diff --git a/014_Image Retrieval.py b/014_Image Retrieval.py
new file mode 100644
index 0000000..0928964
--- /dev/null
+++ b/014_Image Retrieval.py	
@@ -0,0 +1,159 @@
+import streamlit as st
+from pathlib import Path
+import sys
+import requests
+import os
+import json
+import numpy as np
+import cv2
+import glob
+import numpy as np
+import base64
+import random
+from streamlit_image_select import image_select
+from PIL import Image
+
+# API_query_one = 'http://172.18.5.30:2014/query_one'
+
+
+PATH_DATASET = "/storage/computervision/longnth/models/image_retrieval/NetVLAD/data/database"
+
+
+def select_image(images,captions=None):
+    img = image_select(
+    label="Select a photo",
+    images=images,
+    captions=captions,
+    use_container_width=False
+    )
+    return img
+
+
+    
+list_label_structure = ["british_museum", "florence_cathedral_side", "lincoln_memorial", "milan_cathedral", "mount_rushmore", "piazza_san_macro", "reichstag", "sacre_coeur", "sangrada_familia", "st_pauls_cathedral", "st_peters_square"]
+list_dataset = ["train", "validation"]
+dict_att = {}
+
+number_img_sample = 10
+
+sys.path.append('..')
+st.set_page_config(
+    page_title="Image Retrieval",layout='wide'
+)
+st.markdown("<h1 style='text-align: center; color: white;'>Image Retrieval</h1>", unsafe_allow_html=True)
+
+# att = os.listdir("/storage/computervision/longnth/models/image_retrieval/NetVLAD/data/database")
+att = os.listdir("/storage/computervision/longnth/models/image_retrieval/NetVLAD/data")
+
+
+# --------- query_one ---------------
+# import sys
+# # caution: path[0] is reserved for script path (or '' in REPL)
+# sys.path.insert(1, '/storage/computervision/longnth/models/image_retrieval/NetVLAD')
+from pathlib import Path
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.models as models
+from torchvision.models import VGG16_Weights
+
+import importlib.util
+
+def module_from_file(module_name, file_path):
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+NetVLADLayer = module_from_file("NetVLADLayer", "/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/netvlad.py")
+load_checkpoint = module_from_file("load_checkpoint", "/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/utils.py")
+query = module_from_file("query", "/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/query.py")
+
+cuda = torch.cuda.is_available()
+if cuda:
+    device =  torch.device("cuda")
+else:
+    device =  torch.device("cpu")
+    
+encoder = models.vgg16(weights=None)
+encoder_k = 512 ##TODO
+layers = list(encoder.features.children())[:-2]
+
+model = nn.Module()
+encoder = nn.Sequential(*layers)
+model.add_module('encoder', encoder)
+
+n_vocabs = 16
+net_vlad = NetVLADLayer.NetVLADLayer(n_vocabs = n_vocabs, k = encoder_k)
+model.add_module('netvlad', net_vlad)
+
+model = model.to(device)
+
+
+#Set up output paths
+dbPath = '/storage/computervision/longnth/models/image_retrieval/NetVLAD/data/database'
+queryPath = '/storage/computervision/longnth/models/image_retrieval/NetVLAD/data/query'
+outPath = '/storage/computervision/longnth/models/image_retrieval/NetVLAD/out'
+query_features = Path(outPath) / 'q_features.h5'
+db_features = Path(outPath) / 'db_features.h5'
+retrieval = Path(outPath) / 'retrieved.h5'
+
+# db_dataset = ImageDataset(Path(dbPath))
+loadPath = '/storage/computervision/longnth/models/image_retrieval/NetVLAD/model/BatchAll/best.pth.tar'
+
+startEpoch, train_loss, val_loss = load_checkpoint.load_checkpoint(Path(loadPath), 
+                                                        device,
+                                                        model)
+
+query_one = module_from_file("query_one", "/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/query_one.py")
+# plot_retrieval_images_one = module_from_file("plot_retrieval_images_one", "/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/query_one.py")
+
+# API_query_one = query_one.query_one(pil_image, device, model,
+#                       db_features,
+#                       n_result=10)
+
+# --------- process label ---------------
+st.write("### Upload query image")
+source = st.file_uploader("Choose a file")
+
+if source is not None : 
+    file = Path('Data/014_Image Retrieval/Uploaded_Image/source.jpg')
+    content = source.read()
+    file.write_bytes(content)
+    st.image('Data/014_Image Retrieval/Uploaded_Image/source.jpg',caption = 'Successful upload' ,use_column_width = 'auto')
+else:        
+    option_source_dataset = st.selectbox(
+        'Selected your dataset',
+        list_dataset)
+
+    option_source_label = st.selectbox(
+        'Selected your structure label',
+        list_label_structure)
+    db_dict = glob.glob(f"/storage/computervision/longnth/models/image_retrieval/NetVLAD/data/{option_source_dataset}/{option_source_label}/*")[:number_img_sample]
+    img = select_image(images = db_dict)
+    file = str(img)
+if st.button('Find Similars') :
+    with st.spinner("Processing..."):
+        # call api
+        files = {'file': open(file, 'rb')}
+#         response = requests.post(API_query_one, files=files)
+        retrieved_dict = query_one.query_one(Image.open(file), device, model, db_features, n_result=10)
+#         plot_retrieval_images_one.plot_retrieval_images_one(query_img = Image.open(file), retrieved_dict = retrieved_dict, db_dir = dbPath)
+        st.image(Image.open(file))
+# #         result = response.json()
+# #         response = list(response.values())[0]  
+        col1,col2,col3,col4 = st.columns(4)
+        cols = [col1,col2,col3,col4]
+        i = 0
+        for similar_image in retrieved_dict :
+            j = i%4
+            i+=1
+            with cols[j] : 
+                encode = Image.open(dbPath+"/"+similar_image)
+                open_cv_image = cv2.cvtColor(np.array(encode), cv2.COLOR_RGB2BGR)
+#                 im_bytes = base64.b64decode(encode)
+#                 im_arr = np.frombuffer(im_bytes, dtype=np.uint8)  # im_arr is one-dim Numpy array
+#                 img = cv2.imdecode(im_arr, flags=cv2.IMREAD_COLOR)
+                img = cv2.resize(open_cv_image, (300,300))
+                st.image(img[:,:,::-1],caption ='Similar' ,use_column_width = 'auto')
diff --git a/api.py b/api.py
new file mode 100644
index 0000000..03a7eba
--- /dev/null
+++ b/api.py
@@ -0,0 +1,67 @@
+import os
+from PIL import Image
+import numpy as np
+import  pandas as pd
+from fastapi import FastAPI, File, UploadFile
+import uvicorn
+from typing import List
+from pathlib import Path
+from tqdm import tqdm
+import base64
+import cv2
+from scr.query_one import query_one, plot_retrieval_images_one
+from scr.utils import read_image
+from scr.netvlad import NetVLADLayer
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.models as models
+from torchvision.models import VGG16_Weights
+
+app = FastAPI()
+
+savePath = './model/BatchAll'
+loadPath = 'model/BatchAll/best.pth.tar'
+outPath = 'out'
+db_features = Path(outPath) / 'db_features.h5'
+dbPath = 'data/database'
+
+#device
+cuda = torch.cuda.is_available()
+if cuda:
+    device =  torch.device("cuda:0")
+else:
+    device =  torch.device("cpu")
+    print("No GPU found, please get one")
+    
+#model
+encoder = models.vgg16(weights=None)
+encoder_k = 512 ##TODO
+layers = list(encoder.features.children())[:-2]
+model = nn.Module()
+encoder = nn.Sequential(*layers)
+model.add_module('encoder', encoder)
+n_vocabs = 16
+net_vlad = NetVLADLayer(n_vocabs = n_vocabs, k = encoder_k)
+model.add_module('netvlad', net_vlad)
+model = model.to(device)
+
+
+
+
+@app.post("/query_one", response_model = query_one)
+async def fas(file: UploadFile = File()):
+    file_path = './data/'+ file.filename
+    file_result_path = "./data/result.jpg"
+    with open(file_path, 'wb') as f:
+        f.write(file.file.read())
+    
+    image = read_image(file_path)
+    retrived_dict = query_one(image, device, model, db_features = db_features)
+    db_imgs = [read_image(Path(dbPath) / r) for r in retrived_dict]
+    
+    return db_imgs
+       
+if __name__ == '__main__' : 
+    uvicorn.run(app,host="0.0.0.0",port=2014)
diff --git a/demo-NetVLAD.ipynb b/demo-NetVLAD.ipynb
new file mode 100644
index 0000000..f406d6a
--- /dev/null
+++ b/demo-NetVLAD.ipynb
@@ -0,0 +1,1835 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "link: https://github.com/ginofft/NetVLAD\n",
+    "author: Nguyen Tuan Nghia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !source activate nthlongcv3d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libc10_cuda.so: cannot open shared object file: No such file or directory\n",
+      "  warn(f\"Failed to load image Python extension: {e}\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision.models as models\n",
+    "from torchvision.models import VGG16_Weights\n",
+    "\n",
+    "from scr.netvlad import NetVLADLayer\n",
+    "from scr.dataset import OnlineTripletImageDataset, ImageDataset\n",
+    "from scr.loss import OnlineTripletLoss\n",
+    "# from scr.utils import save_checkpoint, load_checkpoint, plot_retrievals_images, str2bool\n",
+    "from scr.utils import save_checkpoint, load_checkpoint, str2bool\n",
+    "from scr.train import train, validate\n",
+    "from scr.query import query, calculate_netvlads"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "| ID | GPU | MEM |\n",
+      "------------------\n",
+      "|  0 |  0% | 52% |\n",
+      "|  1 |  0% | 30% |\n"
+     ]
+    }
+   ],
+   "source": [
+    "from GPUtil import showUtilization as gpu_usage\n",
+    "gpu_usage()   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No GPU found, please get one\n"
+     ]
+    }
+   ],
+   "source": [
+    "cuda = torch.cuda.is_available()\n",
+    "if cuda:\n",
+    "    device =  torch.device(\"cuda\")\n",
+    "else:\n",
+    "    print(\"No GPU found, please get one\")\n",
+    "    device =  torch.device(\"cpu\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "Torch not compiled with CUDA enabled",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[41], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# import pycuda.driver as cuda\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# cuda.init()\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m## Get Id of default device\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/cuda/__init__.py:482\u001b[0m, in \u001b[0;36mcurrent_device\u001b[0;34m()\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcurrent_device\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m    481\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Returns the index of a currently selected device.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 482\u001b[0m     \u001b[43m_lazy_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    483\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_cuda_getDevice()\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/cuda/__init__.py:211\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    208\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiprocessing, you must use the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspawn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m start method\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(torch\u001b[38;5;241m.\u001b[39m_C, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_cuda_getDeviceCount\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 211\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTorch not compiled with CUDA enabled\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _cudart \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[1;32m    214\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
+     ]
+    }
+   ],
+   "source": [
+    "# import pycuda.driver as cuda\n",
+    "# cuda.init()\n",
+    "## Get Id of default device\n",
+    "torch.cuda.current_device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gc\n",
+    "gc.collect()\n",
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder = models.vgg16(weights=None)\n",
+    "encoder_k = 512 ##TODO\n",
+    "layers = list(encoder.features.children())[:-2]\n",
+    "\n",
+    "model = nn.Module()\n",
+    "encoder = nn.Sequential(*layers)\n",
+    "model.add_module('encoder', encoder)\n",
+    "\n",
+    "n_vocabs = 16\n",
+    "net_vlad = NetVLADLayer(n_vocabs = n_vocabs, k = encoder_k)\n",
+    "model.add_module('netvlad', net_vlad)\n",
