Commit
•
abb203b
1
Parent(s):
ce67e64
push changes
Browse files- app.py +86 -0
- config.py +185 -0
- model.py +176 -0
- requirements.txt +8 -0
- utils.py +215 -0
app.py
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import torch
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import albumentations as A
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import cv2
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from albumentations.pytorch import ToTensorV2
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import numpy as np
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import config
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from PIL import Image
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from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.image import show_cam_on_image
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from model import YOLOv3
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import gradio as gr
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import os
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import matplotlib.pyplot as plt
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from utils import *
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model = YOLOv3(num_classes=config.NUM_CLASSES).to(config.DEVICE)
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model.load_state_dict(torch.load("custom_yolo_v3.pt", map_location=torch.device('cpu')), strict=False)
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IMAGE_SIZE = config.IMAGE_SIZE
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test_transforms = A.Compose(
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[
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A.LongestMaxSize(max_size=IMAGE_SIZE),
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A.PadIfNeeded(
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min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
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),
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A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
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ToTensorV2(),
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])
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anchors = (
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torch.tensor(config.ANCHORS)
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* torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1,3,2)
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).to(config.DEVICE)
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def inference(input_img, transparency = 0.5, thresh=0.8, iou_thresh=0.3):
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x = test_transforms(image=input_img)['image'].unsqueeze(0).to(config.DEVICE)
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with torch.no_grad():
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out = model(x)
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bboxes = [[] for _ in range(x.shape[0])]
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for i in range(3):
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batch_size, A, S, _, _ = out[i].shape
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anchor = anchors[i]
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boxes_scale_i = cells_to_bboxes(
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out[i], anchor, S=S, is_preds=True
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)
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for idx, (box) in enumerate(boxes_scale_i):
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bboxes[idx] += box
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model.train()
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nms_boxes = non_max_suppression(
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bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint",
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)
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visualization = plot_image(x[0].permute(1,2,0).detach().cpu(), nms_boxes)
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return visualization
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title = "Object Detection using YOLOv3"
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description = "A simple Gradio interface to show object detection on images"
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examples = [['./images/006294.jpg'],
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['./images/005898.jpg'],
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['./images/003785.jpg'],
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['./images/001624.jpg'],
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['./images/006796.jpg'],
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['./images/003388.jpg'],
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['./images/002216.jpg'],
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['./images/000341.jpg'],
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['./images/006818.jpg'],
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]
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demo = gr.Interface(
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inference,
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inputs = [gr.Image(label="Input Image", type='numpy'),
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gr.Slider(0, 1, value = 0.75, label="Threshold"),
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gr.Slider(0, 1, value = 0.75, label="IoU Threshold"),
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gr.Slider(0, 1, value = 0.8, label="Opacity of GradCAM")],
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outputs = [gr.Image(label="Output").style(width=600, height=600)],
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title = title,
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description = description,
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examples = examples,
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)
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demo.launch()
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config.py
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import albumentations as A
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import cv2
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import torch
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from albumentations.pytorch import ToTensorV2
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# from utils import seed_everything
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DATASET = 'PASCAL_VOC'
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#DATASET = '/kaggle/input/pascal-voc-dataset-used-in-yolov3-video/PASCAL_VOC'
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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# seed_everything() # If you want deterministic behavior
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NUM_WORKERS = 2
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BATCH_SIZE = 32
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IMAGE_SIZE = 416
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NUM_CLASSES = 20
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LEARNING_RATE = 1e-3
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WEIGHT_DECAY = 1e-4
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NUM_EPOCHS = 100
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CONF_THRESHOLD = 0.05
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MAP_IOU_THRESH = 0.5
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NMS_IOU_THRESH = 0.45
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S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8]
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PIN_MEMORY = True
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LOAD_MODEL = False
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SAVE_MODEL = True
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CHECKPOINT_FILE = "checkpoint.pth.tar"
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IMG_DIR = DATASET + "/images/"
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LABEL_DIR = DATASET + "/labels/"
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ANCHORS = [
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[(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)],
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[(0.07, 0.15), (0.15, 0.11), (0.14, 0.29)],
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[(0.02, 0.03), (0.04, 0.07), (0.08, 0.06)],
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] # Note these have been rescaled to be between [0, 1]
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means = [0.485, 0.456, 0.406]
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scale = 1.1
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train_transforms = A.Compose(
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[
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A.LongestMaxSize(max_size=int(IMAGE_SIZE * scale)),
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A.PadIfNeeded(
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min_height=int(IMAGE_SIZE * scale),
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min_width=int(IMAGE_SIZE * scale),
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border_mode=cv2.BORDER_CONSTANT,
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),
