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import torch, torchvision
from torchvision import transforms
import numpy as np
import gradio as gr
from PIL import Image
from torch.utils.data import DataLoader
import itertools
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2
import config as config
from model import YOLOv3
from loss import YoloLoss
from utils import get_loaders
import utils
new_state_dict = {}
state_dict = torch.load('results/Yolov3_Lavanya.pth', map_location=torch.device('cpu'))
for key, value in state_dict.items():
new_key = key.replace('model.', '')
new_state_dict[new_key] = value
model = YOLOv3(in_channels=3, num_classes=config.NUM_CLASSES)
model.load_state_dict(new_state_dict, strict=True)
model.eval()
classes = ("aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor")
import grad_cam_func as gcf
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
from pytorch_grad_cam.utils.image import show_cam_on_image
def inference(input_img=None, iou_threshold=0.6, conf_threshold=0.5, gc_trans=0.3):
if input_img is not None:
tranform_img = config.infer_transforms(image=input_img)
transform_img = tranform_img['image'].unsqueeze(0)
transform_img_visual = config.infer_transforms_visualization(image=input_img)['image']
with torch.no_grad():
outputs = model(transform_img)
bboxes = [[] for _ in range(transform_img.shape[0])] # range of Batch size
for i in range(3):
batch_size, A, S, _, _ = outputs[i].shape
anchor = np.array(config.SCALED_ANCHORS[i])
boxes_scale_i = utils.cells_to_bboxes(
outputs[i], anchor, S=S, is_preds=True)
for idx, (box) in enumerate(boxes_scale_i):
bboxes[idx] += box
nms_boxes = utils.non_max_suppression(bboxes[0], iou_threshold=iou_threshold,
threshold=conf_threshold, box_format="midpoint",)
image, boxes = transform_img_visual.permute(1,2,0), nms_boxes
"""Plots predicted bounding boxes on the image"""
cmap = plt.get_cmap("tab20b")
class_labels = config.PASCAL_CLASSES
colors = [cmap(i) for i in np.linspace(0, 1, len(class_labels))]
im = np.array(image)
height, width, _ = im.shape
# Create figure and axes
fig, ax = plt.subplots(1)
# Display the image
ax.imshow(im)
# box[0] is x midpoint, box[2] is width
# box[1] is y midpoint, box[3] is height
# Create a Rectangle patch
for box in boxes:
assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
class_pred = box[0]
box = box[2:]
upper_left_x = box[0] - box[2] / 2
upper_left_y = box[1] - box[3] / 2
rect = patches.Rectangle(
(upper_left_x * width, upper_left_y * height),
box[2] * width,
box[3] * height,
linewidth=2,
edgecolor=colors[int(class_pred)],
facecolor="none",
)
# Add the patch to the Axes
ax.add_patch(rect)
plt.text(
upper_left_x * width,
upper_left_y * height,
s=class_labels[int(class_pred)],
color="white",
verticalalignment="top",
bbox={"color": colors[int(class_pred)], "pad": 0},
)
plt.axis('off')
fig.canvas.draw()
fig_img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig_img = fig_img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close(fig)
outputs_inference_bb = fig_img
### GradCAM
target_layer = [model.layers[-2]]
cam = gcf.BaseCAM(model, target_layer)
AnG = ActivationsAndGradients(model, target_layer, None)
outputs = AnG(transform_img)
bboxes = [[] for _ in range(1)]
for i in range(3):
batch_size, A, S, _, _ = outputs[i].shape
anchor = config.SCALED_ANCHORS[i]
boxes_scale_i = utils.cells_to_bboxes(
outputs[i], anchor, S=S, is_preds=True
)
for idx, (box) in enumerate(boxes_scale_i):
bboxes[idx] += box
nms_boxes = utils.non_max_suppression(
bboxes[0], iou_threshold=0.5, threshold=0.4, box_format="midpoint",
)
target_categories = [box[0] for box in nms_boxes]
targets = [ClassifierOutputTarget(
category) for category in target_categories]
help_ = cam.compute_cam_per_layer(transform_img, targets, False)
output_gc = cam.aggregate_multi_layers(help_)[0, :, :]
img = cv2.resize(input_img, (416, 416))
img = np.float32(img) / 255
cam_image = show_cam_on_image(img, output_gc, use_rgb=True, image_weight=gc_trans)
outputs_inference_gc = cam_image
else:
outputs_inference_bb = None
outputs_inference_gc = None
return outputs_inference_bb, outputs_inference_gc
title = "PASCAL VOC trained on Yolov3"
description = "A simple Gradio interface to infer on Yolov3 model, and get GradCAM results. PASCAL VOC has the following object classes: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor"
examples = [['examples/test_'+str(i)+'.jpg', 0.6, 0.5, 0.3] for i in range(10)]
demo = gr.Interface(inference,
inputs = [gr.Image(label="Input image"),
gr.Slider(0, 1, value=0.6, label="IOU Threshold"),
gr.Slider(0, 1, value=0.4, label="Threshold"),
gr.Slider(0, 1, value=0.5, label="GradCAM Transparency"),
],
outputs = [
gr.Image(label="Yolov3 Prediction"),
gr.Image(label="GradCAM Output"),],
title = title,
description = description,
examples = examples
)
demo.launch() |