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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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53 lines
1.2 KiB
53 lines
1.2 KiB
import timm.models as tmm |
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import torchvision.models as tm |
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# input shape: (batch_size, 3, 224, 224) |
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tm_models = [ |
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tm.alexnet, |
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tm.convnext_base, |
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tm.densenet121, |
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# tm.efficientnet_v2_s, |
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# tm.googlenet, # output bad case |
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# tm.inception_v3, # bad case |
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tm.mobilenet_v2, |
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tm.mobilenet_v3_small, |
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tm.mnasnet0_5, |
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tm.resnet18, |
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tm.regnet_x_16gf, |
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tm.resnext50_32x4d, |
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tm.shufflenet_v2_x0_5, |
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tm.squeezenet1_0, |
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# tm.swin_s, # fx bad case |
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tm.vgg11, |
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tm.vit_b_16, |
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tm.wide_resnet50_2, |
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] |
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tmm_models = [ |
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tmm.beit_base_patch16_224, |
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tmm.beitv2_base_patch16_224, |
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tmm.cait_s24_224, |
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tmm.coat_lite_mini, |
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tmm.convit_base, |
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tmm.deit3_base_patch16_224, |
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tmm.dm_nfnet_f0, |
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tmm.eca_nfnet_l0, |
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tmm.efficientformer_l1, |
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# tmm.ese_vovnet19b_dw, |
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tmm.gmixer_12_224, |
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tmm.gmlp_b16_224, |
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# tmm.hardcorenas_a, |
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tmm.hrnet_w18_small, |
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tmm.inception_v3, |
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tmm.mixer_b16_224, |
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tmm.nf_ecaresnet101, |
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tmm.nf_regnet_b0, |
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# tmm.pit_b_224, # pretrained only |
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# tmm.regnetv_040, |
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# tmm.skresnet18, |
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# tmm.swin_base_patch4_window7_224, # fx bad case |
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# tmm.tnt_b_patch16_224, # bad case |
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tmm.vgg11, |
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tmm.vit_base_patch16_18x2_224, |
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tmm.wide_resnet50_2, |
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]
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