Datasets:
TMarcus
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InternVL

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4.9908e-07, 2.8762e-02], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 6.8206e-09, 2.7895e-10, 4.3513e-09, 8.0736e-12, 9.0561e-11,
2.4014e-11, 1.4875e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.8206e-09, 2.7895e-10, 4.3513e-09, 8.0736e-12, 9.0561e-11,
2.4014e-11, 1.4875e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.7895e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.7895e-10, device='cuda:1', grad_fn=<DivBackward0>)}
{True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.2033e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a round canopy bed in the image?')
ANSWER1=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Are the doors open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
tensor([8.3563e-01, 3.6576e-02, 3.4583e-02, 8.7063e-03, 7.3261e-02, 6.0849e-05,
9.8503e-03, 1.3328e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
20 *************
['20', '21', '22', '26', '30', '48', '27', '28'] tensor([8.3563e-01, 3.6576e-02, 3.4583e-02, 8.7063e-03, 7.3261e-02, 6.0849e-05,
9.8503e-03, 1.3328e-03], 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(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['Are the doors open?'], 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are in the image?'], responses:['2']
question: ['Is there a round canopy bed in the image?'], responses:['yes']
[('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']]
[('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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 9.5628e-09, 2.5907e-08, 1.7462e-08, 1.1997e-10, 4.5990e-10,
1.1694e-10, 2.2665e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.5628e-09, 2.5907e-08, 1.7462e-08, 1.1997e-10, 4.5990e-10,
1.1694e-10, 2.2665e-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(2.5907e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.5907e-08, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 1.4275e-10, 3.3577e-11, 6.5740e-11, 3.7457e-11, 5.2621e-09,
2.6466e-09, 6.4669e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4275e-10, 3.3577e-11, 6.5740e-11, 3.7457e-11, 5.2621e-09,
2.6466e-09, 6.4669e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.6466e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.6537e-06, 1.8371e-08, 1.2099e-06, 3.7099e-10, 2.0811e-10,
1.1929e-09, 2.3984e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.6537e-06, 1.8371e-08, 1.2099e-06, 3.7099e-10, 2.0811e-10,
1.1929e-09, 2.3984e-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(2.8837e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.3139e-09, 1.1997e-10, 1.8544e-08, 4.5243e-10, 3.9560e-11,
6.2570e-11, 1.1393e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3139e-09, 1.1997e-10, 1.8544e-08, 4.5243e-10, 3.9560e-11,
6.2570e-11, 1.1393e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.1997e-10, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.1997e-10, device='cuda:2', grad_fn=<SubBackward0>)}
[2024-10-24 09:53:52,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 09:53:52,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5890.25 | backward_microstep: 6640.94 | backward_inner_microstep: 5461.14 | backward_allreduce_microstep: 1179.73 | step_microstep: 7.92
[2024-10-24 09:53:52,068] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5890.25 | backward: 6640.93 | backward_inner: 5461.16 | backward_allreduce: 1179.72 | step: 7.93
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4619/4844 [19:12:35<53:35, 14.29s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='What color is the plate the food is in?')
ANSWER1=EVAL(expr='{ANSWER0} == "brown"')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many bottles are depicted in the artwork?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many skin products are standing upright on the counter?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many people are in the car?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([3, 3, 448, 448])