datitran
data_flow_ops.py, line 91, in _as_name_list raise ValueError when run with Python3.5 but everything run well with Python2.7
When I run train.py with model faster_rcnn_resnet101 by Python2.7 interpreter on my customized data set, it work well, however when I run the same code in the same context and the same data set with Python3.5 interpreter, it report below error:
I am running with below setting:
GeForce GTX 1070
Ubuntu 16.04.2
tensorflow 1.4.1
and below is my config:
model {
faster_rcnn {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 400
width: 400
}
}
feature_extractor {
type: 'faster_rcnn_resnet101'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0002
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: { batch_size: 1 optimizer { momentum_optimizer: { learning_rate: { manual_step_learning_rate { initial_learning_rate: 0.0001 schedule { step: 0 learning_rate: .0001 } schedule { step: 5000 learning_rate: .00001 } schedule { step: 7000 learning_rate: .000001 } } } momentum_optimizer_value: 0.9 } use_moving_average: false } gradient_clipping_by_norm: 10.0 batch_queue_capacity: 2 prefetch_queue_capacity: 2 fine_tune_checkpoint: "models/model.ckpt" from_detection_checkpoint: true num_steps: 2000 }
train_input_reader: { tf_record_input_reader { input_path: "data/train.record" } label_map_path: "data/label_map.pbtxt" }
eval_config: { num_examples: 272 num_visualizations: 272 }
eval_input_reader: { tf_record_input_reader { input_path: "data/test.record" } label_map_path: "data/label_map.pbtxt" shuffle: true }
@datitran Can you help over this?
