Merge pull request #11 from QVPR/reconfig

Reconfiguration of codebase

.gitignore

@@ -1,18 +1,10 @@
 __pycache__/
-.ipynb_checkpoints/
-src/__pycache__/
-dataset/conv/
-dataset/Dusk/
-dataset/fall/
-dataset/model/
-dataset/output_database/
-dataset/output_query/
-dataset/Rain/
-dataset/spring/
-dataset/summer/
-dataset/Sun/
-dataset/winter/
-dataset/event.csv/
-models/VPRTempo78415685001.pth
-models/VPRTempoQuant78415685001.pth
-VPRTempo.egg-info/
+.pyest_cache/
+vprtempo/__pycache__/
+vprtempo/dataset/fall/
+vprtempo/dataset/spring/
+vprtempo/dataset/summer/
+vprtempo/dataset/winter/
+vprtempo/dataset/event.csv
+vprtempo/output/
+vprtempo/src/__pycache__/

README.md

@@ -56,7 +56,7 @@ Once downloaded, please install the required dependencies to run the network thr
 Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/).
 
 ```python
-pip3 install VPRTempo
+pip install vprtempo
 ```
 If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system.
 

main.py

@@ -23,17 +23,16 @@
 '''
 Imports
 '''
-import argparse
 import sys
-sys.path.append('./src')
-sys.path.append('./vprtempo')
+import argparse
+
 import torch.quantization as quantization
 
-from VPRTempoTrain import VPRTempoTrain, generate_model_name, check_pretrained_model, train_new_model
-from VPRTempo import VPRTempo, run_inference
-from VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant
-from VPRTempoQuant import VPRTempoQuant, run_inference_quant
-from loggers import model_logger, model_logger_quant
+from vprtempo.VPRTempo import VPRTempo, run_inference
+from vprtempo.src.loggers import model_logger, model_logger_quant
+from vprtempo.VPRTempoQuant import VPRTempoQuant, run_inference_quant
+from vprtempo.VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant
+from vprtempo.VPRTempoTrain import VPRTempoTrain, generate_model_name, check_pretrained_model, train_new_model
 
 def initialize_and_run_model(args,dims):
     # If user wants to train a new network
@@ -109,7 +108,7 @@ def parse_network(use_quantize=False, train_new_model=False):
     # Define the dataset arguments
     parser.add_argument('--dataset', type=str, default='nordland',
                             help="Dataset to use for training and/or inferencing")
-    parser.add_argument('--data_dir', type=str, default='./dataset/',
+    parser.add_argument('--data_dir', type=str, default='./vprtempo/dataset/',
                             help="Directory where dataset files are stored")
     parser.add_argument('--num_places', type=int, default=500,
                             help="Number of places to use for training and/or inferencing")

setup.py

@@ -21,7 +21,7 @@
 # define the setup
 setup(
     name="VPRTempo",
-    version="1.1.2",
+    version="1.1.3",
     description='VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition',
     long_description=long_description,
     long_description_content_type='text/markdown',

vprtempo/VPRTempo.py

@@ -26,22 +26,16 @@
 
 import os
 import torch
-import gc
-import sys
-sys.path.append('./src')
-sys.path.append('./models')
-sys.path.append('./output')
-sys.path.append('./dataset')
-
-import blitnet as bn
+
 import numpy as np
 import torch.nn as nn
+import vprtempo.src.blitnet as bn
 
-from dataset import CustomImageDataset, ProcessImage
-from torch.utils.data import DataLoader
 from tqdm import tqdm
 from prettytable import PrettyTable
-from metrics import recallAtK
+from torch.utils.data import DataLoader
+from vprtempo.src.metrics import recallAtK
+from vprtempo.src.dataset import CustomImageDataset, ProcessImage
 
 class VPRTempo(nn.Module):
     def __init__(self, dims, args=None, logger=None):
@@ -60,7 +54,7 @@ def __init__(self, dims, args=None, logger=None):
 
         self.logger = logger
         # Set the dataset file
-        self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')  
+        self.dataset_file = os.path.join('./vprtempo/dataset', self.dataset + '.csv')  
 
