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If you are wondering where the data of this site comes from, please visit https://api.github.com/users/ShoufaChen/events. GitMemory does not store any data, but only uses NGINX to cache data for a period of time. The idea behind GitMemory is simply to give users a better reading experience.
Shoufa Chen ShoufaChen The University of Hong Kong Hong Kong https://www.shoufachen.com Ph.D. student, The University of Hong Kong

ShoufaChen/CycleMLP 151

Implementation of "CycleMLP: A MLP-like Architecture for Dense Prediction"

ShoufaChen/clone-anonymous4open 18

clone/download codes from https://anonymous.4open.science/

ShoufaChen/WOO 12

[ICCV'21] Implementation of "Watch Only Once: An End-to-End Video Action Detection Framework"

ShoufaChen/apex 0

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

ShoufaChen/awesome-mlp-papers 0

Recent Advances in MLP-based Models (MLP is all you need!)

ShoufaChen/FaceRecognition 0

Face Recognition Using Python and MySQL

ShoufaChen/fvcore 0

Collection of common code that's shared among different research projects in FAIR computer vision team.

ShoufaChen/gpu-burn 0

Multi-GPU CUDA stress test

issue closedShoufaChen/CycleMLP

About fine tuning

Hello. Is your implementation able to do fine tuning on classification of custom dataset?

If not, do you have the plan to provide one?

Thanks

closed time in 5 days

hyuan1991

issue closedShoufaChen/CycleMLP

1D

Hi Would it be possible to change the CycleMLP for 1D data instead of the 2D images? And would there be any benefit?

closed time in 5 days

aslarsen

startedifzhang/ByteTrack

started time in 6 days

PublicEvent

startedChongjianGE/CARE

started time in 7 days

issue commentShoufaChen/CycleMLP

question about the offset

Thanks for your interest and question.

You are right that the real implement is some what different with the figure in the arxiv version. However, they are equivalent as the channel order doesn't matter (ignore broad case).

We think it would be much better to correct the figure and have updated it as follows: stepsizesv2

Many thanks.

lartpang

comment created time in 7 days

issue closedShoufaChen/CycleMLP

question about the offset

Hi, thank you very much for your excellent work. In Fig.4 of your paper, you show the pseudo-kernel when kernel size is 1x3. But I when I find that function "gen_offset" does not generate the same offset as Fig.4. The offset it generates is "0,1,0,-1,0,0,0,1..." instead of "0,1,0,-1,0,1,0,-1', which is shown in Fig.4. So could you please tell me the reason? image image

closed time in 19 days

linjing7

issue commentrwightman/pytorch-image-models

use APEX DistributedDataParallel if `use_amp` is None

Hi, @rwightman

Thanks for your reply. I pull a request https://github.com/rwightman/pytorch-image-models/pull/882 for this issue.

ShoufaChen

comment created time in 24 days

PR opened rwightman/pytorch-image-models

fix `use_amp`

Fix https://github.com/rwightman/pytorch-image-models/issues/881

+2 -2

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push eventShoufaChen/pytorch-image-models

Shoufa Chen

commit sha 908563d060d3c7f2e46583e0e431ab5331f7e558

fix `use_amp` Fix https://github.com/rwightman/pytorch-image-models/issues/881

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fork ShoufaChen/pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

https://rwightman.github.io/pytorch-image-models/

fork in 24 days

issue openedrwightman/pytorch-image-models

use APEX DistributedDataParallel if `use_amp` is None

https://github.com/rwightman/pytorch-image-models/blob/3d9c23af879283e80c2c208786d5613351ca040b/train.py#L454

Hi, I found that if we don't activate amp but the apex is installed in our env, it will choose the ApexDDP by default.

I was wondering is it a best choice or did I miss something?

Thanks in advance.

created time in 24 days

issue commentashkamath/mdetr

CUDA error: out of memory when synchronize between processes on refexp at evaluation stage

Hi, @alcinos

I found a related bug and pull a request https://github.com/ashkamath/mdetr/pull/42.

Please have a check. Thanks.

ShoufaChen

comment created time in a month

PR opened ashkamath/mdetr

fix: reduce gpu memory https://github.com/ashkamath/mdetr/issues/41

Related to https://github.com/ashkamath/mdetr/issues/41.

