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cef04ce
1
Parent(s):
a77f5fd
Update inference.py
Browse files- inference.py +6 -6
inference.py
CHANGED
@@ -52,7 +52,7 @@ def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None
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# Load hyperparameters
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if 'model' not in hyperparams: hyperparams['model'] = WEIGHTS
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-
if 'conf_thresh' not in hyperparams: hyperparams['
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if 'iou_thresh' not in hyperparams: hyperparams['iou_thresh'] = NMS_IOU
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if 'min_hits' not in hyperparams: hyperparams['min_hits'] = MIN_HITS
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if 'max_age' not in hyperparams: hyperparams['max_age'] = MAX_AGE
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@@ -87,13 +87,13 @@ def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None
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return
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-
outputs = do_suppression(inference, conf_thres=hyperparams['
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if hyperparams['use_associative_tracking']:
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-
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do_confidence_boost(inference, outputs, gp=gp)
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outputs = do_suppression(inference, conf_thres=hyperparams['
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all_preds, real_width, real_height = format_predictions(image_shapes, outputs, width, height, gp=gp)
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@@ -288,13 +288,13 @@ def do_confidence_boost(inference, safe_preds, gp=None, batch_size=BATCH_SIZE, v
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pbar.update(1*batch_size)
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def boost_frame(safe_frame, base_frame, dt):
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safe_boxes = safe_frame[:, :4]
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boxes = xywh2xyxy(base_frame[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
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ious = box_iou(boxes, safe_boxes)
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score = torch.matmul(ious, safe_frame[:, 4])
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# score = iou(safe_box, base_box) * confidence(safe_box)
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base_frame[:, 4] *= 1 + (score)*math.exp(-dt*dt)
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return base_frame
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def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, verbose=True):
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# Load hyperparameters
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if 'model' not in hyperparams: hyperparams['model'] = WEIGHTS
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+
if 'conf_thresh' not in hyperparams: hyperparams['conf_thresh'] = CONF_THRES
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if 'iou_thresh' not in hyperparams: hyperparams['iou_thresh'] = NMS_IOU
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if 'min_hits' not in hyperparams: hyperparams['min_hits'] = MIN_HITS
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if 'max_age' not in hyperparams: hyperparams['max_age'] = MAX_AGE
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return
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outputs = do_suppression(inference, conf_thres=hyperparams['conf_thresh'], iou_thres=hyperparams['iou_thresh'], gp=gp)
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if hyperparams['use_associative_tracking']:
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+
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do_confidence_boost(inference, outputs, gp=gp)
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outputs = do_suppression(inference, conf_thres=hyperparams['conf_thresh'], iou_thres=hyperparams['iou_thresh'], gp=gp)
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all_preds, real_width, real_height = format_predictions(image_shapes, outputs, width, height, gp=gp)
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pbar.update(1*batch_size)
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def boost_frame(safe_frame, base_frame, dt, decay=1):
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safe_boxes = safe_frame[:, :4]
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boxes = xywh2xyxy(base_frame[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
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ious = box_iou(boxes, safe_boxes)
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score = torch.matmul(ious, safe_frame[:, 4])
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# score = iou(safe_box, base_box) * confidence(safe_box)
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base_frame[:, 4] *= 1 + (score)*math.exp(-decay*dt*dt)
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return base_frame
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def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, verbose=True):
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