtruth_area,
        fields.InputDataFields.groundtruth_weights
    ]).intersection(set(unbatched_tensor_dict.keys()))

    for key in unpad_keys:
      unpadded_tensor_list = []
      for num_gt, padded_tensor in zip(
          unbatched_tensor_dict[fields.InputDataFields.num_groundtruth_boxes],
          unbatched_tensor_dict[key]):
        tensor_shape = shape_utils.combined_static_and_dynamic_shape(
            padded_tensor)
        slice_begin = tf.zeros([len(tensor_shape)], dtype=tf.int32)
        slice_size = tf.stack(
            [num_gt] + [-1 if dim is None else dim for dim in tensor_shape[1:]])
        unpadded_tensor = tf.slice(padded_tensor, slice_begin, slice_size)
        unpadded_tensor_list.append(unpadded_tensor)
      unbatched_unpadded_tensor_dict[key] = unpadded_tensor_list
    unbatched_tensor_dict.update(unbatched_unpadded_tensor_dict)

  return unbatched_tensor_dict


def create_model_fn(detection_model_fn, configs, hparams, use_tpu=False):
  """Creates a model function for `Estimator`.

  Args:
    detection_model_fn: Function that returns a `DetectionModel` instance.
    configs: Dictionary of pipeline config objects.
    hparams: `HParams` object.
    use_tpu: Boolean indicating whether model should be constructed for
        use on TPU.

  Returns:
    `model_fn` for `Estimator`.
  """
  train_config = configs['train_config']
  eval_input_config = configs['eval_input_config']
192
  eval_config = configs['eval_config']
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210

  def model_fn(features, labels, mode, params=None):
    """Constructs the object detection model.

    Args:
      features: Dictionary of feature tensors, returned from `input_fn`.
      labels: Dictionary of groundtruth tensors if mode is TRAIN or EVAL,
        otherwise None.
      mode: Mode key from tf.estimator.ModeKeys.
      params: Parameter dictionary passed from the estimator.

    Returns:
      An `EstimatorSpec` that encapsulates the model and its serving
        configurations.
    """
    params = params or {}
    total_loss, train_op, detections, export_outputs = None, None, None, None
    is_training = mode == tf.estimator.ModeKeys.TRAIN
211
212
213
214

    # Make sure to set the Keras learning phase. True during training,
    # False for inference.
    tf.keras.backend.set_learning_phase(is_training)
215
216
    detection_model = detection_model_fn(
        is_training=is_training, add_summaries=(not use_tpu))
217
218
219
220
221
222
223
    scaffold_fn = None

    if mode == tf.estimator.ModeKeys.TRAIN:
      labels = unstack_batch(
          labels,
          unpad_groundtruth_tensors=train_config.unpad_groundtruth_tensors)
    elif mode == tf.estimator.ModeKeys.EVAL:
224
225
226
227
228
229
230
231
      # For evaling on train data, it is necessary to check whether groundtruth
      # must be unpadded.
      boxes_shape = (
          labels[fields.InputDataFields.groundtruth_boxes].get_shape()
          .as_list())
      unpad_groundtruth_tensors = True if boxes_shape[1] is not None else False
      labels = unstack_batch(
          labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
232
233
234
235
236
237
238
239
240
241
242

    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
      gt_boxes_list = labels[fields.InputDataFields.groundtruth_boxes]
      gt_classes_list = labels[fields.InputDataFields.groundtruth_classes]
      gt_masks_list = None
      if fields.InputDataFields.groundtruth_instance_masks in labels:
        gt_masks_list = labels[
            fields.InputDataFields.groundtruth_instance_masks]
      gt_keypoints_list = None
      if fields.InputDataFields.groundtruth_keypoints in labels:
        gt_keypoints_list = labels[fields.InputDataFields.groundtruth_keypoints]
243
244
245
      gt_weights_list = None
      if fields.InputDataFields.groundtruth_weights in labels:
        gt_weights_list = labels[fields.InputDataFields.groundtruth_weights]
246
      gt_is_crowd_list = None
247
248
      if fields.InputDataFields.groundtruth_is_crowd in labels:
        gt_is_crowd_list = labels[fields.InputDataFields.groundtruth_is_crowd]
249
250
251
252
      detection_model.provide_groundtruth(
          groundtruth_boxes_list=gt_boxes_list,
          groundtruth_classes_list=gt_classes_list,
          groundtruth_masks_list=gt_masks_list,
253
          groundtruth_keypoints_list=gt_keypoints_list,
254
          groundtruth_weights_list=gt_weights_list,
255
          groundtruth_is_crowd_list=gt_is_crowd_list)
256
257

