Instance Segmentation with Model Garden

This tutorial fine-tunes a Mask R-CNN with Mobilenet V2 as backbone model from the TensorFlow Model Garden package (tensorflow-models).

Model Garden contains a collection of state-of-the-art models, implemented with TensorFlow's high-level APIs. The implementations demonstrate the best practices for modeling, letting users to take full advantage of TensorFlow for their research and product development.

This tutorial demonstrates how to:

Use models from the TensorFlow Models package.
Train/Fine-tune a pre-built Mask R-CNN with mobilenet as backbone for Object Detection and Instance Segmentation
Export the trained/tuned Mask R-CNN model

Install Necessary Dependencies

pip install -U -q "tf-models-official"
pip install -U -q remotezip tqdm opencv-python einops

Import required libraries

import os
import io
import json
import tqdm
import shutil
import pprint
import pathlib
import tempfile
import requests
import collections
import matplotlib
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

from PIL import Image
from six import BytesIO
from etils import epath
from IPython import display
from urllib.request import urlopen

2023-11-30 12:05:19.630836: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-30 12:05:19.630880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-30 12:05:19.632442: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

import orbit
import tensorflow as tf
import tensorflow_models as tfm
import tensorflow_datasets as tfds

from official.core import exp_factory
from official.core import config_definitions as cfg
from official.vision.data import tfrecord_lib
from official.vision.serving import export_saved_model_lib
from official.vision.dataloaders.tf_example_decoder import TfExampleDecoder
from official.vision.utils.object_detection import visualization_utils
from official.vision.ops.preprocess_ops import normalize_image, resize_and_crop_image
from official.vision.data.create_coco_tf_record import coco_annotations_to_lists

pp = pprint.PrettyPrinter(indent=4) # Set Pretty Print Indentation
print(tf.__version__) # Check the version of tensorflow used

%matplotlib inline

2.15.0

Download subset of lvis dataset

LVIS: A dataset for large vocabulary instance segmentation.

# @title Download annotation files

wget https://dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip
unzip -q lvis_v1_train.json.zip
rm lvis_v1_train.json.zip

wget https://dl.fbaipublicfiles.com/LVIS/lvis_v1_val.json.zip
unzip -q lvis_v1_val.json.zip
rm lvis_v1_val.json.zip

wget https://dl.fbaipublicfiles.com/LVIS/lvis_v1_image_info_test_dev.json.zip
unzip -q lvis_v1_image_info_test_dev.json.zip
rm lvis_v1_image_info_test_dev.json.zip

--2023-11-30 12:05:23--  https://dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip
Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.163.189.51, 3.163.189.108, 3.163.189.14, ...
Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.163.189.51|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 350264821 (334M) [application/zip]
Saving to: ‘lvis_v1_train.json.zip’

lvis_v1_train.json. 100%[===================>] 334.04M   295MB/s    in 1.1s    

2023-11-30 12:05:25 (295 MB/s) - ‘lvis_v1_train.json.zip’ saved [350264821/350264821]

--2023-11-30 12:05:34--  https://dl.fbaipublicfiles.com/LVIS/lvis_v1_val.json.zip
Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.163.189.51, 3.163.189.108, 3.163.189.14, ...
Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.163.189.51|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 64026968 (61M) [application/zip]
Saving to: ‘lvis_v1_val.json.zip’

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2023-11-30 12:05:34 (184 MB/s) - ‘lvis_v1_val.json.zip’ saved [64026968/64026968]

--2023-11-30 12:05:36--  https://dl.fbaipublicfiles.com/LVIS/lvis_v1_image_info_test_dev.json.zip
Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.163.189.51, 3.163.189.108, 3.163.189.14, ...
Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.163.189.51|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 384629 (376K) [application/zip]
Saving to: ‘lvis_v1_image_info_test_dev.json.zip’

lvis_v1_image_info_ 100%[===================>] 375.61K  --.-KB/s    in 0.03s   

2023-11-30 12:05:37 (12.3 MB/s) - ‘lvis_v1_image_info_test_dev.json.zip’ saved [384629/384629]

# @title Lvis annotation parsing

# Annotations with invalid bounding boxes. Will not be used.
_INVALID_ANNOTATIONS = [
    # Train split.
    662101,
    81217,
    462924,
    227817,
    29381,
    601484,
    412185,
    504667,
    572573,
    91937,
    239022,
    181534,
    101685,
    # Validation split.
    36668,
    57541,
    33126,
    10932,
]

def get_category_map(annotation_path, num_classes):
  with epath.Path(annotation_path).open() as f:
      data = json.load(f)

  category_map = {id+1: {'id': cat_dict['id'],
                       'name': cat_dict['name']}
                  for id, cat_dict in enumerate(data['categories'][:num_classes])}
  return category_map

class LvisAnnotation:
  """LVIS annotation helper class.
  The format of the annations is explained on
  https://www.lvisdataset.org/dataset.
  """

  def __init__(self, annotation_path):
    with epath.Path(annotation_path).open() as f:
      data = json.load(f)
    self._data = data

    img_id2annotations = collections.defaultdict(list)
    for a in self._data.get('annotations', []):
      if a['category_id'] in category_ids:
        img_id2annotations[a['image_id']].append(a)
    self._img_id2annotations = {
        k: list(sorted(v, key=lambda a: a['id']))
        for k, v in img_id2annotations.items()
    }

  @property
  def categories(self):
    """Return the category dicts, as sorted in the file."""
    return self._data['categories']

  @property
  def images(self):
    """Return the image dicts, as sorted in the file."""
    sub_images = []
    for image_info in self._data['images']:
      if image_info['id'] in self._img_id2annotations:
        sub_images.append(image_info)
    return sub_images

  def get_annotations(self, img_id):
    """Return all annotations associated with the image id string."""
    # Some images don't have any annotations. Return empty list instead.
    return self._img_id2annotations.get(img_id, [])

def _generate_tf_records(prefix, images_zip, annotation_file, num_shards=5):
    """Generate TFRecords."""

    lvis_annotation = LvisAnnotation(annotation_file)

    def _process_example(prefix, image_info, id_to_name_map):
      # Search image dirs.
      filename = pathlib.Path(image_info['coco_url']).name
      image = tf.io.read_file(os.path.join(IMGS_DIR, filename))
      instances = lvis_annotation.get_annotations(img_id=image_info['id'])
      instances = [x for x in instances if x['id'] not in _INVALID_ANNOTATIONS]
      # print([x['category_id'] for x in instances])
      is_crowd = {'iscrowd': 0}
      instances = [dict(x, **is_crowd) for x in instances]
      neg_category_ids = image_info.get('neg_category_ids', [])
      not_exhaustive_category_ids = image_info.get(
          'not_exhaustive_category_ids', []
      )
      data, _ = coco_annotations_to_lists(instances,
                                          id_to_name_map,
                                          image_info['height'],
                                          image_info['width'],
                                          include_masks=True)
      # data['category_id'] = [id-1 for id in data['category_id']]
      keys_to_features = {
          'image/encoded':
              tfrecord_lib.convert_to_feature(image.numpy()),
          'image/filename':
               tfrecord_lib.convert_to_feature(filename.encode('utf8')),
          'image/format':
              tfrecord_lib.convert_to_feature('jpg'.encode('utf8')),
          'image/height':
              tfrecord_lib.convert_to_feature(image_info['height']),
          'image/width':
              tfrecord_lib.convert_to_feature(image_info['width']),
          'image/source_id':
              tfrecord_lib.convert_to_feature(str(image_info['id']).encode('utf8')),
          'image/object/bbox/xmin':
              tfrecord_lib.convert_to_feature(data['xmin']),
          'image/object/bbox/xmax':
              tfrecord_lib.convert_to_feature(data['xmax']),
          'image/object/bbox/ymin':
              tfrecord_lib.convert_to_feature(data['ymin']),
          'image/object/bbox/ymax':
              tfrecord_lib.convert_to_feature(data['ymax']),
          'image/object/class/text':
              tfrecord_lib.convert_to_feature(data['category_names']),
          'image/object/class/label':
              tfrecord_lib.convert_to_feature(data['category_id']),
          'image/object/is_crowd':
              tfrecord_lib.convert_to_feature(data['is_crowd']),
          'image/object/area':
              tfrecord_lib.convert_to_feature(data['area'], 'float_list'),
          'image/object/mask':
              tfrecord_lib.convert_to_feature(data['encoded_mask_png'])
      }
      # print(keys_to_features['image/object/class/label'])
      example = tf.train.Example(
          features=tf.train.Features(feature=keys_to_features))
      return example



