Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
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>)}
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
question: ['Is the dog against a white background?'], responses:['yes']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
question: ['How many people are in the image?'], responses:['1']
[('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']]
[('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: 5, images per sample: 5.0, dynamic token length: 1351
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
question: ['Is the house behind a fence?'], responses:['yes']
tensor([9.9998e-01, 1.1793e-07, 2.3773e-08, 2.8896e-08, 1.5142e-07, 1.7232e-05,
5.3687e-07, 2.3680e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9998e-01, 1.1793e-07, 2.3773e-08, 2.8896e-08, 1.5142e-07, 1.7232e-05,
5.3687e-07, 2.3680e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7649e-05, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
[('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']]
ANSWER0=VQA(image=RIGHT,question='Is there at least one person standing on a curb by the open door of a parked yellow bus with a non-flat front?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['Is there at least one person standing on a curb by the open door of a parked yellow bus with a non-flat front?'], responses:['no']
[('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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1878
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1878
tensor([1.0000e+00, 3.1924e-09, 6.2862e-10, 7.1639e-11, 3.3315e-11, 1.5317e-09,
5.8382e-08, 6.6682e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.1924e-09, 6.2862e-10, 7.1639e-11, 3.3315e-11, 1.5317e-09,
5.8382e-08, 6.6682e-11], 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(6.3907e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1879
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1878
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1878
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1879
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1879
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1879
tensor([1.0000e+00, 2.5451e-09, 3.6129e-07, 3.7683e-10, 2.3859e-08, 1.0781e-07,
1.6223e-08, 2.6984e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.5451e-09, 3.6129e-07, 3.7683e-10, 2.3859e-08, 1.0781e-07,
1.6223e-08, 2.6984e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.5451e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 2.7682e-09, 5.7563e-11, 1.8078e-09, 1.7099e-10, 1.9172e-10,
4.0385e-11, 7.7361e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7682e-09, 5.7563e-11, 1.8078e-09, 1.7099e-10, 1.9172e-10,
4.0385e-11, 7.7361e-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(5.7563e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.7563e-11, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many dogs 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.2399e-09, 5.9642e-09, 8.3719e-09, 5.4922e-11, 1.1628e-10,
8.9127e-12, 1.1052e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2399e-09, 5.9642e-09, 8.3719e-09, 5.4922e-11, 1.1628e-10,
8.9127e-12, 1.1052e-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(5.9642e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.9642e-09, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.8381e-08, 1.6085e-08, 1.6991e-08, 8.0704e-10, 4.0986e-09,
2.9501e-09, 5.6999e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.8381e-08, 1.6085e-08, 1.6991e-08, 8.0704e-10, 4.0986e-09,
2.9501e-09, 5.6999e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.8381e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:06:23,340] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.34
[2024-10-24 10:06:23,341] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4475.90 | backward_microstep: 5602.69 | backward_inner_microstep: 4259.86 | backward_allreduce_microstep: 1342.68 | step_microstep: 7.66
[2024-10-24 10:06:23,341] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4475.91 | backward: 5602.68 | backward_inner: 4259.90 | backward_allreduce: 1342.66 | step: 7.67
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4668/4844 [19:25:07<40:28, 13.80s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many stingrays are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step