Datasets:
TMarcus
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InternVL

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torch.Size([5, 3, 448, 448])
question: ['Are people standing outside the bus?'], responses:['yes']
[('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']]
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are in the image?'], responses:['2']
tensor([4.5036e-01, 5.4955e-01, 2.8475e-09, 6.7817e-05, 6.9958e-07, 3.8430e-08,
4.9321e-06, 1.2994e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([4.5036e-01, 5.4955e-01, 2.8475e-09, 6.7817e-05, 6.9958e-07, 3.8430e-08,
4.9321e-06, 1.2994e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.8430e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there any humans in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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']]
torch.Size([13, 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: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
question: ['Are there any humans in the image?'], responses:['no']
tensor([1.0000e+00, 7.2808e-09, 3.0942e-09, 7.4586e-09, 3.8939e-11, 9.6401e-11,
2.5132e-11, 9.4296e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.2808e-09, 3.0942e-09, 7.4586e-09, 3.8939e-11, 9.6401e-11,
2.5132e-11, 9.4296e-10], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 5.5392e-07, 9.6054e-09, 1.5230e-08, 9.6604e-10, 7.5530e-10,
1.7467e-09, 3.9639e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.5392e-07, 9.6054e-09, 1.5230e-08, 9.6604e-10, 7.5530e-10,
1.7467e-09, 3.9639e-10], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 6.2862e-10, 2.9278e-07, 3.3185e-11, 1.2773e-10, 1.3789e-08,
3.8065e-09, 9.4003e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.2862e-10, 2.9278e-07, 3.3185e-11, 1.2773e-10, 1.3789e-08,
3.8065e-09, 9.4003e-07], device='cuda:3', grad_fn=<SelectBackward0>)
[('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']]
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.5230e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.0942e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.0942e-09, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many women are lined up for a photo?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.2862e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many women are lined up for a photo?'], responses:['six']
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)]
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many bottles are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([5.7708e-05, 1.3875e-02, 1.4547e-03, 2.1518e-01, 2.7761e-01, 2.9757e-03,
1.9214e-01, 2.9670e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
sunrise *************
['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([5.7708e-05, 1.3875e-02, 1.4547e-03, 2.1518e-01, 2.7761e-01, 2.9757e-03,
1.9214e-01, 2.9670e-01], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.2632e-10, 7.5710e-07, 3.2425e-09, 3.5294e-09, 1.2464e-07,
4.6684e-09, 5.9886e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.2632e-10, 7.5710e-07, 3.2425e-09, 3.5294e-09, 1.2464e-07,
4.6684e-09, 5.9886e-07], 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(8.2632e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.4576e-01, 5.6237e-03, 3.0401e-02, 1.1902e-02, 1.4984e-04, 4.6615e-03,
1.6979e-04, 1.3356e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4576e-01, 5.6237e-03, 3.0401e-02, 1.1902e-02, 1.4984e-04, 4.6615e-03,
1.6979e-04, 1.3356e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:55:01,562] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.33
[2024-10-24 09:55:01,562] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2565.20 | backward_microstep: 11420.17 | backward_inner_microstep: 2403.44 | backward_allreduce_microstep: 9016.67 | step_microstep: 7.63
[2024-10-24 09:55:01,562] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2565.21 | backward: 11420.16 | backward_inner: 2403.45 | backward_allreduce: 9016.66 | step: 7.65
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4624/4844 [19:13:45<50:40, 13.82s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step