+    "\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Init Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainPath = 'data/train'\n",
+    "train_set = OnlineTripletImageDataset(Path(trainPath))\n",
+    "\n",
+    "validationPath = 'data/validation'\n",
+    "val_set = OnlineTripletImageDataset(Path(validationPath))\n",
+    "\n",
+    "savePath = 'model/BatchAll'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "startEpoch = 0\n",
+    "val_loss = 1\n",
+    "train_loss = 1\n",
+    "nEpochs =100\n",
+    "margin = 0.1**0.5\n",
+    "\n",
+    "criterion = OnlineTripletLoss(margin = margin, hard=False).to(device) #tiêu chuẩn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train epochs with savepoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[1](50/215): Loss: 0.3160\n",
+      "Epoch[1](100/215): Loss: 0.3151\n",
+      "Epoch[1](150/215): Loss: 0.3129\n",
+      "Epoch[1](200/215): Loss: 0.3105\n",
+      "---> Epoch 1 compledted: Train Avg. Loss: 0.3164\n",
+      "----> Validation loss: 0.3126\n",
+      "Epoch[2](50/215): Loss: 0.4356\n",
+      "Epoch[2](100/215): Loss: 0.3116\n",
+      "Epoch[2](150/215): Loss: 0.3157\n",
+      "Epoch[2](200/215): Loss: 0.3160\n",
+      "---> Epoch 2 compledted: Train Avg. Loss: 0.3190\n",
+      "----> Validation loss: 0.3152\n",
+      "Epoch[3](50/215): Loss: 0.3158\n",
+      "Epoch[3](100/215): Loss: 0.3142\n",
+      "Epoch[3](150/215): Loss: 0.3157\n",
+      "Epoch[3](200/215): Loss: 0.3125\n",
+      "---> Epoch 3 compledted: Train Avg. Loss: 0.3197\n",
+      "----> Validation loss: 0.3152\n",
+      "Epoch[4](50/215): Loss: 0.3117\n",
+      "Epoch[4](100/215): Loss: 0.3160\n",
+      "Epoch[4](150/215): Loss: 0.3144\n",
+      "Epoch[4](200/215): Loss: 0.3010\n",
+      "---> Epoch 4 compledted: Train Avg. Loss: 0.3133\n",
+      "----> Validation loss: 0.3106\n",
+      "Epoch[5](50/215): Loss: 0.2948\n",
+      "Epoch[5](100/215): Loss: 0.2661\n",
+      "Epoch[5](150/215): Loss: 0.2898\n",
+      "Epoch[5](200/215): Loss: 0.2736\n",
+      "---> Epoch 5 compledted: Train Avg. Loss: 0.3029\n",
+      "----> Validation loss: 0.3370\n",
+      "Epoch[6](50/215): Loss: 0.3385\n",
+      "Epoch[6](100/215): Loss: 0.2492\n",
+      "Epoch[6](150/215): Loss: 0.3343\n",
+      "Epoch[6](200/215): Loss: 0.2687\n",
+      "---> Epoch 6 compledted: Train Avg. Loss: 0.3069\n",
+      "----> Validation loss: 0.3155\n",
+      "Epoch[7](50/215): Loss: 0.3206\n",
+      "Epoch[7](100/215): Loss: 0.2743\n",
+      "Epoch[7](150/215): Loss: 0.3780\n",
+      "Epoch[7](200/215): Loss: 0.3287\n",
+      "---> Epoch 7 compledted: Train Avg. Loss: 0.3060\n",
+      "----> Validation loss: 0.3047\n",
+      "Epoch[8](50/215): Loss: 0.2776\n",
+      "Epoch[8](100/215): Loss: 0.3295\n",
+      "Epoch[8](150/215): Loss: 0.2282\n",
+      "Epoch[8](200/215): Loss: 0.3209\n",
+      "---> Epoch 8 compledted: Train Avg. Loss: 0.3004\n",
+      "----> Validation loss: 0.3273\n",
+      "Epoch[9](50/215): Loss: 0.2466\n",
+      "Epoch[9](100/215): Loss: 0.3319\n",
+      "Epoch[9](150/215): Loss: 0.3773\n",
+      "Epoch[9](200/215): Loss: 0.3245\n",
+      "---> Epoch 9 compledted: Train Avg. Loss: 0.3047\n",
+      "----> Validation loss: 0.3238\n",
+      "Epoch[10](50/215): Loss: 0.3252\n",
+      "Epoch[10](100/215): Loss: 0.2913\n",
+      "Epoch[10](150/215): Loss: 0.3019\n",
+      "Epoch[10](200/215): Loss: 0.3070\n",
+      "---> Epoch 10 compledted: Train Avg. Loss: 0.3126\n",
+      "----> Validation loss: 0.3005\n",
+      "Epoch[11](50/215): Loss: 0.3337\n",
+      "Epoch[11](100/215): Loss: 0.3872\n",
+      "Epoch[11](150/215): Loss: 0.3161\n",
+      "Epoch[11](200/215): Loss: 0.2636\n",
+      "---> Epoch 11 compledted: Train Avg. Loss: 0.3074\n",
+      "----> Validation loss: 0.2976\n",
+      "Epoch[12](50/215): Loss: 0.3459\n",
+      "Epoch[12](100/215): Loss: 0.3184\n",
+      "Epoch[12](150/215): Loss: 0.3588\n",
+      "Epoch[12](200/215): Loss: 0.3401\n",
+      "---> Epoch 12 compledted: Train Avg. Loss: 0.3164\n",
+      "----> Validation loss: 0.3062\n",
+      "Epoch[13](50/215): Loss: 0.2987\n",
+      "Epoch[13](100/215): Loss: 0.3421\n",
+      "Epoch[13](150/215): Loss: 0.3293\n",
+      "Epoch[13](200/215): Loss: 0.2831\n",
+      "---> Epoch 13 compledted: Train Avg. Loss: 0.3356\n",
+      "----> Validation loss: 0.3208\n",
+      "Epoch[14](50/215): Loss: 0.3287\n",
+      "Epoch[14](100/215): Loss: 0.2529\n",
+      "Epoch[14](150/215): Loss: 0.2762\n",
+      "Epoch[14](200/215): Loss: 0.2711\n",
+      "---> Epoch 14 compledted: Train Avg. Loss: 0.3296\n",
+      "----> Validation loss: 0.3233\n",
+      "Epoch[15](50/215): Loss: 0.3007\n",
+      "Epoch[15](100/215): Loss: 0.2883\n",
+      "Epoch[15](150/215): Loss: 0.3176\n",
+      "Epoch[15](200/215): Loss: 0.2981\n",
+      "---> Epoch 15 compledted: Train Avg. Loss: 0.3094\n",
+      "----> Validation loss: 0.3131\n",
+      "Epoch[16](50/215): Loss: 0.4024\n",
+      "Epoch[16](100/215): Loss: 0.2519\n",
+      "Epoch[16](150/215): Loss: 0.2869\n",
+      "Epoch[16](200/215): Loss: 0.3208\n",
+      "---> Epoch 16 compledted: Train Avg. Loss: 0.3203\n",
+      "----> Validation loss: 0.2976\n",
+      "Epoch[17](50/215): Loss: 0.3343\n",
+      "Epoch[17](100/215): Loss: 0.2921\n",
+      "Epoch[17](150/215): Loss: 0.3160\n",
+      "Epoch[17](200/215): Loss: 0.2585\n",
+      "---> Epoch 17 compledted: Train Avg. Loss: 0.2974\n",
+      "----> Validation loss: 0.3084\n",
+      "Epoch[18](50/215): Loss: 0.3098\n",
+      "Epoch[18](100/215): Loss: 0.2616\n",
+      "Epoch[18](150/215): Loss: 0.2912\n",
+      "Epoch[18](200/215): Loss: 0.2485\n",
+      "---> Epoch 18 compledted: Train Avg. Loss: 0.2997\n",
+      "----> Validation loss: 0.2824\n",
+      "Epoch[19](50/215): Loss: 0.3308\n",
+      "Epoch[19](100/215): Loss: 0.3042\n",
+      "Epoch[19](150/215): Loss: 0.2924\n",
+      "Epoch[19](200/215): Loss: 0.3045\n",
+      "---> Epoch 19 compledted: Train Avg. Loss: 0.3006\n",
+      "----> Validation loss: 0.2615\n",
+      "Epoch[20](50/215): Loss: 0.2320\n",
+      "Epoch[20](100/215): Loss: 0.2849\n",
+      "Epoch[20](150/215): Loss: 0.2807\n",
+      "Epoch[20](200/215): Loss: 0.2708\n",
+      "---> Epoch 20 compledted: Train Avg. Loss: 0.2823\n",
+      "----> Validation loss: 0.2806\n",
+      "Epoch[21](50/215): Loss: 0.2768\n",
+      "Epoch[21](100/215): Loss: 0.2714\n",
+      "Epoch[21](150/215): Loss: 0.2500\n",
+      "Epoch[21](200/215): Loss: 0.3340\n",
+      "---> Epoch 21 compledted: Train Avg. Loss: 0.2798\n",
+      "----> Validation loss: 0.2931\n",
+      "Epoch[22](50/215): Loss: 0.2791\n",
+      "Epoch[22](100/215): Loss: 0.2986\n",
+      "Epoch[22](150/215): Loss: 0.3036\n",
+      "Epoch[22](200/215): Loss: 0.3654\n",
+      "---> Epoch 22 compledted: Train Avg. Loss: 0.2888\n",
+      "----> Validation loss: 0.2704\n",
+      "Epoch[23](50/215): Loss: 0.2513\n",
+      "Epoch[23](100/215): Loss: 0.3259\n",
+      "Epoch[23](150/215): Loss: 0.3104\n",
+      "Epoch[23](200/215): Loss: 0.3053\n",
+      "---> Epoch 23 compledted: Train Avg. Loss: 0.2839\n",
+      "----> Validation loss: 0.2959\n",
+      "Epoch[24](50/215): Loss: 0.2814\n",
+      "Epoch[24](100/215): Loss: 0.3029\n",
+      "Epoch[24](150/215): Loss: 0.2184\n",
+      "Epoch[24](200/215): Loss: 0.2955\n",
+      "---> Epoch 24 compledted: Train Avg. Loss: 0.2829\n",
+      "----> Validation loss: 0.2661\n",
+      "Epoch[25](50/215): Loss: 0.2939\n",
+      "Epoch[25](100/215): Loss: 0.3268\n",
+      "Epoch[25](150/215): Loss: 0.2332\n",
+      "Epoch[25](200/215): Loss: 0.2839\n",
+      "---> Epoch 25 compledted: Train Avg. Loss: 0.2763\n",
+      "----> Validation loss: 0.2957\n",
+      "Epoch[26](50/215): Loss: 0.2739\n",
+      "Epoch[26](100/215): Loss: 0.2905\n",
+      "Epoch[26](150/215): Loss: 0.2612\n",
+      "Epoch[26](200/215): Loss: 0.3004\n",
+      "---> Epoch 26 compledted: Train Avg. Loss: 0.2802\n",
+      "----> Validation loss: 0.2787\n",
+      "Epoch[27](50/215): Loss: 0.2951\n",
+      "Epoch[27](100/215): Loss: 0.2925\n",
+      "Epoch[27](150/215): Loss: 0.3398\n",
+      "Epoch[27](200/215): Loss: 0.2513\n",
+      "---> Epoch 27 compledted: Train Avg. Loss: 0.2700\n",
+      "----> Validation loss: 0.2860\n",
+      "Epoch[28](50/215): Loss: 0.2131\n",
+      "Epoch[28](100/215): Loss: 0.2622\n",
+      "Epoch[28](150/215): Loss: 0.2642\n",
+      "Epoch[28](200/215): Loss: 0.2952\n",
+      "---> Epoch 28 compledted: Train Avg. Loss: 0.2856\n",
+      "----> Validation loss: 0.3364\n",
+      "Epoch[29](50/215): Loss: 0.3271\n",
+      "Epoch[29](100/215): Loss: 0.2859\n",
+      "Epoch[29](150/215): Loss: 0.3024\n",
+      "Epoch[29](200/215): Loss: 0.3149\n",
+      "---> Epoch 29 compledted: Train Avg. Loss: 0.3091\n",
+      "----> Validation loss: 0.2942\n",
+      "Epoch[30](50/215): Loss: 0.2789\n",
+      "Epoch[30](100/215): Loss: 0.2713\n",
+      "Epoch[30](150/215): Loss: 0.2677\n",
+      "Epoch[30](200/215): Loss: 0.2960\n",
+      "---> Epoch 30 compledted: Train Avg. Loss: 0.3069\n",
+      "----> Validation loss: 0.3018\n",
+      "Epoch[31](50/215): Loss: 0.2854\n",
+      "Epoch[31](100/215): Loss: 0.2975\n",
+      "Epoch[31](150/215): Loss: 0.2902\n",
+      "Epoch[31](200/215): Loss: 0.2805\n",
+      "---> Epoch 31 compledted: Train Avg. Loss: 0.3044\n",
+      "----> Validation loss: 0.3163\n",
+      "Epoch[32](50/215): Loss: 0.2630\n",
+      "Epoch[32](100/215): Loss: 0.2917\n",
+      "Epoch[32](150/215): Loss: 0.3103\n",
+      "Epoch[32](200/215): Loss: 0.2837\n",
+      "---> Epoch 32 compledted: Train Avg. Loss: 0.2902\n",
+      "----> Validation loss: 0.3070\n",
+      "Epoch[33](50/215): Loss: 0.3057\n",
+      "Epoch[33](100/215): Loss: 0.2552\n",
+      "Epoch[33](150/215): Loss: 0.2386\n",
+      "Epoch[33](200/215): Loss: 0.2370\n",
+      "---> Epoch 33 compledted: Train Avg. Loss: 0.2743\n",
+      "----> Validation loss: 0.2916\n",
+      "Epoch[34](50/215): Loss: 0.2483\n",
+      "Epoch[34](100/215): Loss: 0.2155\n",
+      "Epoch[34](150/215): Loss: 0.3358\n",
+      "Epoch[34](200/215): Loss: 0.2660\n",
+      "---> Epoch 34 compledted: Train Avg. Loss: 0.2741\n",
+      "----> Validation loss: 0.2867\n",
+      "Epoch[35](50/215): Loss: 0.2994\n",
+      "Epoch[35](100/215): Loss: 0.2623\n",
+      "Epoch[35](150/215): Loss: 0.2868\n",
+      "Epoch[35](200/215): Loss: 0.2528\n",
+      "---> Epoch 35 compledted: Train Avg. Loss: 0.2670\n",
+      "----> Validation loss: 0.2943\n",
+      "Epoch[36](50/215): Loss: 0.3284\n",
+      "Epoch[36](100/215): Loss: 0.2551\n",
+      "Epoch[36](150/215): Loss: 0.2301\n",
+      "Epoch[36](200/215): Loss: 0.1911\n",
+      "---> Epoch 36 compledted: Train Avg. Loss: 0.2806\n",
+      "----> Validation loss: 0.2583\n",
+      "Epoch[37](50/215): Loss: 0.1854\n",
+      "Epoch[37](100/215): Loss: 0.3067\n",
+      "Epoch[37](150/215): Loss: 0.2751\n",
+      "Epoch[37](200/215): Loss: 0.2960\n",
+      "---> Epoch 37 compledted: Train Avg. Loss: 0.2809\n",
+      "----> Validation loss: 0.3024\n",
+      "Epoch[38](50/215): Loss: 0.3252\n",
+      "Epoch[38](100/215): Loss: 0.3108\n",
+      "Epoch[38](150/215): Loss: 0.3343\n",
+      "Epoch[38](200/215): Loss: 0.2717\n",
+      "---> Epoch 38 compledted: Train Avg. Loss: 0.2913\n",
+      "----> Validation loss: 0.2863\n",
+      "Epoch[39](50/215): Loss: 0.2953\n",
+      "Epoch[39](100/215): Loss: 0.2804\n",
+      "Epoch[39](150/215): Loss: 0.3054\n",
+      "Epoch[39](200/215): Loss: 0.2573\n",
+      "---> Epoch 39 compledted: Train Avg. Loss: 0.2720\n",
+      "----> Validation loss: 0.2626\n",
+      "Epoch[40](50/215): Loss: 0.3265\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[40](100/215): Loss: 0.2379\n",
+      "Epoch[40](150/215): Loss: 0.2856\n",
+      "Epoch[40](200/215): Loss: 0.2995\n",
+      "---> Epoch 40 compledted: Train Avg. Loss: 0.2746\n",
+      "----> Validation loss: 0.2967\n",
+      "Epoch[41](50/215): Loss: 0.2809\n",
+      "Epoch[41](100/215): Loss: 0.2097\n",
+      "Epoch[41](150/215): Loss: 0.2468\n",
+      "Epoch[41](200/215): Loss: 0.2874\n",
+      "---> Epoch 41 compledted: Train Avg. Loss: 0.2734\n",
+      "----> Validation loss: 0.3066\n",
+      "Epoch[42](50/215): Loss: 0.2521\n",
+      "Epoch[42](100/215): Loss: 0.2998\n",
+      "Epoch[42](150/215): Loss: 0.2998\n",
