Datasets:
TMarcus
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InternVL

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[('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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
tensor([1.0000e+00, 1.1062e-08, 8.2332e-08, 1.4592e-08, 1.2326e-11, 6.5228e-11,
7.8920e-11, 5.8227e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1062e-08, 8.2332e-08, 1.4592e-08, 1.2326e-11, 6.5228e-11,
7.8920e-11, 5.8227e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(8.2332e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.6877e-08, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is the food in a solid white bowl?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([2.3063e-04, 7.1016e-06, 1.5842e-03, 8.3764e-02, 1.2041e-04, 6.0455e-05,
8.1941e-02, 8.3229e-01], device='cuda:1', grad_fn=<SoftmaxBackward0>)
mustache *************
['d', 'm', 'l', 'closet', 's', 'h', 'ge', 'mustache'] tensor([2.3063e-04, 7.1016e-06, 1.5842e-03, 8.3764e-02, 1.2041e-04, 6.0455e-05,
8.1941e-02, 8.3229e-01], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 4.5191e-08, 2.2197e-10, 1.3044e-08, 5.5130e-09, 1.3199e-09,
1.0178e-10, 1.0585e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.5191e-08, 2.2197e-10, 1.3044e-08, 5.5130e-09, 1.3199e-09,
1.0178e-10, 1.0585e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.2197e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1899e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pink jellyfish are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many syringes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([11, 3, 448, 448])
question: ['How many pink jellyfish are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many syringes 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']]
tensor([1.0000e+00, 4.1712e-10, 1.0881e-10, 2.0892e-10, 1.5196e-10, 2.2863e-08,
9.5307e-09, 4.5604e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.1712e-10, 1.0881e-10, 2.0892e-10, 1.5196e-10, 2.2863e-08,
9.5307e-09, 4.5604e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.3736e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
tensor([1.0000e+00, 3.3391e-09, 3.2290e-11, 4.3391e-09, 6.6857e-10, 5.7116e-11,
7.8578e-12, 1.4015e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3391e-09, 3.2290e-11, 4.3391e-09, 6.6857e-10, 5.7116e-11,
7.8578e-12, 1.4015e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.2290e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.2290e-11, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a hunter posing near the wild pig?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
question: ['Is there a hunter posing near the wild pig?'], responses:['no']
[('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: 11, images per sample: 11.0, dynamic token length: 2886
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
tensor([9.9995e-01, 6.0233e-08, 9.9876e-09, 5.1442e-05, 2.0308e-10, 4.3706e-10,
2.8777e-10, 5.2935e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9995e-01, 6.0233e-08, 9.9876e-09, 5.1442e-05, 2.0308e-10, 4.3706e-10,
2.8777e-10, 5.2935e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.1442e-05, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.5990e-10, 7.5375e-07, 2.8780e-10, 3.0570e-09, 1.1167e-07,
1.7762e-09, 5.9575e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.5990e-10, 7.5375e-07, 2.8780e-10, 3.0570e-09, 1.1167e-07,
1.7762e-09, 5.9575e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.5990e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:34:51,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.23 | optimizer_step: 0.31
[2024-10-24 09:34:51,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6406.43 | backward_microstep: 7598.78 | backward_inner_microstep: 6150.00 | backward_allreduce_microstep: 1448.68 | step_microstep: 7.28
[2024-10-24 09:34:51,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6406.43 | backward: 7598.77 | backward_inner: 6150.02 | backward_allreduce: 1448.66 | step: 7.29
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4543/4844 [18:53:35<1:16:39, 15.28s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step