         # Layer dict to keep track of layer names and their order
         self.layer_dict = {}
@@ -220,7 +214,7 @@ def run_inference(models, model_name):
                              persistent_workers=True)
 
     # Load the model
-    models[0].load_model(models, os.path.join('./models', model_name))
+    models[0].load_model(models, os.path.join('./vprtempo/models', model_name))
 
     # Retrieve layer names for inference
     layer_names = list(models[0].layer_dict.keys())

vprtempo/VPRTempoQuant.py

@@ -26,26 +26,17 @@
 
 import os
 import torch
-import subprocess
-import sys
-sys.path.append('./src')
-sys.path.append('./models')
-sys.path.append('./output')
-sys.path.append('./dataset')
-
-import blitnet as bn
+
 import numpy as np
 import torch.nn as nn
-import torch.quantization as quantization
+import vprtempo.src.blitnet as bn
 
-from loggers import model_logger_quant
-from VPRTempoQuantTrain import generate_model_name_quant
-from dataset import CustomImageDataset, ProcessImage
-from torch.utils.data import DataLoader
-from torch.ao.quantization import QuantStub, DeQuantStub
 from tqdm import tqdm
 from prettytable import PrettyTable
-from metrics import recallAtK
+from torch.utils.data import DataLoader
+from vprtempo.src.metrics import recallAtK
+from torch.ao.quantization import QuantStub, DeQuantStub
+from vprtempo.src.dataset import CustomImageDataset, ProcessImage
 
 #from main import parse_network
 
@@ -59,7 +50,7 @@ def __init__(self, dims, args=None, logger=None):
             setattr(self, arg, getattr(args, arg))
         setattr(self, 'dims', dims)
         # Set the dataset file
-        self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+        self.dataset_file = os.path.join('./vprtempo/dataset', self.dataset + '.csv')
 
         # Set the model logger and return the device
         self.logger = logger
@@ -240,7 +231,7 @@ def run_inference_quant(models, model_name, qconfig):
                              persistent_workers=True)
 
     # Load the model
-    models[0].load_model(models, os.path.join('./models', model_name))
+    models[0].load_model(models, os.path.join('./vprtempo/models', model_name))
 
     # Use evaluate method for inference accuracy
     with torch.no_grad():

vprtempo/VPRTempoQuantTrain.py

@@ -26,23 +26,17 @@
 
 import os
 import torch
-import gc
-import sys
-sys.path.append('./src')
-sys.path.append('./models')
-sys.path.append('./output')
-sys.path.append('./dataset')
 
-import blitnet as bn
 import numpy as np
 import torch.nn as nn
+import vprtempo.src.blitnet as bn
 import torch.quantization as quantization
 import torchvision.transforms as transforms
 
-from dataset import CustomImageDataset, ProcessImage
+from tqdm import tqdm
 from torch.utils.data import DataLoader
 from torch.ao.quantization import QuantStub, DeQuantStub
-from tqdm import tqdm
+from vprtempo.src.dataset import CustomImageDataset, ProcessImage
 
 class VPRTempoQuantTrain(nn.Module):
     def __init__(self, args, dims, logger):
@@ -61,7 +55,7 @@ def __init__(self, args, dims, logger):
         self.logger = logger
 
         # Set the dataset file
-        self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+        self.dataset_file = os.path.join('./vprtempo/dataset', self.dataset + '.csv')
 
         # Add quantization stubs for Quantization Aware Training (QAT)
         self.quant = QuantStub()
@@ -288,4 +282,4 @@ def train_new_model_quant(models, model_name, qconfig):
         # After training the current layer, add it to the list of trained layer
 
     # Save the model
-    model.save_model(trained_models,os.path.join('./models', model_name))   
+    model.save_model(trained_models,os.path.join('./vprtempo/models', model_name))