+      "Epoch[42](200/215): Loss: 0.2880\n",
+      "---> Epoch 42 compledted: Train Avg. Loss: 0.2770\n",
+      "----> Validation loss: 0.2898\n",
+      "Epoch[43](50/215): Loss: 0.2670\n",
+      "Epoch[43](100/215): Loss: 0.2496\n",
+      "Epoch[43](150/215): Loss: 0.2924\n",
+      "Epoch[43](200/215): Loss: 0.3234\n",
+      "---> Epoch 43 compledted: Train Avg. Loss: 0.2717\n",
+      "----> Validation loss: 0.2951\n",
+      "Epoch[44](50/215): Loss: 0.2530\n",
+      "Epoch[44](100/215): Loss: 0.2613\n",
+      "Epoch[44](150/215): Loss: 0.2987\n",
+      "Epoch[44](200/215): Loss: 0.2706\n",
+      "---> Epoch 44 compledted: Train Avg. Loss: 0.2637\n",
+      "----> Validation loss: 0.2592\n",
+      "Epoch[45](50/215): Loss: 0.2507\n",
+      "Epoch[45](100/215): Loss: 0.2285\n",
+      "Epoch[45](150/215): Loss: 0.2862\n",
+      "Epoch[45](200/215): Loss: 0.2333\n",
+      "---> Epoch 45 compledted: Train Avg. Loss: 0.2586\n",
+      "----> Validation loss: 0.3003\n",
+      "Epoch[46](50/215): Loss: 0.2698\n",
+      "Epoch[46](100/215): Loss: 0.2298\n",
+      "Epoch[46](150/215): Loss: 0.1905\n",
+      "Epoch[46](200/215): Loss: 0.1907\n",
+      "---> Epoch 46 compledted: Train Avg. Loss: 0.2489\n",
+      "----> Validation loss: 0.2765\n",
+      "Epoch[47](50/215): Loss: 0.2789\n",
+      "Epoch[47](100/215): Loss: 0.2803\n",
+      "Epoch[47](150/215): Loss: 0.2382\n",
+      "Epoch[47](200/215): Loss: 0.2960\n",
+      "---> Epoch 47 compledted: Train Avg. Loss: 0.2584\n",
+      "----> Validation loss: 0.2746\n",
+      "Epoch[48](50/215): Loss: 0.3492\n",
+      "Epoch[48](100/215): Loss: 0.2630\n",
+      "Epoch[48](150/215): Loss: 0.2671\n",
+      "Epoch[48](200/215): Loss: 0.2196\n",
+      "---> Epoch 48 compledted: Train Avg. Loss: 0.2510\n",
+      "----> Validation loss: 0.2666\n",
+      "Epoch[49](50/215): Loss: 0.2869\n",
+      "Epoch[49](100/215): Loss: 0.2886\n",
+      "Epoch[49](150/215): Loss: 0.2953\n",
+      "Epoch[49](200/215): Loss: 0.2884\n",
+      "---> Epoch 49 compledted: Train Avg. Loss: 0.2450\n",
+      "----> Validation loss: 0.2731\n",
+      "Epoch[50](50/215): Loss: 0.2338\n",
+      "Epoch[50](100/215): Loss: 0.2669\n",
+      "Epoch[50](150/215): Loss: 0.2887\n",
+      "Epoch[50](200/215): Loss: 0.1422\n",
+      "---> Epoch 50 compledted: Train Avg. Loss: 0.2380\n",
+      "----> Validation loss: 0.2201\n",
+      "Epoch[51](50/215): Loss: 0.1483\n",
+      "Epoch[51](100/215): Loss: 0.1759\n",
+      "Epoch[51](150/215): Loss: 0.1681\n",
+      "Epoch[51](200/215): Loss: 0.2360\n",
+      "---> Epoch 51 compledted: Train Avg. Loss: 0.2311\n",
+      "----> Validation loss: 0.2510\n",
+      "Epoch[52](50/215): Loss: 0.1806\n",
+      "Epoch[52](100/215): Loss: 0.1698\n",
+      "Epoch[52](150/215): Loss: 0.1911\n",
+      "Epoch[52](200/215): Loss: 0.2214\n",
+      "---> Epoch 52 compledted: Train Avg. Loss: 0.2145\n",
+      "----> Validation loss: 0.2488\n",
+      "Epoch[53](50/215): Loss: 0.3232\n",
+      "Epoch[53](100/215): Loss: 0.2386\n",
+      "Epoch[53](150/215): Loss: 0.1897\n",
+      "Epoch[53](200/215): Loss: 0.1811\n",
+      "---> Epoch 53 compledted: Train Avg. Loss: 0.2163\n",
+      "----> Validation loss: 0.2456\n",
+      "Epoch[54](50/215): Loss: 0.2527\n",
+      "Epoch[54](100/215): Loss: 0.1658\n",
+      "Epoch[54](150/215): Loss: 0.3351\n",
+      "Epoch[54](200/215): Loss: 0.3111\n",
+      "---> Epoch 54 compledted: Train Avg. Loss: 0.2576\n",
+      "----> Validation loss: 0.2785\n",
+      "Epoch[55](50/215): Loss: 0.3114\n",
+      "Epoch[55](100/215): Loss: 0.2509\n",
+      "Epoch[55](150/215): Loss: 0.1810\n",
+      "Epoch[55](200/215): Loss: 0.2618\n",
+      "---> Epoch 55 compledted: Train Avg. Loss: 0.2871\n",
+      "----> Validation loss: 0.2739\n",
+      "Epoch[56](50/215): Loss: 0.2625\n",
+      "Epoch[56](100/215): Loss: 0.2776\n",
+      "Epoch[56](150/215): Loss: 0.1323\n",
+      "Epoch[56](200/215): Loss: 0.1994\n",
+      "---> Epoch 56 compledted: Train Avg. Loss: 0.2208\n",
+      "----> Validation loss: 0.2353\n",
+      "Epoch[57](50/215): Loss: 0.2805\n",
+      "Epoch[57](100/215): Loss: 0.2459\n",
+      "Epoch[57](150/215): Loss: 0.2071\n",
+      "Epoch[57](200/215): Loss: 0.2609\n",
+      "---> Epoch 57 compledted: Train Avg. Loss: 0.2024\n",
+      "----> Validation loss: 0.2335\n",
+      "Epoch[58](50/215): Loss: 0.1986\n",
+      "Epoch[58](100/215): Loss: 0.2233\n",
+      "Epoch[58](150/215): Loss: 0.2647\n",
+      "Epoch[58](200/215): Loss: 0.1257\n",
+      "---> Epoch 58 compledted: Train Avg. Loss: 0.2032\n",
+      "----> Validation loss: 0.2338\n",
+      "Epoch[59](50/215): Loss: 0.2490\n",
+      "Epoch[59](100/215): Loss: 0.0960\n",
+      "Epoch[59](150/215): Loss: 0.2741\n",
+      "Epoch[59](200/215): Loss: 0.1619\n",
+      "---> Epoch 59 compledted: Train Avg. Loss: 0.1899\n",
+      "----> Validation loss: 0.2399\n",
+      "Epoch[60](50/215): Loss: 0.1188\n",
+      "Epoch[60](100/215): Loss: 0.1672\n",
+      "Epoch[60](150/215): Loss: 0.1520\n",
+      "Epoch[60](200/215): Loss: 0.1676\n",
+      "---> Epoch 60 compledted: Train Avg. Loss: 0.1829\n",
+      "----> Validation loss: 0.2501\n",
+      "Epoch[61](50/215): Loss: 0.1738\n",
+      "Epoch[61](100/215): Loss: 0.3109\n",
+      "Epoch[61](150/215): Loss: 0.2667\n",
+      "Epoch[61](200/215): Loss: 0.1852\n",
+      "---> Epoch 61 compledted: Train Avg. Loss: 0.1718\n",
+      "----> Validation loss: 0.2354\n",
+      "Epoch[62](50/215): Loss: 0.1870\n",
+      "Epoch[62](100/215): Loss: 0.1415\n",
+      "Epoch[62](150/215): Loss: 0.2935\n",
+      "Epoch[62](200/215): Loss: 0.1793\n",
+      "---> Epoch 62 compledted: Train Avg. Loss: 0.1626\n",
+      "----> Validation loss: 0.2168\n",
+      "Epoch[63](50/215): Loss: 0.1893\n",
+      "Epoch[63](100/215): Loss: 0.2970\n",
+      "Epoch[63](150/215): Loss: 0.1814\n",
+      "Epoch[63](200/215): Loss: 0.0561\n",
+      "---> Epoch 63 compledted: Train Avg. Loss: 0.1571\n",
+      "----> Validation loss: 0.2324\n",
+      "Epoch[64](50/215): Loss: 0.1099\n",
+      "Epoch[64](100/215): Loss: 0.1187\n",
+      "Epoch[64](150/215): Loss: 0.1314\n",
+      "Epoch[64](200/215): Loss: 0.2044\n",
+      "---> Epoch 64 compledted: Train Avg. Loss: 0.1481\n",
+      "----> Validation loss: 0.2251\n",
+      "Epoch[65](50/215): Loss: 0.1235\n",
+      "Epoch[65](100/215): Loss: 0.1660\n",
+      "Epoch[65](150/215): Loss: 0.1248\n",
+      "Epoch[65](200/215): Loss: 0.1765\n",
+      "---> Epoch 65 compledted: Train Avg. Loss: 0.1485\n",
+      "----> Validation loss: 0.2366\n",
+      "Epoch[66](50/215): Loss: 0.0769\n",
+      "Epoch[66](100/215): Loss: 0.0744\n",
+      "Epoch[66](150/215): Loss: 0.1270\n",
+      "Epoch[66](200/215): Loss: 0.2521\n",
+      "---> Epoch 66 compledted: Train Avg. Loss: 0.1475\n",
+      "----> Validation loss: 0.2528\n",
+      "Epoch[67](50/215): Loss: 0.1228\n",
+      "Epoch[67](100/215): Loss: 0.0958\n",
+      "Epoch[67](150/215): Loss: 0.1001\n",
+      "Epoch[67](200/215): Loss: 0.1510\n",
+      "---> Epoch 67 compledted: Train Avg. Loss: 0.1364\n",
+      "----> Validation loss: 0.2458\n",
+      "Epoch[68](50/215): Loss: 0.0879\n",
+      "Epoch[68](100/215): Loss: 0.1780\n",
+      "Epoch[68](150/215): Loss: 0.1627\n",
+      "Epoch[68](200/215): Loss: 0.2815\n",
+      "---> Epoch 68 compledted: Train Avg. Loss: 0.1429\n",
+      "----> Validation loss: 0.2003\n",
+      "Epoch[69](50/215): Loss: 0.0948\n",
+      "Epoch[69](100/215): Loss: 0.1598\n",
+      "Epoch[69](150/215): Loss: 0.2011\n",
+      "Epoch[69](200/215): Loss: 0.0968\n",
+      "---> Epoch 69 compledted: Train Avg. Loss: 0.1228\n",
+      "----> Validation loss: 0.2429\n",
+      "Epoch[70](50/215): Loss: 0.2555\n",
+      "Epoch[70](100/215): Loss: 0.1558\n",
+      "Epoch[70](150/215): Loss: 0.1721\n",
+      "Epoch[70](200/215): Loss: 0.1373\n",
+      "---> Epoch 70 compledted: Train Avg. Loss: 0.1344\n",
+      "----> Validation loss: 0.1932\n",
+      "Epoch[71](50/215): Loss: 0.3040\n",
+      "Epoch[71](100/215): Loss: 0.0790\n",
+      "Epoch[71](150/215): Loss: 0.0360\n",
+      "Epoch[71](200/215): Loss: 0.1428\n",
+      "---> Epoch 71 compledted: Train Avg. Loss: 0.1124\n",
+      "----> Validation loss: 0.2209\n",
+      "Epoch[72](50/215): Loss: 0.0488\n",
+      "Epoch[72](100/215): Loss: 0.1509\n",
+      "Epoch[72](150/215): Loss: 0.0000\n",
+      "Epoch[72](200/215): Loss: 0.0745\n",
+      "---> Epoch 72 compledted: Train Avg. Loss: 0.1047\n",
+      "----> Validation loss: 0.1990\n",
+      "Epoch[73](50/215): Loss: 0.0000\n",
+      "Epoch[73](100/215): Loss: 0.2421\n",
+      "Epoch[73](150/215): Loss: 0.1066\n",
+      "Epoch[73](200/215): Loss: 0.0671\n",
+      "---> Epoch 73 compledted: Train Avg. Loss: 0.0886\n",
+      "----> Validation loss: 0.1974\n",
+      "Epoch[74](50/215): Loss: 0.1622\n",
+      "Epoch[74](100/215): Loss: 0.0651\n",
+      "Epoch[74](150/215): Loss: 0.0402\n",
+      "Epoch[74](200/215): Loss: 0.1581\n",
+      "---> Epoch 74 compledted: Train Avg. Loss: 0.1112\n",
+      "----> Validation loss: 0.2070\n",
+      "Epoch[75](50/215): Loss: 0.1400\n",
+      "Epoch[75](100/215): Loss: 0.1426\n",
+      "Epoch[75](150/215): Loss: 0.1151\n",
+      "Epoch[75](200/215): Loss: 0.1256\n",
+      "---> Epoch 75 compledted: Train Avg. Loss: 0.1279\n",
+      "----> Validation loss: 0.2154\n",
+      "Epoch[76](50/215): Loss: 0.0983\n",
+      "Epoch[76](100/215): Loss: 0.0603\n",
+      "Epoch[76](150/215): Loss: 0.0057\n",
+      "Epoch[76](200/215): Loss: 0.1815\n",
+      "---> Epoch 76 compledted: Train Avg. Loss: 0.0934\n",
+      "----> Validation loss: 0.1908\n",
+      "Epoch[77](50/215): Loss: 0.0506\n",
+      "Epoch[77](100/215): Loss: 0.0089\n",
+      "Epoch[77](150/215): Loss: 0.2515\n",
+      "Epoch[77](200/215): Loss: 0.1096\n",
+      "---> Epoch 77 compledted: Train Avg. Loss: 0.0780\n",
+      "----> Validation loss: 0.2317\n",
+      "Epoch[78](50/215): Loss: 0.0317\n",
+      "Epoch[78](100/215): Loss: 0.0382\n",
+      "Epoch[78](150/215): Loss: 0.2004\n",
+      "Epoch[78](200/215): Loss: 0.2151\n",
+      "---> Epoch 78 compledted: Train Avg. Loss: 0.1148\n",
+      "----> Validation loss: 0.1962\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[79](50/215): Loss: 0.1047\n",
+      "Epoch[79](100/215): Loss: 0.0778\n",
+      "Epoch[79](150/215): Loss: 0.0730\n",
+      "Epoch[79](200/215): Loss: 0.0530\n",
+      "---> Epoch 79 compledted: Train Avg. Loss: 0.0775\n",
+      "----> Validation loss: 0.2129\n",
+      "Epoch[80](50/215): Loss: 0.0400\n",
+      "Epoch[80](100/215): Loss: 0.0914\n",
+      "Epoch[80](150/215): Loss: 0.0000\n",
+      "Epoch[80](200/215): Loss: 0.0028\n",
+      "---> Epoch 80 compledted: Train Avg. Loss: 0.0575\n",
+      "----> Validation loss: 0.2081\n",
+      "Epoch[81](50/215): Loss: 0.1043\n",
+      "Epoch[81](100/215): Loss: 0.0276\n",
+      "Epoch[81](150/215): Loss: 0.0952\n",
+      "Epoch[81](200/215): Loss: 0.0000\n",
+      "---> Epoch 81 compledted: Train Avg. Loss: 0.0592\n",
+      "----> Validation loss: 0.1915\n",
+      "Epoch[82](50/215): Loss: 0.0000\n",
+      "Epoch[82](100/215): Loss: 0.0000\n",
+      "Epoch[82](150/215): Loss: 0.0000\n",
+      "Epoch[82](200/215): Loss: 0.0657\n",
+      "---> Epoch 82 compledted: Train Avg. Loss: 0.0520\n",
+      "----> Validation loss: 0.1990\n",
+      "Epoch[83](50/215): Loss: 0.0435\n",
+      "Epoch[83](100/215): Loss: 0.0733\n",
+      "Epoch[83](150/215): Loss: 0.0160\n",
+      "Epoch[83](200/215): Loss: 0.0063\n",
+      "---> Epoch 83 compledted: Train Avg. Loss: 0.0413\n",
+      "----> Validation loss: 0.1997\n",
+      "Epoch[84](50/215): Loss: 0.0000\n",
+      "Epoch[84](100/215): Loss: 0.0570\n",
+      "Epoch[84](150/215): Loss: 0.0000\n",
+      "Epoch[84](200/215): Loss: 0.0921\n",
+      "---> Epoch 84 compledted: Train Avg. Loss: 0.0537\n",
+      "----> Validation loss: 0.2303\n",
+      "Epoch[85](50/215): Loss: 0.0706\n",
+      "Epoch[85](100/215): Loss: 0.1829\n",
+      "Epoch[85](150/215): Loss: 0.0218\n",
+      "Epoch[85](200/215): Loss: 0.0537\n",
+      "---> Epoch 85 compledted: Train Avg. Loss: 0.0773\n",
+      "----> Validation loss: 0.2047\n",
+      "Epoch[86](50/215): Loss: 0.0193\n",
