Datasets:
TMarcus
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InternVL

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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.2067e-10, 1.8398e-11, 6.6765e-11, 5.1986e-11, 2.8132e-09,
5.4304e-09, 3.6126e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.4304e-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>)}
tensor([1.0000e+00, 5.4711e-09, 1.1248e-10, 5.3913e-08, 2.4255e-09, 1.5803e-09,
1.0696e-10, 1.8266e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.4711e-09, 1.1248e-10, 5.3913e-08, 2.4255e-09, 1.5803e-09,
1.0696e-10, 1.8266e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.1248e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-24 10:29:15,247] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.33
[2024-10-24 10:29:15,248] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7030.89 | backward_microstep: 10862.52 | backward_inner_microstep: 6772.42 | backward_allreduce_microstep: 4089.98 | step_microstep: 7.78
[2024-10-24 10:29:15,248] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7030.90 | backward: 10862.50 | backward_inner: 6772.44 | backward_allreduce: 4089.96 | step: 7.79
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4761/4844 [19:47:59<21:42, 15.69s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many lipsticks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is a woman wearing a stethoscope in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([3, 3, 448, 448])
question: ['How many lipsticks 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([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many animals are in the image?'], responses:['5']
question: ['Is a woman wearing a stethoscope in the image?'], responses:['yes']
[('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']]
[('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([3, 3, 448, 448]) knan debug pixel values shape
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
tensor([9.7589e-01, 1.1425e-03, 2.0252e-02, 2.5496e-04, 2.7180e-07, 1.8838e-03,
1.8286e-06, 5.7453e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.7589e-01, 1.1425e-03, 2.0252e-02, 2.5496e-04, 2.7180e-07, 1.8838e-03,
1.8286e-06, 5.7453e-04], 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>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
question: ['How many animals are in the image?'], responses:['1']
ANSWER0=VQA(image=LEFT,question='How many zebras are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 842
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 842
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 842
tensor([9.9670e-01, 1.9380e-07, 6.1318e-06, 2.0482e-03, 5.3129e-11, 1.2423e-03,
2.0955e-07, 3.9704e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9670e-01, 1.9380e-07, 6.1318e-06, 2.0482e-03, 5.3129e-11, 1.2423e-03,
2.0955e-07, 3.9704e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 7.9119e-09, 5.6671e-11, 8.3019e-08, 1.9923e-10, 8.8649e-10,
1.6722e-11, 2.1638e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.9119e-09, 5.6671e-11, 8.3019e-08, 1.9923e-10, 8.8649e-10,
1.6722e-11, 2.1638e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9967, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0033, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.6671e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1915e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is one of the locks black?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many ferrets are in the image?'], responses:['2']
question: ['How many zebras 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape