Datasets:
TMarcus
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InternVL

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2.6573e-09, 2.7238e-09], 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(4.8364e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([4.7607e-07, 3.8050e-01, 2.2405e-02, 5.4033e-04, 5.9586e-01, 3.6370e-04,
9.9419e-05, 2.3275e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([4.7607e-07, 3.8050e-01, 2.2405e-02, 5.4033e-04, 5.9586e-01, 3.6370e-04,
9.9419e-05, 2.3275e-04], 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 09:28:23,226] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.24 | optimizer_step: 0.31
[2024-10-24 09:28:23,226] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4433.23 | backward_microstep: 7948.42 | backward_inner_microstep: 4222.19 | backward_allreduce_microstep: 3726.14 | step_microstep: 7.41
[2024-10-24 09:28:23,226] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4433.25 | backward: 7948.42 | backward_inner: 4222.22 | backward_allreduce: 3726.11 | step: 7.42
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4518/4844 [18:47:07<1:21:08, 14.93s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many canines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many people are standing outside the book shop?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='What is the position of the dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "side profile"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many bottles are in the image?'], responses:['δΈ‰']
question: ['What is the position of the dog in the image?'], responses:['st']
question: ['How many canines are in the image?'], responses:['11']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
[('m', 0.12581326269018167), ('santa', 0.1253264086837026), ('broom', 0.12500836712840702), ('hood', 0.12488960815771866), ('virgin', 0.12482165846042056), ('batter', 0.12480295794283948), ('brand', 0.12468266634423), ('rear', 0.1246550705925001)]
[['m', 'santa', 'broom', 'hood', 'virgin', 'batter', 'brand', 'rear']]
[('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']]
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
question: ['How many people are standing outside the book shop?'], responses:['0']
[('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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([2.9384e-04, 1.2781e-01, 8.1475e-02, 2.1566e-02, 3.4946e-01, 2.3542e-01,
1.0709e-04, 1.8387e-01], device='cuda:1', grad_fn=<SoftmaxBackward0>)
virgin *************
['m', 'santa', 'broom', 'hood', 'virgin', 'batter', 'brand', 'rear'] tensor([2.9384e-04, 1.2781e-01, 8.1475e-02, 2.1566e-02, 3.4946e-01, 2.3542e-01,
1.0709e-04, 1.8387e-01], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([3.1302e-05, 1.6862e-03, 2.0163e-01, 4.6179e-01, 2.5335e-01, 5.5000e-02,
1.8343e-02, 8.1639e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([3.1302e-05, 1.6862e-03, 2.0163e-01, 4.6179e-01, 2.5335e-01, 5.5000e-02,
1.8343e-02, 8.1639e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.0483e-01, 1.0733e-02, 7.7600e-04, 2.7393e-02, 1.2807e-03, 4.4305e-04,
5.4436e-02, 1.0500e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.0483e-01, 1.0733e-02, 7.7600e-04, 2.7393e-02, 1.2807e-03, 4.4305e-04,
5.4436e-02, 1.0500e-04], device='cuda:3', 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>)}
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many glass bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
question: ['How many 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([1.0000e+00, 1.9976e-09, 3.8067e-07, 5.8958e-07, 9.0545e-11, 5.8342e-08,
3.4634e-10, 1.1661e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.9976e-09, 3.8067e-07, 5.8958e-07, 9.0545e-11, 5.8342e-08,
3.4634e-10, 1.1661e-08], device='cuda:3', grad_fn=<SelectBackward0>)