Datasets:
TMarcus
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InternVL

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2.5758e-10, 6.2717e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7630e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many safety pins are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.7979e-10, 5.5143e-11, 1.3703e-10, 8.8278e-11, 4.4954e-09,
4.7180e-09, 5.5018e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7979e-10, 5.5143e-11, 1.3703e-10, 8.8278e-11, 4.4954e-09,
4.7180e-09, 5.5018e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.9287e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many safety pins are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.9932e-01, 5.5114e-04, 1.3143e-04, 3.4140e-10, 4.4507e-09, 4.1873e-07,
4.3423e-08, 3.0729e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9932e-01, 5.5114e-04, 1.3143e-04, 3.4140e-10, 4.4507e-09, 4.1873e-07,
4.3423e-08, 3.0729e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9993, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:38:01,045] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.34
[2024-10-24 10:38:01,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3853.93 | backward_microstep: 9956.38 | backward_inner_microstep: 3581.50 | backward_allreduce_microstep: 6374.73 | step_microstep: 7.83
[2024-10-24 10:38:01,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3853.94 | backward: 9956.37 | backward_inner: 3581.54 | backward_allreduce: 6374.70 | step: 7.84
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 4796/4844 [19:56:44<10:47, 13.49s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many cell phones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Are the animals in an enclosure?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many jellyfish are swimming in the water?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([13, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the bottle of soap have a top pump dispenser?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many cell phones 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Does the bottle of soap have a top pump dispenser?'], 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([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many jellyfish are swimming in the water?'], responses:['5']
[('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']]
tensor([1.0000e+00, 8.4946e-08, 3.8891e-08, 1.1254e-07, 4.2534e-10, 1.9283e-09,
1.1999e-09, 2.8554e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.4946e-08, 3.8891e-08, 1.1254e-07, 4.2534e-10, 1.9283e-09,
1.1999e-09, 2.8554e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.2768e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Are the animals in an enclosure?'], responses:['no']
ANSWER0=VQA(image=LEFT,question='How many bottles of wine are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
[('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']]
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
torch.Size([13, 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([1.0000e+00, 3.9738e-09, 2.9632e-11, 2.2403e-09, 1.2073e-10, 2.1388e-10,
7.6781e-12, 1.6616e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.9738e-09, 2.9632e-11, 2.2403e-09, 1.2073e-10, 2.1388e-10,
7.6781e-12, 1.6616e-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(2.9632e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.9632e-11, device='cuda:1', grad_fn=<DivBackward0>)}
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])
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
question: ['How many syringes 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 bottles of wine are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]