+      "Epoch[86](100/215): Loss: 0.0780\n",
+      "Epoch[86](150/215): Loss: 0.0586\n",
+      "Epoch[86](200/215): Loss: 0.0000\n",
+      "---> Epoch 86 compledted: Train Avg. Loss: 0.0503\n",
+      "----> Validation loss: 0.1978\n",
+      "Epoch[87](50/215): Loss: 0.1485\n",
+      "Epoch[87](100/215): Loss: 0.0015\n",
+      "Epoch[87](150/215): Loss: 0.0000\n",
+      "Epoch[87](200/215): Loss: 0.0000\n",
+      "---> Epoch 87 compledted: Train Avg. Loss: 0.0315\n",
+      "----> Validation loss: 0.1812\n",
+      "Epoch[88](50/215): Loss: 0.0845\n",
+      "Epoch[88](100/215): Loss: 0.1461\n",
+      "Epoch[88](150/215): Loss: 0.0000\n",
+      "Epoch[88](200/215): Loss: 0.0000\n",
+      "---> Epoch 88 compledted: Train Avg. Loss: 0.0493\n",
+      "----> Validation loss: 0.2324\n",
+      "Epoch[89](50/215): Loss: 0.0500\n",
+      "Epoch[89](100/215): Loss: 0.0000\n",
+      "Epoch[89](150/215): Loss: 0.0000\n",
+      "Epoch[89](200/215): Loss: 0.0000\n",
+      "---> Epoch 89 compledted: Train Avg. Loss: 0.0338\n",
+      "----> Validation loss: 0.2179\n",
+      "Epoch[90](50/215): Loss: 0.0000\n",
+      "Epoch[90](100/215): Loss: 0.0000\n",
+      "Epoch[90](150/215): Loss: 0.0354\n",
+      "Epoch[90](200/215): Loss: 0.0418\n",
+      "---> Epoch 90 compledted: Train Avg. Loss: 0.0229\n",
+      "----> Validation loss: 0.1796\n",
+      "Epoch[91](50/215): Loss: 0.1815\n",
+      "Epoch[91](100/215): Loss: 0.0000\n",
+      "Epoch[91](150/215): Loss: 0.0342\n",
+      "Epoch[91](200/215): Loss: 0.0976\n",
+      "---> Epoch 91 compledted: Train Avg. Loss: 0.0247\n",
+      "----> Validation loss: 0.2123\n",
+      "Epoch[92](50/215): Loss: 0.0000\n",
+      "Epoch[92](100/215): Loss: 0.0000\n",
+      "Epoch[92](150/215): Loss: 0.0000\n",
+      "Epoch[92](200/215): Loss: 0.0000\n",
+      "---> Epoch 92 compledted: Train Avg. Loss: 0.0348\n",
+      "----> Validation loss: 0.2259\n",
+      "Epoch[93](50/215): Loss: 0.0342\n",
+      "Epoch[93](100/215): Loss: 0.0674\n",
+      "Epoch[93](150/215): Loss: 0.0628\n",
+      "Epoch[93](200/215): Loss: 0.1564\n",
+      "---> Epoch 93 compledted: Train Avg. Loss: 0.0483\n",
+      "----> Validation loss: 0.2003\n",
+      "Epoch[94](50/215): Loss: 0.0312\n",
+      "Epoch[94](100/215): Loss: 0.0000\n",
+      "Epoch[94](150/215): Loss: 0.0000\n",
+      "Epoch[94](200/215): Loss: 0.0417\n",
+      "---> Epoch 94 compledted: Train Avg. Loss: 0.0238\n",
+      "----> Validation loss: 0.2168\n",
+      "Epoch[95](50/215): Loss: 0.0000\n",
+      "Epoch[95](100/215): Loss: 0.0517\n",
+      "Epoch[95](150/215): Loss: 0.0013\n",
+      "Epoch[95](200/215): Loss: 0.0269\n",
+      "---> Epoch 95 compledted: Train Avg. Loss: 0.0224\n",
+      "----> Validation loss: 0.2176\n",
+      "Epoch[96](50/215): Loss: 0.0000\n",
+      "Epoch[96](100/215): Loss: 0.0000\n",
+      "Epoch[96](150/215): Loss: 0.0429\n",
+      "Epoch[96](200/215): Loss: 0.0000\n",
+      "---> Epoch 96 compledted: Train Avg. Loss: 0.0187\n",
+      "----> Validation loss: 0.1970\n",
+      "Epoch[97](50/215): Loss: 0.0192\n",
+      "Epoch[97](100/215): Loss: 0.0000\n",
+      "Epoch[97](150/215): Loss: 0.0000\n",
+      "Epoch[97](200/215): Loss: 0.0061\n",
+      "---> Epoch 97 compledted: Train Avg. Loss: 0.0273\n",
+      "----> Validation loss: 0.1953\n",
+      "Epoch[98](50/215): Loss: 0.0000\n",
+      "Epoch[98](100/215): Loss: 0.0000\n",
+      "Epoch[98](150/215): Loss: 0.0831\n",
+      "Epoch[98](200/215): Loss: 0.0463\n",
+      "---> Epoch 98 compledted: Train Avg. Loss: 0.0208\n",
+      "----> Validation loss: 0.1918\n",
+      "Epoch[99](50/215): Loss: 0.0000\n",
+      "Epoch[99](100/215): Loss: 0.0384\n",
+      "Epoch[99](150/215): Loss: 0.0637\n",
+      "Epoch[99](200/215): Loss: 0.0000\n",
+      "---> Epoch 99 compledted: Train Avg. Loss: 0.0131\n",
+      "----> Validation loss: 0.1806\n",
+      "Epoch[100](50/215): Loss: 0.0000\n",
+      "Epoch[100](100/215): Loss: 0.0642\n",
+      "Epoch[100](150/215): Loss: 0.0401\n",
+      "Epoch[100](200/215): Loss: 0.0000\n",
+      "---> Epoch 100 compledted: Train Avg. Loss: 0.0512\n",
+      "----> Validation loss: 0.2434\n"
+     ]
+    }
+   ],
+   "source": [
+    "P = 4\n",
+    "K = 8\n",
+    "saveEvery = 25\n",
+    "lr = 0.001\n",
+    "optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), \n",
+    "                             lr = lr)\n",
+    "\n",
+    "for epoch in range(startEpoch+1, nEpochs+1):\n",
+    "      # train & validate\n",
+    "      epoch_train_loss = train(device, model, epoch,\n",
+    "                            train_set, P, K,\n",
+    "                            criterion, optimizer)  \n",
+    "      if validationPath:\n",
+    "        epoch_val_loss = validate(device, model, \n",
+    "                                  val_set, P, K,\n",
+    "                                  criterion)\n",
+    "      else:\n",
+    "        epoch_val_loss = 1\n",
+    " \n",
+    "\n",
+    "      #saving stuff\n",
+    "      if (epoch_train_loss < train_loss): #lowest loss on train set\n",
+    "        train_loss = epoch_train_loss\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'best_train.pth.tar')\n",
+    "\n",
+    "      if (epoch_val_loss < val_loss): #lowest loss on val set\n",
+    "        val_loss = epoch_val_loss\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'best.pth.tar')\n",
+    "        \n",
+    "      if (epoch % saveEvery) == 0: #save every epoch\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'epoch{}.pth.tar'.format(epoch))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Switch to BatchHard, train again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P = 4\n",
+    "K = 8\n",
+    "saveEvery = 25\n",
+    "lr = 0.001\n",
+    "optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), \n",
+    "                             lr = lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[36], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m loadPath \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel/BatchAll/best.pth.tar\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m startEpoch, train_loss, val_loss \u001b[38;5;241m=\u001b[39m \u001b[43mload_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloadPath\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                                                        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                                                        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                                                        \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/utils.py:81\u001b[0m, in \u001b[0;36mload_checkpoint\u001b[0;34m(path, device, model, optimizer)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_checkpoint\u001b[39m(path, device, model, optimizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 81\u001b[0m   state \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m   epoch \u001b[38;5;241m=\u001b[39m state[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     83\u001b[0m   train_loss \u001b[38;5;241m=\u001b[39m state[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_loss\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:712\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    710\u001b[0m             opened_file\u001b[38;5;241m.\u001b[39mseek(orig_position)\n\u001b[1;32m    711\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mload(opened_file)\n\u001b[0;32m--> 712\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopened_zipfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _legacy_load(opened_file, map_location, pickle_module, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:1049\u001b[0m, in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1047\u001b[0m unpickler \u001b[38;5;241m=\u001b[39m UnpicklerWrapper(data_file, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpickle_load_args)\n\u001b[1;32m   1048\u001b[0m unpickler\u001b[38;5;241m.\u001b[39mpersistent_load \u001b[38;5;241m=\u001b[39m persistent_load\n\u001b[0;32m-> 1049\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43munpickler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1051\u001b[0m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_validate_loaded_sparse_tensors()\n\u001b[1;32m   1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:1019\u001b[0m, in \u001b[0;36m_load.<locals>.persistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m   1017\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m loaded_storages:\n\u001b[1;32m   1018\u001b[0m     nbytes \u001b[38;5;241m=\u001b[39m numel \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_element_size(dtype)\n\u001b[0;32m-> 1019\u001b[0m     \u001b[43mload_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_maybe_decode_ascii\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1021\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loaded_storages[key]\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:1001\u001b[0m, in \u001b[0;36m_load.<locals>.load_tensor\u001b[0;34m(dtype, numel, key, location)\u001b[0m\n\u001b[1;32m    997\u001b[0m storage \u001b[38;5;241m=\u001b[39m zip_file\u001b[38;5;241m.\u001b[39mget_storage_from_record(name, numel, torch\u001b[38;5;241m.\u001b[39m_UntypedStorage)\u001b[38;5;241m.\u001b[39mstorage()\u001b[38;5;241m.\u001b[39m_untyped()\n\u001b[1;32m    998\u001b[0m \u001b[38;5;66;03m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[1;32m    999\u001b[0m \u001b[38;5;66;03m# stop wrapping with _TypedStorage\u001b[39;00m\n\u001b[1;32m   1000\u001b[0m loaded_storages[key] \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstorage\u001b[38;5;241m.\u001b[39m_TypedStorage(\n\u001b[0;32m-> 1001\u001b[0m     wrap_storage\u001b[38;5;241m=\u001b[39m\u001b[43mrestore_location\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1002\u001b[0m     dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:175\u001b[0m, in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault_restore_location\u001b[39m(storage, location):\n\u001b[1;32m    174\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _, _, fn \u001b[38;5;129;01min\u001b[39;00m _package_registry:\n\u001b[0;32m--> 175\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    176\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    177\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:152\u001b[0m, in \u001b[0;36m_cuda_deserialize\u001b[0;34m(obj, location)\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_cuda_deserialize\u001b[39m(obj, location):\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m location\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 152\u001b[0m         device \u001b[38;5;241m=\u001b[39m \u001b[43mvalidate_cuda_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_torch_load_uninitialized\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    154\u001b[0m             \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mdevice(device):\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/serialization.py:136\u001b[0m, in \u001b[0;36mvalidate_cuda_device\u001b[0;34m(location)\u001b[0m\n\u001b[1;32m    133\u001b[0m device \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39m_utils\u001b[38;5;241m.\u001b[39m_get_device_index(location, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available():\n\u001b[0;32m--> 136\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAttempting to deserialize object on a CUDA \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    137\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdevice but torch.cuda.is_available() is False. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    138\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIf you are running on a CPU-only machine, \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    139\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    140\u001b[0m                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mto map your storages to the CPU.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    141\u001b[0m device_count \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mdevice_count()\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m device \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m device_count:\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU."