When pretraining with MDETR_CPU_REDUCE=1, GPU memory before and after torch.load are:

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A   2390563      C   ...da3/envs/mdetr/bin/python     9333MiB |
|    1   N/A  N/A   2390564      C   ...da3/envs/mdetr/bin/python     9031MiB |
|    2   N/A  N/A   2390565      C   ...da3/envs/mdetr/bin/python     8651MiB |
|    3   N/A  N/A   2390566      C   ...da3/envs/mdetr/bin/python     9857MiB |
+-----------------------------------------------------------------------------+

and

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A   2337080      C   ...da3/envs/mdetr/bin/python    13587MiB |
|    0   N/A  N/A   2337081      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    0   N/A  N/A   2337082      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    0   N/A  N/A   2337083      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    1   N/A  N/A   2337080      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    1   N/A  N/A   2337081      C   ...da3/envs/mdetr/bin/python    13301MiB |
|    1   N/A  N/A   2337082      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    1   N/A  N/A   2337083      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    2   N/A  N/A   2337080      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    2   N/A  N/A   2337081      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    2   N/A  N/A   2337082      C   ...da3/envs/mdetr/bin/python    11397MiB |
|    2   N/A  N/A   2337083      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    3   N/A  N/A   2337080      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    3   N/A  N/A   2337081      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    3   N/A  N/A   2337082      C   ...da3/envs/mdetr/bin/python     1103MiB |
|    3   N/A  N/A   2337083      C   ...da3/envs/mdetr/bin/python    13251MiB |
+-----------------------------------------------------------------------------+

Use map_location=device solves this issue.

+1 -1

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push eventShoufaChen/mdetr-1

Shoufa Chen

commit sha 3d9e40891ffdd39d6a5bf56730d468ace142752f

fix: reduce gpu memory

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issue commentashkamath/mdetr

CUDA error: out of memory when synchronize between processes on refexp at evaluation stage

Hi, @alcinos

Thanks for your reply.

As mentioned above, I have set CUBLAS_WORKSPACE_CONFIG=:4096:8 MDETR_CPU_REDUCE=1.

ShoufaChen

comment created time in a month

issue commentashkamath/mdetr

CUDA error: out of memory when synchronize between processes on refexp at evaluation stage

Kindly ping @alcinos @ashkamath

I found a similar issue https://github.com/ashkamath/mdetr/issues/40.

Any help would be much appriciated. Please let me know if you need further information.

ShoufaChen

comment created time in a month

issue openedashkamath/mdetr

CUDA error: out of memory when synchronize between processes on refexp at evaluation stage

Hi,

Thanks for your great work.