    preprocessed_images = features[fields.InputDataFields.image]
258
259
260
261
262
263
264
265
266
267
268
269
    if use_tpu and train_config.use_bfloat16:
      with tf.contrib.tpu.bfloat16_scope():
        prediction_dict = detection_model.predict(
            preprocessed_images,
            features[fields.InputDataFields.true_image_shape])
        for k, v in prediction_dict.items():
          if v.dtype == tf.bfloat16:
            prediction_dict[k] = tf.cast(v, tf.float32)
    else:
      prediction_dict = detection_model.predict(
          preprocessed_images,
          features[fields.InputDataFields.true_image_shape])
270
271
272
    if mode in (tf.estimator.ModeKeys.EVAL, tf.estimator.ModeKeys.PREDICT):
      detections = detection_model.postprocess(
          prediction_dict, features[fields.InputDataFields.true_image_shape])
273
274
275

    if mode == tf.estimator.ModeKeys.TRAIN:
      if train_config.fine_tune_checkpoint and hparams.load_pretrained:
276
277
278
279
280
281
282
283
        if not train_config.fine_tune_checkpoint_type:
          # train_config.from_detection_checkpoint field is deprecated. For
          # backward compatibility, set train_config.fine_tune_checkpoint_type
          # based on train_config.from_detection_checkpoint.
          if train_config.from_detection_checkpoint:
            train_config.fine_tune_checkpoint_type = 'detection'
          else:
            train_config.fine_tune_checkpoint_type = 'classification'
284
        asg_map = detection_model.restore_map(
285
            fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
286
287
288
289
            load_all_detection_checkpoint_vars=(
                train_config.load_all_detection_checkpoint_vars))
        available_var_map = (
            variables_helper.get_variables_available_in_checkpoint(
290
291
                asg_map,
                train_config.fine_tune_checkpoint,
292
293
                include_global_step=False))
        if use_tpu:
294

295
296
297
298
          def tpu_scaffold():
            tf.train.init_from_checkpoint(train_config.fine_tune_checkpoint,
                                          available_var_map)
            return tf.train.Scaffold()
299

300
301
302
303
304
305
306
307
          scaffold_fn = tpu_scaffold
        else:
          tf.train.init_from_checkpoint(train_config.fine_tune_checkpoint,
                                        available_var_map)

    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
      losses_dict = detection_model.loss(
          prediction_dict, features[fields.InputDataFields.true_image_shape])
308
      losses = [loss_tensor for loss_tensor in losses_dict.values()]
309
      if train_config.add_regularization_loss:
310
        regularization_losses = detection_model.regularization_losses()
311
        if regularization_losses:
312
313
          regularization_loss = tf.add_n(
              regularization_losses, name='regularization_loss')
314
          losses.append(regularization_loss)
315
          losses_dict['Loss/regularization_loss'] = regularization_loss
316
      total_loss = tf.add_n(losses, name='total_loss')
317
      losses_dict['Loss/total_loss'] = total_loss
318

319
320
321
322
323
      if 'graph_rewriter_config' in configs:
        graph_rewriter_fn = graph_rewriter_builder.build(
            configs['graph_rewriter_config'], is_training=is_training)
        graph_rewriter_fn()

324
325
      # TODO(rathodv): Stop creating optimizer summary vars in EVAL mode once we
      # can write learning rate summaries on TPU without host calls.
326
327
328
329
      global_step = tf.train.get_or_create_global_step()
      training_optimizer, optimizer_summary_vars = optimizer_builder.build(
          train_config.optimizer)