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A.Rotate(limit = 10, interpolation=1, border_mode=4),
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A.RandomCrop(width=IMAGE_SIZE, height=IMAGE_SIZE),
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A.ColorJitter(brightness=0.6, contrast=0.6, saturation=0.6, hue=0.6, p=0.4),
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A.OneOf(
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[
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A.ShiftScaleRotate(
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rotate_limit=20, p=0.5, border_mode=cv2.BORDER_CONSTANT
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),
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# A.Affine(shear=15, p=0.5, mode="constant"),
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],
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p=1.0,
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),
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A.HorizontalFlip(p=0.5),
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A.Blur(p=0.1),
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A.CLAHE(p=0.1),
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A.Posterize(p=0.1),
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A.ToGray(p=0.1),
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A.ChannelShuffle(p=0.05),
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A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
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ToTensorV2(),
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],
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bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[],),
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)
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test_transforms = A.Compose(
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[
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A.LongestMaxSize(max_size=IMAGE_SIZE),
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A.PadIfNeeded(
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min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
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),
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A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
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ToTensorV2(),
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],
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bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[]),
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)
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PASCAL_CLASSES = [
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"aeroplane",
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"bicycle",
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"bird",
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"boat",
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"bottle",
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"bus",
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"car",
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"cat",
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"chair",
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"cow",
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"diningtable",
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"dog",
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"horse",
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"motorbike",
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"person",
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"pottedplant",
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"sheep",
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"sofa",
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"train",
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"tvmonitor"
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]
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COCO_LABELS = ['person',
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'bicycle',
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'car',
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'motorcycle',
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'airplane',
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'bus',
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'train',
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'truck',
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'boat',
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'traffic light',
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'fire hydrant',
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'stop sign',
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'parking meter',
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'bench',
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'bird',
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'cat',
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'dog',
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'horse',
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'sheep',
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'cow',
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'elephant',
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'bear',
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'zebra',
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'giraffe',
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'backpack',
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'umbrella',
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'handbag',
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'tie',
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'suitcase',
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'frisbee',
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'skis',
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'snowboard',
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'sports ball',
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'kite',
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'baseball bat',
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'baseball glove',
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'skateboard',
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'surfboard',
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'tennis racket',
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'bottle',
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'wine glass',
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'cup',
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'fork',
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'knife',
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'spoon',
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'bowl',
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'banana',
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'apple',
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'sandwich',
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'orange',
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'broccoli',
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'carrot',
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'hot dog',
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'pizza',
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'donut',
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'cake',
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'chair',
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'couch',
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'potted plant',
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'bed',
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'dining table',
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'toilet',
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'tv',
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'laptop',
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'mouse',
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'remote',
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'keyboard',
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'cell phone',
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'microwave',
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'oven',
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'toaster',
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'sink',
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'refrigerator',
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'book',
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'clock',
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'vase',
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'scissors',
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'teddy bear',
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'hair drier',
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'toothbrush'
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]
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model.py
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"""
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2 |
+
Implementation of YOLOv3 architecture
|
3 |
+
"""
|
4 |
+
|
5 |
+
import torch
|
6 |
+
import torch.nn as nn
|
7 |
+
|
8 |
+
"""
|
9 |
+
Information about architecture config:
|
10 |
+
Tuple is structured by (filters, kernel_size, stride)
|
11 |
+
Every conv is a same convolution.
