Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug 11 18:08:06 2023
+@author: prarthana.ts
+"""
+
+import torch
+import torch.optim as optim
+import lightning.pytorch as pl
+from lightning.pytorch.tuner import Tuner
+
+# import pytorch_lightning as pl
+from tqdm import tqdm
+
+from torch.optim.lr_scheduler import OneCycleLR
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import albumentations as A
+import cv2
+import torch
+from pytorch_grad_cam.utils.image import show_cam_on_image
+import numpy as np
+from albumentations.pytorch import ToTensorV2
+
+from utils_for_app import cells_to_bboxes,non_max_suppression,plot_image,YoloCAM
+from yolov3 import YOLOv3
+from loss import YoloLoss
+from utils import LearningRateFinder
+# Create your config module or import it from the existing config.py file.
+import config
+from main_yolov3_lightening import YOLOv3Lightning
+import torch
+import cv2
+import numpy as np
+import gradio as gr
+
+model = YOLOv3Lightning()
+model.load_state_dict(torch.load("yolov3_model_without_75_mosaic.pth", map_location=torch.device('cpu')), strict=False)
+model.setup(stage="test")
+
+IMAGE_SIZE = 416
+transforms = A.Compose(
+    [
+        A.LongestMaxSize(max_size=IMAGE_SIZE),
+        A.PadIfNeeded(
+            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
+        ),
+        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+        ToTensorV2(),
+    ],
+)
+
+ANCHORS = [
+    [(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)],
+    [(0.07, 0.15), (0.15, 0.11), (0.14, 0.29)],
+    [(0.02, 0.03), (0.04, 0.07), (0.08, 0.06)],
+]  # Note these have been rescaled to be between [0, 1]
+S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8]
+
+scaled_anchors = (
+    torch.tensor(config.ANCHORS)
+    * torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
+)
+
+def process_image_and_plot(image, model, scaled_anchors):
+
+    transformed_image = transforms(image=image)["image"].unsqueeze(0)
+    output = model(transformed_image)
+    bboxes = [[] for _ in range(1)]
+
+    for i in range(3):
+        batch_size, A, S, _, _ = output[i].shape
+        anchor = scaled_anchors[i]
+        boxes_scale_i = cells_to_bboxes(output[i], anchor, S=S, is_preds=True)
+        for idx, box in enumerate(boxes_scale_i):
+            bboxes[idx] += box
+
+    nms_boxes = non_max_suppression(
+        bboxes[0], iou_threshold=0.5, threshold=0.4, box_format="midpoint",
+    )
+    fig = plot_image(transformed_image[0].permute(1, 2, 0), nms_boxes)
+
+    cam = YoloCAM(model=model, target_layers=[model.model.layers[-2]], use_cuda=False)
+    grayscale_cam = cam(transformed_image, scaled_anchors)[0, :, :]
+    img = cv2.resize(image, (416, 416))
+    img = np.float32(img) / 255
+    cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True)
+
+    return fig,cam_image
+
+
+examples = [
+    ["/content/images/automobile.jpg"],
+    ["/content/images/cycle.jpg"],
+    ["/content/images/dog-kitten.jpg"],
+    ["/content/images/human.jpg"],
+]
+
+def processed_image(image):
+    figure,gradcam = process_image_and_plot(image, model, scaled_anchors)
+    return figure,gradcam
+
+title = "YoloV3 on Pascal VOC Dataset with GradCAM"
+description = "Pytorch Lightening Implemetation of YoloV3 trained from scratch"
+demo = gr.Interface(processed_image,
+    inputs=[
+        gr.Image(label="Input Image"),
+    ],
+    outputs=[gr.Plot(),gr.Image(shape=(32, 32), label="Model Prediction")],
+    title=title,
+    description=description,
+    examples=examples,
+)
+demo.launch()