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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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Encountered ExecuteError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[2024-10-22 17:04:08,902] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 5.16 | optimizer_gradients: 0.37 | optimizer_step: 0.33
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[2024-10-22 17:04:08,903] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2728.81 | backward_microstep: 2.75 | backward_inner_microstep: 1.25 | backward_allreduce_microstep: 1.41 | step_microstep: 2188.53
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[2024-10-22 17:04:08,903] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2728.78 | backward: 2.74 | backward_inner: 1.25 | backward_allreduce: 1.41 | step: 2188.53
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0%| | 1/26352 [00:18<135:17:43, 18.48s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Is there a black eared boar facing right with its snout facing forward left?')
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ANSWER1=VQA(image=RIGHT,question='Is there a black eared boar facing right with its snout facing forward left?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Are the penguins walking through the waves?')
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ANSWER1=VQA(image=RIGHT,question='Are the penguins walking through the waves?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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ANSWER0=VQA(image=LEFT,question='How many upright tubes of lipstick are in the image?')
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ANSWER1=VQA(image=RIGHT,question='How many upright tubes of lipstick are in the image?')
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ANSWER2=EVAL(expr='{ANSWER1} > {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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ANSWER0=VQA(image=LEFT,question='Is there a single purple headed crab crawling in the ground?')
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ANSWER1=VQA(image=RIGHT,question='Is there a single purple headed crab crawling in the ground?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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torch.Size([7, 3, 448, 448])
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torch.Size([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many upright tubes of lipstick are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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question: ['Is there a black eared boar facing right with its snout facing forward left?'], responses:['yes']
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B: 8
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question: ['Are the penguins walking through the waves?'], responses:['yes']
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question: ['Is there a single purple headed crab crawling in the ground?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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Encountered ExecuteError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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Encountered ExecuteError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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Encountered ExecuteError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
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ANSWER1=VQA(image=RIGHT,question='How many dogs are in the image?')
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ANSWER2=EVAL(expr='{ANSWER0} == 1 and {ANSWER1} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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ANSWER0=VQA(image=LEFT,question='Is there a feather in the image?')
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ANSWER1=VQA(image=RIGHT,question='Is there a feather in the image?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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ANSWER0=VQA(image=LEFT,question='How many ducks are in the water?')
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ANSWER1=VQA(image=RIGHT,question='How many ducks are in the water?')
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ANSWER2=EVAL(expr='{ANSWER0} + {ANSWER1} > 3')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there a feather in the image?'], responses:['no']
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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Encountered ExecuteError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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Encountered ExecuteError: shape '[8, 1351]' is invalid for input of size 1
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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question: ['How many dogs are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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B: 8
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dynamic ViT batch size: 7, images per sample: 0.875, dynamic token length: 1860
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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ANSWER0=VQA(image=LEFT,question='Are there dogs wearing colored socks in the image?')
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ANSWER1=VQA(image=RIGHT,question='Are there dogs wearing colored socks in the image?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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torch.Size([7, 3, 448, 448])
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[2024-10-22 17:07:45,217] torch.distributed.run: [WARNING]
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[2024-10-22 17:07:45,217] torch.distributed.run: [WARNING] *****************************************
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[2024-10-22 17:07:45,217] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
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[2024-10-22 17:07:45,217] torch.distributed.run: [WARNING] *****************************************
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