330
    if mode == tf.estimator.ModeKeys.TRAIN:
331
      if use_tpu:
332
        training_optimizer = tf.contrib.tpu.CrossShardOptimizer(
333
334
335
336
            training_optimizer)

      # Optionally freeze some layers by setting their gradients to be zero.
      trainable_variables = None
337
338
339
340
341
342
343
344
345
346
      include_variables = (
          train_config.update_trainable_variables
          if train_config.update_trainable_variables else None)
      exclude_variables = (
          train_config.freeze_variables
          if train_config.freeze_variables else None)
      trainable_variables = tf.contrib.framework.filter_variables(
          tf.trainable_variables(),
          include_patterns=include_variables,
          exclude_patterns=exclude_variables)
347
348
349
350
351
352
353
354
355

      clip_gradients_value = None
      if train_config.gradient_clipping_by_norm > 0:
        clip_gradients_value = train_config.gradient_clipping_by_norm

      if not use_tpu:
        for var in optimizer_summary_vars:
          tf.summary.scalar(var.op.name, var)
      summaries = [] if use_tpu else None
356
357
      if train_config.summarize_gradients:
        summaries = ['gradients', 'gradient_norm', 'global_gradient_norm']
358
359
360
361
362
363
      train_op = tf.contrib.layers.optimize_loss(
          loss=total_loss,
          global_step=global_step,
          learning_rate=None,
          clip_gradients=clip_gradients_value,
          optimizer=training_optimizer,
364
          update_ops=detection_model.updates(),
365
366
367
368
369
          variables=trainable_variables,
          summaries=summaries,
          name='')  # Preventing scope prefix on all variables.

    if mode == tf.estimator.ModeKeys.PREDICT:
370
      exported_output = exporter_lib.add_output_tensor_nodes(detections)
371
372
      export_outputs = {
          tf.saved_model.signature_constants.PREDICT_METHOD_NAME:
373
              tf.estimator.export.PredictOutput(exported_output)
374
375
376
      }

    eval_metric_ops = None
377
    scaffold = None
378
    if mode == tf.estimator.ModeKeys.EVAL:
379
380
381
382
      class_agnostic = (
          fields.DetectionResultFields.detection_classes not in detections)
      groundtruth = _prepare_groundtruth_for_eval(detection_model,
                                                  class_agnostic)
383
      use_original_images = fields.InputDataFields.original_image in features
pkulzc's avatar
pkulzc committed
384
385
386
387
388
389
390
391
      if use_original_images:
        eval_images = tf.cast(tf.image.resize_bilinear(
            features[fields.InputDataFields.original_image][0:1],
            features[fields.InputDataFields.original_image_spatial_shape][0]),
                              tf.uint8)
      else:
        eval_images = features[fields.InputDataFields.image]

392
      eval_dict = eval_util.result_dict_for_single_example(
393
          eval_images[0:1],
394
395
396
397
          features[inputs.HASH_KEY][0],
          detections,
          groundtruth,
          class_agnostic=class_agnostic,
398
          scale_to_absolute=True)
399
400
401
402
403
404

      if class_agnostic:
        category_index = label_map_util.create_class_agnostic_category_index()
      else:
        category_index = label_map_util.create_category_index_from_labelmap(
            eval_input_config.label_map_path)
405
      vis_metric_ops = None
406
      if not use_tpu and use_original_images:
407
408
409
410
411
412
413
414
        eval_metric_op_vis = vis_utils.VisualizeSingleFrameDetections(
            category_index,
            max_examples_to_draw=eval_config.num_visualizations,
            max_boxes_to_draw=eval_config.max_num_boxes_to_visualize,
            min_score_thresh=eval_config.min_score_threshold,
            use_normalized_coordinates=False)
        vis_metric_ops = eval_metric_op_vis.get_estimator_eval_metric_ops(
            eval_dict)
415

