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heatmap.py
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import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import torch, yaml, cv2, os, shutil
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from ultralytics.nn.tasks import DetectionModel as Model
from ultralytics.utils.torch_utils import intersect_dicts
from ultralytics.utils.ops import xywh2xyxy
from pytorch_grad_cam import GradCAMPlusPlus, GradCAM, XGradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
class yolov8_heatmap:
def __init__(self, weight, cfg, device, method, layer, backward_type, conf_threshold, ratio):
device = torch.device(device)
ckpt = torch.load(weight)
model_names = ckpt['model'].names
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
model = Model(cfg, ch=3, nc=len(model_names)).to(device)
csd = intersect_dicts(csd, model.state_dict(), exclude=['anchor']) # intersect
model.load_state_dict(csd, strict=False) # load
model.eval()
print(f'Transferred {len(csd)}/{len(model.state_dict())} items')
target_layers = [eval(layer)]
method = eval(method)
colors = np.random.uniform(0, 255, size=(len(model_names), 3)).astype(np.int)
self.__dict__.update(locals())
def post_process(self, result):
logits_ = result[:, 4:]
boxes_ = result[:, :4]
sorted, indices = torch.sort(logits_.max(1)[0], descending=True)
return torch.transpose(logits_[0], dim0=0, dim1=1)[indices[0]], torch.transpose(boxes_[0], dim0=0, dim1=1)[indices[0]], xywh2xyxy(torch.transpose(boxes_[0], dim0=0, dim1=1)[indices[0]]).cpu().detach().numpy()
def draw_detections(self, box, color, name, img):
xmin, ymin, xmax, ymax = list(map(int, list(box)))
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), tuple(int(x) for x in color), 2)
cv2.putText(img, str(name), (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, tuple(int(x) for x in color), 2, lineType=cv2.LINE_AA)
return img
def __call__(self, img_path, save_path):
# remove dir if exist
if os.path.exists(save_path):
shutil.rmtree(save_path)
# make dir if not exist
os.makedirs(save_path, exist_ok=True)
# img process
img = cv2.imread(img_path)
img = letterbox(img)[0]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.float32(img) / 255.0
tensor = torch.from_numpy(np.transpose(img, axes=[2, 0, 1])).unsqueeze(0).to(self.device)
# init ActivationsAndGradients
grads = ActivationsAndGradients(self.model, self.target_layers, reshape_transform=None)
# get ActivationsAndResult
result = grads(tensor)
activations = grads.activations[0].cpu().detach().numpy()
# postprocess to yolo output
post_result, pre_post_boxes, post_boxes = self.post_process(result[0])
for i in trange(int(post_result.size(0) * self.ratio)):
if float(post_result[i].max()) < self.conf_threshold:
break
self.model.zero_grad()
# get max probability for this prediction
if self.backward_type == 'class' or self.backward_type == 'all':
score = post_result[i].max()
score.backward(retain_graph=True)
if self.backward_type == 'box' or self.backward_type == 'all':
for j in range(4):
score = pre_post_boxes[i, j]
score.backward(retain_graph=True)
# process heatmap
if self.backward_type == 'class':
gradients = grads.gradients[0]
elif self.backward_type == 'box':
gradients = grads.gradients[0] + grads.gradients[1] + grads.gradients[2] + grads.gradients[3]
else:
gradients = grads.gradients[0] + grads.gradients[1] + grads.gradients[2] + grads.gradients[3] + grads.gradients[4]
b, k, u, v = gradients.size()
weights = self.method.get_cam_weights(self.method, None, None, None, activations, gradients.detach().numpy())
weights = weights.reshape((b, k, 1, 1))
saliency_map = np.sum(weights * activations, axis=1)
saliency_map = np.squeeze(np.maximum(saliency_map, 0))
saliency_map = cv2.resize(saliency_map, (tensor.size(3), tensor.size(2)))
saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
if (saliency_map_max - saliency_map_min) == 0:
continue
saliency_map = (saliency_map - saliency_map_min) / (saliency_map_max - saliency_map_min)
# add heatmap and box to image
cam_image = show_cam_on_image(img.copy(), saliency_map, use_rgb=True)
cam_image = self.draw_detections(post_boxes[i], self.colors[int(post_result[i, :].argmax())], f'{self.model_names[int(post_result[i, :].argmax())]} {float(post_result[i].max()):.2f}', cam_image)
cam_image = Image.fromarray(cam_image)
cam_image.save(f'{save_path}/{i}.png')
def get_params():
params = {
'weight': 'yolov8n.pt',
'cfg': 'ultralytics/cfg/models/v8/yolov8n.yaml',
'device': 'cuda:0',
'method': 'GradCAM', # GradCAMPlusPlus, GradCAM, XGradCAM
'layer': 'model.model[9]',
'backward_type': 'all', # class, box, all
'conf_threshold': 0.6, # 0.6
'ratio': 0.02 # 0.02-0.1
}
return params
if __name__ == '__main__':
model = yolov8_heatmap(**get_params())
model(r'ultralytics/assets/bus.jpg', 'result')