Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([9.8764e-01, 1.1269e-03, 1.7900e-03, 2.5944e-04, 1.1418e-03, 2.7117e-04,
3.7786e-04, 7.3903e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8764e-01, 1.1269e-03, 1.7900e-03, 2.5944e-04, 1.1418e-03, 2.7117e-04,
3.7786e-04, 7.3903e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9876, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0124, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many beetles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['How many beetles 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
tensor([9.7666e-01, 2.2970e-02, 1.6134e-05, 3.0474e-05, 1.5459e-04, 6.6795e-05,
8.4837e-05, 1.8906e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.7666e-01, 2.2970e-02, 1.6134e-05, 3.0474e-05, 1.5459e-04, 6.6795e-05,
8.4837e-05, 1.8906e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0230, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9767, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([0.0568, 0.0220, 0.0541, 0.4527, 0.3179, 0.0350, 0.0197, 0.0419],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
diamond *************
['in', 'on', 'grill', 'diamond', 'dome', 'opaque', 'focus', 'fireplace'] tensor([0.0568, 0.0220, 0.0541, 0.4527, 0.3179, 0.0350, 0.0197, 0.0419],
device='cuda:1', 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>)}
tensor([9.3800e-01, 8.3715e-03, 2.7183e-03, 1.2827e-03, 1.7548e-03, 1.0897e-03,
4.6700e-02, 8.4908e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.3800e-01, 8.3715e-03, 2.7183e-03, 1.2827e-03, 1.7548e-03, 1.0897e-03,
4.6700e-02, 8.4908e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0467, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9533, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([5.7651e-01, 4.2179e-01, 6.5305e-05, 1.7764e-04, 2.8585e-04, 7.5383e-04,
3.8554e-04, 3.3154e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7651e-01, 4.2179e-01, 6.5305e-05, 1.7764e-04, 2.8585e-04, 7.5383e-04,
3.8554e-04, 3.3154e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4218, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5765, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0017, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:52:47,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.34 | optimizer_step: 0.32
[2024-10-23 14:52:47,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5114.02 | backward_microstep: 8840.50 | backward_inner_microstep: 4827.17 | backward_allreduce_microstep: 4013.16 | step_microstep: 7.71
[2024-10-23 14:52:47,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5114.02 | backward: 8840.49 | backward_inner: 4827.27 | backward_allreduce: 4013.15 | step: 7.73
1%| | 45/4844 [11:30<19:58:01, 14.98s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a potted plant?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many dogs 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='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many adult gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the image contain a potted plant?'], responses:['yes']
[('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
question: ['How many dogs are in the image?'], responses:['2']
question: ['How many adult gorillas are in the image?'], responses:['1']
[('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']]
[('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
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.0854e-01, 1.0833e-02, 7.9387e-02, 6.3621e-04, 3.2021e-05, 1.3442e-04,
2.0428e-05, 4.2217e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.0854e-01, 1.0833e-02, 7.9387e-02, 6.3621e-04, 3.2021e-05, 1.3442e-04,
2.0428e-05, 4.2217e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9085, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0794, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0121, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([9.4306e-01, 2.3608e-02, 3.0966e-03, 2.8476e-02, 1.0372e-03, 3.2535e-04,
3.8109e-04, 1.6507e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.4306e-01, 2.3608e-02, 3.0966e-03, 2.8476e-02, 1.0372e-03, 3.2535e-04,