    # file_names = [f"{prefix}/{pathlib.Path(image_info['coco_url']).name}"
    #               for image_info in lvis_annotation.images]
    # _extract_images(images_zip, file_names)
    writers = [
        tf.io.TFRecordWriter(
            tf_records_dir + prefix +'-%05d-of-%05d.tfrecord' % (i, num_shards))
        for i in range(num_shards)
    ]
    id_to_name_map = {cat_dict['id']: cat_dict['name']
                      for cat_dict in lvis_annotation.categories[:NUM_CLASSES]}
    # print(id_to_name_map)
    for idx, image_info in enumerate(tqdm.tqdm(lvis_annotation.images)):
      img_data = requests.get(image_info['coco_url'], stream=True).content
      img_name = image_info['coco_url'].split('/')[-1]
      with open(os.path.join(IMGS_DIR, img_name), 'wb') as handler:
          handler.write(img_data)
      tf_example = _process_example(prefix, image_info, id_to_name_map)
      writers[idx % num_shards].write(tf_example.SerializeToString())

    del lvis_annotation

_URLS = {
    'train_images': 'http://images.cocodataset.org/zips/train2017.zip',
    'validation_images': 'http://images.cocodataset.org/zips/val2017.zip',
    'test_images': 'http://images.cocodataset.org/zips/test2017.zip',
}

train_prefix = 'train'
valid_prefix = 'val'

train_annotation_path = './lvis_v1_train.json'
valid_annotation_path = './lvis_v1_val.json'

IMGS_DIR = './lvis_sub_dataset/'
tf_records_dir = './lvis_tfrecords/'


if not os.path.exists(IMGS_DIR):
  os.mkdir(IMGS_DIR)

if not os.path.exists(tf_records_dir):
  os.mkdir(tf_records_dir)



NUM_CLASSES = 3
category_index = get_category_map(valid_annotation_path, NUM_CLASSES)
category_ids = list(category_index.keys())

# Below helper function are taken from github tensorflow dataset lvis
# https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/datasets/lvis/lvis_dataset_builder.py
_generate_tf_records(train_prefix,
                     _URLS['train_images'],
                     train_annotation_path)

100%|██████████| 2338/2338 [16:14<00:00,  2.40it/s]

_generate_tf_records(valid_prefix,
                     _URLS['validation_images'],
                     valid_annotation_path)

100%|██████████| 422/422 [02:56<00:00,  2.40it/s]

Configure the MaskRCNN Resnet FPN COCO model for custom dataset

train_data_input_path = './lvis_tfrecords/train*'
valid_data_input_path = './lvis_tfrecords/val*'
test_data_input_path = './lvis_tfrecords/test*'
model_dir = './trained_model/'
export_dir ='./exported_model/'

if not os.path.exists(model_dir):
  os.mkdir(model_dir)

In Model Garden, the collections of parameters that define a model are called configs. Model Garden can create a config based on a known set of parameters via a factory.

Use the retinanet_mobilenet_coco experiment configuration, as defined by tfm.vision.configs.maskrcnn.maskrcnn_mobilenet_coco.

Please find all the registered experiements here

The configuration defines an experiment to train a Mask R-CNN model with mobilenet as backbone and FPN as decoder. Default Congiguration is trained on COCO train2017 and evaluated on COCO val2017.

There are also other alternative experiments available such as maskrcnn_resnetfpn_coco, maskrcnn_spinenet_coco and more. One can switch to them by changing the experiment name argument to the get_exp_config function.

exp_config = exp_factory.get_exp_config('maskrcnn_mobilenet_coco')

model_ckpt_path = './model_ckpt/'
if not os.path.exists(model_ckpt_path):
  os.mkdir(model_ckpt_path)

!gsutil cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.data-00000-of-00001 './model_ckpt/'
!gsutil cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.index './model_ckpt/'

Copying gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.data-00000-of-00001...

Operation completed over 1 objects/26.9 MiB.                                     
Copying gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.index...

Operation completed over 1 objects/7.5 KiB.

Adjust the model and dataset configurations so that it works with custom dataset.

BATCH_SIZE = 8
HEIGHT, WIDTH = 256, 256
IMG_SHAPE = [HEIGHT, WIDTH, 3]


# Backbone Config
exp_config.task.annotation_file = None
exp_config.task.freeze_backbone = True
exp_config.task.init_checkpoint = "./model_ckpt/ckpt-180648"
exp_config.task.init_checkpoint_modules = "backbone"

# Model Config
exp_config.task.model.num_classes = NUM_CLASSES + 1
exp_config.task.model.input_size = IMG_SHAPE

# Training Data Config
exp_config.task.train_data.input_path = train_data_input_path
exp_config.task.train_data.dtype = 'float32'
exp_config.task.train_data.global_batch_size = BATCH_SIZE
exp_config.task.train_data.shuffle_buffer_size = 64
exp_config.task.train_data.parser.aug_scale_max = 1.0
exp_config.task.train_data.parser.aug_scale_min = 1.0

# Validation Data Config
exp_config.task.validation_data.input_path = valid_data_input_path
exp_config.task.validation_data.dtype = 'float32'
exp_config.task.validation_data.global_batch_size = BATCH_SIZE

Adjust the trainer configuration.

logical_device_names = [logical_device.name for logical_device in tf.config.list_logical_devices()]

if 'GPU' in ''.join(logical_device_names):
  print('This may be broken in Colab.')
  device = 'GPU'
elif 'TPU' in ''.join(logical_device_names):
  print('This may be broken in Colab.')
  device = 'TPU'
else:
  print('Running on CPU is slow, so only train for a few steps.')
  device = 'CPU'


train_steps = 2000
exp_config.trainer.steps_per_loop = 200 # steps_per_loop = num_of_training_examples // train_batch_size

exp_config.trainer.summary_interval = 200
exp_config.trainer.checkpoint_interval = 200
exp_config.trainer.validation_interval = 200
exp_config.trainer.validation_steps =  200 # validation_steps = num_of_validation_examples // eval_batch_size
exp_config.trainer.train_steps = train_steps
exp_config.trainer.optimizer_config.warmup.linear.warmup_steps = 200
exp_config.trainer.optimizer_config.learning_rate.type = 'cosine'
exp_config.trainer.optimizer_config.learning_rate.cosine.decay_steps = train_steps
exp_config.trainer.optimizer_config.learning_rate.cosine.initial_learning_rate = 0.07
exp_config.trainer.optimizer_config.warmup.linear.warmup_learning_rate = 0.05

This may be broken in Colab.

Print the modified configuration.

pp.pprint(exp_config.as_dict())
display.Javascript("google.colab.output.setIframeHeight('500px');")