+     ]
+    }
+   ],
+   "source": [
+    "loadPath = 'model/BatchAll/best.pth.tar'\n",
+    "startEpoch, train_loss, val_loss = load_checkpoint(Path(loadPath),\n",
+    "                                                        device,\n",
+    "                                                        model, \n",
+    "                                                        optimizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_loss = 1\n",
+    "train_loss = 1\n",
+    "nEpochs = 99 + startEpoch\n",
+    "\n",
+    "criterion = OnlineTripletLoss(margin = margin, hard=True).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[95](50/215): Loss: 0.0000\n",
+      "Epoch[95](100/215): Loss: 0.0000\n",
+      "Epoch[95](150/215): Loss: 0.0000\n",
+      "Epoch[95](200/215): Loss: 0.0000\n",
+      "---> Epoch 95 compledted: Train Avg. Loss: 0.0003\n",
+      "----> Validation loss: 0.2017\n",
+      "Epoch[96](50/215): Loss: 0.0000\n",
+      "Epoch[96](100/215): Loss: 0.0000\n",
+      "Epoch[96](150/215): Loss: 0.0000\n",
+      "Epoch[96](200/215): Loss: 0.0000\n",
+      "---> Epoch 96 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.2287\n",
+      "Epoch[97](50/215): Loss: 0.0000\n",
+      "Epoch[97](100/215): Loss: 0.0000\n",
+      "Epoch[97](150/215): Loss: 0.0000\n",
+      "Epoch[97](200/215): Loss: 0.0000\n",
+      "---> Epoch 97 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.1632\n",
+      "Epoch[98](50/215): Loss: 0.0000\n",
+      "Epoch[98](100/215): Loss: 0.0000\n",
+      "Epoch[98](150/215): Loss: 0.0000\n",
+      "Epoch[98](200/215): Loss: 0.0000\n",
+      "---> Epoch 98 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1921\n",
+      "Epoch[99](50/215): Loss: 0.0000\n",
+      "Epoch[99](100/215): Loss: 0.0000\n",
+      "Epoch[99](150/215): Loss: 0.0000\n",
+      "Epoch[99](200/215): Loss: 0.0000\n",
+      "---> Epoch 99 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.1960\n",
+      "Epoch[100](50/215): Loss: 0.0000\n",
+      "Epoch[100](100/215): Loss: 0.0000\n",
+      "Epoch[100](150/215): Loss: 0.0000\n",
+      "Epoch[100](200/215): Loss: 0.0000\n",
+      "---> Epoch 100 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1733\n",
+      "Epoch[101](50/215): Loss: 0.0000\n",
+      "Epoch[101](100/215): Loss: 0.0000\n",
+      "Epoch[101](150/215): Loss: 0.0000\n",
+      "Epoch[101](200/215): Loss: 0.0000\n",
+      "---> Epoch 101 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.2228\n",
+      "Epoch[102](50/215): Loss: 0.0000\n",
+      "Epoch[102](100/215): Loss: 0.0000\n",
+      "Epoch[102](150/215): Loss: 0.0000\n",
+      "Epoch[102](200/215): Loss: 0.0000\n",
+      "---> Epoch 102 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1904\n",
+      "Epoch[103](50/215): Loss: 0.0000\n",
+      "Epoch[103](100/215): Loss: 0.0000\n",
+      "Epoch[103](150/215): Loss: 0.0000\n",
+      "Epoch[103](200/215): Loss: 0.0150\n",
+      "---> Epoch 103 compledted: Train Avg. Loss: 0.0041\n",
+      "----> Validation loss: 0.2233\n",
+      "Epoch[104](50/215): Loss: 0.1861\n",
+      "Epoch[104](100/215): Loss: 0.0251\n",
+      "Epoch[104](150/215): Loss: 0.0483\n",
+      "Epoch[104](200/215): Loss: 0.1740\n",
+      "---> Epoch 104 compledted: Train Avg. Loss: 0.0814\n",
+      "----> Validation loss: 0.2468\n",
+      "Epoch[105](50/215): Loss: 0.0059\n",
+      "Epoch[105](100/215): Loss: 0.0178\n",
+      "Epoch[105](150/215): Loss: 0.0000\n",
+      "Epoch[105](200/215): Loss: 0.0208\n",
+      "---> Epoch 105 compledted: Train Avg. Loss: 0.0069\n",
+      "----> Validation loss: 0.2268\n",
+      "Epoch[106](50/215): Loss: 0.0490\n",
+      "Epoch[106](100/215): Loss: 0.0000\n",
+      "Epoch[106](150/215): Loss: 0.0000\n",
+      "Epoch[106](200/215): Loss: 0.0000\n",
+      "---> Epoch 106 compledted: Train Avg. Loss: 0.0032\n",
+      "----> Validation loss: 0.3048\n",
+      "Epoch[107](50/215): Loss: 0.0000\n",
+      "Epoch[107](100/215): Loss: 0.0107\n",
+      "Epoch[107](150/215): Loss: 0.0021\n",
+      "Epoch[107](200/215): Loss: 0.0000\n",
+      "---> Epoch 107 compledted: Train Avg. Loss: 0.0145\n",
+      "----> Validation loss: 0.2275\n",
+      "Epoch[108](50/215): Loss: 0.0000\n",
+      "Epoch[108](100/215): Loss: 0.0000\n",
+      "Epoch[108](150/215): Loss: 0.0000\n",
+      "Epoch[108](200/215): Loss: 0.0000\n",
+      "---> Epoch 108 compledted: Train Avg. Loss: 0.0003\n",
+      "----> Validation loss: 0.1930\n",
+      "Epoch[109](50/215): Loss: 0.0000\n",
+      "Epoch[109](100/215): Loss: 0.0000\n",
+      "Epoch[109](150/215): Loss: 0.0000\n",
+      "Epoch[109](200/215): Loss: 0.0000\n",
+      "---> Epoch 109 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.1591\n",
+      "Epoch[110](50/215): Loss: 0.0000\n",
+      "Epoch[110](100/215): Loss: 0.0000\n",
+      "Epoch[110](150/215): Loss: 0.0000\n",
+      "Epoch[110](200/215): Loss: 0.0000\n",
+      "---> Epoch 110 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1837\n",
+      "Epoch[111](50/215): Loss: 0.0000\n",
+      "Epoch[111](100/215): Loss: 0.0000\n",
+      "Epoch[111](150/215): Loss: 0.0000\n",
+      "Epoch[111](200/215): Loss: 0.0008\n",
+      "---> Epoch 111 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.2203\n",
+      "Epoch[112](50/215): Loss: 0.0000\n",
+      "Epoch[112](100/215): Loss: 0.0000\n",
+      "Epoch[112](150/215): Loss: 0.0000\n",
+      "Epoch[112](200/215): Loss: 0.0000\n",
+      "---> Epoch 112 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1863\n",
+      "Epoch[113](50/215): Loss: 0.0000\n",
+      "Epoch[113](100/215): Loss: 0.0000\n",
+      "Epoch[113](150/215): Loss: 0.0000\n",
+      "Epoch[113](200/215): Loss: 0.0000\n",
+      "---> Epoch 113 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.2181\n",
+      "Epoch[114](50/215): Loss: 0.0000\n",
+      "Epoch[114](100/215): Loss: 0.0061\n",
+      "Epoch[114](150/215): Loss: 0.0064\n",
+      "Epoch[114](200/215): Loss: 0.0000\n",
+      "---> Epoch 114 compledted: Train Avg. Loss: 0.0014\n",
+      "----> Validation loss: 0.2052\n",
+      "Epoch[115](50/215): Loss: 0.0000\n",
+      "Epoch[115](100/215): Loss: 0.0000\n",
+      "Epoch[115](150/215): Loss: 0.0000\n",
+      "Epoch[115](200/215): Loss: 0.0000\n",
+      "---> Epoch 115 compledted: Train Avg. Loss: 0.0026\n",
+      "----> Validation loss: 0.2055\n",
+      "Epoch[116](50/215): Loss: 0.0111\n",
+      "Epoch[116](100/215): Loss: 0.0000\n",
+      "Epoch[116](150/215): Loss: 0.0000\n",
+      "Epoch[116](200/215): Loss: 0.0000\n",
+      "---> Epoch 116 compledted: Train Avg. Loss: 0.0014\n",
+      "----> Validation loss: 0.2332\n",
+      "Epoch[117](50/215): Loss: 0.0000\n",
+      "Epoch[117](100/215): Loss: 0.0000\n",
+      "Epoch[117](150/215): Loss: 0.0000\n",
+      "Epoch[117](200/215): Loss: 0.0000\n",
+      "---> Epoch 117 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1911\n",
+      "Epoch[118](50/215): Loss: 0.0000\n",
+      "Epoch[118](100/215): Loss: 0.0000\n",
+      "Epoch[118](150/215): Loss: 0.0000\n",
+      "Epoch[118](200/215): Loss: 0.0000\n",
+      "---> Epoch 118 compledted: Train Avg. Loss: 0.0006\n",
+      "----> Validation loss: 0.2186\n",
+      "Epoch[119](50/215): Loss: 0.0000\n",
+      "Epoch[119](100/215): Loss: 0.0271\n",
+      "Epoch[119](150/215): Loss: 0.0031\n",
+      "Epoch[119](200/215): Loss: 0.0821\n",
+      "---> Epoch 119 compledted: Train Avg. Loss: 0.0115\n",
+      "----> Validation loss: 0.2890\n",
+      "Epoch[120](50/215): Loss: 0.0000\n",
+      "Epoch[120](100/215): Loss: 0.0000\n",
+      "Epoch[120](150/215): Loss: 0.3262\n",
+      "Epoch[120](200/215): Loss: 0.0127\n",
+      "---> Epoch 120 compledted: Train Avg. Loss: 0.1074\n",
+      "----> Validation loss: 0.2356\n",
+      "Epoch[121](50/215): Loss: 0.0036\n",
+      "Epoch[121](100/215): Loss: 0.0000\n",
+      "Epoch[121](150/215): Loss: 0.0068\n",
+      "Epoch[121](200/215): Loss: 0.0000\n",
+      "---> Epoch 121 compledted: Train Avg. Loss: 0.0032\n",
+      "----> Validation loss: 0.1961\n",
+      "Epoch[122](50/215): Loss: 0.0000\n",
+      "Epoch[122](100/215): Loss: 0.0000\n",
+      "Epoch[122](150/215): Loss: 0.0000\n",
+      "Epoch[122](200/215): Loss: 0.0000\n",
+      "---> Epoch 122 compledted: Train Avg. Loss: 0.0010\n",
+      "----> Validation loss: 0.2111\n",
+      "Epoch[123](50/215): Loss: 0.0000\n",
+      "Epoch[123](100/215): Loss: 0.0000\n",
+      "Epoch[123](150/215): Loss: 0.0000\n",
+      "Epoch[123](200/215): Loss: 0.0000\n",
+      "---> Epoch 123 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1927\n",
+      "Epoch[124](50/215): Loss: 0.0000\n",
+      "Epoch[124](100/215): Loss: 0.0000\n",
+      "Epoch[124](150/215): Loss: 0.0000\n",
+      "Epoch[124](200/215): Loss: 0.0000\n",
+      "---> Epoch 124 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.2018\n",
+      "Epoch[125](50/215): Loss: 0.0000\n",
+      "Epoch[125](100/215): Loss: 0.0000\n",
+      "Epoch[125](150/215): Loss: 0.0000\n",
+      "Epoch[125](200/215): Loss: 0.0000\n",
+      "---> Epoch 125 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.2216\n",
+      "Epoch[126](50/215): Loss: 0.0009\n",
+      "Epoch[126](100/215): Loss: 0.0000\n",
+      "Epoch[126](150/215): Loss: 0.0000\n",
+      "Epoch[126](200/215): Loss: 0.0000\n",
+      "---> Epoch 126 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.2063\n",
+      "Epoch[127](50/215): Loss: 0.0000\n",
+      "Epoch[127](100/215): Loss: 0.0000\n",
+      "Epoch[127](150/215): Loss: 0.0000\n",
+      "Epoch[127](200/215): Loss: 0.0000\n",
+      "---> Epoch 127 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.1985\n",
+      "Epoch[128](50/215): Loss: 0.0000\n",
+      "Epoch[128](100/215): Loss: 0.0000\n",
+      "Epoch[128](150/215): Loss: 0.0000\n",
+      "Epoch[128](200/215): Loss: 0.0000\n",
+      "---> Epoch 128 compledted: Train Avg. Loss: 0.0003\n",
+      "----> Validation loss: 0.2024\n",
+      "Epoch[129](50/215): Loss: 0.0000\n",
+      "Epoch[129](100/215): Loss: 0.0000\n",
+      "Epoch[129](150/215): Loss: 0.0000\n",
+      "Epoch[129](200/215): Loss: 0.0000\n",
+      "---> Epoch 129 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.1897\n",
+      "Epoch[130](50/215): Loss: 0.0000\n",
+      "Epoch[130](100/215): Loss: 0.0000\n",
+      "Epoch[130](150/215): Loss: 0.0000\n",
+      "Epoch[130](200/215): Loss: 0.0000\n",
+      "---> Epoch 130 compledted: Train Avg. Loss: 0.0000\n",
+      "----> Validation loss: 0.1581\n",
+      "Epoch[131](50/215): Loss: 0.0000\n",
+      "Epoch[131](100/215): Loss: 0.0000\n",
+      "Epoch[131](150/215): Loss: 0.0010\n",
+      "Epoch[131](200/215): Loss: 0.0000\n",