416
417
      # Eval metrics on a single example.
      eval_metric_ops = eval_util.get_eval_metric_ops_for_evaluators(
418
          eval_config, category_index.values(), eval_dict)
419
420
421
422
      for loss_key, loss_tensor in iter(losses_dict.items()):
        eval_metric_ops[loss_key] = tf.metrics.mean(loss_tensor)
      for var in optimizer_summary_vars:
        eval_metric_ops[var.op.name] = (var, tf.no_op())
423
424
      if vis_metric_ops is not None:
        eval_metric_ops.update(vis_metric_ops)
425
      eval_metric_ops = {str(k): v for k, v in eval_metric_ops.items()}
426

427
428
429
430
431
432
433
434
435
436
      if eval_config.use_moving_averages:
        variable_averages = tf.train.ExponentialMovingAverage(0.0)
        variables_to_restore = variable_averages.variables_to_restore()
        keep_checkpoint_every_n_hours = (
            train_config.keep_checkpoint_every_n_hours)
        saver = tf.train.Saver(
            variables_to_restore,
            keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
        scaffold = tf.train.Scaffold(saver=saver)

437
438
    # EVAL executes on CPU, so use regular non-TPU EstimatorSpec.
    if use_tpu and mode != tf.estimator.ModeKeys.EVAL:
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
      return tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          scaffold_fn=scaffold_fn,
          predictions=detections,
          loss=total_loss,
          train_op=train_op,
          eval_metrics=eval_metric_ops,
          export_outputs=export_outputs)
    else:
      return tf.estimator.EstimatorSpec(
          mode=mode,
          predictions=detections,
          loss=total_loss,
          train_op=train_op,
          eval_metric_ops=eval_metric_ops,
454
455
          export_outputs=export_outputs,
          scaffold=scaffold)
456
457
458
459

  return model_fn


460
461
462
def create_estimator_and_inputs(run_config,
                                hparams,
                                pipeline_config_path,
463
                                config_override=None,
464
                                train_steps=None,
465
466
                                sample_1_of_n_eval_examples=1,
                                sample_1_of_n_eval_on_train_examples=1,
467
468
469
470
471
                                model_fn_creator=create_model_fn,
                                use_tpu_estimator=False,
                                use_tpu=False,
                                num_shards=1,
                                params=None,
472
                                override_eval_num_epochs=True,
473
                                save_final_config=False,
474
475
                                **kwargs):
  """Creates `Estimator`, input functions, and steps.
476
477
478
479
480

  Args:
    run_config: A `RunConfig`.
    hparams: A `HParams`.
    pipeline_config_path: A path to a pipeline config file.
481
482
    config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to
      override the config from `pipeline_config_path`.
483
484
    train_steps: Number of training steps. If None, the number of training steps
      is set from the `TrainConfig` proto.
485
486
487
488
489
    sample_1_of_n_eval_examples: Integer representing how often an eval example
      should be sampled. If 1, will sample all examples.
    sample_1_of_n_eval_on_train_examples: Similar to
      `sample_1_of_n_eval_examples`, except controls the sampling of training
      data for evaluation.
490
491
492
493
494
495
496
497
498
499
    model_fn_creator: A function that creates a `model_fn` for `Estimator`.
      Follows the signature:

      * Args:
        * `detection_model_fn`: Function that returns `DetectionModel` instance.
        * `configs`: Dictionary of pipeline config objects.
        * `hparams`: `HParams` object.
      * Returns:
        `model_fn` for `Estimator`.

500
501
502
503
504
505
506
507
    use_tpu_estimator: Whether a `TPUEstimator` should be returned. If False,
      an `Estimator` will be returned.
    use_tpu: Boolean, whether training and evaluation should run on TPU. Only
      used if `use_tpu_estimator` is True.
    num_shards: Number of shards (TPU cores). Only used if `use_tpu_estimator`
      is True.
    params: Parameter dictionary passed from the estimator. Only used if
      `use_tpu_estimator` is True.
508
509
    override_eval_num_epochs: Whether to overwrite the number of epochs to
      1 for eval_input.
510
511
    save_final_config: Whether to save final config (obtained after applying
      overrides) to `estimator.model_dir`.
512
513
514
    **kwargs: Additional keyword arguments for configuration override.