{   'runtime': {   'all_reduce_alg': None,
                   'batchnorm_spatial_persistent': False,
                   'dataset_num_private_threads': None,
                   'default_shard_dim': -1,
                   'distribution_strategy': 'mirrored',
                   'enable_xla': False,
                   'gpu_thread_mode': None,
                   'loss_scale': None,
                   'mixed_precision_dtype': 'bfloat16',
                   'num_cores_per_replica': 1,
                   'num_gpus': 0,
                   'num_packs': 1,
                   'per_gpu_thread_count': 0,
                   'run_eagerly': False,
                   'task_index': -1,
                   'tpu': None,
                   'tpu_enable_xla_dynamic_padder': None,
                   'use_tpu_mp_strategy': False,
                   'worker_hosts': None},
    'task': {   'allow_image_summary': False,
                'allowed_mask_class_ids': None,
                'annotation_file': None,
                'differential_privacy_config': None,
                'freeze_backbone': True,
                'init_checkpoint': './model_ckpt/ckpt-180648',
                'init_checkpoint_modules': 'backbone',
                'losses': {   'class_weights': None,
                              'frcnn_box_weight': 1.0,
                              'frcnn_class_loss_top_k_percent': 1.0,
                              'frcnn_class_use_binary_cross_entropy': False,
                              'frcnn_class_weight': 1.0,
                              'frcnn_huber_loss_delta': 1.0,
                              'l2_weight_decay': 4e-05,
                              'loss_weight': 1.0,
                              'mask_weight': 1.0,
                              'rpn_box_weight': 1.0,
                              'rpn_huber_loss_delta': 0.1111111111111111,
                              'rpn_score_weight': 1.0},
                'model': {   'anchor': {   'anchor_size': 3,
                                           'aspect_ratios': [0.5, 1.0, 2.0],
                                           'num_scales': 1},
                             'backbone': {   'mobilenet': {   'filter_size_scale': 1.0,
                                                              'model_id': 'MobileNetV2',
                                                              'output_intermediate_endpoints': False,
                                                              'output_stride': None,
                                                              'stochastic_depth_drop_rate': 0.0},
                                             'type': 'mobilenet'},
                             'decoder': {   'fpn': {   'fusion_type': 'sum',
                                                       'num_filters': 128,
                                                       'use_keras_layer': False,
                                                       'use_separable_conv': True},
                                            'type': 'fpn'},
                             'detection_generator': {   'apply_nms': True,
                                                        'max_num_detections': 100,
                                                        'nms_iou_threshold': 0.5,
                                                        'nms_version': 'v2',
                                                        'pre_nms_score_threshold': 0.05,
                                                        'pre_nms_top_k': 5000,
                                                        'soft_nms_sigma': None,
                                                        'use_cpu_nms': False,
                                                        'use_sigmoid_probability': False},
                             'detection_head': {   'cascade_class_ensemble': False,
                                                   'class_agnostic_bbox_pred': False,
                                                   'fc_dims': 512,
                                                   'num_convs': 4,
                                                   'num_fcs': 1,
                                                   'num_filters': 128,
                                                   'use_separable_conv': True},
                             'include_mask': True,
                             'input_size': [256, 256, 3],
                             'mask_head': {   'class_agnostic': False,
                                              'num_convs': 4,
                                              'num_filters': 128,
                                              'upsample_factor': 2,
                                              'use_separable_conv': True},
                             'mask_roi_aligner': {   'crop_size': 14,
                                                     'sample_offset': 0.5},
                             'mask_sampler': {'num_sampled_masks': 128},
                             'max_level': 6,
                             'min_level': 3,
                             'norm_activation': {   'activation': 'relu6',
                                                    'norm_epsilon': 0.001,
                                                    'norm_momentum': 0.99,
                                                    'use_sync_bn': True},
                             'num_classes': 4,
                             'outer_boxes_scale': 1.0,
                             'roi_aligner': {   'crop_size': 7,
                                                'sample_offset': 0.5},
                             'roi_generator': {   'nms_iou_threshold': 0.7,
                                                  'num_proposals': 1000,
                                                  'pre_nms_min_size_threshold': 0.0,
                                                  'pre_nms_score_threshold': 0.0,
                                                  'pre_nms_top_k': 2000,
                                                  'test_nms_iou_threshold': 0.7,
                                                  'test_num_proposals': 1000,
                                                  'test_pre_nms_min_size_threshold': 0.0,
                                                  'test_pre_nms_score_threshold': 0.0,
                                                  'test_pre_nms_top_k': 1000,
                                                  'use_batched_nms': False},
                             'roi_sampler': {   'background_iou_high_threshold': 0.5,
                                                'background_iou_low_threshold': 0.0,
                                                'cascade_iou_thresholds': None,
                                                'foreground_fraction': 0.25,
                                                'foreground_iou_threshold': 0.5,
                                                'mix_gt_boxes': True,
                                                'num_sampled_rois': 512},
                             'rpn_head': {   'num_convs': 1,
                                             'num_filters': 128,
                                             'use_separable_conv': True} },
                'name': None,
                'per_category_metrics': False,
                'train_data': {   'apply_tf_data_service_before_batching': False,
                                  'autotune_algorithm': None,
                                  'block_length': 1,
                                  'cache': False,
                                  'cycle_length': None,
                                  'decoder': {   'simple_decoder': {   'attribute_names': [   ],
                                                                       'mask_binarize_threshold': None,
                                                                       'regenerate_source_id': False},
                                                 'type': 'simple_decoder'},
                                  'deterministic': None,
                                  'drop_remainder': True,
                                  'dtype': 'float32',
                                  'enable_shared_tf_data_service_between_parallel_trainers': False,
                                  'enable_tf_data_service': False,
                                  'file_type': 'tfrecord',
                                  'global_batch_size': 8,
                                  'input_path': './lvis_tfrecords/train*',
                                  'is_training': True,
                                  'num_examples': -1,
                                  'parser': {   'aug_rand_hflip': True,
                                                'aug_rand_vflip': False,
                                                'aug_scale_max': 1.0,
                                                'aug_scale_min': 1.0,
                                                'aug_type': None,
                                                'mask_crop_size': 112,
                                                'match_threshold': 0.5,
                                                'max_num_instances': 100,
                                                'num_channels': 3,
                                                'pad': True,
                                                'rpn_batch_size_per_im': 256,
                                                'rpn_fg_fraction': 0.5,
                                                'rpn_match_threshold': 0.7,
                                                'rpn_unmatched_threshold': 0.3,
                                                'skip_crowd_during_training': True,
                                                'unmatched_threshold': 0.5},
                                  'prefetch_buffer_size': None,
                                  'seed': None,
                                  'sharding': True,
                                  'shuffle_buffer_size': 64,
                                  'tf_data_service_address': None,
                                  'tf_data_service_job_name': None,
                                  'tfds_as_supervised': False,
                                  'tfds_data_dir': '',
                                  'tfds_name': '',
                                  'tfds_skip_decoding_feature': '',
                                  'tfds_split': '',
                                  'trainer_id': None,
                                  'weights': None},
                'use_approx_instance_metrics': False,
                'use_coco_metrics': True,
                'use_wod_metrics': False,
                'validation_data': {   'apply_tf_data_service_before_batching': False,
                                       'autotune_algorithm': None,
                                       'block_length': 1,
                                       'cache': False,
                                       'cycle_length': None,
                                       'decoder': {   'simple_decoder': {   'attribute_names': [   ],
                                                                            'mask_binarize_threshold': None,
                                                                            'regenerate_source_id': False},
                                                      'type': 'simple_decoder'},
                                       'deterministic': None,
                                       'drop_remainder': False,
                                       'dtype': 'float32',
                                       'enable_shared_tf_data_service_between_parallel_trainers': False,
                                       'enable_tf_data_service': False,
                                       'file_type': 'tfrecord',
                                       'global_batch_size': 8,
                                       'input_path': './lvis_tfrecords/val*',
                                       'is_training': False,
                                       'num_examples': -1,
                                       'parser': {   'aug_rand_hflip': False,
                                                     'aug_rand_vflip': False,
                                                     'aug_scale_max': 1.0,
                                                     'aug_scale_min': 1.0,
                                                     'aug_type': None,
                                                     'mask_crop_size': 112,
                                                     'match_threshold': 0.5,
                                                     'max_num_instances': 100,
                                                     'num_channels': 3,
                                                     'pad': True,
                                                     'rpn_batch_size_per_im': 256,
                                                     'rpn_fg_fraction': 0.5,
                                                     'rpn_match_threshold': 0.7,
                                                     'rpn_unmatched_threshold': 0.3,
                                                     'skip_crowd_during_training': True,
                                                     'unmatched_threshold': 0.5},
                                       'prefetch_buffer_size': None,
                                       'seed': None,
                                       'sharding': True,
                                       'shuffle_buffer_size': 10000,
                                       'tf_data_service_address': None,
                                       'tf_data_service_job_name': None,
                                       'tfds_as_supervised': False,
                                       'tfds_data_dir': '',
                                       'tfds_name': '',
                                       'tfds_skip_decoding_feature': '',
                                       'tfds_split': '',
                                       'trainer_id': None,
                                       'weights': None} },
    'trainer': {   'allow_tpu_summary': False,
                   'best_checkpoint_eval_metric': '',
                   'best_checkpoint_export_subdir': '',
                   'best_checkpoint_metric_comp': 'higher',
                   'checkpoint_interval': 200,
                   'continuous_eval_timeout': 3600,
                   'eval_tf_function': True,
                   'eval_tf_while_loop': False,
                   'loss_upper_bound': 1000000.0,
                   'max_to_keep': 5,
                   'optimizer_config': {   'ema': None,
                                           'learning_rate': {   'cosine': {   'alpha': 0.0,
                                                                              'decay_steps': 2000,
                                                                              'initial_learning_rate': 0.07,
                                                                              'name': 'CosineDecay',
                                                                              'offset': 0},
                                                                'type': 'cosine'},
                                           'optimizer': {   'sgd': {   'clipnorm': None,
                                                                       'clipvalue': None,
                                                                       'decay': 0.0,
                                                                       'global_clipnorm': None,
                                                                       'momentum': 0.9,
                                                                       'name': 'SGD',
                                                                       'nesterov': False},
                                                            'type': 'sgd'},
                                           'warmup': {   'linear': {   'name': 'linear',
                                                                       'warmup_learning_rate': 0.05,
                                                                       'warmup_steps': 200},
                                                         'type': 'linear'} },
                   'preemption_on_demand_checkpoint': True,
                   'recovery_begin_steps': 0,
                   'recovery_max_trials': 0,
                   'steps_per_loop': 200,
                   'summary_interval': 200,
                   'train_steps': 2000,
                   'train_tf_function': True,
                   'train_tf_while_loop': True,
                   'validation_interval': 200,
                   'validation_steps': 200,
                   'validation_summary_subdir': 'validation'} }
<IPython.core.display.Javascript object>

Set up the distribution strategy.

