Kaggle competition: Global Wheat Detection
<SOS> metric: custom metric Kaggle data source: global-wheat-detection<compute_train_metric>
def load_image(path):
with open(path, "rb")as f:
buf = f.read()
return mx.image.imdecode(buf)
def weight_opt(oof_lgb, oof_xgb, y_true):
weight_lgb = np.inf
best_f1 = np.inf
for i in np.arange(0, 1.01, 0.10):
combined_oof = i * oof_lgb +(1-i)* oof_xgb
blend = np.round(np.clip(combined_oof,0,10)).astype(int)
 f1_blend = metrics.f1_score(y_true, blend, average = 'macro')
 if np.mean(f1_blend)< best_f1:
best_f1 = np.mean(f1_blend)
 weight_lgb = round(i, 2)
 print(str(round(i, 2)) + ' : mean F1(Blend)is ', round(np.mean(f1_blend), 6))
print('-'*36)
 print('Best weight for LGB: ', weight_lgb)
 print('Best weight for XGB: ', round(1-weight_lgb, 2))
print('Best mean F1(Blend): ', round(best_f1, 6))
return weight_lgb, round(1-weight_lgb, 2 )<compute_test_metric>
def load_model(path, ctx=mx.cpu()):
net = gcv.model_zoo.yolo3_darknet53_custom(["wheat"], pretrained_base=False)
 net.set_nms(post_nms=150)
 net.load_parameters(path, ctx=ctx)
 return net
<define_variables><EOS> 
folds = 5
threshold = 0.1
img_s = 512
context = mx.gpu()
print("Loading test images...")
 images = [ 
(os.path.join(dirname, filename), os.path.splitext(filename)[0])
 for dirname, _, filenames in os.walk('/kaggle/input/global-wheat-detection/test')for filename in filenames
]
print("Loading models...")
 models = [load_model("/kaggle/input/global-wheat-detection-private/global-wheat-yolo3-darknet53_fold%d.params" % i, ctx=context)for i in range(folds)]
print("Inference...")
 results = []
for path, image_id in images:
print(path)
 raw = load_image(path)
 rh, rw, _ = raw.shape
classes_list = []
scores_list = []
bboxes_list = []
for _ in range(5):
img, flips = gcv.data.transforms.image.random_flip(raw, px=0.5, py=0.5)
 x, _ = gcv.data.transforms.presets.yolo.transform_test(img, short=img_s)
 _, _, xh, xw = x.shape
rot = random.randint(0, 3)
 if rot > 0:
x = np.rot90(x.asnumpy() , k=rot, axes=(2, 3))
for model in models:
classes, scores, bboxes = model(mx.nd.array(x, ctx=context))
if rot > 0:
if rot == 1:
raw_bboxes = bboxes.copy()
bboxes[0, :, [0, 2]] = xh - raw_bboxes[0, :, [1, 3]]
bboxes[0, :, [1, 3]] = raw_bboxes[0, :, [2, 0]]
elif rot == 2:
bboxes[0, :, [0, 1, 2, 3]] = mx.nd.array([[xw], [xh], [xw], [xh]], ctx=context)- bboxes[0, :, [2, 3, 0, 1]]
elif rot == 3:
raw_bboxes = bboxes.copy()
bboxes[0, :, [0, 2]] = raw_bboxes[0, :, [1, 3]]
bboxes[0, :, [1, 3]] = xw - raw_bboxes[0, :, [2, 0]]
raw_bboxes = bboxes.copy()
bboxes[0, :, 0] = raw_bboxes[0, :, [0, 2]].min(axis=0)
 bboxes[0, :, 1] = raw_bboxes[0, :, [1, 3]].min(axis=0)
 bboxes[0, :, 2] = raw_bboxes[0, :, [0, 2]].max(axis=0)
 bboxes[0, :, 3] = raw_bboxes[0, :, [1, 3]].max(axis=0)
 bboxes[0, :, :] = gcv.data.transforms.bbox.flip(bboxes[0, :, :],(xw, xh), flip_x=flips[0], flip_y=flips[1])
 bboxes[0, :, 0::2] =(bboxes[0, :, 0::2] /(xw - 1)).clip(0.0, 1.0)
 bboxes[0, :, 1::2] =(bboxes[0, :, 1::2] /(xh - 1)).clip(0.0, 1.0)
 classes_list.append([
int(classes[0, i].asscalar())for i in range(classes.shape[1])
 if classes[0, i].asscalar() >= 0.0
])
 scores_list.append([
scores[0, i].asscalar() for i in range(classes.shape[1])
 if classes[0, i].asscalar() >= 0.0
])
 bboxes_list.append([
bboxes[0, i].asnumpy().tolist() for i in range(classes.shape[1])
 if classes[0, i].asscalar() >= 0.0
])
 bboxes, scores, classes = weighted_boxes_fusion(bboxes_list, scores_list, classes_list)
 bboxes[:, 0::2] *= rw - 1
bboxes[:, 1::2] *= rh - 1
bboxes[:, 2:4] -= bboxes[:, 0:2]
results.append({
"image_id": image_id,
"PredictionString": " ".join([
" ".join([str(x)for x in [scores[i]] + [round(x)for x in bboxes[i].tolist() ]])
 for i in range(classes.shape[0])
 if model.classes[int(classes[i])] == "wheat" and scores[i] > threshold
])
 })
 pd.DataFrame(results, columns=['image_id', 'PredictionString'] ).to_csv('submission.csv', index=False)