+      "---> Epoch 131 compledted: Train Avg. Loss: 0.0001\n",
+      "----> Validation loss: 0.2162\n",
+      "Epoch[132](50/215): Loss: 0.0002\n",
+      "Epoch[132](100/215): Loss: 0.1554\n",
+      "Epoch[132](150/215): Loss: 0.6277\n",
+      "Epoch[132](200/215): Loss: 0.5078\n",
+      "---> Epoch 132 compledted: Train Avg. Loss: 0.2852\n",
+      "----> Validation loss: 0.6249\n",
+      "Epoch[133](50/215): Loss: 0.6463\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[133](100/215): Loss: 0.7863\n",
+      "Epoch[133](150/215): Loss: 0.5474\n",
+      "Epoch[133](200/215): Loss: 0.7881\n",
+      "---> Epoch 133 compledted: Train Avg. Loss: 0.6366\n",
+      "----> Validation loss: 0.6497\n",
+      "Epoch[134](50/215): Loss: 0.4880\n",
+      "Epoch[134](100/215): Loss: 0.6315\n",
+      "Epoch[134](150/215): Loss: 0.5500\n",
+      "Epoch[134](200/215): Loss: 0.4902\n",
+      "---> Epoch 134 compledted: Train Avg. Loss: 0.5775\n",
+      "----> Validation loss: 0.5804\n",
+      "Epoch[135](50/215): Loss: 0.3596\n",
+      "Epoch[135](100/215): Loss: 0.5395\n",
+      "Epoch[135](150/215): Loss: 0.3470\n",
+      "Epoch[135](200/215): Loss: 0.3385\n",
+      "---> Epoch 135 compledted: Train Avg. Loss: 0.4752\n",
+      "----> Validation loss: 0.5018\n",
+      "Epoch[136](50/215): Loss: 0.3451\n",
+      "Epoch[136](100/215): Loss: 0.3349\n",
+      "Epoch[136](150/215): Loss: 0.5378\n",
+      "Epoch[136](200/215): Loss: 0.5308\n",
+      "---> Epoch 136 compledted: Train Avg. Loss: 0.4958\n",
+      "----> Validation loss: 0.5243\n",
+      "Epoch[137](50/215): Loss: 0.7926\n",
+      "Epoch[137](100/215): Loss: 1.0215\n",
+      "Epoch[137](150/215): Loss: 0.3268\n",
+      "Epoch[137](200/215): Loss: 0.5324\n",
+      "---> Epoch 137 compledted: Train Avg. Loss: 0.5270\n",
+      "----> Validation loss: 0.5324\n",
+      "Epoch[138](50/215): Loss: 1.0241\n",
+      "Epoch[138](100/215): Loss: 0.3375\n",
+      "Epoch[138](150/215): Loss: 0.5307\n",
+      "Epoch[138](200/215): Loss: 0.7982\n",
+      "---> Epoch 138 compledted: Train Avg. Loss: 0.5245\n",
+      "----> Validation loss: 0.5159\n",
+      "Epoch[139](50/215): Loss: 0.5317\n",
+      "Epoch[139](100/215): Loss: 0.5308\n",
+      "Epoch[139](150/215): Loss: 0.4992\n",
+      "Epoch[139](200/215): Loss: 0.3232\n",
+      "---> Epoch 139 compledted: Train Avg. Loss: 0.5239\n",
+      "----> Validation loss: 0.6994\n",
+      "Epoch[140](50/215): Loss: 0.3254\n",
+      "Epoch[140](100/215): Loss: 0.7902\n",
+      "Epoch[140](150/215): Loss: 1.0277\n",
+      "Epoch[140](200/215): Loss: 0.7912\n",
+      "---> Epoch 140 compledted: Train Avg. Loss: 0.5366\n",
+      "----> Validation loss: 0.6310\n",
+      "Epoch[141](50/215): Loss: 0.7901\n",
+      "Epoch[141](100/215): Loss: 0.3264\n",
+      "Epoch[141](150/215): Loss: 0.3218\n",
+      "Epoch[141](200/215): Loss: 0.3229\n",
+      "---> Epoch 141 compledted: Train Avg. Loss: 0.5192\n",
+      "----> Validation loss: 0.5057\n",
+      "Epoch[142](50/215): Loss: 0.3225\n",
+      "Epoch[142](100/215): Loss: 0.7919\n",
+      "Epoch[142](150/215): Loss: 0.5271\n",
+      "Epoch[142](200/215): Loss: 0.7893\n",
+      "---> Epoch 142 compledted: Train Avg. Loss: 0.4772\n",
+      "----> Validation loss: 0.4766\n",
+      "Epoch[143](50/215): Loss: 0.3225\n",
+      "Epoch[143](100/215): Loss: 0.5268\n",
+      "Epoch[143](150/215): Loss: 0.5273\n",
+      "Epoch[143](200/215): Loss: 0.3203\n",
+      "---> Epoch 143 compledted: Train Avg. Loss: 0.4611\n",
+      "----> Validation loss: 0.4904\n",
+      "Epoch[144](50/215): Loss: 0.3206\n",
+      "Epoch[144](100/215): Loss: 0.5249\n",
+      "Epoch[144](150/215): Loss: 0.5270\n",
+      "Epoch[144](200/215): Loss: 0.5259\n",
+      "---> Epoch 144 compledted: Train Avg. Loss: 0.4395\n",
+      "----> Validation loss: 0.4761\n",
+      "Epoch[145](50/215): Loss: 0.5260\n",
+      "Epoch[145](100/215): Loss: 0.5261\n",
+      "Epoch[145](150/215): Loss: 0.7913\n",
+      "Epoch[145](200/215): Loss: 0.5255\n",
+      "---> Epoch 145 compledted: Train Avg. Loss: 0.4617\n",
+      "----> Validation loss: 0.4752\n",
+      "Epoch[146](50/215): Loss: 0.7917\n",
+      "Epoch[146](100/215): Loss: 0.4964\n",
+      "Epoch[146](150/215): Loss: 0.5270\n",
+      "Epoch[146](200/215): Loss: 0.3187\n",
+      "---> Epoch 146 compledted: Train Avg. Loss: 0.4368\n",
+      "----> Validation loss: 0.4917\n",
+      "Epoch[147](50/215): Loss: 0.3194\n",
+      "Epoch[147](100/215): Loss: 0.3186\n",
+      "Epoch[147](150/215): Loss: 0.3188\n",
+      "Epoch[147](200/215): Loss: 0.3188\n",
+      "---> Epoch 147 compledted: Train Avg. Loss: 0.4248\n",
+      "----> Validation loss: 0.4435\n",
+      "Epoch[148](50/215): Loss: 0.3190\n",
+      "Epoch[148](100/215): Loss: 0.3186\n",
+      "Epoch[148](150/215): Loss: 0.5247\n",
+      "Epoch[148](200/215): Loss: 0.3182\n",
+      "---> Epoch 148 compledted: Train Avg. Loss: 0.4146\n",
+      "----> Validation loss: 0.4573\n",
+      "Epoch[149](50/215): Loss: 0.3186\n",
+      "Epoch[149](100/215): Loss: 0.7910\n",
+      "Epoch[149](150/215): Loss: 0.3176\n",
+      "Epoch[149](200/215): Loss: 0.3174\n",
+      "---> Epoch 149 compledted: Train Avg. Loss: 0.3847\n",
+      "----> Validation loss: 0.4502\n",
+      "Epoch[150](50/215): Loss: 0.3176\n",
+      "Epoch[150](100/215): Loss: 0.7915\n",
+      "Epoch[150](150/215): Loss: 0.3172\n",
+      "Epoch[150](200/215): Loss: 0.5248\n",
+      "---> Epoch 150 compledted: Train Avg. Loss: 0.3957\n",
+      "----> Validation loss: 0.4340\n",
+      "Epoch[151](50/215): Loss: 0.3171\n",
+      "Epoch[151](100/215): Loss: 0.3172\n",
+      "Epoch[151](150/215): Loss: 0.5251\n",
+      "Epoch[151](200/215): Loss: 0.3171\n",
+      "---> Epoch 151 compledted: Train Avg. Loss: 0.3857\n",
+      "----> Validation loss: 0.4687\n",
+      "Epoch[152](50/215): Loss: 0.5998\n",
+      "Epoch[152](100/215): Loss: 0.3617\n",
+      "Epoch[152](150/215): Loss: 0.5417\n",
+      "Epoch[152](200/215): Loss: 0.3269\n",
+      "---> Epoch 152 compledted: Train Avg. Loss: 0.4041\n",
+      "----> Validation loss: 0.4235\n",
+      "Epoch[153](50/215): Loss: 0.7927\n",
+      "Epoch[153](100/215): Loss: 0.3212\n",
+      "Epoch[153](150/215): Loss: 0.3846\n",
+      "Epoch[153](200/215): Loss: 0.3210\n",
+      "---> Epoch 153 compledted: Train Avg. Loss: 0.3810\n",
+      "----> Validation loss: 0.4142\n",
+      "Epoch[154](50/215): Loss: 0.3337\n",
+      "Epoch[154](100/215): Loss: 0.3205\n",
+      "Epoch[154](150/215): Loss: 0.3212\n",
+      "Epoch[154](200/215): Loss: 0.5252\n",
+      "---> Epoch 154 compledted: Train Avg. Loss: 0.3725\n",
+      "----> Validation loss: 0.3992\n",
+      "Epoch[155](50/215): Loss: 0.5254\n",
+      "Epoch[155](100/215): Loss: 0.5382\n",
+      "Epoch[155](150/215): Loss: 0.5266\n",
+      "Epoch[155](200/215): Loss: 0.3193\n",
+      "---> Epoch 155 compledted: Train Avg. Loss: 0.3731\n",
+      "----> Validation loss: 0.3336\n",
+      "Epoch[156](50/215): Loss: 0.3185\n",
+      "Epoch[156](100/215): Loss: 0.3184\n",
+      "Epoch[156](150/215): Loss: 0.3170\n",
+      "Epoch[156](200/215): Loss: 0.3175\n",
+      "---> Epoch 156 compledted: Train Avg. Loss: 0.3378\n",
+      "----> Validation loss: 0.3342\n",
+      "Epoch[157](50/215): Loss: 0.3185\n",
+      "Epoch[157](100/215): Loss: 0.3405\n",
+      "Epoch[157](150/215): Loss: 0.3361\n",
+      "Epoch[157](200/215): Loss: 0.3173\n",
+      "---> Epoch 157 compledted: Train Avg. Loss: 0.3388\n",
+      "----> Validation loss: 0.3176\n",
+      "Epoch[158](50/215): Loss: 0.3171\n",
+      "Epoch[158](100/215): Loss: 0.3171\n",
+      "Epoch[158](150/215): Loss: 0.3169\n",
+      "Epoch[158](200/215): Loss: 0.3170\n",
+      "---> Epoch 158 compledted: Train Avg. Loss: 0.3311\n",
+      "----> Validation loss: 0.3172\n",
+      "Epoch[159](50/215): Loss: 0.3168\n",
+      "Epoch[159](100/215): Loss: 0.3169\n",
+      "Epoch[159](150/215): Loss: 0.3167\n",
+      "Epoch[159](200/215): Loss: 0.3168\n",
+      "---> Epoch 159 compledted: Train Avg. Loss: 0.3329\n",
+      "----> Validation loss: 0.3171\n",
+      "Epoch[160](50/215): Loss: 0.3172\n",
+      "Epoch[160](100/215): Loss: 0.3167\n",
+      "Epoch[160](150/215): Loss: 0.3169\n",
+      "Epoch[160](200/215): Loss: 0.3296\n",
+      "---> Epoch 160 compledted: Train Avg. Loss: 0.3345\n",
+      "----> Validation loss: 0.3169\n",
+      "Epoch[161](50/215): Loss: 0.3167\n",
+      "Epoch[161](100/215): Loss: 0.3168\n",
+      "Epoch[161](150/215): Loss: 0.3167\n",
+      "Epoch[161](200/215): Loss: 0.3166\n",
+      "---> Epoch 161 compledted: Train Avg. Loss: 0.3294\n",
+      "----> Validation loss: 0.3169\n",
+      "Epoch[162](50/215): Loss: 0.3165\n",
+      "Epoch[162](100/215): Loss: 0.3166\n",
+      "Epoch[162](150/215): Loss: 0.3168\n",
+      "Epoch[162](200/215): Loss: 0.3167\n",
+      "---> Epoch 162 compledted: Train Avg. Loss: 0.3295\n",
+      "----> Validation loss: 0.3168\n",
+      "Epoch[163](50/215): Loss: 0.3167\n",
+      "Epoch[163](100/215): Loss: 0.3167\n",
+      "Epoch[163](150/215): Loss: 0.3867\n",
+      "Epoch[163](200/215): Loss: 0.3167\n",
+      "---> Epoch 163 compledted: Train Avg. Loss: 0.3316\n",
+      "----> Validation loss: 0.3168\n",
+      "Epoch[164](50/215): Loss: 0.3167\n",
+      "Epoch[164](100/215): Loss: 0.3166\n",
+      "Epoch[164](150/215): Loss: 0.3167\n",
+      "Epoch[164](200/215): Loss: 0.3166\n",
+      "---> Epoch 164 compledted: Train Avg. Loss: 0.3273\n",
+      "----> Validation loss: 0.3168\n",
+      "Epoch[165](50/215): Loss: 0.3165\n",
+      "Epoch[165](100/215): Loss: 0.3166\n",
+      "Epoch[165](150/215): Loss: 0.3168\n",
+      "Epoch[165](200/215): Loss: 0.3166\n",
+      "---> Epoch 165 compledted: Train Avg. Loss: 0.3267\n",
+      "----> Validation loss: 0.3167\n",
+      "Epoch[166](50/215): Loss: 0.3167\n",
+      "Epoch[166](100/215): Loss: 0.3166\n",
+      "Epoch[166](150/215): Loss: 0.3167\n",
+      "Epoch[166](200/215): Loss: 0.3167\n",
+      "---> Epoch 166 compledted: Train Avg. Loss: 0.3324\n",
+      "----> Validation loss: 0.3167\n",
+      "Epoch[167](50/215): Loss: 0.3167\n",
+      "Epoch[167](100/215): Loss: 0.3167\n",
+      "Epoch[167](150/215): Loss: 0.3166\n",
+      "Epoch[167](200/215): Loss: 0.3166\n",
+      "---> Epoch 167 compledted: Train Avg. Loss: 0.3276\n",
+      "----> Validation loss: 0.3167\n",
+      "Epoch[168](50/215): Loss: 0.3167\n",
+      "Epoch[168](100/215): Loss: 0.3166\n",
+      "Epoch[168](150/215): Loss: 0.3167\n",
+      "Epoch[168](200/215): Loss: 0.3167\n",
+      "---> Epoch 168 compledted: Train Avg. Loss: 0.3281\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[169](50/215): Loss: 0.3167\n",
