Datasets:
TMarcus
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InternVL

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tensor([9.9996e-01, 3.1202e-05, 1.8370e-08, 7.8890e-06, 8.4586e-10, 2.2855e-10,
1.4845e-09, 4.0817e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9996e-01, 3.1202e-05, 1.8370e-08, 7.8890e-06, 8.4586e-10, 2.2855e-10,
1.4845e-09, 4.0817e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.9112e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([4.1724e-05, 1.4736e-03, 6.7728e-02, 5.3824e-01, 1.3512e-01, 5.6983e-02,
3.1598e-03, 1.9725e-01], device='cuda:0', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([4.1724e-05, 1.4736e-03, 6.7728e-02, 5.3824e-01, 1.3512e-01, 5.6983e-02,
3.1598e-03, 1.9725e-01], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.1344e-09, 1.8294e-10, 7.5241e-09, 1.2569e-10, 1.9474e-10,
1.9346e-11, 4.7560e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.1344e-09, 1.8294e-10, 7.5241e-09, 1.2569e-10, 1.9474e-10,
1.9346e-11, 4.7560e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.8294e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.8294e-10, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-24 09:44:34,746] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 09:44:34,747] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7079.53 | backward_microstep: 6916.21 | backward_inner_microstep: 6730.46 | backward_allreduce_microstep: 185.56 | step_microstep: 7.66
[2024-10-24 09:44:34,747] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7079.53 | backward: 6916.20 | backward_inner: 6730.54 | backward_allreduce: 185.44 | step: 7.68
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4583/4844 [19:03:18<1:05:44, 15.11s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='What is the foot rest of the buggy made from?')
ANSWER1=EVAL(expr='{ANSWER0} == "wooden slats"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many open laptops can be seen in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the roof pink on the structure?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many open laptops can be seen in the image?'], responses:['0']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['What is the foot rest of the buggy made from?'], responses:['le']
[('la', 0.1253174324179604), ('spiral', 0.12507818232057966), ('grill', 0.12499808609355235), ('panda', 0.12497563862337473), ('greyhound', 0.12494815273468146), ('pan', 0.12492097545398419), ('tan', 0.12489943593506406), ('ge', 0.12486209642080312)]
[['la', 'spiral', 'grill', 'panda', 'greyhound', 'pan', 'tan', 'ge']]
tensor([1.0000e+00, 5.6149e-07, 4.5952e-08, 1.7195e-10, 5.1559e-07, 3.3410e-08,
1.9369e-07, 9.0815e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 5.6149e-07, 4.5952e-08, 1.7195e-10, 5.1559e-07, 3.3410e-08,
1.9369e-07, 9.0815e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.2650e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pugs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
question: ['How many animals are in the image?'], responses:['2']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
question: ['How many pugs are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
question: ['Is the roof pink on the structure?'], responses:['no']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([5.4669e-05, 9.1232e-02, 8.1179e-01, 3.4293e-03, 4.3924e-02, 1.1305e-02,
2.9905e-02, 8.3557e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
grill *************
['la', 'spiral', 'grill', 'panda', 'greyhound', 'pan', 'tan', 'ge'] tensor([5.4669e-05, 9.1232e-02, 8.1179e-01, 3.4293e-03, 4.3924e-02, 1.1305e-02,
2.9905e-02, 8.3557e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.4663e-07, 7.9012e-09, 8.4108e-09, 1.8728e-10, 1.5770e-10,
3.0279e-10, 2.7200e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)