sannyjo
commented
6 years ago my pipeline config:
model { ssd { num_classes: 20 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 1 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } } feature_extractor { type: 'ssd_mobilenet_v1' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } loss { classification_loss { weighted_sigmoid { } } localization_loss { weighted_smooth_l1 { } } hard_example_miner { num_hard_examples: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } } }
train_config: { batch_size: 24 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } fine_tune_checkpoint: "/home/d4/tensorflow/data/pre_model/model.ckpt" from_detection_checkpoint: true
num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } }
train_input_reader: { tf_record_input_reader { input_path: "/home/d4/tensorflow/data/tensorflow/pascal_train.record" } label_map_path: "/home/d4/tensorflow/data/tensorflow/pascal_label_map.pbtxt" }
eval_config: { num_examples: 8000
max_evals: 10 }
eval_input_reader: { tf_record_input_reader { input_path: "/home/d4/tensorflow/data/tensorflow/pascal_val.record" } label_map_path: "/home/d4/tensorflow/data/tensorflow/pascal_label_map.pbtxt" shuffle: false num_readers: 1 }
clemkoa
System information
The error is following when I train my data:
data: VOCtrainval_11-May-2012.tar ssd_mobilenet_v1_coco.config ssd_mobilenet_v1_coco_2017_11_17
WARNING:tensorflow:From /home/grg-d4/models/research/object_detection/trainer.py:257: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.create_global_step Traceback (most recent call last): File "object_detection/train.py", line 167, in
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 124, in run
_sys.exit(main(argv))
File "object_detection/train.py", line 163, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "/home/grg-d4/models/research/object_detection/trainer.py", line 275, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
File "/home/grg-d4/tensorflow/models_bak/research/slim/deployment/model_deploy.py", line 193, in create_clones
outputs = model_fn(*args, kwargs)
File "/home/grg-d4/models/research/object_detection/trainer.py", line 198, in _create_losses
prediction_dict = detection_model.predict(images, true_image_shapes)
File "/home/grg-d4/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 384, in predict
preprocessed_inputs)
File "/home/grg-d4/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py", line 121, in extract_features
scope=scope)
File "/home/grg-d4/tensorflow/models_bak/research/slim/nets/mobilenet_v1.py", line 267, in mobilenet_v1_base
scope=end_point)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
return func(args, current_args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1066, in convolution
outputs = normalizer_fn(outputs, normalizer_params)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
return func(args, current_args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 667, in batch_norm
outputs = layer.apply(inputs, training=is_training)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 762, in apply
return self.call(inputs, *args, *kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 652, in call
outputs = self.call(inputs, args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/normalization.py", line 544, in call
training_value = utils.constant_value(training)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/utils.py", line 234, in constant_value
raise TypeError('
predmust be a Tensor, a Variable, or a Python bool.') TypeError:predmust be a Tensor, a Variable, or a Python bool.please pleas help me.........