+      "Epoch[169](100/215): Loss: 0.3166\n",
+      "Epoch[169](150/215): Loss: 0.3166\n",
+      "Epoch[169](200/215): Loss: 0.3167\n",
+      "---> Epoch 169 compledted: Train Avg. Loss: 0.3261\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[170](50/215): Loss: 0.3167\n",
+      "Epoch[170](100/215): Loss: 0.5723\n",
+      "Epoch[170](150/215): Loss: 0.3167\n",
+      "Epoch[170](200/215): Loss: 0.3167\n",
+      "---> Epoch 170 compledted: Train Avg. Loss: 0.3297\n",
+      "----> Validation loss: 0.3167\n",
+      "Epoch[171](50/215): Loss: 0.3167\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch[171](100/215): Loss: 0.3165\n",
+      "Epoch[171](150/215): Loss: 0.3166\n",
+      "Epoch[171](200/215): Loss: 0.3165\n",
+      "---> Epoch 171 compledted: Train Avg. Loss: 0.3273\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[172](50/215): Loss: 0.3166\n",
+      "Epoch[172](100/215): Loss: 0.3167\n",
+      "Epoch[172](150/215): Loss: 0.3866\n",
+      "Epoch[172](200/215): Loss: 0.3166\n",
+      "---> Epoch 172 compledted: Train Avg. Loss: 0.3264\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[173](50/215): Loss: 0.3166\n",
+      "Epoch[173](150/215): Loss: 0.3166\n",
+      "Epoch[173](200/215): Loss: 0.3166\n",
+      "---> Epoch 173 compledted: Train Avg. Loss: 0.3312\n",
+      "----> Validation loss: 0.3167\n",
+      "Epoch[174](50/215): Loss: 0.3166\n",
+      "Epoch[174](100/215): Loss: 0.3166\n",
+      "Epoch[174](150/215): Loss: 0.3165\n",
+      "Epoch[174](200/215): Loss: 0.3165\n",
+      "---> Epoch 174 compledted: Train Avg. Loss: 0.3269\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[175](50/215): Loss: 0.3166\n",
+      "Epoch[175](100/215): Loss: 0.3166\n",
+      "Epoch[175](150/215): Loss: 0.3166\n",
+      "Epoch[175](200/215): Loss: 0.3166\n",
+      "---> Epoch 175 compledted: Train Avg. Loss: 0.3282\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[176](50/215): Loss: 0.3167\n",
+      "Epoch[176](100/215): Loss: 0.3165\n",
+      "Epoch[176](150/215): Loss: 0.3166\n",
+      "Epoch[176](200/215): Loss: 0.3166\n",
+      "---> Epoch 176 compledted: Train Avg. Loss: 0.3319\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[177](50/215): Loss: 0.3166\n",
+      "Epoch[177](100/215): Loss: 0.3165\n",
+      "Epoch[177](150/215): Loss: 0.3166\n",
+      "Epoch[177](200/215): Loss: 0.3167\n",
+      "---> Epoch 177 compledted: Train Avg. Loss: 0.3232\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[178](50/215): Loss: 0.3165\n",
+      "Epoch[178](100/215): Loss: 0.3167\n",
+      "Epoch[178](150/215): Loss: 0.3166\n",
+      "Epoch[178](200/215): Loss: 0.3166\n",
+      "---> Epoch 178 compledted: Train Avg. Loss: 0.3222\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[179](50/215): Loss: 0.3165\n",
+      "Epoch[179](100/215): Loss: 0.3166\n",
+      "Epoch[179](150/215): Loss: 0.3166\n",
+      "Epoch[179](200/215): Loss: 0.3308\n",
+      "---> Epoch 179 compledted: Train Avg. Loss: 0.3203\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[180](50/215): Loss: 0.3166\n",
+      "Epoch[180](100/215): Loss: 0.3166\n",
+      "Epoch[180](150/215): Loss: 0.3166\n",
+      "Epoch[180](200/215): Loss: 0.3166\n",
+      "---> Epoch 180 compledted: Train Avg. Loss: 0.3192\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[181](50/215): Loss: 0.3165\n",
+      "Epoch[181](100/215): Loss: 0.3166\n",
+      "Epoch[181](150/215): Loss: 0.3166\n",
+      "Epoch[181](200/215): Loss: 0.3167\n",
+      "---> Epoch 181 compledted: Train Avg. Loss: 0.3192\n",
+      "----> Validation loss: 0.3165\n",
+      "Epoch[182](50/215): Loss: 0.3166\n",
+      "Epoch[182](100/215): Loss: 0.3165\n",
+      "Epoch[182](150/215): Loss: 0.3166\n",
+      "Epoch[182](200/215): Loss: 0.3165\n",
+      "---> Epoch 182 compledted: Train Avg. Loss: 0.3195\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[183](50/215): Loss: 0.3166\n",
+      "Epoch[183](100/215): Loss: 0.3164\n",
+      "Epoch[183](150/215): Loss: 0.3165\n",
+      "Epoch[183](200/215): Loss: 0.3165\n",
+      "---> Epoch 183 compledted: Train Avg. Loss: 0.3182\n",
+      "----> Validation loss: 0.3166\n",
+      "Epoch[184](50/215): Loss: 0.3165\n",
+      "Epoch[184](100/215): Loss: 0.3165\n",
+      "Epoch[184](150/215): Loss: 0.3164\n",
+      "Epoch[184](200/215): Loss: 0.3166\n",
+      "---> Epoch 184 compledted: Train Avg. Loss: 0.3193\n",
+      "----> Validation loss: 0.3165\n",
+      "Epoch[185](50/215): Loss: 0.3165\n",
+      "Epoch[185](100/215): Loss: 0.3165\n",
+      "Epoch[185](150/215): Loss: 0.3165\n",
+      "Epoch[185](200/215): Loss: 0.3165\n",
+      "---> Epoch 185 compledted: Train Avg. Loss: 0.3225\n",
+      "----> Validation loss: 0.3165\n",
+      "Epoch[186](50/215): Loss: 0.3165\n",
+      "Epoch[186](100/215): Loss: 0.3165\n",
+      "Epoch[186](150/215): Loss: 0.3166\n",
+      "Epoch[186](200/215): Loss: 0.3164\n",
+      "---> Epoch 186 compledted: Train Avg. Loss: 0.3216\n",
+      "----> Validation loss: 0.3251\n",
+      "Epoch[187](50/215): Loss: 0.3166\n",
+      "Epoch[187](100/215): Loss: 0.3165\n",
+      "Epoch[187](150/215): Loss: 0.3164\n",
+      "Epoch[187](200/215): Loss: 0.3166\n",
+      "---> Epoch 187 compledted: Train Avg. Loss: 0.3268\n",
+      "----> Validation loss: 0.3559\n",
+      "Epoch[188](50/215): Loss: 0.3164\n",
+      "Epoch[188](100/215): Loss: 0.3165\n",
+      "Epoch[188](150/215): Loss: 0.3166\n",
+      "Epoch[188](200/215): Loss: 0.3166\n",
+      "---> Epoch 188 compledted: Train Avg. Loss: 0.3369\n",
+      "----> Validation loss: 0.3755\n",
+      "Epoch[189](50/215): Loss: 0.3166\n",
+      "Epoch[189](100/215): Loss: 0.3165\n",
+      "Epoch[189](150/215): Loss: 0.3165\n",
+      "Epoch[189](200/215): Loss: 0.3165\n",
+      "---> Epoch 189 compledted: Train Avg. Loss: 0.3425\n",
+      "----> Validation loss: 0.3756\n",
+      "Epoch[190](50/215): Loss: 0.3166\n",
+      "Epoch[190](100/215): Loss: 0.3166\n",
+      "Epoch[190](150/215): Loss: 0.3164\n",
+      "Epoch[190](200/215): Loss: 0.3167\n",
+      "---> Epoch 190 compledted: Train Avg. Loss: 0.3663\n",
+      "----> Validation loss: 0.3624\n",
+      "Epoch[191](50/215): Loss: 0.3164\n",
+      "Epoch[191](100/215): Loss: 0.3166\n",
+      "Epoch[191](150/215): Loss: 0.5724\n",
+      "Epoch[191](200/215): Loss: 0.5723\n",
+      "---> Epoch 191 compledted: Train Avg. Loss: 0.3601\n",
+      "----> Validation loss: 0.3624\n",
+      "Epoch[192](50/215): Loss: 0.3166\n",
+      "Epoch[192](100/215): Loss: 0.5721\n",
+      "Epoch[192](150/215): Loss: 0.3163\n",
+      "Epoch[192](200/215): Loss: 0.3166\n",
+      "---> Epoch 192 compledted: Train Avg. Loss: 0.3613\n",
+      "----> Validation loss: 0.3493\n",
+      "Epoch[193](50/215): Loss: 0.5723\n",
+      "Epoch[193](100/215): Loss: 0.3165\n",
+      "Epoch[193](150/215): Loss: 0.5721\n",
+      "Epoch[193](200/215): Loss: 0.3165\n",
+      "---> Epoch 193 compledted: Train Avg. Loss: 0.3693\n",
+      "----> Validation loss: 0.3493\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(startEpoch+1, nEpochs+1):\n",
+    "      # train & validate\n",
+    "      epoch_train_loss = train(device, model, epoch,\n",
+    "                            train_set, P, K,\n",
+    "                            criterion, optimizer)  \n",
+    "      if validationPath:\n",
+    "        epoch_val_loss = validate(device, model, \n",
+    "                                  val_set, P, K,\n",
+    "                                  criterion)\n",
+    "      else:\n",
+    "        epoch_val_loss = 1\n",
+    " \n",
+    "\n",
+    "      #saving stuff\n",
+    "      if (epoch_train_loss < train_loss): #lowest loss on train set\n",
+    "        train_loss = epoch_train_loss\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'best_train.pth.tar')\n",
+    "\n",
+    "      if (epoch_val_loss < val_loss): #lowest loss on val set\n",
+    "        val_loss = epoch_val_loss\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'best.pth.tar')\n",
+    "        \n",
+    "      if (epoch % saveEvery) == 0: #save every epoch\n",
+    "        save_checkpoint({\n",
+    "            'epoch': epoch,\n",
+    "            'train_loss': epoch_train_loss,\n",
+    "            'val_loss': epoch_val_loss,\n",
+    "            'model': model.state_dict(),\n",
+    "            'optimizer': optimizer.state_dict(),\n",
+    "        }, Path(savePath), 'epoch{}.pth.tar'.format(epoch))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set up output paths\n",
+    "dbPath = 'data/database'\n",
+    "queryPath = 'data/query'\n",
+    "outPath = 'out'\n",
+    "query_features = Path(outPath) / 'q_features.h5'\n",
+    "db_features = Path(outPath) / 'db_features.h5'\n",
+    "retrieval = Path(outPath) / 'retrieved.h5'\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=> loaded checkpoint 'True' (epoch 130)\n",
+      "Checkpoint's train loss is: 0.0000\n",
+      "Checkpoint's validation loss is: 0.1581\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 8\u001b[0m\n\u001b[1;32m      3\u001b[0m loadPath \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel/BatchAll/best.pth.tar\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      5\u001b[0m startEpoch, train_loss, val_loss \u001b[38;5;241m=\u001b[39m load_checkpoint(Path(loadPath), \n\u001b[1;32m      6\u001b[0m                                                         device,\n\u001b[1;32m      7\u001b[0m                                                         model)\n\u001b[0;32m----> 8\u001b[0m \u001b[43mcalculate_netvlads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdb_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdb_features\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/query.py:41\u001b[0m, in \u001b[0;36mcalculate_netvlads\u001b[0;34m(device, model, dataset, out_path)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, query \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dataset):\n\u001b[1;32m     40\u001b[0m   name \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mnames[i]\n\u001b[0;32m---> 41\u001b[0m   v \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_NetVLAD\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mFile(\u001b[38;5;28mstr\u001b[39m(out_path), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m, libver\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlatest\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fd:\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[0;32m/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/query.py:19\u001b[0m, in \u001b[0;36mcalculate_NetVLAD\u001b[0;34m(device, model, img)\u001b[0m\n\u001b[1;32m     17\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     18\u001b[0m img_encoding \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mencoder(img)\n\u001b[0;32m---> 19\u001b[0m v \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnetvlad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg_encoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m v \u001b[38;5;241m=\u001b[39m v\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m v\n",
