code stringlengths 3 6.57k |
|---|
str(timestamp) |
method.upper() |
join(payloadArray) |
self.hmac(self.encode(payload) |
self.encode(self.secret) |
handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody) |
self.safe_string(response, 'label') |
self.throw_exactly_matched_exception(self.exceptions['exact'], label, feedback) |
ExchangeError(feedback) |
CollectorRegistry(auto_describe=True) |
PlatformCollector(registry=REGISTRY, namespace=NAMESPACE) |
ProcessCollector(namespace=NAMESPACE, registry=REGISTRY) |
GCCollector(registry=REGISTRY, namespace=NAMESPACE) |
UniqueOperationTypesRule(SDLValidationRule) |
__init__(self, context: SDLValidationContext) |
super() |
__init__(context) |
self.defined_operation_types.get(operation) |
self.existing_operation_types.get(operation) |
CostAllocationPolicyType(str, Enum) |
CostAllocationResourceType(str, Enum) |
RuleStatus(str, Enum) |
np.random.seed(1024) |
save_kitti_format(sample_id, calib, bbox3d, kitti_output_dir, scores, img_shape) |
kitti_utils.boxes3d_to_corners3d(bbox3d) |
calib.corners3d_to_img_boxes(corners3d) |
np.clip(img_boxes[:, 0], 0, img_shape[1] - 1) |
np.clip(img_boxes[:, 1], 0, img_shape[0] - 1) |
np.clip(img_boxes[:, 2], 0, img_shape[1] - 1) |
np.clip(img_boxes[:, 3], 0, img_shape[0] - 1) |
os.path.join(kitti_output_dir, '%06d.txt' % sample_id) |
open(kitti_output_file, 'w') |
range(bbox3d.shape[0]) |
np.arctan2(z, x) |
np.sign(beta) |
eval_one_epoch_joint(model, dataloader, epoch_id, result_dir) |
print("-----------------joint____________________________*******") |
np.random.seed(666) |
torch.from_numpy(cfg.CLS_MEAN_SIZE[0]) |
cuda() |
os.path.join(result_dir, 'final_result', 'data') |
os.makedirs(final_output_dir, exist_ok=True) |
print("------------save_result__________________*******") |
os.path.join(result_dir, 'roi_result', 'data') |
os.path.join(result_dir, 'refine_result', 'data') |
os.path.join(result_dir, 'rpn_result', 'data') |
os.makedirs(rpn_output_dir, exist_ok=True) |
os.makedirs(roi_output_dir, exist_ok=True) |
os.makedirs(refine_output_dir, exist_ok=True) |
model.eval() |
tqdm.tqdm(total=len(dataloader) |
len(sample_id) |
torch.from_numpy(pts_input) |
cuda(non_blocking=True) |
float() |
model(input_data) |
print(ret_dict.key() |
long() |
decode_bbox_target(roi_boxes3d.view(-1, 7) |
rcnn_reg.view(-1, rcnn_reg.shape[-1]) |
view(batch_size, -1, 7) |
torch.sigmoid(raw_scores) |
long() |
torch.argmax(rcnn_cls, dim=1) |
view(-1) |
F.softmax(rcnn_cls, dim=1) |
cuda(non_blocking=True) |
long() |
range(batch_size) |
cur_gt_boxes3d.__len__() |
sum() |
cuda(non_blocking=True) |
float() |
iou3d.max(dim=0) |
iou3d.max(dim=1) |
enumerate(thresh_list) |
sum() |
item() |
sum() |
item() |
iou3d_in.max(dim=0) |
enumerate(thresh_list) |
sum() |
item() |
sum() |
float() |
fg_mask.sum() |
float() |
sum() |
float() |
torch.clamp(union, min=1.0) |
rpn_iou.item() |
progress_bar.set_postfix(disp_dict) |
progress_bar.update() |
roi_boxes3d.cpu() |
numpy() |
pred_boxes3d.cpu() |
numpy() |
roi_scores_raw.cpu() |
numpy() |
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