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InternVL

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6.6821e-11, 6.0162e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0171e-08, 6.3222e-11, 5.0317e-08, 4.6747e-10, 2.5004e-10,
6.6821e-11, 6.0162e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(6.3222e-11, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1915e-07, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-24 09:58:03,865] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-24 09:58:03,866] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8985.40 | backward_microstep: 8724.87 | backward_inner_microstep: 8718.43 | backward_allreduce_microstep: 6.30 | step_microstep: 9.90
[2024-10-24 09:58:03,866] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8985.42 | backward: 8724.85 | backward_inner: 8718.50 | backward_allreduce: 6.29 | step: 9.92
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4636/4844 [19:16:47<57:32, 16.60s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are the golfballs in the image in shadow?')
ANSWER1=EVAL(expr='not {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
ANSWER0=VQA(image=LEFT,question='Are there chairs visible in front of the window?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many perfume containers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many pillows are stacked up in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([11, 3, 448, 448])
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the golfballs in the image in shadow?'], 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']]
question: ['How many pillows are stacked up in the image?'], responses:['4']
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.3356e-09, 1.0586e-07, 3.7166e-11, 5.8271e-11, 5.7780e-09,
2.2244e-10, 2.9344e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.3356e-09, 1.0586e-07, 3.7166e-11, 5.8271e-11, 5.7780e-09,
2.2244e-10, 2.9344e-07], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([9.9156e-01, 2.1674e-03, 6.2716e-03, 1.9547e-08, 1.2572e-06, 4.2216e-07,
2.0055e-07, 8.1315e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9156e-01, 2.1674e-03, 6.2716e-03, 1.9547e-08, 1.2572e-06, 4.2216e-07,
2.0055e-07, 8.1315e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.3356e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dog feet are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9916, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0084, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many canines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many dog feet are visible in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many canines are in the image?'], responses:['0']
question: ['Are there chairs visible in front of the window?'], responses:['yes']
[('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']]
[('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
tensor([9.9903e-01, 5.5889e-06, 1.3407e-05, 8.5580e-04, 7.4779e-05, 3.2841e-09,
2.1425e-05, 6.4697e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9903e-01, 5.5889e-06, 1.3407e-05, 8.5580e-04, 7.4779e-05, 3.2841e-09,
2.1425e-05, 6.4697e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9990, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0010, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many perfume containers are in the image?'], responses:['1']
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
[('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: 11, images per sample: 11.0, dynamic token length: 2886
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2889
tensor([1.0000e+00, 6.0187e-07, 1.1045e-08, 1.1993e-11, 6.4299e-07, 2.3962e-08,
9.5124e-08, 1.0998e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 6.0187e-07, 1.1045e-08, 1.1993e-11, 6.4299e-07, 2.3962e-08,
9.5124e-08, 1.0998e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2887
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2887