Update Regnet model weights

[kazhang]

When I uploaded the RegNet model weights, I picked the last checkpoint rather than the checkpoints that generated best Acc@1. (for regnet_y_800mf and regnet_x_400mf, the best checkpoints are the last checkpoints, so they are not updated.)

In addition, regnet_y_1_6gf, regnet_y_3_2gf, regnet_y_16gf, regnet_y_32gf, regnet_x_1_6gf, regnet_x_3_2gf, regnet_x_16gf, regnet_x_32gf are also included. We have weights for all variants now.

Resolves #2655

cc @datumbox

[kazhang]

Tested pretrained weights on ImageNet.

updated weights

model	Acc@1	Paper Acc@1	Acc@5	command
regnet_y_400mf	74.046	74.1	91.716	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_400mf --batch-size 128 --test-only --pretrained
regnet_y_8gf	80.032	79.9	95.048	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_8gf --batch-size 64 --test-only --pretrained
regnet_x_800mf	75.212	75.2	92.348	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_800mf --batch-size 128 --test-only --pretrained
regnet_x_8gf	79.344	79.3	94.686	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_8gf --batch-size 64 --test-only --pretrained

new weights

model	Acc@1	Paper Acc@1	Acc@5	command
regnet_y_1_6gf	77.95	78	93.966	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_1_6gf --batch-size 128 --test-only --pretrained
regnet_y_3_2gf	78.948	79	94.576	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_3_2gf --batch-size 64 --test-only --pretrained
regnet_y_16gf	80.424	80.4	95.24	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_16gf --batch-size 32 --test-only --pretrained
regnet_y_32gf	80.878	81	95.34	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_y_32gf --batch-size 32 --test-only --pretrained
regnet_x_1_6gf	77.04	77	93.44	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_1_6gf --batch-size 128 --test-only --pretrained
regnet_x_3_2gf	78.364	78.3	93.992	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_3_2gf --batch-size 64 --test-only --pretrained
regnet_x_16gf	80.058	80	94.944	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_16gf --batch-size 64 --test-only --pretrained
regnet_x_32gf	80.622	80.5	95.248	srun -p $TRAIN_QUEUE --cpus-per-task=96 --gpus-per-node=8 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model regnet_x_32gf --batch-size 32 --test-only --pretrained

[datumbox]

Thanks @kazhang, looks good to me. Merge when ready.