This hands-on tutorial shows how to fine-tune a Mask R-CNN with a MobileNetV2 backbone using TensorFlow Model Garden. You’ll download LVIS annotations, generate TFRecords from COCO images, load a registered Mask R-CNN experiment config, adapt it for a small custom class set, set training hyperparameters, run distributed training, evaluate, and finally export a SavedModel for deployment. Code and configs follow TensorFlow best practices; original samples are CC BY 4.0 / Apache 2.0.This hands-on tutorial shows how to fine-tune a Mask R-CNN with a MobileNetV2 backbone using TensorFlow Model Garden. You’ll download LVIS annotations, generate TFRecords from COCO images, load a registered Mask R-CNN experiment config, adapt it for a small custom class set, set training hyperparameters, run distributed training, evaluate, and finally export a SavedModel for deployment. Code and configs follow TensorFlow best practices; original samples are CC BY 4.0 / Apache 2.0.
Train a Mask R-CNN for Instance Segmentation with TF Model Garden
Content Overview
- Install Necessary Dependencies
- Import required libraries
- Download subset of Ivis dataset
- Configure the MaskRCNN Resnet FPN COCO model for custom dataset
- Create the Task object (tfm.core.basetask.Task) from the configdefinitions.TaskConfig
\ 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.
\
:::tip Note: LVIS uses the COCO 2017 train, validation, and test image sets. If you have already downloaded the COCO images, you only need to download the LVIS annotations. LVIS val set contains images from COCO 2017 train in addition to the COCO 2017 val split.
:::
\
# @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’ lvis_v1_val.json.zi 100%[===================>] 61.06M 184MB/s in 0.3s 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) \ \
:::info Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License.
:::
\
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact [email protected] for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.
You May Also Like
Wilma now a low-pressure area, to drench Palawan, Western Visayas
Tropical Cyclone Wilma, which has weakened into a low-pressure area (LPA), is likely to bring thunderstorms to Palawan and Western Visayas, according to the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) on Monday. The former storm Wilma was last located 265 kilometers south of Cuyo, Palawan, according to PAGASA’s 10:00 a.m. advisory. In its earlier 4:00 a.m. […]
Share
Bworldonline2025/12/08 12:37
The Future of Secure Messaging: Why Decentralization Matters
The post The Future of Secure Messaging: Why Decentralization Matters appeared on BitcoinEthereumNews.com. From encrypted chats to decentralized messaging Encrypted messengers are having a second wave. Apps like WhatsApp, iMessage and Signal made end-to-end encryption (E2EE) a default expectation. But most still hinge on phone numbers, centralized servers and a lot of metadata, such as who you talk to, when, from which IP and on which device. That is what Vitalik Buterin is aiming at in his recent X post and donation. He argues the next steps for secure messaging are permissionless account creation with no phone numbers or Know Your Customer (KYC) and much stronger metadata privacy. In that context he highlighted Session and SimpleX and sent 128 Ether (ETH) to each to keep pushing in that direction. Session is a good case study because it tries to combine E2E encryption with decentralization. There is no central message server, traffic is routed through onion paths, and user IDs are keys instead of phone numbers. Did you know? Forty-three percent of people who use public WiFi report experiencing a data breach, with man-in-the-middle attacks and packet sniffing against unencrypted traffic among the most common causes. How Session stores your messages Session is built around public key identities. When you sign up, the app generates a keypair locally and derives a Session ID from it with no phone number or email required. Messages travel through a network of service nodes using onion routing so that no single node can see both the sender and the recipient. (You can see your message’s node path in the settings.) For asynchronous delivery when you are offline, messages are stored in small groups of nodes called “swarms.” Each Session ID is mapped to a specific swarm, and your messages are stored there encrypted until your client fetches them. Historically, messages had a default time-to-live of about two weeks…
Share
BitcoinEthereumNews2025/12/08 14:40
Grayscale Files Sui Trust as 21Shares Launches First SUI ETF Amid Rising Demand
The post Grayscale Files Sui Trust as 21Shares Launches First SUI ETF Amid Rising Demand appeared on BitcoinEthereumNews.com. The Grayscale Sui Trust filing and 21Shares’ launch of the first SUI ETF highlight surging interest in regulated Sui investments. These products offer investors direct exposure to the SUI token through spot-style structures, simplifying access to the Sui blockchain’s growth without direct custody needs, amid expanding altcoin ETF options. Grayscale’s spot Sui Trust seeks to track SUI price performance for long-term holders. 21Shares’ SUI ETF provides leveraged exposure, targeting traders with 2x daily returns. Early trading data shows over 4,700 shares exchanged, with volumes exceeding $24 per unit in the debut session. Explore Grayscale Sui Trust filing and 21Shares SUI ETF launch: Key developments in regulated Sui investments for 2025. Stay informed on altcoin ETF trends. What is the Grayscale Sui Trust? The Grayscale Sui Trust is a proposed spot-style investment product filed via S-1 registration with the U.S. Securities and Exchange Commission, aimed at providing investors with direct exposure to the SUI token’s price movements. This trust mirrors the performance of SUI, the native cryptocurrency of the Sui blockchain, minus applicable fees, offering a regulated avenue for long-term participation in the network’s ecosystem. By holding SUI assets on behalf of investors, it eliminates the need for individuals to manage token storage or transactions directly. ⚡ LATEST: GRAYSCALE FILES S-1 FOR $SUI TRUSTThe “Grayscale Sui Trust,” is a spot-style ETF designed to provide direct exposure to the $SUI token. Grayscale’s goal is to mirror SUI’s market performance, minus fees, giving long-term investors a regulated, hassle-free way to… pic.twitter.com/mPQMINLrYC — CryptosRus (@CryptosR_Us) December 6, 2025 How does the 21Shares SUI ETF differ from traditional funds? The 21Shares SUI ETF, launched under the ticker TXXS, introduces a leveraged approach with 2x daily exposure to SUI’s price fluctuations, utilizing derivatives for amplified returns rather than direct spot holdings. This structure appeals to short-term…
Share
BitcoinEthereumNews2025/12/08 14:20