|
12 |
+
List is structured by "B" indicating a residual block followed by the number of repeats
|
13 |
+
"S" is for scale prediction block and computing the yolo loss
|
14 |
+
"U" is for upsampling the feature map and concatenating with a previous layer
|
15 |
+
"""
|
16 |
+
config = [
|
17 |
+
(32, 3, 1),
|
18 |
+
(64, 3, 2),
|
19 |
+
["B", 1],
|
20 |
+
(128, 3, 2),
|
21 |
+
["B", 2],
|
22 |
+
(256, 3, 2),
|
23 |
+
["B", 8],
|
24 |
+
(512, 3, 2),
|
25 |
+
["B", 8],
|
26 |
+
(1024, 3, 2),
|
27 |
+
["B", 4], # To this point is Darknet-53
|
28 |
+
(512, 1, 1),
|
29 |
+
(1024, 3, 1),
|
30 |
+
"S",
|
31 |
+
(256, 1, 1),
|
32 |
+
"U",
|
33 |
+
(256, 1, 1),
|
34 |
+
(512, 3, 1),
|
35 |
+
"S",
|
36 |
+
(128, 1, 1),
|
37 |
+
"U",
|
38 |
+
(128, 1, 1),
|
39 |
+
(256, 3, 1),
|
40 |
+
"S",
|
41 |
+
]
|
42 |
+
|
43 |
+
|
44 |
+
class CNNBlock(nn.Module):
|
45 |
+
def __init__(self, in_channels, out_channels, bn_act=True, **kwargs):
|
46 |
+
super().__init__()
|
47 |
+
self.conv = nn.Conv2d(in_channels, out_channels, bias=not bn_act, **kwargs)
|
48 |
+
self.bn = nn.BatchNorm2d(out_channels)
|
49 |
+
self.leaky = nn.LeakyReLU(0.1)
|
50 |
+
self.use_bn_act = bn_act
|
51 |
+
|
52 |
+
def forward(self, x):
|
53 |
+
if self.use_bn_act:
|
54 |
+
return self.leaky(self.bn(self.conv(x)))
|
55 |
+
else:
|
56 |
+
return self.conv(x)
|
57 |
+
|
58 |
+
|
59 |
+
class ResidualBlock(nn.Module):
|
60 |
+
def __init__(self, channels, use_residual=True, num_repeats=1):
|
61 |
+
super().__init__()
|
62 |
+
self.layers = nn.ModuleList()
|
63 |
+
for repeat in range(num_repeats):
|
64 |
+
self.layers += [
|
65 |
+
nn.Sequential(
|
66 |
+
CNNBlock(channels, channels // 2, kernel_size=1),
|
67 |
+
CNNBlock(channels // 2, channels, kernel_size=3, padding=1),
|
68 |
+
)
|
69 |
+
]
|
70 |
+
|
71 |
+
self.use_residual = use_residual
|
72 |
+
self.num_repeats = num_repeats
|
73 |
+
|
74 |
+
def forward(self, x):
|
75 |
+
for layer in self.layers:
|
76 |
+
if self.use_residual:
|
77 |
+
x = x + layer(x)
|
78 |
+
else:
|
79 |
+
x = layer(x)
|
80 |
+
|
81 |
+
return x
|
82 |
+
|
83 |
+
|
84 |
+
class ScalePrediction(nn.Module):
|
85 |
+
def __init__(self, in_channels, num_classes):
|
86 |
+
super().__init__()
|
87 |
+
self.pred = nn.Sequential(
|
88 |
+
CNNBlock(in_channels, 2 * in_channels, kernel_size=3, padding=1),
|
89 |
+
CNNBlock(
|
90 |
+
2 * in_channels, (num_classes + 5) * 3, bn_act=False, kernel_size=1
|
91 |
+
),
|
92 |
+
)
|
93 |
+
self.num_classes = num_classes
|
94 |
+
|
95 |
+
def forward(self, x):
|
96 |
+
return (
|
97 |
+
self.pred(x)
|
98 |
+
.reshape(x.shape[0], 3, self.num_classes + 5, x.shape[2], x.shape[3])
|
99 |
+
.permute(0, 1, 3, 4, 2)
