Datasets:
TMarcus
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InternVL

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question: ['Is there a body of water in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
tensor([7.7044e-01, 2.0979e-02, 2.0654e-01, 1.0990e-03, 9.3732e-05, 2.0007e-04,
8.4193e-05, 5.7128e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7044e-01, 2.0979e-02, 2.0654e-01, 1.0990e-03, 9.3732e-05, 2.0007e-04,
8.4193e-05, 5.7128e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7704, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.2065, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0230, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([8.8419e-01, 1.4880e-02, 9.9130e-02, 1.0930e-03, 7.0560e-05, 2.3784e-04,
1.6477e-05, 3.8576e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8419e-01, 1.4880e-02, 9.9130e-02, 1.0930e-03, 7.0560e-05, 2.3784e-04,
1.6477e-05, 3.8576e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8842, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0991, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0167, device='cuda:1', grad_fn=<SubBackward0>)}
[2024-10-23 14:47:46,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-23 14:47:46,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3794.51 | backward_microstep: 10040.51 | backward_inner_microstep: 3523.82 | backward_allreduce_microstep: 6516.61 | step_microstep: 7.66
[2024-10-23 14:47:46,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3794.52 | backward: 10040.50 | backward_inner: 3523.84 | backward_allreduce: 6516.59 | step: 7.67
1%| | 26/4844 [06:30<18:02:24, 13.48s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is wine pouring into the glass?')
ANSWER1=EVAL(expr='{ANSWER0}')
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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many zipper pouches are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many parrots with a red head are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many zipper pouches are in the image?'], responses:['3']
question: ['How many parrots with a red head are in the image?'], responses:['0']
question: ['How many animals are in the image?'], responses:['2']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['Is wine pouring into the glass?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([0.3582, 0.2248, 0.0741, 0.0997, 0.0117, 0.1882, 0.0396, 0.0037],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3582, 0.2248, 0.0741, 0.0997, 0.0117, 0.1882, 0.0396, 0.0037],
device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.6975e-01, 2.7787e-03, 4.4752e-03, 1.0102e-03, 3.4250e-03, 7.2675e-04,
3.0511e-03, 1.4782e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.6975e-01, 2.7787e-03, 4.4752e-03, 1.0102e-03, 3.4250e-03, 7.2675e-04,
3.0511e-03, 1.4782e-02], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([4.7714e-01, 3.0807e-01, 1.0001e-01, 5.9384e-02, 4.1692e-02, 5.5235e-03,
7.9570e-03, 2.1500e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.7714e-01, 3.0807e-01, 1.0001e-01, 5.9384e-02, 4.1692e-02, 5.5235e-03,
7.9570e-03, 2.1500e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7377, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2623, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4771, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5229, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9698, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0302, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many keys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='What color are the vases?')
ANSWER1=EVAL(expr='{ANSWER0} == "silver"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)