Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth

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/var/lib/workspace/intermediate_source/engine.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
  with torch.cuda.amp.autocast(enabled=scaler is not None):
Epoch: [0]  [ 0/60]  eta: 0:00:51  lr: 0.000090  loss: 5.0142 (5.0142)  loss_classifier: 0.6467 (0.6467)  loss_box_reg: 0.3455 (0.3455)  loss_mask: 3.9954 (3.9954)  loss_objectness: 0.0207 (0.0207)  loss_rpn_box_reg: 0.0058 (0.0058)  time: 0.8572  data: 0.0140  max mem: 2144
Epoch: [0]  [10/60]  eta: 0:00:13  lr: 0.000936  loss: 1.7787 (2.8318)  loss_classifier: 0.4449 (0.4134)  loss_box_reg: 0.3037 (0.2730)  loss_mask: 0.9447 (2.1163)  loss_objectness: 0.0237 (0.0255)  loss_rpn_box_reg: 0.0033 (0.0037)  time: 0.2663  data: 0.0166  max mem: 2432
Epoch: [0]  [20/60]  eta: 0:00:09  lr: 0.001783  loss: 1.0360 (1.8413)  loss_classifier: 0.2012 (0.2987)  loss_box_reg: 0.2441 (0.2786)  loss_mask: 0.4199 (1.2388)  loss_objectness: 0.0153 (0.0203)  loss_rpn_box_reg: 0.0041 (0.0050)  time: 0.2078  data: 0.0169  max mem: 2551
Epoch: [0]  [30/60]  eta: 0:00:06  lr: 0.002629  loss: 0.5565 (1.4080)  loss_classifier: 0.1181 (0.2296)  loss_box_reg: 0.1978 (0.2499)  loss_mask: 0.2211 (0.9055)  loss_objectness: 0.0097 (0.0176)  loss_rpn_box_reg: 0.0054 (0.0054)  time: 0.2025  data: 0.0161  max mem: 2551
Epoch: [0]  [40/60]  eta: 0:00:04  lr: 0.003476  loss: 0.4310 (1.1655)  loss_classifier: 0.0596 (0.1856)  loss_box_reg: 0.1598 (0.2301)  loss_mask: 0.1767 (0.7295)  loss_objectness: 0.0071 (0.0152)  loss_rpn_box_reg: 0.0041 (0.0051)  time: 0.1940  data: 0.0149  max mem: 2551
Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3635 (1.0068)  loss_classifier: 0.0443 (0.1572)  loss_box_reg: 0.1468 (0.2100)  loss_mask: 0.1750 (0.6217)  loss_objectness: 0.0037 (0.0129)  loss_rpn_box_reg: 0.0036 (0.0051)  time: 0.1926  data: 0.0141  max mem: 2551
Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3605 (0.9150)  loss_classifier: 0.0436 (0.1409)  loss_box_reg: 0.1428 (0.1995)  loss_mask: 0.1683 (0.5578)  loss_objectness: 0.0030 (0.0115)  loss_rpn_box_reg: 0.0053 (0.0054)  time: 0.2062  data: 0.0149  max mem: 2982
Epoch: [0] Total time: 0:00:12 (0.2136 s / it)
creating index...
index created!