|
100 |
+
)
|
101 |
+
|
102 |
+
|
103 |
+
class YOLOv3(nn.Module):
|
104 |
+
def __init__(self, in_channels=3, num_classes=80):
|
105 |
+
super().__init__()
|
106 |
+
self.num_classes = num_classes
|
107 |
+
self.in_channels = in_channels
|
108 |
+
self.layers = self._create_conv_layers()
|
109 |
+
|
110 |
+
def forward(self, x):
|
111 |
+
outputs = [] # for each scale
|
112 |
+
route_connections = []
|
113 |
+
for layer in self.layers:
|
114 |
+
if isinstance(layer, ScalePrediction):
|
115 |
+
outputs.append(layer(x))
|
116 |
+
continue
|
117 |
+
|
118 |
+
x = layer(x)
|
119 |
+
|
120 |
+
if isinstance(layer, ResidualBlock) and layer.num_repeats == 8:
|
121 |
+
route_connections.append(x)
|
122 |
+
|
123 |
+
elif isinstance(layer, nn.Upsample):
|
124 |
+
x = torch.cat([x, route_connections[-1]], dim=1)
|
125 |
+
route_connections.pop()
|
126 |
+
|
127 |
+
return outputs
|
128 |
+
|
129 |
+
def _create_conv_layers(self):
|
130 |
+
layers = nn.ModuleList()
|
131 |
+
in_channels = self.in_channels
|
132 |
+
|
133 |
+
for module in config:
|
134 |
+
if isinstance(module, tuple):
|
135 |
+
out_channels, kernel_size, stride = module
|
136 |
+
layers.append(
|
137 |
+
CNNBlock(
|
138 |
+
in_channels,
|
139 |
+
out_channels,
|
140 |
+
kernel_size=kernel_size,
|
141 |
+
stride=stride,
|
142 |
+
padding=1 if kernel_size == 3 else 0,
|
143 |
+
)
|
144 |
+
)
|
145 |
+
in_channels = out_channels
|
146 |
+
|
147 |
+
elif isinstance(module, list):
|
148 |
+
num_repeats = module[1]
|
149 |
+
layers.append(ResidualBlock(in_channels, num_repeats=num_repeats,))
|
150 |
+
|
151 |
+
elif isinstance(module, str):
|
152 |
+
if module == "S":
|
153 |
+
layers += [
|
154 |
+
ResidualBlock(in_channels, use_residual=False, num_repeats=1),
|
155 |
+
CNNBlock(in_channels, in_channels // 2, kernel_size=1),
|
156 |
+
ScalePrediction(in_channels // 2, num_classes=self.num_classes),
|
157 |
+
]
|
158 |
+
in_channels = in_channels // 2
|
159 |
+
|
160 |
+
elif module == "U":
|
161 |
+
layers.append(nn.Upsample(scale_factor=2),)
|
162 |
+
in_channels = in_channels * 3
|
163 |
+
|
164 |
+
return layers
|
165 |
+
|
166 |
+
|
167 |
+
if __name__ == "__main__":
|
168 |
+
num_classes = 20
|
169 |
+
IMAGE_SIZE = 416
|
170 |
+
model = YOLOv3(num_classes=num_classes)
|
171 |
+
x = torch.randn((2, 3, IMAGE_SIZE, IMAGE_SIZE))
|
172 |
+
out = model(x)
|
173 |
+
assert model(x)[0].shape == (2, 3, IMAGE_SIZE//32, IMAGE_SIZE//32, num_classes + 5)
|
174 |
+
assert model(x)[1].shape == (2, 3, IMAGE_SIZE//16, IMAGE_SIZE//16, num_classes + 5)
|
175 |
+
assert model(x)[2].shape == (2, 3, IMAGE_SIZE//8, IMAGE_SIZE//8, num_classes + 5)
|
176 |
+
print("Success!")