  Returns:
515
516
517
    A dictionary with the following fields:
    'estimator': An `Estimator` or `TPUEstimator`.
    'train_input_fn': A training input function.
518
519
    'eval_input_fns': A list of all evaluation input functions.
    'eval_input_names': A list of names for each evaluation input.
520
    'eval_on_train_input_fn': An evaluation-on-train input function.
521
522
523
    'predict_input_fn': A prediction input function.
    'train_steps': Number of training steps. Either directly from input or from
      configuration.
524
  """
525
526
527
528
  get_configs_from_pipeline_file = MODEL_BUILD_UTIL_MAP[
      'get_configs_from_pipeline_file']
  merge_external_params_with_configs = MODEL_BUILD_UTIL_MAP[
      'merge_external_params_with_configs']
529
530
  create_pipeline_proto_from_configs = MODEL_BUILD_UTIL_MAP[
      'create_pipeline_proto_from_configs']
531
532
533
534
  create_train_input_fn = MODEL_BUILD_UTIL_MAP['create_train_input_fn']
  create_eval_input_fn = MODEL_BUILD_UTIL_MAP['create_eval_input_fn']
  create_predict_input_fn = MODEL_BUILD_UTIL_MAP['create_predict_input_fn']

535
536
  configs = get_configs_from_pipeline_file(pipeline_config_path,
                                           config_override=config_override)
537
538
  kwargs.update({
      'train_steps': train_steps,
pkulzc's avatar
pkulzc committed
539
      'sample_1_of_n_eval_examples': sample_1_of_n_eval_examples
540
541
542
543
544
  })
  if override_eval_num_epochs:
    kwargs.update({'eval_num_epochs': 1})
    tf.logging.warning(
        'Forced number of epochs for all eval validations to be 1.')
545
  configs = merge_external_params_with_configs(
546
      configs, hparams, kwargs_dict=kwargs)
547
548
549
550
  model_config = configs['model']
  train_config = configs['train_config']
  train_input_config = configs['train_input_config']
  eval_config = configs['eval_config']
551
552
553
554
555
556
557
558
559
560
561
  eval_input_configs = configs['eval_input_configs']
  eval_on_train_input_config = copy.deepcopy(train_input_config)
  eval_on_train_input_config.sample_1_of_n_examples = (
      sample_1_of_n_eval_on_train_examples)
  if override_eval_num_epochs and eval_on_train_input_config.num_epochs != 1:
    tf.logging.warning('Expected number of evaluation epochs is 1, but '
                       'instead encountered `eval_on_train_input_config'
                       '.num_epochs` = '
                       '{}. Overwriting `num_epochs` to 1.'.format(
                           eval_on_train_input_config.num_epochs))
    eval_on_train_input_config.num_epochs = 1
562

563
564
565
  # update train_steps from config but only when non-zero value is provided
  if train_steps is None and train_config.num_steps != 0:
    train_steps = train_config.num_steps
566
567
568
569

  detection_model_fn = functools.partial(
      model_builder.build, model_config=model_config)

570
  # Create the input functions for TRAIN/EVAL/PREDICT.
571
  train_input_fn = create_train_input_fn(
572
573
574
      train_config=train_config,
      train_input_config=train_input_config,
      model_config=model_config)
575
576
577
578
579
580
581
582
583
  eval_input_fns = [
      create_eval_input_fn(
          eval_config=eval_config,
          eval_input_config=eval_input_config,
          model_config=model_config) for eval_input_config in eval_input_configs
  ]
  eval_input_names = [
      eval_input_config.name for eval_input_config in eval_input_configs
  ]
584
585
  eval_on_train_input_fn = create_eval_input_fn(
      eval_config=eval_config,
586
      eval_input_config=eval_on_train_input_config,
587
      model_config=model_config)
588
  predict_input_fn = create_predict_input_fn(
589
      model_config=model_config, predict_input_config=eval_input_configs[0])
590

