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prune_channels_and_layers/kmeans_args.py

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+# coding: utf-8
+# This file contains the parameter used in train.py
+
+from __future__ import division, print_function
+
+from utils.misc_utils import parse_anchors, read_class_names
+import math
+
+### Some paths
+root = '/home/pcl/tf_work/my_github/yolov3_prune/'
+train_file = root + '/data/my_data/train.txt'  # The path of the training txt file.
+val_file = root + '/data/my_data/val.txt'  # The path of the validation txt file.
+# restore_path = root + '/dinali_608/scale_gamma_checkpoint/best_model_Epoch_1_step_5517_mAP_0.6280_loss_4.9122_lr_1e-05'  # The path of the weights to restore.
+finetune_restore_path = root + '/kmeans_yolo/kmeans_checkpoint/kmeans_prune_restore_model_all.ckpt'
+save_dir =root + '/kmeans_yolo/kmeans_checkpoint/'  # The directory of the weights to save.
+log_dir = root  + '/data/logs/'  # The directory to store the tensorboard log files.
+progress_log_path = root + '/data/progress.log'  # The path to record the training progress.
+anchor_path = root + '/data/yolo_anchors.txt'  # The path of the anchor txt file.
+class_name_path = root + '/data/my_data/voc.names'  # The path of the class names.
+
+### Training releated numbers
+batch_size = 6
+img_size = [416, 416]  # Images will be resized to `img_size` and fed to the network, size format: [width, height]
+letterbox_resize = False  # Whether to use the letterbox resize, i.e., keep the original aspect ratio in the resized image.
+total_epoches = 100
+train_evaluation_step = 100  # Evaluate on the training batch after some steps.
+val_evaluation_epoch = 1  # Evaluate on the whole validation dataset after some steps. Set to None to evaluate every epoch.
+save_epoch = 1  # Save the model after some epochs.
+batch_norm_decay = 0.99  # decay in bn ops
+weight_decay = 5e-4  # l2 weight decay
+global_step = 0  # used when resuming training
+
+### tf.data parameters
+num_threads = 10  # Number of threads for image processing used in tf.data pipeline.
+prefetech_buffer = 5  # Prefetech_buffer used in tf.data pipeline.
+
+### Learning rate and optimizer
+optimizer_name = 'momentum'  # Chosen from [sgd, momentum, adam, rmsprop]
+save_optimizer = False  # Whether to save the optimizer parameters into the checkpoint file.
+learning_rate_init = 1e-4
+lr_type = 'piecewise'  # Chosen from [fixed, exponential, cosine_decay, cosine_decay_restart, piecewise]
+lr_decay_epoch = 5  # Epochs after which learning rate decays. Int or float. Used when chosen `exponential` and `cosine_decay_restart` lr_type.
+lr_decay_factor = 0.96  # The learning rate decay factor. Used when chosen `exponential` lr_type.
+lr_lower_bound = 1e-6  # The minimum learning rate.
+# piecewise params
+pw_boundaries = [25, 40]  # epoch based boundaries
+pw_values = [learning_rate_init, 3e-5, 1e-4]
+
+### Load and finetune
+# Choose the parts you want to restore the weights. List form.
+# restore_include: None, restore_exclude: None  => restore the whole model
+# restore_include: None, restore_exclude: scope  => restore the whole model except `scope`
+# restore_include: scope1, restore_exclude: scope2  => if scope1 contains scope2, restore scope1 and not restore scope2 (scope1 - scope2)
+# choise 1: only restore the darknet body
+restore_include = ['yolov3/darknet53_body', 'yolov3/yolov3_head']
+restore_exclude = None
+# choise 2: restore all layers except the last 3 conv2d layers in 3 scale
+# restore_include = None
+# restore_exclude = ['yolov3/yolov3_head/Conv_14', 'yolov3/yolov3_head/Conv_6', 'yolov3/yolov3_head/Conv_22']
+# Choose the parts you want to finetune. List form.
+# Set to None to train the whole model.
+update_part = None
+
+### other training strategies
+multi_scale_train = True  # Whether to apply multi-scale training strategy. Image size varies from [320, 320] to [640, 640] by default.
+use_label_smooth = True # Whether to use class label smoothing strategy.
+use_focal_loss = True  # Whether to apply focal loss on the conf loss.
+use_mix_up = True  # Whether to use mix up data augmentation strategy.
+use_warm_up = False  # whether to use warm up strategy to prevent from gradient exploding.
+warm_up_epoch = 3  # Warm up training epoches. Set to a larger value if gradient explodes.
+
+### some constants in validation
+# nms
+nms_threshold = 0.45  # iou threshold in nms operation
+score_threshold = 0.01 # threshold of the probability of the classes in nms operation, i.e. score = pred_confs * pred_probs. set lower for higher recall.
+nms_topk = 150  # keep at most nms_topk outputs after nms
+# mAP eval
+eval_threshold = 0.5  # the iou threshold applied in mAP evaluation
+use_voc_07_metric = False  # whether to use voc 2007 evaluation metric, i.e. the 11-point metric
+
+### parse some params
+anchors = parse_anchors(anchor_path)
+classes = read_class_names( class_name_path)
+class_num = len(classes)
+train_img_cnt = len(open(train_file, 'r').readlines())
+val_img_cnt = len(open(val_file, 'r').readlines())
+train_batch_num = int(math.ceil(float(train_img_cnt) / batch_size))
+
+lr_decay_freq = int(train_batch_num * lr_decay_epoch)
+pw_boundaries = [float(i) * train_batch_num + global_step for i in pw_boundaries]