Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.0843e-09, 7.9919e-11, 2.1607e-08, 3.0353e-10, 2.2067e-10,
4.6680e-11, 9.5560e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0843e-09, 7.9919e-11, 2.1607e-08, 3.0353e-10, 2.2067e-10,
4.6680e-11, 9.5560e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.9919e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.9919e-11, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['Does the dog on the right have a blue collar?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many hyenas 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 5.7728e-09, 6.0800e-11, 7.8187e-09, 1.4517e-10, 9.4907e-11,
1.1773e-11, 1.3742e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.7728e-09, 6.0800e-11, 7.8187e-09, 1.4517e-10, 9.4907e-11,
1.1773e-11, 1.3742e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(6.0800e-11, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-6.0800e-11, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 6.4310e-09, 1.5080e-09, 3.4987e-08, 1.4459e-10, 3.6877e-11,
8.1109e-11, 6.3969e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.4310e-09, 1.5080e-09, 3.4987e-08, 1.4459e-10, 3.6877e-11,
8.1109e-11, 6.3969e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.5080e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.5080e-09, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([2.3161e-03, 2.0259e-02, 2.3493e-05, 2.5476e-01, 1.2238e-01, 4.7023e-02,
5.5167e-01, 1.5722e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
vegetable *************
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([2.3161e-03, 2.0259e-02, 2.3493e-05, 2.5476e-01, 1.2238e-01, 4.7023e-02,
5.5167e-01, 1.5722e-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(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 5.3350e-10, 1.6019e-10, 6.3353e-10, 2.2611e-10, 1.2236e-08,
4.7180e-09, 9.0536e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.3350e-10, 1.6019e-10, 6.3353e-10, 2.2611e-10, 1.2236e-08,
4.7180e-09, 9.0536e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.7180e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:38:12,837] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 09:38:12,837] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9089.23 | backward_microstep: 8728.04 | backward_inner_microstep: 8721.84 | backward_allreduce_microstep: 6.08 | step_microstep: 7.31
[2024-10-24 09:38:12,837] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9089.23 | backward: 8728.03 | backward_inner: 8721.87 | backward_allreduce: 6.01 | step: 7.32
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4556/4844 [18:56:56<1:17:29, 16.14s/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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many chimps are outside in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many beetles are pushing a dung ball in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many train cars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many parrots are in the image?'], responses:['5']
question: ['How many beetles are pushing a dung ball in the image?'], responses:['1']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
[('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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([8.5891e-01, 2.8204e-08, 1.4085e-01, 1.7591e-04, 1.4703e-06, 5.6899e-05,
1.2088e-07, 8.1635e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([1.0000e+00, 1.0819e-09, 1.8294e-10, 6.6648e-10, 4.0586e-10, 3.3725e-08,
1.5961e-08, 1.7847e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.5891e-01, 2.8204e-08, 1.4085e-01, 1.7591e-04, 1.4703e-06, 5.6899e-05,
1.2088e-07, 8.1635e-08], device='cuda:1', grad_fn=<SelectBackward0>)