Datasets:
TMarcus
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InternVL

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2.7349e-03, 3.9927e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7373e-01, 1.3889e-02, 9.5460e-03, 4.3774e-06, 4.7059e-05, 4.4197e-05,
2.7349e-03, 3.9927e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0263, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9737, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many animals 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 6.6916e-10, 3.4302e-07, 1.1072e-11, 1.9950e-10, 5.3250e-09,
2.6869e-10, 2.0265e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.6916e-10, 3.4302e-07, 1.1072e-11, 1.9950e-10, 5.3250e-09,
2.6869e-10, 2.0265e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.6916e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many sled dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many sled dogs are in the image?'], responses:['six']
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)]
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.1399e-08, 1.8044e-09, 9.0942e-09, 4.3543e-10, 8.5253e-10,
1.9207e-09, 6.5360e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.1399e-08, 1.8044e-09, 9.0942e-09, 4.3543e-10, 8.5253e-10,
1.9207e-09, 6.5360e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.6160e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([0.0026, 0.0032, 0.0026, 0.0872, 0.4537, 0.0010, 0.3974, 0.0524],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
peach *************
['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([0.0026, 0.0032, 0.0026, 0.0872, 0.4537, 0.0010, 0.3974, 0.0524],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:27:21,352] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 09:27:21,352] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3783.81 | backward_microstep: 13897.11 | backward_inner_microstep: 3544.27 | backward_allreduce_microstep: 10352.72 | step_microstep: 7.87
[2024-10-24 09:27:21,352] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3783.81 | backward: 13897.10 | backward_inner: 3544.33 | backward_allreduce: 10352.68 | step: 7.89
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4514/4844 [18:46:05<1:18:31, 14.28s/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=RIGHT,question='Do some of the bottles have rounded tops in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many green glass bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are standing in the grass?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many partially open shades are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many green glass bottles 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
question: ['How many dogs are standing in the grass?'], 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
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
tensor([1.0000e+00, 8.5920e-07, 2.8447e-06, 2.1566e-07, 1.2691e-08, 1.7369e-07,
1.6218e-08, 3.2515e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.5920e-07, 2.8447e-06, 2.1566e-07, 1.2691e-08, 1.7369e-07,
1.6218e-08, 3.2515e-08], 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(4.1547e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
tensor([1.0000e+00, 1.1611e-07, 2.2159e-08, 2.1478e-08, 3.6311e-10, 6.1644e-10,
8.4918e-10, 6.4439e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.1611e-07, 2.2159e-08, 2.1478e-08, 3.6311e-10, 6.1644e-10,
8.4918e-10, 6.4439e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.6164e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')