I met the OOM error at the evaluation stage after 1 epoch training. The log is

Test: Total time: 0:01:42 (0.2476 s / it)
Averaged stats: loss: 113.5333 (96.1866)  loss_bbox: 0.5625 (0.5173)  loss_bbox_0: 0.6505 (0.5977)  loss_bbox_1: 0.5762 (0.5255)  loss_bbox_2: 0.5703 (0.5273)  loss_bbox_3: 0.5712 (0.5109)  loss_bbox_4: 0.5651 (0.5138)  loss_ce: 11.5826 (9.1250)  loss_ce_0: 11.4480 (9.4460)  loss_ce_1: 11.7980 (9.5058)  loss_ce_2: 11.8104 (9.4749)  loss_ce_3: 11.6550 (9.2512)  loss_ce_4: 11.5774 (9.0949)  loss_contrastive_align: 6.1482 (5.6187)  loss_contrastive_align_0: 6.1950 (5.8909)  loss_contrastive_align_1: 6.1946 (5.7864)  loss_contrastive_align_2: 6.1133 (5.7674)  loss_contrastive_align_3: 6.1261 (5.6713)  loss_contrastive_align_4: 6.0199 (5.5644)  loss_giou: 0.4890 (0.4578)  loss_giou_0: 0.5642 (0.5090)  loss_giou_1: 0.5024 (0.4579)  loss_giou_2: 0.4965 (0.4619)  loss_giou_3: 0.5086 (0.4525)  loss_giou_4: 0.4900 (0.4579)  cardinality_error_unscaled: 8.3906 (4.8554)  cardinality_error_0_unscaled: 6.5000 (4.3573)  cardinality_error_1_unscaled: 9.4062 (5.9682)  cardinality_error_2_unscaled: 10.3125 (6.3725)  cardinality_error_3_unscaled: 9.2969 (5.2416)  cardinality_error_4_unscaled: 8.8281 (5.0047)  loss_bbox_unscaled: 0.1125 (0.1035)  loss_bbox_0_unscaled: 0.1301 (0.1195)  loss_bbox_1_unscaled: 0.1152 (0.1051)  loss_bbox_2_unscaled: 0.1141 (0.1055)  loss_bbox_3_unscaled: 0.1142 (0.1022)  loss_bbox_4_unscaled: 0.1130 (0.1028)  loss_ce_unscaled: 11.5826 (9.1250)  loss_ce_0_unscaled: 11.4480 (9.4460)  loss_ce_1_unscaled: 11.7980 (9.5058)  loss_ce_2_unscaled: 11.8104 (9.4749)  loss_ce_3_unscaled: 11.6550 (9.2512)  loss_ce_4_unscaled: 11.5774 (9.0949)  loss_contrastive_align_unscaled: 6.1482 (5.6187)  loss_contrastive_align_0_unscaled: 6.1950 (5.8909)  loss_contrastive_align_1_unscaled: 6.1946 (5.7864)  loss_contrastive_align_2_unscaled: 6.1133 (5.7674)  loss_contrastive_align_3_unscaled: 6.1261 (5.6713)  loss_contrastive_align_4_unscaled: 6.0199 (5.5644)  loss_giou_unscaled: 0.2445 (0.2289)  loss_giou_0_unscaled: 0.2821 (0.2545)  loss_giou_1_unscaled: 0.2512 (0.2289)  loss_giou_2_unscaled: 0.2483 (0.2309)  loss_giou_3_unscaled: 0.2543 (0.2263)  loss_giou_4_unscaled: 0.2450 (0.2289)
gathering on cpu
gathering on cpu
gathering on cpu
Traceback (most recent call last):
  File \"main.py\", line 655, in <module>
    main(args)
  File \"main.py\", line 598, in main
    curr_test_stats = evaluate(
  File \"/usr/local/lib/python3.8/site-packages/torch/autograd/grad_mode.py\", line 26, in decorate_context
    return func(*args, **kwargs)
  File \"/worksapce/mdetr/trainer/engine.py\", line 230, in evaluate
    evaluator.synchronize_between_processes()
  File \"/worksapce/mdetr/trainer/datasets/refexp.py\", line 38, in synchronize_between_processes
    all_predictions = dist.all_gather(self.predictions)
  File \"/worksapce/mdetr/trainer/util/dist.py\", line 86, in all_gather
    obj = torch.load(buffer)
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 594, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 853, in _load
    result = unpickler.load()
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 845, in persistent_load
    load_tensor(data_type, size, key, _maybe_decode_ascii(location))
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 834, in load_tensor
    loaded_storages[key] = restore_location(storage, location)
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 175, in default_restore_location
    result = fn(storage, location)
  File \"/usr/local/lib/python3.8/site-packages/torch/serialization.py\", line 157, in _cuda_deserialize
    return obj.cuda(device)
  File \"/usr/local/lib/python3.8/site-packages/torch/_utils.py\", line 79, in _cuda
    return new_type(self.size()).copy_(self, non_blocking)
  File \"/usr/local/lib/python3.8/site-packages/torch/cuda/__init__.py\", line 462, in _lazy_new
    return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
RuntimeError: CUDA error: out of memory

I use 32G V100 GPUs, with 2 samples per GPU following default settings. I also set CUBLAS_WORKSPACE_CONFIG=:4096:8 MDETR_CPU_REDUCE=1.

created time in a month

issue commentashkamath/mdetr

Issues about flickr30k images dataset

I met the same issue, too but I didn't seperate the flickr-images dir to 'train', 'val'.

I just softlink them by:

ln -s flickr-images train
ln -s flickr-images val

I think it is ok as the dataset is split by train.txt, val.txt.

However, I am not sure about it. Maybe @ashkamath can help us with this issue. Is this softlink ok?

Thanks.

1338199

comment created time in a month

issue commentpytorch/pytorch

add torch.unravel_index

Hi, @francois-rozet

Thanks for your great work.

I found the this implementation with pytorch has a different behavior with numpy in some case:

    import jax.numpy as jnp
    import numpy as np

    indices = list(range(15))
    shape = (1920, )

    out_jax = jnp.unravel_index(indices, shape)
    out_np = np.unravel_index(indices, shape)
    out_torch = unravel_index(torch.Tensor(indices), shape)

    print(out_jax)
    print(out_np)
    print(out_torch)

Results:

(DeviceArray([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14], dtype=int32),)
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),)
(tensor([0.]), tensor([1.]), tensor([2.]), tensor([3.]), tensor([4.]), tensor([5.]), tensor([6.]), tensor([7.]), tensor([8.]), tensor([9.]), tensor([10.]), tensor([11.]), tensor([12.]), tensor([13.]), tensor([14.]))

