changed api refactor

.gitignore

@@ -1,4 +1,5 @@
 api/data/
+api/data.*
 api/saved_models/
 api/testimages/
 **/__pycache__/

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./api"
+    ]
+}

api/src/bird_cnn/train_bird.py

@@ -1,16 +1,14 @@
+from enum import Enum
+
 import torch
 import torch.nn as nn
 import torchvision
+from PIL import Image
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader, random_split
-import matplotlib.pyplot as plt
 
-from util import TransformedSubset, visualizeData
+from src.bird_cnn.bird_cnn import Bird_CNN, sample, trainCNN
-from .bird_cnn import Bird_CNN, sample, trainCNN
+from src.util import TransformedSubset, visualizeData
-
-from enum import Enum
-
-from PIL import Image
 
 SAVE_PATH = "../saved_models"
 IMAGE_SIZE = 128

api/src/main.py

@@ -1,10 +1,4 @@
-import sys
+from src.yolo.train_yolo_faces import train_yolo
-
-sys.path.insert(1, './src/yolo')
-from train_yolo_faces import train_yolo
-
-sys.path.insert(2, './src/bird_cnn')
-from bird_cnn import Bird_CNN
 
 
 def main():

api/src/r_cnn/createObjectDetectionDataset.py

@@ -1,12 +1,14 @@
+import random
 from pathlib import Path
-from tqdm import tqdm
+
-from ..util import iou
-from .cocoDetectionDataset import CocoDetectionDataset
-from torchvision.transforms import ToPILImage, ToTensor
 import torch
 import torchvision.transforms.functional as TF
+from torchvision.transforms import ToPILImage, ToTensor
 from torch.utils.data import DataLoader
-import random
+from tqdm import tqdm
+
+from src.r_cnn.cocoDetectionDataset import CocoDetectionDataset
+from src.util import iou
 
 
 

api/src/r_cnn/r_cnn.py

@@ -1,12 +1,14 @@
 import os
+import random
+
 import cv2
 import torch
+import torch.nn as nn
+import torch.nn.functional as F
 import torchvision.transforms.functional as TF
 from tqdm import tqdm
-import torch.nn as nn
+
-from util import iou, visualizeImage
+from src.util import iou, visualizeImage
-import random
-import torch.nn.functional as F
 
 
 class ObjectDetectionCNN(nn.Module):

api/src/r_cnn/r_cnn_test.py

@@ -5,8 +5,8 @@ from torch.utils.data import DataLoader
 from torchvision.transforms import ToTensor
 import torch.nn as nn
 
-from .r_cnn import ObjectDetectionCNN, train, eval
+from src.r_cnn.cocoDetectionDataset import CocoDetectionDataset
-from .cocoDetectionDataset import CocoDetectionDataset
+from src.r_cnn.r_cnn import ObjectDetectionCNN, eval, train
 
 
 def get_transform():

api/src/r_cnn/train_face_detection_r_cnn.py

@@ -1,17 +1,14 @@
 import os
 
-from torchvision import transforms
-from tqdm import tqdm
-from util import TransformedSubset, visualizeImage
-from api.src.r_cnn.r_cnn import ObjectDetectionCNN, trainNormalDataset, eval
-from torch.utils.data import DataLoader, random_split
-from torchvision import datasets
 import torch
 import torch.nn as nn
+from torchvision import datasets, transforms
 from torchvision.io import read_image
-from torch.utils.data import WeightedRandomSampler
+from torch.utils.data import DataLoader, WeightedRandomSampler, random_split
-
+from tqdm import tqdm
 
+from src.r_cnn.r_cnn import ObjectDetectionCNN, eval, trainNormalDataset
+from src.util import TransformedSubset, visualizeImage
 
 SAVE_PATH = "./saved_models"
 PADDING = 20

api/src/yolo/train_yolo_faces.py

@@ -2,17 +2,17 @@ import os
 
 import cv2
 from tqdm import tqdm
-from yolo_dataset import YoloDataset
+import torch
-from yolo_model import train, Yolo_model, sample
+import torch.nn.functional as F
-from yolo_loss import YoloLoss
-from util import TransformedSubset, test_workers_speed, visualizeImage
 from torch.utils.data import DataLoader, random_split
 from torchvision import datasets
-import torch
 from torchvision.utils import draw_bounding_boxes
-import torch.nn.functional as F
 
-from production.yolo_model_production import convert_prediction
+from src.util import TransformedSubset, test_workers_speed, visualizeImage
+from src.production.yolo_model_production import convert_prediction
+from src.yolo.yolo_dataset import YoloDataset
+from src.yolo.yolo_loss import YoloLoss
+from src.yolo.yolo_model import Yolo_model, sample, train
 
 def view_data(dataset):
     dataloader = DataLoader(dataset=dataset, batch_size=1, shuffle=True)
@@ -140,8 +140,8 @@ def train_yolo():
     loss_module = YoloLoss()
 
 
-    #view_data(dataset_valid)
+    view_data(dataset_valid)
-    #exit()
+    exit()
 
 
     #sample_data(dataloader=val_loader, model=model, device=device, SAVE_PATH=SAVE_PATH)