|
requirements.txt
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
git+https://github.com/albu/albumentations
|
2 |
+
torch
|
3 |
+
torchvision
|
4 |
+
torch-lr-finder
|
5 |
+
pytorch-lightning
|
6 |
+
grad-cam
|
7 |
+
pillow
|
8 |
+
numpy
|
utils.py
ADDED
@@ -0,0 +1,215 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import config
|
2 |
+
import matplotlib.pyplot as plt
|
3 |
+
import matplotlib.patches as patches
|
4 |
+
import numpy as np
|
5 |
+
import os
|
6 |
+
import random
|
7 |
+
import torch
|
8 |
+
from PIL import Image
|
9 |
+
from io import BytesIO
|
10 |
+
|
11 |
+
from collections import Counter
|
12 |
+
|
13 |
+
|
14 |
+
|
15 |
+
def iou_width_height(boxes1, boxes2):
|
16 |
+
"""
|
17 |
+
Parameters:
|
18 |
+
boxes1 (tensor): width and height of the first bounding boxes
|
19 |
+
boxes2 (tensor): width and height of the second bounding boxes
|
20 |
+
Returns:
|
21 |
+
tensor: Intersection over union of the corresponding boxes
|
22 |
+
"""
|
23 |
+
intersection = torch.min(boxes1[..., 0], boxes2[..., 0]) * torch.min(
|
24 |
+
boxes1[..., 1], boxes2[..., 1]
|
25 |
+
)
|
26 |
+
union = (
|
27 |
+
boxes1[..., 0] * boxes1[..., 1] + boxes2[..., 0] * boxes2[..., 1] - intersection
|
28 |
+
)
|
29 |
+
return intersection / union
|
30 |
+
|
31 |
+
|
32 |
+
def intersection_over_union(boxes_preds, boxes_labels, box_format="midpoint"):
|
33 |
+
"""
|
34 |
+
Video explanation of this function:
|
35 |
+
https://youtu.be/XXYG5ZWtjj0
|
36 |
+
|
37 |
+
This function calculates intersection over union (iou) given pred boxes
|
38 |
+
and target boxes.
|
39 |
+
|
40 |
+
Parameters:
|
41 |
+
boxes_preds (tensor): Predictions of Bounding Boxes (BATCH_SIZE, 4)
|
42 |
+
boxes_labels (tensor): Correct labels of Bounding Boxes (BATCH_SIZE, 4)
|
43 |
+
box_format (str): midpoint/corners, if boxes (x,y,w,h) or (x1,y1,x2,y2)
|
44 |
+
|
45 |
+
Returns:
|
46 |
+
tensor: Intersection over union for all examples
|
47 |
+
"""
|
48 |
+
|
49 |
+
if box_format == "midpoint":
|
50 |
+
box1_x1 = boxes_preds[..., 0:1] - boxes_preds[..., 2:3] / 2
|
51 |
+
box1_y1 = boxes_preds[..., 1:2] - boxes_preds[..., 3:4] / 2
|
52 |
+
box1_x2 = boxes_preds[..., 0:1] + boxes_preds[..., 2:3] / 2
|
53 |
+
box1_y2 = boxes_preds[..., 1:2] + boxes_preds[..., 3:4] / 2
|
54 |
+
box2_x1 = boxes_labels[..., 0:1] - boxes_labels[..., 2:3] / 2
|
55 |
+
box2_y1 = boxes_labels[..., 1:2] - boxes_labels[..., 3:4] / 2
|
56 |
+
box2_x2 = boxes_labels[..., 0:1] + boxes_labels[..., 2:3] / 2
|
57 |
+
box2_y2 = boxes_labels[..., 1:2] + boxes_labels[..., 3:4] / 2
|
58 |
+
|
59 |
+
if box_format == "corners":
|
60 |
+
box1_x1 = boxes_preds[..., 0:1]
|
61 |
+
box1_y1 = boxes_preds[..., 1:2]
|
62 |
+
box1_x2 = boxes_preds[..., 2:3]
|
63 |
+
box1_y2 = boxes_preds[..., 3:4]
|
64 |
+
box2_x1 = boxes_labels[..., 0:1]
|
65 |
+
box2_y1 = boxes_labels[..., 1:2]
|
66 |
+
