def generate(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest):	
	'''
		lst=["bird","eagle","chicken"]
	'''
	x='''#Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset.
#Users should configure the fine_tune_checkpoint field in the train config as
#well as the label_map_path and input_path fields in the train_input_reader and
#eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
#should be configured.

model {
faster_rcnn {
	num_classes: %s
	image_resizer {
	keep_aspect_ratio_resizer {
		min_dimension: 600
		max_dimension: 1024
	}
	}
	feature_extractor {
	type: 'faster_rcnn_inception_v2'
	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: 0.0
		}
	}
	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: 0.0
			}
		}
		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: 0.0002
		schedule {
			step: 1
			learning_rate: .0002
		}
		schedule {
			step: 900000
			learning_rate: .00002
		}
		schedule {
			step: 1200000
			learning_rate: .000002
		}
		}
	}
	momentum_optimizer_value: 0.9
	}
	use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "%s"
from_detection_checkpoint: true
#Note: The below line limits the training process to 200K steps, which we
#empirically found to be sufficient enough to train the pets dataset. This
#effectively bypasses the learning rate schedule (the learning rate will
#never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
	random_horizontal_flip {
	}
}
}


train_input_reader: {
tf_record_input_reader {
	input_path: "%s"
}
label_map_path: "%s"
}

eval_config: {
num_examples: %s
#Note: The below line limits the evaluation process to 10 evaluations.
#Remove the below line to evaluate indefinitely.
max_evals: 10
}

eval_input_reader: {
tf_record_input_reader {
	input_path: "%s"
}
label_map_path: "%s"
shuffle: false
num_readers: 1
}
'''%(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest)
		
	return x
if __name__ == '__main__':
	num_classes='3'#todo
	fine_tune_checkpoint='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt'
	input_pathtrain='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/train.record'
	label_map_pathtrain='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/training/labelmap.pbtxt'
	num_examples='num_examples67'#todo
	input_pathtest='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/test.record'
	label_map_pathtest='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/training/labelmap.pbtxt'
	strs=generate(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest)
	print(strs)
	

def generate(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest):	
	'''
		lst=["bird","eagle","chicken"]
	'''
	x='''#Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset.
#Users should configure the fine_tune_checkpoint field in the train config as
#well as the label_map_path and input_path fields in the train_input_reader and
#eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
#should be configured.

model {
faster_rcnn {
	num_classes: %s
	image_resizer {
	keep_aspect_ratio_resizer {
		min_dimension: 600
		max_dimension: 1024
	}
	}
	feature_extractor {
	type: 'faster_rcnn_inception_v2'
	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: 0.0
		}
	}
	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: 0.0
			}
		}
		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: 0.0002
		schedule {
			step: 1
			learning_rate: .0002
		}
		schedule {
			step: 900000
			learning_rate: .00002
		}
		schedule {
			step: 1200000
			learning_rate: .000002
		}
		}
	}
	momentum_optimizer_value: 0.9
	}
	use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "%s"
from_detection_checkpoint: true
#Note: The below line limits the training process to 200K steps, which we
#empirically found to be sufficient enough to train the pets dataset. This
#effectively bypasses the learning rate schedule (the learning rate will
#never decay). Remove the below line to train indefinitely.
num_steps: 200000
data_augmentation_options {
	random_horizontal_flip {
	}
}
}


train_input_reader: {
tf_record_input_reader {
	input_path: "%s"
}
label_map_path: "%s"
}

eval_config: {
num_examples: %s
#Note: The below line limits the evaluation process to 10 evaluations.
#Remove the below line to evaluate indefinitely.
max_evals: 10
}

eval_input_reader: {
tf_record_input_reader {
	input_path: "%s"
}
label_map_path: "%s"
shuffle: false
num_readers: 1
}
'''%(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest)
		
	return x
if __name__ == '__main__':
	num_classes='3'#todo
	fine_tune_checkpoint='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt'
	input_pathtrain='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/train.record'
	label_map_pathtrain='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/training/labelmap.pbtxt'
	num_examples='num_examples67'#todo
	input_pathtest='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/test.record'
	label_map_pathtest='/content/drive/My Drive/Colab Notebooks/models/research/object_detection/training/labelmap.pbtxt'
	strs=generate(num_classes,fine_tune_checkpoint,input_pathtrain,label_map_pathtrain,num_examples,input_pathtest,label_map_pathtest)
	print(strs)
	

lst=["bird","eagle","chicken"] #Faster R-CNN with Inception v2, configured for Oxford-IIIT Pets Dataset. #Users should configure the fine_tune_checkpoint field in the train config as #well as the label_map_path and input_path fields in the train_input_reader and #eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that #should be configured.  model { faster_rcnn { 	num_classes: %s 	image_resizer { 	keep_aspect_ratio_resizer { 		min_dimension: 600 		max_dimension: 1024 	} 	} 	feature_extractor { 	type: 'faster_rcnn_inception_v2' 	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: 0.0 		} 	} 	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: 0.0 			} 		} 		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: 0.0002 		schedule { 			step: 1 			learning_rate: .0002 		} 		schedule { 			step: 900000 			learning_rate: .00002 		} 		schedule { 			step: 1200000 			learning_rate: .000002 		} 		} 	} 	momentum_optimizer_value: 0.9 	} 	use_moving_average: false } gradient_clipping_by_norm: 10.0 fine_tune_checkpoint: "%s" from_detection_checkpoint: true #Note: The below line limits the training process to 200K steps, which we #empirically found to be sufficient enough to train the pets dataset. This #effectively bypasses the learning rate schedule (the learning rate will #never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { 	random_horizontal_flip { 	} } }   train_input_reader: { tf_record_input_reader { 	input_path: "%s" } label_map_path: "%s" }  eval_config: { num_examples: %s #Note: The below line limits the evaluation process to 10 evaluations. #Remove the below line to evaluate indefinitely. max_evals: 10 }  eval_input_reader: { tf_record_input_reader { 	input_path: "%s" } label_map_path: "%s" shuffle: false num_readers: 1 }