Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2887
tensor([1.0000e+00, 9.0336e-09, 2.0408e-10, 1.8446e-08, 2.1540e-10, 6.5879e-10,
6.4628e-11, 5.4046e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.0336e-09, 2.0408e-10, 1.8446e-08, 2.1540e-10, 6.5879e-10,
6.4628e-11, 5.4046e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.0408e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.0408e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left image contain a dark brown bookshelf?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
tensor([1.0000e+00, 8.5087e-10, 1.2091e-10, 1.5891e-10, 2.0568e-10, 1.3606e-08,
3.6535e-08, 3.6606e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.5087e-10, 1.2091e-10, 1.5891e-10, 2.0568e-10, 1.3606e-08,
3.6535e-08, 3.6606e-10], device='cuda:1', grad_fn=<SelectBackward0>)
question: ['Does the left image contain a dark brown bookshelf?'], responses:['yes']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.1843e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many cats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
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: 1354
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: 1352
question: ['How many cats are in the image?'], responses:['11']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
[('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']]
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: 1352
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([1.0000e+00, 1.7497e-09, 1.0505e-10, 3.5262e-09, 2.0325e-10, 2.0407e-10,
1.3628e-11, 9.8416e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7497e-09, 1.0505e-10, 3.5262e-09, 2.0325e-10, 2.0407e-10,
1.3628e-11, 9.8416e-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(1.0505e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.0505e-10, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([8.8803e-01, 9.8781e-03, 1.4364e-02, 4.6665e-03, 2.4714e-04, 1.6246e-02,
6.4378e-02, 2.1922e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([8.8803e-01, 9.8781e-03, 1.4364e-02, 4.6665e-03, 2.4714e-04, 1.6246e-02,
6.4378e-02, 2.1922e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 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 09:58:17,653] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.24 | optimizer_step: 0.31
[2024-10-24 09:58:17,653] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5854.72 | backward_microstep: 7910.39 | backward_inner_microstep: 5558.62 | backward_allreduce_microstep: 2351.70 | step_microstep: 7.85
[2024-10-24 09:58:17,653] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5854.72 | backward: 7910.38 | backward_inner: 5558.63 | backward_allreduce: 2351.69 | step: 7.86
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4637/4844 [19:17:01<54:20, 15.75s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Do the bottles in the image have caps?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Are there elephants standing in or beside water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Do the bottles in the image have caps?'], 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 6.6810e-09, 2.9988e-09, 4.4548e-09, 6.0487e-10, 3.5261e-10,
8.4792e-11, 4.2983e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.6810e-09, 2.9988e-09, 4.4548e-09, 6.0487e-10, 3.5261e-10,
8.4792e-11, 4.2983e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.9988e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.9988e-09, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many people are in the image?'], responses:['1']
torch.Size([7, 3, 448, 448])
question: ['How many animals are in the image?'], responses:['1']
question: ['Are there elephants standing in or beside water?'], responses:['yes']
[('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']]