box2_x2 = boxes_labels[..., 2:3]
|
67 |
+
box2_y2 = boxes_labels[..., 3:4]
|
68 |
+
|
69 |
+
x1 = torch.max(box1_x1, box2_x1)
|
70 |
+
y1 = torch.max(box1_y1, box2_y1)
|
71 |
+
x2 = torch.min(box1_x2, box2_x2)
|
72 |
+
y2 = torch.min(box1_y2, box2_y2)
|
73 |
+
|
74 |
+
intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
|
75 |
+
box1_area = abs((box1_x2 - box1_x1) * (box1_y2 - box1_y1))
|
76 |
+
box2_area = abs((box2_x2 - box2_x1) * (box2_y2 - box2_y1))
|
77 |
+
|
78 |
+
return intersection / (box1_area + box2_area - intersection + 1e-6)
|
79 |
+
|
80 |
+
|
81 |
+
def non_max_suppression(bboxes, iou_threshold, threshold, box_format="corners"):
|
82 |
+
"""
|
83 |
+
Video explanation of this function:
|
84 |
+
https://youtu.be/YDkjWEN8jNA
|
85 |
+
|
86 |
+
Does Non Max Suppression given bboxes
|
87 |
+
|
88 |
+
Parameters:
|
89 |
+
bboxes (list): list of lists containing all bboxes with each bboxes
|
90 |
+
specified as [class_pred, prob_score, x1, y1, x2, y2]
|
91 |
+
iou_threshold (float): threshold where predicted bboxes is correct
|
92 |
+
threshold (float): threshold to remove predicted bboxes (independent of IoU)
|
93 |
+
box_format (str): "midpoint" or "corners" used to specify bboxes
|
94 |
+
|
95 |
+
Returns:
|
96 |
+
list: bboxes after performing NMS given a specific IoU threshold
|
97 |
+
"""
|
98 |
+
|
99 |
+
assert type(bboxes) == list
|
100 |
+
|
101 |
+
bboxes = [box for box in bboxes if box[1] > threshold]
|
102 |
+
bboxes = sorted(bboxes, key=lambda x: x[1], reverse=True)
|
103 |
+
bboxes_after_nms = []
|
104 |
+
|
105 |
+
while bboxes:
|
106 |
+
chosen_box = bboxes.pop(0)
|
107 |
+
|
108 |
+
bboxes = [
|
109 |
+
box
|
110 |
+
for box in bboxes
|
111 |
+
if box[0] != chosen_box[0]
|
112 |
+
or intersection_over_union(
|
113 |
+
torch.tensor(chosen_box[2:]),
|
114 |
+
torch.tensor(box[2:]),
|
115 |
+
box_format=box_format,
|
116 |
+
)
|
117 |
+
< iou_threshold
|
118 |
+
]
|
119 |
+
|
120 |
+
bboxes_after_nms.append(chosen_box)
|
121 |
+
|
122 |
+
return bboxes_after_nms
|
123 |
+
|
124 |
+
|
125 |
+
|
126 |
+
|
127 |
+
def plot_image(image, boxes):
|
128 |
+
"""Plots predicted bounding boxes on the image"""
|
129 |
+
cmap = plt.get_cmap("tab20b")
|
130 |
+
class_labels = config.COCO_LABELS if config.DATASET=='COCO' else config.PASCAL_CLASSES
|
131 |
+
colors = [cmap(i) for i in np.linspace(0, 1, len(class_labels))]
|
132 |
+
im = np.array(image)
|
133 |
+
height, width, _ = im.shape
|
134 |
+
|
135 |
+
# Create figure and axes
|
136 |
+
fig, ax = plt.subplots(1)
|
137 |
+
# Display the image
|
138 |
+
ax.imshow(im)
|
139 |
+
|
140 |
+
# box[0] is x midpoint, box[2] is width
|
141 |
+
# box[1] is y midpoint, box[3] is height
|
142 |
+
|
143 |
+
# Create a Rectangle patch
|
144 |
+
for box in boxes:
|
145 |
+
assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
|
146 |
+
class_pred = box[0]