Test:  [ 0/50]  eta: 0:00:04  model_time: 0.0602 (0.0602)  evaluator_time: 0.0186 (0.0186)  time: 0.0964  data: 0.0169  max mem: 2982
Test:  [49/50]  eta: 0:00:00  model_time: 0.0437 (0.0460)  evaluator_time: 0.0061 (0.0071)  time: 0.0643  data: 0.0108  max mem: 2982
Test: Total time: 0:00:03 (0.0644 s / it)
Averaged stats: model_time: 0.0437 (0.0460)  evaluator_time: 0.0061 (0.0071)
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.712
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.992
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.882
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.625
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.721
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.295
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.759
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.759
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764
IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.992
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.893
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.338
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.284
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.751
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.753
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.714
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.756
Epoch: [1]  [ 0/60]  eta: 0:00:13  lr: 0.005000  loss: 0.2362 (0.2362)  loss_classifier: 0.0231 (0.0231)  loss_box_reg: 0.0745 (0.0745)  loss_mask: 0.1294 (0.1294)  loss_objectness: 0.0056 (0.0056)  loss_rpn_box_reg: 0.0036 (0.0036)  time: 0.2181  data: 0.0172  max mem: 2982
Epoch: [1]  [10/60]  eta: 0:00:10  lr: 0.005000  loss: 0.3038 (0.2900)  loss_classifier: 0.0397 (0.0395)  loss_box_reg: 0.1018 (0.0966)  loss_mask: 0.1396 (0.1459)  loss_objectness: 0.0013 (0.0020)  loss_rpn_box_reg: 0.0050 (0.0062)  time: 0.2040  data: 0.0160  max mem: 2982
Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.2842 (0.2925)  loss_classifier: 0.0330 (0.0383)  loss_box_reg: 0.0894 (0.0898)  loss_mask: 0.1473 (0.1569)  loss_objectness: 0.0010 (0.0022)  loss_rpn_box_reg: 0.0032 (0.0054)  time: 0.1993  data: 0.0157  max mem: 2982
Epoch: [1]  [30/60]  eta: 0:00:05  lr: 0.005000  loss: 0.2823 (0.2911)  loss_classifier: 0.0392 (0.0388)  loss_box_reg: 0.0759 (0.0899)  loss_mask: 0.1533 (0.1555)  loss_objectness: 0.0010 (0.0019)  loss_rpn_box_reg: 0.0040 (0.0050)  time: 0.1968  data: 0.0154  max mem: 2982
Epoch: [1]  [40/60]  eta: 0:00:03  lr: 0.005000  loss: 0.2657 (0.2806)  loss_classifier: 0.0281 (0.0364)  loss_box_reg: 0.0728 (0.0835)  loss_mask: 0.1364 (0.1542)  loss_objectness: 0.0008 (0.0016)  loss_rpn_box_reg: 0.0043 (0.0050)  time: 0.1997  data: 0.0147  max mem: 2982
Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2483 (0.2832)  loss_classifier: 0.0262 (0.0366)  loss_box_reg: 0.0648 (0.0850)  loss_mask: 0.1364 (0.1551)  loss_objectness: 0.0008 (0.0015)  loss_rpn_box_reg: 0.0050 (0.0051)  time: 0.2070  data: 0.0154  max mem: 2982
Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2748 (0.2792)  loss_classifier: 0.0334 (0.0362)  loss_box_reg: 0.0762 (0.0823)  loss_mask: 0.1364 (0.1540)  loss_objectness: 0.0007 (0.0014)  loss_rpn_box_reg: 0.0060 (0.0053)  time: 0.2121  data: 0.0166  max mem: 2982
Epoch: [1] Total time: 0:00:12 (0.2034 s / it)
creating index...
index created!
Test:  [ 0/50]  eta: 0:00:04  model_time: 0.0466 (0.0466)  evaluator_time: 0.0164 (0.0164)  time: 0.0805  data: 0.0170  max mem: 2982
Test:  [49/50]  eta: 0:00:00  model_time: 0.0399 (0.0408)  evaluator_time: 0.0031 (0.0039)  time: 0.0561  data: 0.0108  max mem: 2982
Test: Total time: 0:00:02 (0.0557 s / it)
Averaged stats: model_time: 0.0399 (0.0408)  evaluator_time: 0.0031 (0.0039)
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.781
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.993
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.939
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.384
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.578
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.790
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.818
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.786
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.822
IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.723
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.992
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.883
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.359
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.529
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.731
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.292
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.764
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.765
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.550
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.743
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.770
That's it!