update Supervised

config.py

@@ -7,7 +7,7 @@
 LOG_DIR = 'log/'
 MODEL_FILENAME = "model.ckpt"
 
-DATASET_FOLDER = '../../benchmark_datasets/'
+DATASET_FOLDER = '/home/cc/dm_data/'
 
 # TRAIN
 BATCH_NUM_QUERIES = 2
@@ -18,7 +18,7 @@
 BASE_LEARNING_RATE = 0.000005
 MOMENTUM = 0.9
 OPTIMIZER = 'ADAM'
-MAX_EPOCH = 20
+MAX_EPOCH = 100
 
 MARGIN_1 = 0.5
 MARGIN_2 = 0.2

evaluate.py

@@ -4,7 +4,7 @@
 import socket
 import importlib
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+os.environ["CUDA_VISIBLE_DEVICES"] = "2"
 import sys
 import torch
 import torch.nn as nn
@@ -45,10 +45,12 @@ def evaluate():
     print("ave_one_percent_recall:"+str(ave_one_percent_recall))
 
 
-def evaluate_model(model,epoch,save=False):
+def evaluate_model(model,optimizer,epoch,save=False):
     if save:
         torch.save({
             'state_dict': model.state_dict(),
+            'optimizer': optimizer.state_dict(),
+            'epoch': epoch,
             }, cfg.LOG_DIR + "checkpoint.pth.tar")
 
     #checkpoint = torch.load(cfg.LOG_DIR + "checkpoint.pth.tar")

generating_queries/generate_training_tuples_cc_baseline.py

@@ -27,7 +27,7 @@
 folders = []
 
 # All runs are used for training (both full and partial)
-index_list = range(10)
+index_list = range(18)
 print("Number of runs: "+str(len(index_list)))
 for index in index_list:
     folders.append(all_folders[index])

train_pointnetvlad.py

@@ -202,7 +202,7 @@ def train():
         log_string('EVALUATING...')
         cfg.OUTPUT_FILE = cfg.RESULTS_FOLDER + 'results_' + str(epoch) + '.txt'
 
-        eval_recall = evaluate.evaluate_model(model, epoch,True)
+        eval_recall = evaluate.evaluate_model(model,optimizer,epoch,True)
         log_string('EVAL RECALL: %s' % str(eval_recall))
 
         train_writer.add_scalar("Val Recall", eval_recall, epoch)