-
Notifications
You must be signed in to change notification settings - Fork 0
/
yolov8_heatmep.py
170 lines (143 loc) · 7.42 KB
/
yolov8_heatmep.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
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.yolo.utils.torch_utils import intersect_dicts
# from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.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(layer_id):
params = {
'weight': 'yolov8m.pt',
'cfg': 'ultralytics/models/v8/yolov8m.yaml',
'device': 'cuda:0',
'method': 'XGradCAM', # GradCAMPlusPlus, GradCAM, XGradCAM
'layer': f'model.model[{layer_id}]',
'backward_type': 'class', # class, box, all
'conf_threshold': 0.5, # 0.6
'ratio': 0.02 # 0.02-0.1
}
return params
if __name__ == '__main__':
for layer_id in range(5,20):
model = yolov8_heatmap(**get_params(layer_id=layer_id))
model(r'ultralytics/assets/bus.jpg', f'result/{layer_id}')