591
592
593
  export_to_tpu = hparams.get('export_to_tpu', False)
  tf.logging.info('create_estimator_and_inputs: use_tpu %s, export_to_tpu %s',
                  use_tpu, export_to_tpu)
594
595
  model_fn = model_fn_creator(detection_model_fn, configs, hparams, use_tpu)
  if use_tpu_estimator:
596
    estimator = tf.contrib.tpu.TPUEstimator(
597
598
599
600
601
602
        model_fn=model_fn,
        train_batch_size=train_config.batch_size,
        # For each core, only batch size 1 is supported for eval.
        eval_batch_size=num_shards * 1 if use_tpu else 1,
        use_tpu=use_tpu,
        config=run_config,
603
        # TODO(lzc): Remove conditional after CMLE moves to TF 1.9
604
605
606
        params=params if params else {})
  else:
    estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)
607

608
  # Write the as-run pipeline config to disk.
609
  if run_config.is_chief and save_final_config:
610
    pipeline_config_final = create_pipeline_proto_from_configs(configs)
611
    config_util.save_pipeline_config(pipeline_config_final, estimator.model_dir)
612

613
  return dict(
614
615
      estimator=estimator,
      train_input_fn=train_input_fn,
616
617
      eval_input_fns=eval_input_fns,
      eval_input_names=eval_input_names,
618
      eval_on_train_input_fn=eval_on_train_input_fn,
619
      predict_input_fn=predict_input_fn,
620
      train_steps=train_steps)
621
622
623


def create_train_and_eval_specs(train_input_fn,
624
                                eval_input_fns,
625
                                eval_on_train_input_fn,
626
627
628
629
                                predict_input_fn,
                                train_steps,
                                eval_on_train_data=False,
                                final_exporter_name='Servo',
630
                                eval_spec_names=None):
631
632
633
634
  """Creates a `TrainSpec` and `EvalSpec`s.

  Args:
    train_input_fn: Function that produces features and labels on train data.
635
636
    eval_input_fns: A list of functions that produce features and labels on eval
      data.
637
638
    eval_on_train_input_fn: Function that produces features and labels for
      evaluation on train data.
639
640
641
642
643
    predict_input_fn: Function that produces features for inference.
    train_steps: Number of training steps.
    eval_on_train_data: Whether to evaluate model on training data. Default is
      False.
    final_exporter_name: String name given to `FinalExporter`.
644
    eval_spec_names: A list of string names for each `EvalSpec`.
645
646

  Returns:
647
648
649
    Tuple of `TrainSpec` and list of `EvalSpecs`. If `eval_on_train_data` is
    True, the last `EvalSpec` in the list will correspond to training data. The
    rest EvalSpecs in the list are evaluation datas.
650
651
652
653
  """
  train_spec = tf.estimator.TrainSpec(
      input_fn=train_input_fn, max_steps=train_steps)

654
  if eval_spec_names is None:
655
    eval_spec_names = [str(i) for i in range(len(eval_input_fns))]
656
657

  eval_specs = []
658
659
660
661
662
663
664
665
  for index, (eval_spec_name, eval_input_fn) in enumerate(
      zip(eval_spec_names, eval_input_fns)):
    # Uses final_exporter_name as exporter_name for the first eval spec for
    # backward compatibility.
    if index == 0:
      exporter_name = final_exporter_name
    else:
      exporter_name = '{}_{}'.format(final_exporter_name, eval_spec_name)
666
667
668
669
670
671
672
673
    exporter = tf.estimator.FinalExporter(
        name=exporter_name, serving_input_receiver_fn=predict_input_fn)
    eval_specs.append(
        tf.estimator.EvalSpec(
            name=eval_spec_name,
            input_fn=eval_input_fn,
            steps=None,
            exporters=exporter))
674
675
676
677

  if eval_on_train_data:
    eval_specs.append(
        tf.estimator.EvalSpec(
678
            name='eval_on_train', input_fn=eval_on_train_input_fn, steps=None))
679
680

  return train_spec, eval_specs
681
682


683
def continuous_eval(estimator, model_dir, input_fn, train_steps, name):
684
685
686
687
688
689
690
691
692
693
  """Perform continuous evaluation on checkpoints written to a model directory.