A workaround which is not very elegant here:

    coord = []

    for dim in reversed(shape):
        coord.append(indices % dim)
        indices = indices // dim

    if len(coord) == 1 and coord[0].dim() == 1:
        return coord
    coord = torch.stack(coord[::-1], dim=-1)

    return coord

Would you mind helping to check it?

Thanks.

KiaraGrouwstra

comment created time in a month

issue closeddeepmind/deepmind-research

Perceiver IO Training Script on Language

Hi,

Similar to https://github.com/deepmind/deepmind-research/issues/281,

do you have any plan to relase the training script for language model?

Thanks in advance.

closed time in a month

ShoufaChen

issue commentdeepmind/deepmind-research

Perceiver IO Training Script on Language

Thanks all the way for your reply.

ShoufaChen

comment created time in a month

issue commentShoufaChen/CycleMLP

兄弟,semantic segmentation 还更新吗?等了两个月了

Hi,

Thanks for your interest.

The segmentation code will possibily not be available within a month because of many courseworks and some deadlines in Setptember.

If you are eager to use, you can simply follow pvt repo. Just change the model from PVT to CycleMLP. Nothing else is needed to modify.

Feel free to post your issue if you need further help.

LaiHei

comment created time in a month

issue openedrwightman/pytorch-image-models

[BUG] Checkpoint Mismatch for ResMLP

Hi,

Thanks for your great work.