# Setting up the Strategy
if exp_config.runtime.mixed_precision_dtype == tf.float16:
    tf.keras.mixed_precision.set_global_policy('mixed_float16')

if 'GPU' in ''.join(logical_device_names):
  distribution_strategy = tf.distribute.MirroredStrategy()
elif 'TPU' in ''.join(logical_device_names):
  tf.tpu.experimental.initialize_tpu_system()
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='/device:TPU_SYSTEM:0')
  distribution_strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
  print('Warning: this will be really slow.')
  distribution_strategy = tf.distribute.OneDeviceStrategy(logical_device_names[0])

print("Done")

INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')
Done

Create the `Task` object (`tfm.core.base_task.Task`) from the `config_definitions.TaskConfig`.

The Task object has all the methods necessary for building the dataset, building the model, and running training & evaluation. These methods are driven by tfm.core.train_lib.run_experiment.

with distribution_strategy.scope():
  task = tfm.core.task_factory.get_task(exp_config.task, logging_dir=model_dir)

Visualize a batch of the data.

for images, labels in task.build_inputs(exp_config.task.train_data).take(1):
  print()
  print(f'images.shape: {str(images.shape):16}  images.dtype: {images.dtype!r}')
  print(f'labels.keys: {labels.keys()}')

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/util/deprecation.py:660: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1701347149.948159    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -56 } dim { size: -42 } dim { size: -43 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -3 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
images.shape: (8, 256, 256, 3)  images.dtype: tf.float32
labels.keys: dict_keys(['anchor_boxes', 'image_info', 'rpn_score_targets', 'rpn_box_targets', 'gt_boxes', 'gt_classes', 'gt_outer_boxes', 'gt_masks'])

Create Category Index Dictionary to map the labels to coressponding label names

tf_ex_decoder = TfExampleDecoder(include_mask=True)

Helper Function for Visualizing the results from TFRecords

Use visualize_boxes_and_labels_on_image_array from visualization_utils to draw boudning boxes on the image.

def show_batch(raw_records):
  plt.figure(figsize=(20, 20))
  use_normalized_coordinates=True
  min_score_thresh = 0.30
  for i, serialized_example in enumerate(raw_records):
    plt.subplot(1, 3, i + 1)
    decoded_tensors = tf_ex_decoder.decode(serialized_example)
    image = decoded_tensors['image'].numpy().astype('uint8')
    scores = np.ones(shape=(len(decoded_tensors['groundtruth_boxes'])))
    # print(decoded_tensors['groundtruth_instance_masks'].numpy().shape)
    # print(decoded_tensors.keys())
    visualization_utils.visualize_boxes_and_labels_on_image_array(
        image,
        decoded_tensors['groundtruth_boxes'].numpy(),
        decoded_tensors['groundtruth_classes'].numpy().astype('int'),
        scores,
        category_index=category_index,
        use_normalized_coordinates=use_normalized_coordinates,
        min_score_thresh=min_score_thresh,
        instance_masks=decoded_tensors['groundtruth_instance_masks'].numpy().astype('uint8'),
        line_thickness=4)

    plt.imshow(image)
    plt.axis("off")
    plt.title(f"Image-{i+1}")
  plt.show()

Visualization of Train Data

The bounding box detection has three components

Class label of the object detected.
Percentage of match between predicted and ground truth bounding boxes.
Instance Segmentation Mask

buffer_size = 100
num_of_examples = 3

train_tfrecords = tf.io.gfile.glob(exp_config.task.train_data.input_path)
raw_records = tf.data.TFRecordDataset(train_tfrecords).shuffle(buffer_size=buffer_size).take(num_of_examples)
show_batch(raw_records)

png

Train and evaluate

We follow the COCO challenge tradition to evaluate the accuracy of object detection based on mAP(mean Average Precision). Please check here for detail explanation of how evaluation metrics for detection task is done.

IoU: is defined as the area of the intersection divided by the area of the union of a predicted bounding box and ground truth bounding box.

model, eval_logs = tfm.core.train_lib.run_experiment(
    distribution_strategy=distribution_strategy,
    task=task,
    mode='train_and_eval',
    params=exp_config,
    model_dir=model_dir,
    run_post_eval=True)

INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
WARNING:tensorflow:`tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized` set to True.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/engine/functional.py:642: UserWarning: Input dict contained keys ['6'] which did not match any model input. They will be ignored by the model.
  inputs = self._flatten_to_reference_inputs(inputs)
WARNING:tensorflow:`tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized` set to True.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer VarianceScaling is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.
  warnings.warn(
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
restoring or initializing model...
INFO:tensorflow:Customized initialization is done through the passed `init_fn`.
INFO:tensorflow:Customized initialization is done through the passed `init_fn`.
train | step:      0 | training until step 200...
W0000 00:00:1701347174.666189    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -56 } dim { size: -42 } dim { size: -43 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -3 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 101 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 101 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
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INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 1 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 101 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
INFO:tensorflow:Collective all_reduce tensors: 101 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1
train | step:    200 | steps/sec:    1.3 | output: 
    {'frcnn_box_loss': 0.31850263,
     'frcnn_cls_loss': 0.05660701,
     'learning_rate': 0.06828698,
     'mask_loss': 0.5251324,
     'model_loss': 1.0341916,
     'rpn_box_loss': 0.0608424,
     'rpn_score_loss': 0.073107146,
     'total_loss': 1.3348999,
     'training_loss': 1.3348999}
saved checkpoint to ./trained_model/ckpt-200.
 eval | step:    200 | running 200 steps of evaluation...
W0000 00:00:1701347326.414129    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.55s).
Accumulating evaluation results...
DONE (t=0.37s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.018
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.004
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.009
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.027
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.049
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.096
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.77s).
Accumulating evaluation results...
DONE (t=0.36s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.013
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.024
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.036
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.021
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.099
 eval | step:    200 | steps/sec:    2.5 | eval time:   80.1 sec | output: 
    {'AP': 0.0034739533,
     'AP50': 0.01818383,
     'AP75': 0.000105925246,
     'APl': 0.009990587,
     'APm': 0.0038059496,
     'APs': 0.00014011688,
     'ARl': 0.096254684,
     'ARm': 0.060511984,
     'ARmax1': 0.008508347,
     'ARmax10': 0.026843267,
     'ARmax100': 0.04902959,
     'ARs': 0.0065315315,
     'mask_AP': 0.0021072333,
     'mask_AP50': 0.012642865,
     'mask_AP75': 1.1583788e-05,
     'mask_APl': 0.010214079,
     'mask_APm': 0.001238946,
     'mask_APs': 5.1088673e-06,
     'mask_ARl': 0.09868914,
     'mask_ARm': 0.021187363,
     'mask_ARmax1': 0.008023694,
     'mask_ARmax10': 0.02381474,
     'mask_ARmax100': 0.035661805,
     'mask_ARs': 0.0015765766,
     'steps_per_second': 2.49587810524514,
     'validation_loss': 0.0}
train | step:    200 | training until step 400...
train | step:    400 | steps/sec:    1.7 | output: 
    {'frcnn_box_loss': 0.3148728,
     'frcnn_cls_loss': 0.04683144,
     'learning_rate': 0.06331559,
     'mask_loss': 0.41505823,
     'model_loss': 0.8509541,
     'rpn_box_loss': 0.054795396,
     'rpn_score_loss': 0.019396221,
     'total_loss': 1.1508584,
     'training_loss': 1.1508584}
saved checkpoint to ./trained_model/ckpt-400.
 eval | step:    400 | running 200 steps of evaluation...
W0000 00:00:1701347440.748884    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.96s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.038
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.109
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.018
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.023
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.104
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.049
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.074
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.077
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.005
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.055
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.201
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.08s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.026
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.086
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.003
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.012
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.076
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.036
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.051
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.024
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.153
 eval | step:    400 | steps/sec:    4.5 | eval time:   44.7 sec | output: 
    {'AP': 0.037587434,
     'AP50': 0.1090748,
     'AP75': 0.018366594,
     'APl': 0.10382032,
     'APm': 0.022636896,
     'APs': 0.0011642236,
     'ARl': 0.20149812,
     'ARm': 0.054738563,
     'ARmax1': 0.04948842,
     'ARmax10': 0.07442111,
     'ARmax100': 0.07727517,
     'ARs': 0.0054054055,
     'mask_AP': 0.025703182,
     'mask_AP50': 0.08565975,
     'mask_AP75': 0.0030623602,
     'mask_APl': 0.07599234,
     'mask_APm': 0.012238867,
     'mask_APs': 9.593982e-07,
     'mask_ARl': 0.15299626,
     'mask_ARm': 0.02369281,
     'mask_ARmax1': 0.0364028,
     'mask_ARmax10': 0.050511576,
     'mask_ARmax100': 0.051857837,
     'mask_ARs': 0.00022522523,
     'steps_per_second': 4.477373324644756,
     'validation_loss': 0.0}
train | step:    400 | training until step 600...
train | step:    600 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.27767265,
     'frcnn_cls_loss': 0.049189795,
     'learning_rate': 0.055572484,
     'mask_loss': 0.39033934,
     'model_loss': 0.7880812,
     'rpn_box_loss': 0.051621474,
     'rpn_score_loss': 0.019257905,
     'total_loss': 1.0869627,
     'training_loss': 1.0869627}
saved checkpoint to ./trained_model/ckpt-600.
 eval | step:    600 | running 200 steps of evaluation...
W0000 00:00:1701347519.556857    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.53s).
Accumulating evaluation results...
DONE (t=0.34s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.058
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.147
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.035
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.035
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.161
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.067
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.094
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.108
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.099
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.244
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.71s).
Accumulating evaluation results...
DONE (t=0.33s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.029
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.099
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.005
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.010
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.098
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.041
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.052
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.054
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.155
 eval | step:    600 | steps/sec:    4.2 | eval time:   47.9 sec | output: 
    {'AP': 0.057559177,
     'AP50': 0.14743358,
     'AP75': 0.034844287,
     'APl': 0.1611763,
     'APm': 0.035119582,
     'APs': 0.0034293495,
     'ARl': 0.24419476,
     'ARm': 0.09869281,
     'ARmax1': 0.0665136,
     'ARmax10': 0.0941388,
     'ARmax100': 0.10835528,
     'ARs': 0.026576577,
     'mask_AP': 0.028755136,
     'mask_AP50': 0.0986329,
     'mask_AP75': 0.00467551,
     'mask_APl': 0.09832131,
     'mask_APm': 0.010138089,
     'mask_APs': 1.7697794e-06,
     'mask_ARl': 0.15505618,
     'mask_ARm': 0.030337691,
     'mask_ARmax1': 0.040624514,
     'mask_ARmax10': 0.052256178,
     'mask_ARmax100': 0.05403324,
     'mask_ARs': 0.0009009009,
     'steps_per_second': 4.171504081290655,
     'validation_loss': 0.0}
train | step:    600 | training until step 800...
train | step:    800 | steps/sec:    2.4 | output: 
    {'frcnn_box_loss': 0.28230885,
     'frcnn_cls_loss': 0.043198194,
     'learning_rate': 0.045815594,
     'mask_loss': 0.38323456,
     'model_loss': 0.77104104,
     'rpn_box_loss': 0.046326794,
     'rpn_score_loss': 0.015972756,
     'total_loss': 1.0689586,
     'training_loss': 1.0689586}
saved checkpoint to ./trained_model/ckpt-800.
 eval | step:    800 | running 200 steps of evaluation...
W0000 00:00:1701347601.529665    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.01s).
Accumulating evaluation results...
DONE (t=0.32s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.063
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.147
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.046
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.037
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.177
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.072
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.097
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.103
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.078
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.256
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.14s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.045
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.124
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.017
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.018
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.136
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.053
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.064
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.065
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.192
 eval | step:    800 | steps/sec:    4.4 | eval time:   45.0 sec | output: 
    {'AP': 0.063469686,
     'AP50': 0.1473477,
     'AP75': 0.045820855,
     'APl': 0.17694354,
     'APm': 0.03674903,
     'APs': 0.003920865,
     'ARl': 0.25561798,
     'ARm': 0.07761438,
     'ARmax1': 0.07167474,
     'ARmax10': 0.09676898,
     'ARmax100': 0.10253096,
     'ARs': 0.014639639,
     'mask_AP': 0.04488069,
     'mask_AP50': 0.12370826,
     'mask_AP75': 0.0165427,
     'mask_APl': 0.1360923,
     'mask_APm': 0.017513687,
     'mask_APs': 5.2146588e-05,
     'mask_ARl': 0.19232209,
     'mask_ARm': 0.029575163,
     'mask_ARmax1': 0.053419493,
     'mask_ARmax10': 0.064243406,
     'mask_ARmax100': 0.06532041,
     'mask_ARs': 0.0011261262,
     'steps_per_second': 4.4475057268487275,
     'validation_loss': 0.0}
train | step:    800 | training until step 1000...
train | step:   1000 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.26871693,
     'frcnn_cls_loss': 0.04368285,
     'learning_rate': 0.034999996,
     'mask_loss': 0.38002,
     'model_loss': 0.7526051,
     'rpn_box_loss': 0.044981632,
     'rpn_score_loss': 0.015203744,
     'total_loss': 1.0497146,
     'training_loss': 1.0497146}
saved checkpoint to ./trained_model/ckpt-1000.
 eval | step:   1000 | running 200 steps of evaluation...
W0000 00:00:1701347680.528202    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.98s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.078
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.165
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.065
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.051
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.205
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.083
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.107
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.110
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.05s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.046
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.129
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.017
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.017
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.143
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.055
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.064
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.065
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.190
 eval | step:   1000 | steps/sec:    4.4 | eval time:   45.9 sec | output: 
    {'AP': 0.078015566,
     'AP50': 0.16489986,
     'AP75': 0.06506885,
     'APl': 0.20505275,
     'APm': 0.0508199,
     'APs': 0.009842132,
     'ARl': 0.26666668,
     'ARm': 0.0875817,
     'ARmax1': 0.08303716,
     'ARmax10': 0.10705439,
     'ARmax100': 0.11001615,
     'ARs': 0.018693693,
     'mask_AP': 0.045874245,
     'mask_AP50': 0.12868273,
     'mask_AP75': 0.016570596,
     'mask_APl': 0.14324915,
     'mask_APm': 0.017211707,
     'mask_APs': 9.338933e-05,
     'mask_ARl': 0.18988764,
     'mask_ARm': 0.030392157,
     'mask_ARmax1': 0.0547119,
     'mask_ARmax10': 0.0638126,
     'mask_ARmax100': 0.06483576,
     'mask_ARs': 0.0009009009,
     'steps_per_second': 4.3583434846159985,
     'validation_loss': 0.0}
train | step:   1000 | training until step 1200...
train | step:   1200 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.24772172,
     'frcnn_cls_loss': 0.0436343,
     'learning_rate': 0.024184398,
     'mask_loss': 0.36858365,
     'model_loss': 0.72140527,
     'rpn_box_loss': 0.045137476,
     'rpn_score_loss': 0.016328312,
     'total_loss': 1.0178626,
     'training_loss': 1.0178626}
saved checkpoint to ./trained_model/ckpt-1200.
 eval | step:   1200 | running 200 steps of evaluation...
W0000 00:00:1701347760.342535    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.15s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.084
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.176
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.069
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.010
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.056
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.219
