Datasets:
TMarcus
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InternVL

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tensor([7.3846e-01, 3.2042e-04, 1.0019e-01, 1.2885e-01, 4.7043e-03, 7.2574e-03,
1.9728e-02, 4.8806e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([7.3846e-01, 3.2042e-04, 1.0019e-01, 1.2885e-01, 4.7043e-03, 7.2574e-03,
1.9728e-02, 4.8806e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.0157e-09, 1.3568e-11, 2.6933e-08, 5.5427e-10, 2.3674e-10,
6.7149e-11, 9.8356e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.0157e-09, 1.3568e-11, 2.6933e-08, 5.5427e-10, 2.3674e-10,
6.7149e-11, 9.8356e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.3568e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.3568e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 2.8453e-08, 2.7993e-07, 1.9281e-11, 1.1522e-11, 7.3043e-09,
9.2529e-10, 3.4358e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.8453e-08, 2.7993e-07, 1.9281e-11, 1.1522e-11, 7.3043e-09,
9.2529e-10, 3.4358e-07], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.8453e-08, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many ferrets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many ferrets 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 wolves are in the image?'], responses:['11']
tensor([1.0000e+00, 3.1733e-10, 6.9435e-11, 2.4807e-10, 8.1064e-11, 1.1639e-08,
2.8841e-09, 2.4394e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.1733e-10, 6.9435e-11, 2.4807e-10, 8.1064e-11, 1.1639e-08,
2.8841e-09, 2.4394e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5483e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[('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([9.3657e-01, 6.3063e-03, 5.1797e-04, 4.9625e-02, 4.5734e-04, 5.5128e-04,
5.9291e-03, 4.5261e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.3657e-01, 6.3063e-03, 5.1797e-04, 4.9625e-02, 4.5734e-04, 5.5128e-04,
5.9291e-03, 4.5261e-05], 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(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:43:35,218] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-24 09:43:35,218] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3865.51 | backward_microstep: 13812.62 | backward_inner_microstep: 3636.05 | backward_allreduce_microstep: 10176.45 | step_microstep: 7.75
[2024-10-24 09:43:35,219] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3865.51 | backward: 13812.61 | backward_inner: 3636.09 | backward_allreduce: 10176.43 | step: 7.76
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4579/4844 [19:02:19<1:08:12, 15.44s/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
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many graduation students are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many vertically stacked drawers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the dog positioned on a wooden surface?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many graduation students are in the image?'], responses:['four']
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many vertically stacked drawers are in the image?'], responses:['3']
question: ['Is the dog positioned on a wooden surface?'], responses:['yes']
question: ['How many boats are in the image?'], responses:['2']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
[('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']]
[('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]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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
tensor([1.5991e-14, 9.9997e-01, 8.2162e-06, 1.4616e-05, 4.6825e-06, 8.3052e-07,
1.0542e-06, 2.4698e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([1.5991e-14, 9.9997e-01, 8.2162e-06, 1.4616e-05, 4.6825e-06, 8.3052e-07,
1.0542e-06, 2.4698e-07], 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(4.6825e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0371e-05, device='cuda:2', grad_fn=<DivBackward0>)}