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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-26 14:57:28 +01:00
parent 707d6ea965
commit cb63ce30ec
2 changed files with 18 additions and 17 deletions
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27
test.py
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@ -37,18 +37,15 @@ def test(
model.to(device).eval() model.to(device).eval()
# Get dataloader # Get dataloader
# dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size) # pytorch # dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size)
dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size) dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size)
# Create JSON
jdict = []
float3 = lambda x: float(format(x, '.3f')) # print json to 3 decimals
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0 mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP')) print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], [] outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
[], [], [], [], [], [], [], [], []
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC) AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
coco91class = coco80_to_coco91_class()
for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader): for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader):
output = model(imgs.to(device)) output = model(imgs.to(device))
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres) output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
@ -67,18 +64,18 @@ def test(
detections = detections.cpu().numpy() detections = detections.cpu().numpy()
detections = detections[np.argsort(-detections[:, 4])] detections = detections[np.argsort(-detections[:, 4])]
# Save JSON
if save_json: if save_json:
# rescale box to original image size, top left origin # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
box = torch.from_numpy(detections[:, :4]).clone() # x1y1x2y2 box = torch.from_numpy(detections[:, :4]).clone() # xyxy
scale_coords(img_size, box, shapes[si]) scale_coords(img_size, box, shapes[si]) # to original shape
box = xyxy2xywh(box) box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # origin center to corner box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
# add to json dictionary
for di, d in enumerate(detections): for di, d in enumerate(detections):
jdict.append({ # add to json dictionary jdict.append({
'image_id': int(Path(paths[si]).stem.split('_')[-1]), 'image_id': int(Path(paths[si]).stem.split('_')[-1]),
'category_id': darknet2coco_class(int(d[6])), 'category_id': coco91class(int(d[6])),
'bbox': [float3(x) for x in box[di]], 'bbox': [float3(x) for x in box[di]],
'score': float3(d[4] * d[5]) 'score': float3(d[4] * d[5])
}) })

8
utils/utils.py
View File

@ -12,6 +12,10 @@ torch.set_printoptions(linewidth=1320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
def float3(x): # format floats to 3 decimals
return float(format(x, '.3f'))
def init_seeds(seed=0): def init_seeds(seed=0):
random.seed(seed) random.seed(seed)
np.random.seed(seed) np.random.seed(seed)
@ -49,12 +53,12 @@ def coco_class_weights(): # frequency of each class in coco train2014
return weights return weights
def darknet2coco_class(c): # returns the coco class for each darknet class def coco80_to_coco91_class(): # returns the coco class for each darknet class
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco x = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
return x[c] return x
def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img