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.090
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.114
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.124
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.110
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.277
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.28s).
Accumulating evaluation results...
DONE (t=0.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.048
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.125
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.024
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.016
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.153
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.059
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.067
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.068
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.037
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.196
 eval | step:   1200 | steps/sec:    4.3 | eval time:   46.4 sec | output: 
    {'AP': 0.08426202,
     'AP50': 0.17574485,
     'AP75': 0.06948364,
     'APl': 0.21902785,
     'APm': 0.0561062,
     'APs': 0.010037346,
     'ARl': 0.27734083,
     'ARm': 0.10996732,
     'ARmax1': 0.0897382,
     'ARmax10': 0.114347786,
     'ARmax100': 0.12382544,
     'ARs': 0.03536036,
     'mask_AP': 0.04775647,
     'mask_AP50': 0.12510313,
     'mask_AP75': 0.023735445,
     'mask_APl': 0.15327115,
     'mask_APm': 0.015975025,
     'mask_APs': 9.356999e-05,
     'mask_ARl': 0.19606742,
     'mask_ARm': 0.036764707,
     'mask_ARmax1': 0.05893583,
     'mask_ARmax10': 0.06712107,
     'mask_ARmax100': 0.068467334,
     'mask_ARs': 0.0018018018,
     'steps_per_second': 4.310460102047628,
     'validation_loss': 0.0}
train | step:   1200 | training until step 1400...
train | step:   1400 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.23599713,
     'frcnn_cls_loss': 0.041378804,
     'learning_rate': 0.014427517,
     'mask_loss': 0.35429612,
     'model_loss': 0.68914723,
     'rpn_box_loss': 0.042537488,
     'rpn_score_loss': 0.014937655,
     'total_loss': 0.98513216,
     'training_loss': 0.98513216}
saved checkpoint to ./trained_model/ckpt-1400.
 eval | step:   1400 | running 200 steps of evaluation...
W0000 00:00:1701347840.710868    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.00s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.088
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.177
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.075
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.059
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.226
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.091
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.115
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.122
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.029
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.282
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.09s).
Accumulating evaluation results...
DONE (t=0.29s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.051
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.133
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.027
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.019
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.156
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.060
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.070
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.071
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.203
 eval | step:   1400 | steps/sec:    4.5 | eval time:   44.5 sec | output: 
    {'AP': 0.087541394,
     'AP50': 0.1765836,
     'AP75': 0.074927665,
     'APl': 0.22625497,
     'APm': 0.05886792,
     'APs': 0.011434166,
     'ARl': 0.28220972,
     'ARm': 0.10310458,
     'ARmax1': 0.09084545,
     'ARmax10': 0.11534733,
     'ARmax100': 0.12186322,
     'ARs': 0.029279279,
     'mask_AP': 0.050579414,
     'mask_AP50': 0.13304058,
     'mask_AP75': 0.027463775,
     'mask_APl': 0.15557747,
     'mask_APm': 0.018948099,
     'mask_APs': 0.00015400951,
     'mask_ARl': 0.20262173,
     'mask_ARm': 0.036111113,
     'mask_ARmax1': 0.05988153,
     'mask_ARmax10': 0.070113085,
     'mask_ARmax100': 0.07059774,
     'mask_ARs': 0.0018018018,
     'steps_per_second': 4.490972982132935,
     'validation_loss': 0.0}
train | step:   1400 | training until step 1600...
train | step:   1600 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.2448898,
     'frcnn_cls_loss': 0.040846318,
     'learning_rate': 0.006684403,
     'mask_loss': 0.3572018,
     'model_loss': 0.69579756,
     'rpn_box_loss': 0.038723875,
     'rpn_score_loss': 0.014135833,
     'total_loss': 0.99148893,
     'training_loss': 0.99148893}
saved checkpoint to ./trained_model/ckpt-1600.
 eval | step:   1600 | running 200 steps of evaluation...
W0000 00:00:1701347919.382529    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.00s).
Accumulating evaluation results...
DONE (t=0.29s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.089
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.175
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.081
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.014
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.057
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.232
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.093
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.118
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.124
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.099
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.08s).
Accumulating evaluation results...
DONE (t=0.28s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.054
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.136
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.030
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.021
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.168
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.063
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.074
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.075
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.041
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.212
 eval | step:   1600 | steps/sec:    4.4 | eval time:   45.6 sec | output: 
    {'AP': 0.08937628,
     'AP50': 0.17525133,
     'AP75': 0.0808516,
     'APl': 0.2315524,
     'APm': 0.057085045,
     'APs': 0.014369107,
     'ARl': 0.28820226,
     'ARm': 0.09918301,
     'ARmax1': 0.09332256,
     'ARmax10': 0.11814755,
     'ARmax100': 0.12353258,
     'ARs': 0.03400901,
     'mask_AP': 0.053880908,
     'mask_AP50': 0.1363149,
     'mask_AP75': 0.03003062,
     'mask_APl': 0.16768122,
     'mask_APm': 0.021485755,
     'mask_APs': 0.0002800922,
     'mask_ARl': 0.21161048,
     'mask_ARm': 0.040849674,
     'mask_ARmax1': 0.063327946,
     'mask_ARmax10': 0.07399031,
     'mask_ARmax100': 0.07495961,
     'mask_ARs': 0.0027027028,
     'steps_per_second': 4.384812711815325,
     'validation_loss': 0.0}
train | step:   1600 | training until step 1800...
train | step:   1800 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.23585029,
     'frcnn_cls_loss': 0.039750163,
     'learning_rate': 0.0017130232,
     'mask_loss': 0.35994247,
     'model_loss': 0.6895491,
     'rpn_box_loss': 0.040137492,
     'rpn_score_loss': 0.013868618,
     'total_loss': 0.9850925,
     'training_loss': 0.9850925}
saved checkpoint to ./trained_model/ckpt-1800.
 eval | step:   1800 | running 200 steps of evaluation...
W0000 00:00:1701347999.044772    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.04s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.089
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.177
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.085
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.012
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.230
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.092
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.117
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.123
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.102
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.282
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.12s).
Accumulating evaluation results...
DONE (t=0.29s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.051
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.133
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.025
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.019
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.159
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.060
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.069
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.070
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.200
 eval | step:   1800 | steps/sec:    4.5 | eval time:   44.7 sec | output: 
    {'AP': 0.08882947,
     'AP50': 0.1769001,
     'AP75': 0.084709905,
     'APl': 0.23017395,
     'APm': 0.05795139,
     'APs': 0.012462094,
     'ARl': 0.282397,
     'ARm': 0.10163399,
     'ARmax1': 0.09213786,
     'ARmax10': 0.11690899,
     'ARmax100': 0.122886375,
     'ARs': 0.03445946,
     'mask_AP': 0.050573327,
     'mask_AP50': 0.13258772,
     'mask_AP75': 0.025201378,
     'mask_APl': 0.15921223,
     'mask_APm': 0.019294621,
     'mask_APs': 0.00025428535,
     'mask_ARl': 0.20018727,
     'mask_ARm': 0.03627451,
     'mask_ARmax1': 0.06015078,
     'mask_ARmax10': 0.06919763,
     'mask_ARmax100': 0.07022078,
     'mask_ARs': 0.002927928,
     'steps_per_second': 4.474347551876668,
     'validation_loss': 0.0}
train | step:   1800 | training until step 2000...
train | step:   2000 | steps/sec:    2.5 | output: 
    {'frcnn_box_loss': 0.22732982,
     'frcnn_cls_loss': 0.04367072,
     'learning_rate': 0.0,
     'mask_loss': 0.35671493,
     'model_loss': 0.68381965,
     'rpn_box_loss': 0.040140744,
     'rpn_score_loss': 0.01596347,
     'total_loss': 0.9793183,
     'training_loss': 0.9793183}
saved checkpoint to ./trained_model/ckpt-2000.
 eval | step:   2000 | running 200 steps of evaluation...
W0000 00:00:1701348077.624909    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.05s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.091
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.178
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.086
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.062
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.233
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.093
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.119
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.125
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.286
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.12s).
Accumulating evaluation results...
DONE (t=0.29s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.053
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.136
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.028
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.022
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.164
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.062
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.072
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.073
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.040
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.207
 eval | step:   2000 | steps/sec:    4.4 | eval time:   45.7 sec | output: 
    {'AP': 0.09090912,
     'AP50': 0.17810935,
     'AP75': 0.08558971,
     'APl': 0.23334582,
     'APm': 0.0617902,
     'APs': 0.013224964,
     'ARl': 0.2863296,
     'ARm': 0.10294118,
     'ARmax1': 0.09337641,
     'ARmax10': 0.11895531,
     'ARmax100': 0.124663435,
     'ARs': 0.035135135,
     'mask_AP': 0.053071458,
     'mask_AP50': 0.13569556,
     'mask_AP75': 0.028363172,
     'mask_APl': 0.16439752,
     'mask_APm': 0.022396944,
     'mask_APs': 0.00038678324,
     'mask_ARl': 0.20692883,
     'mask_ARm': 0.039542485,
     'mask_ARmax1': 0.062250942,
     'mask_ARmax10': 0.0724825,
     'mask_ARmax100': 0.073236406,
     'mask_ARs': 0.002927928,
     'steps_per_second': 4.3735250426509005,
     'validation_loss': 0.0}
 eval | step:   2000 | running 200 steps of evaluation...
W0000 00:00:1701348123.440153    8938 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -5 } dim { size: -6 } dim { size: -7 } dim { size: 1 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 112 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2199 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 57671680 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 112 } dim { size: 112 } dim { size: 1 } } }
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=1.01s).
Accumulating evaluation results...
DONE (t=0.29s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.091
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.178
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.086
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.013
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.062
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.233
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.093
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.119
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.125
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.286
Running per image evaluation...
Evaluate annotation type *segm*
DONE (t=1.10s).
Accumulating evaluation results...
DONE (t=0.30s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.053
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.136
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.028
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.022
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.164
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.062
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.072
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.073
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.040
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.207
 eval | step:   2000 | steps/sec:    4.4 | eval time:   45.8 sec | output: 
    {'AP': 0.09090912,
     'AP50': 0.17810935,
     'AP75': 0.08558971,
     'APl': 0.23334582,
     'APm': 0.0617902,
     'APs': 0.013224964,
     'ARl': 0.2863296,
     'ARm': 0.10294118,
     'ARmax1': 0.09337641,
     'ARmax10': 0.11895531,
     'ARmax100': 0.124663435,
     'ARs': 0.035135135,
     'mask_AP': 0.053071458,
     'mask_AP50': 0.13569556,
     'mask_AP75': 0.028363172,
     'mask_APl': 0.16439752,
     'mask_APm': 0.022396944,
     'mask_APs': 0.00038678324,
     'mask_ARl': 0.20692883,
     'mask_ARm': 0.039542485,
     'mask_ARmax1': 0.062250942,
     'mask_ARmax10': 0.0724825,
     'mask_ARmax100': 0.073236406,
     'mask_ARs': 0.002927928,
     'steps_per_second': 4.370002598316903,
     'validation_loss': 0.0}