  Args:
    estimator: Estimator object to use for evaluation.
    model_dir: Model directory to read checkpoints for continuous evaluation.
    input_fn: Input function to use for evaluation.
    train_steps: Number of training steps. This is used to infer the last
      checkpoint and stop evaluation loop.
    name: Namescope for eval summary.
  """
694

695
696
697
698
699
700
701
702
703
704
705
  def terminate_eval():
    tf.logging.info('Terminating eval after 180 seconds of no checkpoints')
    return True

  for ckpt in tf.contrib.training.checkpoints_iterator(
      model_dir, min_interval_secs=180, timeout=None,
      timeout_fn=terminate_eval):

    tf.logging.info('Starting Evaluation.')
    try:
      eval_results = estimator.evaluate(
706
          input_fn=input_fn, steps=None, checkpoint_path=ckpt, name=name)
707
708
709
710
711
712
713
714
715
716
717
718
719
720
      tf.logging.info('Eval results: %s' % eval_results)

      # Terminate eval job when final checkpoint is reached
      current_step = int(os.path.basename(ckpt).split('-')[1])
      if current_step >= train_steps:
        tf.logging.info(
            'Evaluation finished after training step %d' % current_step)
        break

    except tf.errors.NotFoundError:
      tf.logging.info(
          'Checkpoint %s no longer exists, skipping checkpoint' % ckpt)


721
722
723
724
725
726
727
728
def populate_experiment(run_config,
                        hparams,
                        pipeline_config_path,
                        train_steps=None,
                        eval_steps=None,
                        model_fn_creator=create_model_fn,
                        **kwargs):
  """Populates an `Experiment` object.
729

730
731
  EXPERIMENT CLASS IS DEPRECATED. Please switch to
  tf.estimator.train_and_evaluate. As an example, see model_main.py.
732

733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
  Args:
    run_config: A `RunConfig`.
    hparams: A `HParams`.
    pipeline_config_path: A path to a pipeline config file.
    train_steps: Number of training steps. If None, the number of training steps
      is set from the `TrainConfig` proto.
    eval_steps: Number of evaluation steps per evaluation cycle. If None, the
      number of evaluation steps is set from the `EvalConfig` proto.
    model_fn_creator: A function that creates a `model_fn` for `Estimator`.
      Follows the signature:

      * Args:
        * `detection_model_fn`: Function that returns `DetectionModel` instance.
        * `configs`: Dictionary of pipeline config objects.
        * `hparams`: `HParams` object.
      * Returns:
        `model_fn` for `Estimator`.

    **kwargs: Additional keyword arguments for configuration override.

  Returns:
    An `Experiment` that defines all aspects of training, evaluation, and
    export.
  """
  tf.logging.warning('Experiment is being deprecated. Please use '
                     'tf.estimator.train_and_evaluate(). See model_main.py for '
                     'an example.')
  train_and_eval_dict = create_estimator_and_inputs(
      run_config,
      hparams,
      pipeline_config_path,
      train_steps=train_steps,
      eval_steps=eval_steps,
      model_fn_creator=model_fn_creator,
767
      save_final_config=True,
768
769
770
      **kwargs)
  estimator = train_and_eval_dict['estimator']
  train_input_fn = train_and_eval_dict['train_input_fn']
771
  eval_input_fns = train_and_eval_dict['eval_input_fns']
772
773
774
775
776
777
778
779
780
781
782
  predict_input_fn = train_and_eval_dict['predict_input_fn']
  train_steps = train_and_eval_dict['train_steps']

  export_strategies = [
      tf.contrib.learn.utils.saved_model_export_utils.make_export_strategy(
          serving_input_fn=predict_input_fn)
  ]

  return tf.contrib.learn.Experiment(
      estimator=estimator,
      train_input_fn=train_input_fn,
783
      eval_input_fn=eval_input_fns[0],
784
      train_steps=train_steps,
785
      eval_steps=None,
786
      export_strategies=export_strategies,
787
788
      eval_delay_secs=120,
  )