|
147 |
+
box = box[2:]
|
148 |
+
upper_left_x = box[0] - box[2] / 2
|
149 |
+
upper_left_y = box[1] - box[3] / 2
|
150 |
+
rect = patches.Rectangle(
|
151 |
+
(upper_left_x * width, upper_left_y * height),
|
152 |
+
box[2] * width,
|
153 |
+
box[3] * height,
|
154 |
+
linewidth=2,
|
155 |
+
edgecolor=colors[int(class_pred)],
|
156 |
+
facecolor="none",
|
157 |
+
)
|
158 |
+
# Add the patch to the Axes
|
159 |
+
ax.add_patch(rect)
|
160 |
+
plt.text(
|
161 |
+
upper_left_x * width,
|
162 |
+
upper_left_y * height,
|
163 |
+
s=class_labels[int(class_pred)],
|
164 |
+
color="white",
|
165 |
+
verticalalignment="top",
|
166 |
+
bbox={"color": colors[int(class_pred)], "pad": 0},
|
167 |
+
)
|
168 |
+
|
169 |
+
buffer = BytesIO()
|
170 |
+
plt.axis('off')
|
171 |
+
plt.savefig(buffer,format='png', bbox_inches='tight', pad_inches=0)
|
172 |
+
visualization = Image.open(buffer)
|
173 |
+
|
174 |
+
return visualization
|
175 |
+
|
176 |
+
|
177 |
+
def cells_to_bboxes(predictions, anchors, S, is_preds=True):
|
178 |
+
"""
|
179 |
+
Scales the predictions coming from the model to
|
180 |
+
be relative to the entire image such that they for example later
|
181 |
+
can be plotted or.
|
182 |
+
INPUT:
|
183 |
+
predictions: tensor of size (N, 3, S, S, num_classes+5)
|
184 |
+
anchors: the anchors used for the predictions
|
185 |
+
S: the number of cells the image is divided in on the width (and height)
|
186 |
+
is_preds: whether the input is predictions or the true bounding boxes
|
187 |
+
OUTPUT:
|
188 |
+
converted_bboxes: the converted boxes of sizes (N, num_anchors, S, S, 1+5) with class index,
|
189 |
+
object score, bounding box coordinates
|
190 |
+
"""
|
191 |
+
BATCH_SIZE = predictions.shape[0]
|
192 |
+
num_anchors = len(anchors)
|
193 |
+
box_predictions = predictions[..., 1:5]
|
194 |
+
if is_preds:
|
195 |
+
anchors = anchors.reshape(1, len(anchors), 1, 1, 2)
|
196 |
+
box_predictions[..., 0:2] = torch.sigmoid(box_predictions[..., 0:2])
|
197 |
+
box_predictions[..., 2:] = torch.exp(box_predictions[..., 2:]) * anchors
|
198 |
+
scores = torch.sigmoid(predictions[..., 0:1])
|
199 |
+
best_class = torch.argmax(predictions[..., 5:], dim=-1).unsqueeze(-1)
|
200 |
+
else:
|
201 |
+
scores = predictions[..., 0:1]
|
202 |
+
best_class = predictions[..., 5:6]
|
203 |
+
|
204 |
+
cell_indices = (
|
205 |
+
torch.arange(S)
|
206 |
+
.repeat(predictions.shape[0], 3, S, 1)
|
207 |
+
.unsqueeze(-1)
|
208 |
+
.to(predictions.device)
|
209 |
+
)
|
210 |
+
x = 1 / S * (box_predictions[..., 0:1] + cell_indices)
|
211 |
+
y = 1 / S * (box_predictions[..., 1:2] + cell_indices.permute(0, 1, 3, 2, 4))
|
212 |
+
w_h = 1 / S * box_predictions[..., 2:4]
|
213 |
+
converted_bboxes = torch.cat((best_class, scores, x, y, w_h), dim=-1).reshape(BATCH_SIZE, num_anchors * S * S, 6)
|
214 |
+
return converted_bboxes.tolist()
|
215 |
+
|