Load logs in tensorboard

%load_ext tensorboard
%tensorboard --logdir "./trained_model"

Saving and exporting the trained model

The keras.Model object returned by train_lib.run_experiment expects the data to be normalized by the dataset loader using the same mean and variance statiscics in preprocess_ops.normalize_image(image, offset=MEAN_RGB, scale=STDDEV_RGB). This export function handles those details, so you can pass tf.uint8 images and get the correct results.

export_saved_model_lib.export_inference_graph(
    input_type='image_tensor',
    batch_size=1,
    input_image_size=[HEIGHT, WIDTH],
    params=exp_config,
    checkpoint_path=tf.train.latest_checkpoint(model_dir),
    export_dir=export_dir)

/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/engine/functional.py:642: UserWarning: Input dict contained keys ['6'] which did not match any model input. They will be ignored by the model.
  inputs = self._flatten_to_reference_inputs(inputs)
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.maskrcnn_model.MaskRCNNModel object at 0x7f90444a1e50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.maskrcnn_model.MaskRCNNModel object at 0x7f90444a1e50>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f9371926460>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f9371926460>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f902c4d7250>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f902c4d7250>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90c84cc850>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90c84cc850>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f92cd278250>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f92cd278250>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90443add00>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90443add00>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90445fe4c0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f90445fe4c0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f902c505d00>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.src.layers.merging.add.Add object at 0x7f902c505d00>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.detection_generator.DetectionGenerator object at 0x7f90c0603310>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.detection_generator.DetectionGenerator object at 0x7f90c0603310>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.mask_sampler.MaskSampler object at 0x7f9044492a60>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.mask_sampler.MaskSampler object at 0x7f9044492a60>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.roi_sampler.ROISampler object at 0x7f902c2afee0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.roi_sampler.ROISampler object at 0x7f902c2afee0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.box_sampler.BoxSampler object at 0x7f90c0615220>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <official.vision.modeling.layers.box_sampler.BoxSampler object at 0x7f90c0615220>, because it is not built.
INFO:tensorflow:Assets written to: ./exported_model/assets
INFO:tensorflow:Assets written to: ./exported_model/assets

Inference from Trained Model

def load_image_into_numpy_array(path):
  """Load an image from file into a numpy array.

  Puts image into numpy array to feed into tensorflow graph.
  Note that by convention we put it into a numpy array with shape
  (height, width, channels), where channels=3 for RGB.

  Args:
    path: the file path to the image

  Returns:
    uint8 numpy array with shape (img_height, img_width, 3)
  """
  image = None
  if(path.startswith('http')):
    response = urlopen(path)
    image_data = response.read()
    image_data = BytesIO(image_data)
    image = Image.open(image_data)
  else:
    image_data = tf.io.gfile.GFile(path, 'rb').read()
    image = Image.open(BytesIO(image_data))

  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (1, im_height, im_width, 3)).astype(np.uint8)



def build_inputs_for_object_detection(image, input_image_size):
  """Builds Object Detection model inputs for serving."""
  image, _ = resize_and_crop_image(
      image,
      input_image_size,
      padded_size=input_image_size,
      aug_scale_min=1.0,
      aug_scale_max=1.0)
  return image

Visualize test data

num_of_examples = 3

test_tfrecords = tf.io.gfile.glob('./lvis_tfrecords/val*')
test_ds = tf.data.TFRecordDataset(test_tfrecords).take(num_of_examples)
show_batch(test_ds)

png

Importing SavedModel

imported = tf.saved_model.load(export_dir)
model_fn = imported.signatures['serving_default']

