changed api structure and added object detection

api/src/r_cnn/r_cnn_test.py

@@ -0,0 +1,56 @@
+import json
+import os
+import cv2
+from matplotlib import pyplot as plt
+import numpy as np
+from torch import tensor
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision.transforms import ToTensor, functional
+import torchvision.transforms.functional as TF
+from tqdm import tqdm
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .r_cnn import ObjectDetectionCNN, train, eval
+from .cocoDetectionDataset import CocoDetectionDataset
+import random
+
+SAVE_PATH = "./saved_models"
+PADDING = 20
+
+def get_transform():
+    return ToTensor()
+
+train_dataset = CocoDetectionDataset(
+    image_dir="data/football/train", 
+    annotation_path="data/football/train/_annotations.coco.json",
+    transforms=get_transform()
+)
+
+val_dataset = CocoDetectionDataset(
+    image_dir="data/football/valid",
+    annotation_path="data/football/valid/_annotations.coco.json",
+    transforms=get_transform()
+)
+
+train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, collate_fn=lambda x: tuple(zip(*x)))
+val_loader = DataLoader(val_dataset, batch_size=2, shuffle=True, collate_fn=lambda x: tuple(zip(*x)))
+
+device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
+print("Using device", device)
+
+model = ObjectDetectionCNN(c_in=3, c_hidden=32, c_out=2, layers=10)
+model.to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
+loss_module = nn.CrossEntropyLoss()
+
+#train(model=model, loss_module=loss_module, train_loader=train_loader, val_loader=val_loader, 
+#                optimizer=optimizer, SAVE_PATH=SAVE_PATH, saving=True, PADDING=40, device=device)
+
+#exit()
+
+image = next(iter(val_loader))[0][0]
+eval(model=model, image=image, BUILD_PATH=os.path.join(SAVE_PATH, "object_detection", "object_detection"), 
+     device=device, PADDING=40, minSize=5, maxSize=100, minConf=0.8)