+      "File \u001b[0;32m/data/computervision/anaconda3/envs/nthlongcv3d/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "File \u001b[0;32m/storage/computervision/longnth/models/image_retrieval/NetVLAD/scr/netvlad.py:60\u001b[0m, in \u001b[0;36mNetVLADLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     57\u001b[0m residual \u001b[38;5;241m=\u001b[39m x_flatten\u001b[38;5;241m.\u001b[39mexpand(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_vocabs, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m) \u001b[38;5;241m-\u001b[39m \\\n\u001b[1;32m     58\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvocabs\u001b[38;5;241m.\u001b[39mexpand(x_flatten\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     59\u001b[0m residual \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m soft_assignment\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 60\u001b[0m vlad \u001b[38;5;241m=\u001b[39m \u001b[43mresidual\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     62\u001b[0m vlad \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mnormalize(vlad, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)  \u001b[38;5;66;03m# intra-normalization\u001b[39;00m\n\u001b[1;32m     63\u001b[0m vlad \u001b[38;5;241m=\u001b[39m vlad\u001b[38;5;241m.\u001b[39mview(x\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)  \u001b[38;5;66;03m# flatten\u001b[39;00m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "#Load database into Dataset, then calculate db's netvlads\n",
+    "db_dataset = ImageDataset(Path(dbPath))\n",
+    "loadPath = 'model/BatchAll/best.pth.tar'\n",
+    "\n",
+    "startEpoch, train_loss, val_loss = load_checkpoint(Path(loadPath), \n",
+    "                                                        device,\n",
+    "                                                        model)\n",
+    "calculate_netvlads(device, model, db_dataset, db_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Load query into Dataset, then calculate query's netvlads\n",
+    "query_dataset = ImageDataset(Path(queryPath))\n",
+    "calculate_netvlads(device, model, query_dataset, query_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find Retrieval \n",
+    "query(query_features, db_features, retrieval)\n",
+    "# plot_retrievals_images(retrieval, Path(dbPath), Path(queryPath))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Query one image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=> loaded checkpoint 'True' (epoch 130)\n",
+      "Checkpoint's train loss is: 0.0000\n",
+      "Checkpoint's validation loss is: 0.1581\n"
+     ]
+    }
+   ],
+   "source": [
+    "db_dataset = ImageDataset(Path(dbPath))\n",
+    "loadPath = 'model/BatchAll/best.pth.tar'\n",
+    "\n",
+    "startEpoch, train_loss, val_loss = load_checkpoint(Path(loadPath), \n",
+    "                                                        device,\n",
+    "                                                        model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import urllib\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "from PIL import Image\n",
+    "\n",
+    "req = urllib.request.urlopen('https://www.vaticancitytours.it/wp-content/uploads/2019/08/st-peters-square-outside.jpg')\n",
+    "arr = np.asarray(bytearray(req.read()), dtype=np.uint8)\n",
+    "opencv_image = cv2.imdecode(arr, -1)\n",
+    "\n",
+    "rgb_image = cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB)\n",
+    "pil_image = Image.fromarray(rgb_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from scr.query_one import query_one, plot_retrieval_images_one\n",
+    "\n",
+    "retrieved_dict = query_one(pil_image, device, model,\n",
+    "                      db_features,\n",
+    "                      n_result=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1586.53x112.5 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_retrieval_images_one(query_img = pil_image, retrieved_dict = retrieved_dict, db_dir = dbPath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:nthlongcv3d]",
+   "language": "python",
+   "name": "conda-env-nthlongcv3d-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0d591c6e422414675974e227c13f5382000c440fedd3c5006ef2be5d887f0ba7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scr/query_one.py b/scr/query_one.py
new file mode 100644
index 0000000..aedf1ee
--- /dev/null
+++ b/scr/query_one.py
@@ -0,0 +1,157 @@
+from pathlib import Path
+from typing import Optional
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+
+from pathlib import Path
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.models as models
+from torchvision.models import VGG16_Weights
+
+from PIL import Image
+from typing import Optional
+import argparse
+
+def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5,
+                adaptive=True):
+    """Plot a set of images horizontally.
+    Args:
+        imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
+        titles: a list of strings, as titles for each image.
+        cmaps: colormaps for monochrome images.
+        adaptive: whether the figure size should fit the image aspect ratios.
+    """
+    n = len(imgs)
+    if not isinstance(cmaps, (list, tuple)):
+        cmaps = [cmaps] * n
+
+    if adaptive:
+        ratios = [i.size[0] / i.size[1] for i in imgs]  # W / H
+    else:
+        ratios = [4/3] * n
+    figsize = [sum(ratios)*4.5, 4.5]
+    fig, ax = plt.subplots(
+        1, n, figsize=figsize, dpi=dpi, gridspec_kw={'width_ratios': ratios})
+    if n == 1:
+        ax = [ax]
+    for i in range(n):
+        ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
+        ax[i].get_yaxis().set_ticks([])
+        ax[i].get_xaxis().set_ticks([])
+        ax[i].set_axis_off()
+        for spine in ax[i].spines.values():  # remove frame
+            spine.set_visible(False)
+        if titles:
+            ax[i].set_title(titles[i])
+    fig.tight_layout(pad=pad)
+    
+
+def read_image(path: Path):
+    """This function read an image from a path.
+    The read is perform using PIL.Image (cause PyTorch).
+    """
+
+    image = Image.open(path)
+    if image is None:
+        raise ValueError(f'Cannot read image {path}.')
+    return image
+
+    
+def calculate_NetVLAD(device, model, img):
+  '''This function calculate the NetVLAD of an preprocessed image
+  Args
+  ------------------------------------------------------------------------------
+  model: pytorch Module, with our netvlad component
+  img: an image, preprocess according to pytorch format
+
+  Return
+  ------------------------------------------------------------------------------
+  v: NetVLAD vector of that img
+  '''
+  img = img.unsqueeze(0).to(device)
+  img_encoding = model.encoder(img)
+  v = model.netvlad(img_encoding)
+  v = v.view(-1).detach().cpu().numpy()
+  return v
+
+
+def pairs_from_similarity_matrix(sim, n_results):
+    """This function create pair of similar indices from a similarity matrix"""
+    idx = np.argsort(sim, axis =1)
+    n_col = idx.shape[1]-1
+    pairs = []
+    for i,_ in enumerate(sim):
+        for j in range(n_results):
+            pairs.append((i, idx[i,n_col-j]))
+    return pairs
+
+
+def query_one(image, device, model,
+          db_features: Path,
+          n_result=10):
+  '''This function:
+    - read image's and db's netvlads
+    - compare image's netvlads to db's netvlads 
+    - create a retrieval dictionary
+    - save that dictionary into a .h5 file 
+  Args
+  ------------------------------------------------------------------------------
+  image_vector: query_image's NetVLAD vector
+  db_features: .h5 file containing db's netvlads
+  out_path: where we save our retrieval result
+  '''
+  transform = transforms.Compose([
+      transforms.Resize(256),
+      transforms.CenterCrop(256),
+      transforms.ToTensor(),
+      transforms.Normalize(mean = [0.485, 0.456, 0.406],
+                            std = [0.229, 0.224, 0.225]) 
+  ])
+  image = transform(image)
+    
+  def read_netvlads(path: Path):
+    with h5py.File(str(path), 'r', libver='latest') as f:
+      names = []
+      netvlads = []
+      for i, key in enumerate(f.keys()):
+        names.append(key)
+        netvlads.append(f[key][()])
+    netvlads = np.array(netvlads)
+    return names, netvlads
+  
+  image_vector = calculate_NetVLAD(device, model, image)
+  query_names, query_netvlads = "result_one", image_vector
+  db_names, db_netvlads = read_netvlads(db_features)
+
+  query_netvlads = torch.from_numpy(query_netvlads)
+  query_netvlads = torch.unsqueeze(query_netvlads, 0)
+  sim = np.einsum('id, jd -> ij', query_netvlads, db_netvlads)
+  pairs = pairs_from_similarity_matrix(sim, n_result)
+  pairs = [(query_names, db_names[j]) for i,j in pairs]
+  retrieved_dict = {}
+
+  for query_name, db_name in pairs:
+    if query_name in retrieved_dict.keys():
+      retrieved_dict[query_name].append(db_name)
+    else:
+      retrieved_dict[query_name] = [db_name]
+
+  return retrieved_dict["result_one"]
+
+
+def plot_retrieval_images_one(query_img, retrieved_dict, db_dir: Path):
+    """This function plots queries and retrieved images
+    Args
+    ----------------------------------------------------------------
+    retrieved_dict: list storing retrievel results
+    db_dir: path of folder containing database images
+    """
+    db_refs = [db_index for db_index in retrieved_dict]
+#     plot_images([query_img], dpi = 25)
+    plt.imshow(query_img)
+    db_imgs = [read_image(db_dir+ "/"+r) for r in db_refs]
+    plot_images(db_imgs[:10], dpi = 25)
\ No newline at end of file
diff --git a/scr/utils.py b/scr/utils.py
index 98e2e3a..0df1827 100644
--- a/scr/utils.py
+++ b/scr/utils.py
@@ -78,7 +78,10 @@ def save_checkpoint(state, path:Path, filename='lastest.pth.tar'):
   torch.save(state, out_path)
 
 def load_checkpoint(path, device, model, optimizer = None):
-  state = torch.load(path)
+  if torch.cuda.is_available():
+    state = torch.load(path)
+  else:
+    state = torch.load(path, map_location=torch.device('cpu'))
   epoch = state['epoch']
   train_loss = state['train_loss']
   val_loss = state['val_loss']
@@ -101,4 +104,4 @@ def str2bool(v):
         return False
     else:
         raise argparse.ArgumentTypeError('Boolean value expected')
-    
\ No newline at end of file
+