WARNING:absl:Importing a function (__inference_internal_grad_fn_419718) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416667) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
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WARNING:absl:Importing a function (__inference_internal_grad_fn_417423) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417711) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413850) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416748) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418494) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419052) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417837) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417180) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419565) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416640) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417675) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416919) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417342) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414723) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414408) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416226) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415056) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414597) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415983) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418485) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415686) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416649) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415173) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418719) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414876) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417918) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416028) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415866) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414660) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415893) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418305) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419214) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413589) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418062) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419232) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417693) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419421) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418350) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417549) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415884) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414111) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417756) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415821) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418881) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413625) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413859) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417405) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417072) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417639) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419511) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415209) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418278) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416631) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418683) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414084) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416451) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418935) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418566) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413634) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419277) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417000) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416460) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416397) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418224) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415929) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415992) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417513) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415677) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415137) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417864) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419007) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413877) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414867) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417666) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415515) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416262) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417684) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414804) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419196) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419268) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419295) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416163) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416802) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418026) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414453) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419250) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415488) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415416) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417486) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415920) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418242) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418647) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415182) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415587) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413643) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413454) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415425) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419655) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419520) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414903) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418359) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415083) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413733) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419403) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417468) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418377) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414129) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417090) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418845) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415839) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415164) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416271) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415524) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414381) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417144) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416658) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414849) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419583) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417036) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419151) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418791) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419241) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417216) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413940) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416055) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414489) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414093) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414102) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417900) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415947) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414219) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417045) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414012) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415875) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418116) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414939) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413544) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419457) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418611) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414993) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419547) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414696) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413688) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417333) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414615) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417702) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419034) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418584) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415731) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416001) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418170) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418953) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413841) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413706) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414588) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418593) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418665) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419205) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416523) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415767) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417414) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414759) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415398) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416433) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413607) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418971) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417603) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413562) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417558) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416829) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413535) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415245) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418125) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419367) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419412) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419259) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414336) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416478) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416325) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413652) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413517) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418854) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417459) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416550) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415281) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413814) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419691) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416208) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414543) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414039) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414165) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415911) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417999) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414309) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415722) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414975) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414948) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419628) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415299) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415011) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416424) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416532) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417855) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416154) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416064) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415749) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415542) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417378) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419133) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414966) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417225) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419106) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419016) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414570) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414840) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418656) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415353) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415434) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415128) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413481) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418386) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414858) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415065) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419286) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419142) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418728) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416910) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413445) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418458) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417315) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416406) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417936) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419700) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414057) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417621) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416883) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416856) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414003) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413958) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418251) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416199) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414120) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417432) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417810) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416352) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416361) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417018) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415074) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414579) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416784) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414327) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416811) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415668) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413715) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416145) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414624) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416964) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416676) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416082) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418746) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414561) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414066) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419736) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419727) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419376) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414534) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419169) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415002) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419025) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418332) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418233) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416370) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416793) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414669) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413526) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415110) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416613) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414831) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413571) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419187) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413778) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417198) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418926) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414021) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415254) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418629) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419331) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419637) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418143) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417747) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415803) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416172) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417387) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414525) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416901) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419619) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418269) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417720) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414237) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415389) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416109) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415974) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415335) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418989) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419160) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416127) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417909) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417774) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414363) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419313) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414210) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414345) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418800) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417819) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418890) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415659) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413616) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418692) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416541) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417972) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415857) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417630) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415704) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414642) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415569) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416982) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414714) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413751) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413904) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416298) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416991) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418755) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414282) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417540) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415506) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418944) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418530) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418260) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418287) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419178) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415578) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417009) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413868) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415155) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418467) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415218) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415236) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418053) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419061) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414732) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414930) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419529) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415119) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413886) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414633) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416892) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416037) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416487) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414417) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414201) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417207) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415596) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414912) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415794) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416244) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416766) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419322) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418431) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413796) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418521) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414606) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415461) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417234) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415695) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417963) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415632) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414984) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417792) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415407) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414075) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416388) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413508) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419745) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418206) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414822) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414768) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417585) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419448) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419340) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417135) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418215) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417126) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414507) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416874) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418098) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413769) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413931) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414471) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419115) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414813) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414246) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417531) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413436) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417369) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416721) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416379) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417504) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418512) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415533) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414921) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417477) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414498) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418836) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419043) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415227) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415317) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416217) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413553) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418827) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413913) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413922) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419592) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417783) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416973) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418395) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417648) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415290) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418557) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414894) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415614) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413823) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417594) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419538) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415623) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417612) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414138) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419475) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417360) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415650) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418908) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418071) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416622) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416190) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418035) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416712) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418503) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418152) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416586) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419610) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417162) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419682) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418413) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413895) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414777) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418080) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413670) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418980) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416838) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416595) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415497) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413490) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417099) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418764) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417063) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415812) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416730) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415605) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417306) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416334) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414678) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415101) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413472) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415371) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414048) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419664) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416955) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416757) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418314) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417657) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415191) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417495) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418917) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418539) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418161) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418863) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414183) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416505) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418737) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417738) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418134) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415551) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414390) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414264) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417288) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415740) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419502) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418323) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418296) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416577) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415443) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413463) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418638) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415029) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418701) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417441) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417252) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417189) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416136) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414480) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418017) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417576) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418782) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418404) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413832) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416253) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416937) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415713) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415785) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418440) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419574) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417891) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416946) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413598) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414174) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418089) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419394) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417765) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418809) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414426) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413994) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418341) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416442) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418818) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418602) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417981) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417054) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413697) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414705) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417729) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416604) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419466) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418575) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419223) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413742) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418998) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416496) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415965) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_418710) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415848) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_419709) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415956) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417351) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415047) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416865) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414273) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_416820) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415641) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_417027) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414318) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_414444) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_413724) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_internal_grad_fn_415470) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.

Visualize predictions

def reframe_image_corners_relative_to_boxes(boxes):
  """Reframe the image corners ([0, 0, 1, 1]) to be relative to boxes.
  The local coordinate frame of each box is assumed to be relative to
  its own for corners.
  Args:
    boxes: A float tensor of [num_boxes, 4] of (ymin, xmin, ymax, xmax)
      coordinates in relative coordinate space of each bounding box.
  Returns:
    reframed_boxes: Reframes boxes with same shape as input.
  """
  ymin, xmin, ymax, xmax = (boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3])

  height = tf.maximum(ymax - ymin, 1e-4)
  width = tf.maximum(xmax - xmin, 1e-4)

  ymin_out = (0 - ymin) / height
  xmin_out = (0 - xmin) / width
  ymax_out = (1 - ymin) / height
  xmax_out = (1 - xmin) / width
  return tf.stack([ymin_out, xmin_out, ymax_out, xmax_out], axis=1)

def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
                                     image_width, resize_method='bilinear'):
  """Transforms the box masks back to full image masks.
  Embeds masks in bounding boxes of larger masks whose shapes correspond to
  image shape.
  Args:
    box_masks: A tensor of size [num_masks, mask_height, mask_width].
    boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
           corners. Row i contains [ymin, xmin, ymax, xmax] of the box
           corresponding to mask i. Note that the box corners are in
           normalized coordinates.
    image_height: Image height. The output mask will have the same height as
                  the image height.
    image_width: Image width. The output mask will have the same width as the
                 image width.
    resize_method: The resize method, either 'bilinear' or 'nearest'. Note that
      'bilinear' is only respected if box_masks is a float.
  Returns:
    A tensor of size [num_masks, image_height, image_width] with the same dtype
    as `box_masks`.
  """
  resize_method = 'nearest' if box_masks.dtype == tf.uint8 else resize_method
  # TODO(rathodv): Make this a public function.
  def reframe_box_masks_to_image_masks_default():
    """The default function when there are more than 0 box masks."""

    num_boxes = tf.shape(box_masks)[0]
    box_masks_expanded = tf.expand_dims(box_masks, axis=3)

    resized_crops = tf.image.crop_and_resize(
        image=box_masks_expanded,
        boxes=reframe_image_corners_relative_to_boxes(boxes),
        box_indices=tf.range(num_boxes),
        crop_size=[image_height, image_width],
        method=resize_method,
        extrapolation_value=0)
    return tf.cast(resized_crops, box_masks.dtype)

  image_masks = tf.cond(
      tf.shape(box_masks)[0] > 0,
      reframe_box_masks_to_image_masks_default,
      lambda: tf.zeros([0, image_height, image_width, 1], box_masks.dtype))
  return tf.squeeze(image_masks, axis=3)

input_image_size = (HEIGHT, WIDTH)
plt.figure(figsize=(20, 20))
min_score_thresh = 0.40 # Change minimum score for threshold to see all bounding boxes confidences

for i, serialized_example in enumerate(test_ds):
  plt.subplot(1, 3, i+1)
  decoded_tensors = tf_ex_decoder.decode(serialized_example)
  image = build_inputs_for_object_detection(decoded_tensors['image'], input_image_size)
  image = tf.expand_dims(image, axis=0)
  image = tf.cast(image, dtype = tf.uint8)
  image_np = image[0].numpy()
  result = model_fn(image)
  # Visualize detection and masks
  if 'detection_masks' in result:
    # we need to convert np.arrays to tensors
    detection_masks = tf.convert_to_tensor(result['detection_masks'][0])
    detection_boxes = tf.convert_to_tensor(result['detection_boxes'][0])
    detection_masks_reframed = reframe_box_masks_to_image_masks(
              detection_masks, detection_boxes/256.0,
                image_np.shape[0], image_np.shape[1])
    detection_masks_reframed = tf.cast(
        detection_masks_reframed > min_score_thresh,
        np.uint8)

    result['detection_masks_reframed'] = detection_masks_reframed.numpy()
  visualization_utils.visualize_boxes_and_labels_on_image_array(
        image_np,
        result['detection_boxes'][0].numpy(),
        (result['detection_classes'][0] + 0).numpy().astype(int),
        result['detection_scores'][0].numpy(),
        category_index=category_index,
        use_normalized_coordinates=False,
        max_boxes_to_draw=200,
        min_score_thresh=min_score_thresh,
        instance_masks=result.get('detection_masks_reframed', None),
        line_thickness=4)

  plt.imshow(image_np)
  plt.axis("off")

plt.show()

png