I found the checkpoint mismatch problem when loading resmlp-12 model,

Missing key(s) in state_dict: "stem.proj.weight", "stem.proj.bias", "blocks.0.ls1", "blocks.0.ls2", "blocks.0.linear_tokens.weight", "blocks.0.linear_tokens.bias", "blocks.0.mlp_channe
ls.fc1.weight", "blocks.0.mlp_channels.fc1.bias", "blocks.0.mlp_channels.fc2.weight", "blocks.0.mlp_channels.fc2.bias", "blocks.1.ls1", "blocks.1.ls2", "blocks.1.linear_tokens.weight", "blocks
.1.linear_tokens.bias", "blocks.1.mlp_channels.fc1.weight", "blocks.1.mlp_channels.fc1.bias", "blocks.1.mlp_channels.fc2.weight", "blocks.1.mlp_channels.fc2.bias", "blocks.2.ls1", "blocks.2.ls
2", "blocks.2.linear_tokens.weight", "blocks.2.linear_tokens.bias", "blocks.2.mlp_channels.fc1.weight", "blocks.2.mlp_channels.fc1.bias", "blocks.2.mlp_channels.fc2.weight", "blocks.2.mlp_chan
nels.fc2.bias", "blocks.3.ls1", "blocks.3.ls2", "blocks.3.linear_tokens.weight", "blocks.3.linear_tokens.bias", "blocks.3.mlp_channels.fc1.weight", "blocks.3.mlp_channels.fc1.bias", "blocks.3.
mlp_channels.fc2.weight", "blocks.3.mlp_channels.fc2.bias", "blocks.4.ls1", "blocks.4.ls2", "blocks.4.linear_tokens.weight", "blocks.4.linear_tokens.bias", "blocks.4.mlp_channels.fc1.weight", 
"blocks.4.mlp_channels.fc1.bias", "blocks.4.mlp_channels.fc2.weight", "blocks.4.mlp_channels.fc2.bias", "blocks.5.ls1", "blocks.5.ls2", "blocks.5.linear_tokens.weight", "blocks.5.linear_tokens
.bias", "blocks.5.mlp_channels.fc1.weight", "blocks.5.mlp_channels.fc1.bias", "blocks.5.mlp_channels.fc2.weight", "blocks.5.mlp_channels.fc2.bias", "blocks.6.ls1", "blocks.6.ls2", "blocks.6.li
near_tokens.weight", "blocks.6.linear_tokens.bias", "blocks.6.mlp_channels.fc1.weight", "blocks.6.mlp_channels.fc1.bias", "blocks.6.mlp_channels.fc2.weight", "blocks.6.mlp_channels.fc2.bias", 
"blocks.7.ls1", "blocks.7.ls2", "blocks.7.linear_tokens.weight", "blocks.7.linear_tokens.bias", "blocks.7.mlp_channels.fc1.weight", "blocks.7.mlp_channels.fc1.bias", "blocks.7.mlp_channels.fc2
.weight", "blocks.7.mlp_channels.fc2.bias", "blocks.8.ls1", "blocks.8.ls2", "blocks.8.linear_tokens.weight", "blocks.8.linear_tokens.bias", "blocks.8.mlp_channels.fc1.weight", "blocks.8.mlp_ch
annels.fc1.bias", "blocks.8.mlp_channels.fc2.weight", "blocks.8.mlp_channels.fc2.bias", "blocks.9.ls1", "blocks.9.ls2", "blocks.9.linear_tokens.weight", "blocks.9.linear_tokens.bias", "blocks.
9.mlp_channels.fc1.weight", "blocks.9.mlp_channels.fc1.bias", "blocks.9.mlp_channels.fc2.weight", "blocks.9.mlp_channels.fc2.bias", "blocks.10.ls1", "blocks.10.ls2", "blocks.10.linear_tokens.w
eight", "blocks.10.linear_tokens.bias", "blocks.10.mlp_channels.fc1.weight", "blocks.10.mlp_channels.fc1.bias", "blocks.10.mlp_channels.fc2.weight", "blocks.10.mlp_channels.fc2.bias", "blocks.
11.ls1", "blocks.11.ls2", "blocks.11.linear_tokens.weight", "blocks.11.linear_tokens.bias", "blocks.11.mlp_channels.fc1.weight", "blocks.11.mlp_channels.fc1.bias", "blocks.11.mlp_channels.fc2.
weight", "blocks.11.mlp_channels.fc2.bias".                                                                                                                                                     
        Unexpected key(s) in state_dict: "patch_embed.proj.weight", "patch_embed.proj.bias", "blocks.0.gamma_1", "blocks.0.gamma_2", "blocks.0.attn.weight", "blocks.0.attn.bias", "blocks.0.mlp
.fc1.weight", "blocks.0.mlp.fc1.bias", "blocks.0.mlp.fc2.weight", "blocks.0.mlp.fc2.bias", "blocks.1.gamma_1", "blocks.1.gamma_2", "blocks.1.attn.weight", "blocks.1.attn.bias", "blocks.1.mlp.f
c1.weight", "blocks.1.mlp.fc1.bias", "blocks.1.mlp.fc2.weight", "blocks.1.mlp.fc2.bias", "blocks.2.gamma_1", "blocks.2.gamma_2", "blocks.2.attn.weight", "blocks.2.attn.bias", "blocks.2.mlp.fc1
.weight", "blocks.2.mlp.fc1.bias", "blocks.2.mlp.fc2.weight", "blocks.2.mlp.fc2.bias", "blocks.3.gamma_1", "blocks.3.gamma_2", "blocks.3.attn.weight", "blocks.3.attn.bias", "blocks.3.mlp.fc1.w
eight", "blocks.3.mlp.fc1.bias", "blocks.3.mlp.fc2.weight", "blocks.3.mlp.fc2.bias", "blocks.4.gamma_1", "blocks.4.gamma_2", "blocks.4.attn.weight", "blocks.4.attn.bias", "blocks.4.mlp.fc1.wei
ght", "blocks.4.mlp.fc1.bias", "blocks.4.mlp.fc2.weight", "blocks.4.mlp.fc2.bias", "blocks.5.gamma_1", "blocks.5.gamma_2", "blocks.5.attn.weight", "blocks.5.attn.bias", "blocks.5.mlp.fc1.weigh
t", "blocks.5.mlp.fc1.bias", "blocks.5.mlp.fc2.weight", "blocks.5.mlp.fc2.bias", "blocks.6.gamma_1", "blocks.6.gamma_2", "blocks.6.attn.weight", "blocks.6.attn.bias", "blocks.6.mlp.fc1.weight"
, "blocks.6.mlp.fc1.bias", "blocks.6.mlp.fc2.weight", "blocks.6.mlp.fc2.bias", "blocks.7.gamma_1", "blocks.7.gamma_2", "blocks.7.attn.weight", "blocks.7.attn.bias", "blocks.7.mlp.fc1.weight", 
"blocks.7.mlp.fc1.bias", "blocks.7.mlp.fc2.weight", "blocks.7.mlp.fc2.bias", "blocks.8.gamma_1", "blocks.8.gamma_2", "blocks.8.attn.weight", "blocks.8.attn.bias", "blocks.8.mlp.fc1.weight", "b
locks.8.mlp.fc1.bias", "blocks.8.mlp.fc2.weight", "blocks.8.mlp.fc2.bias", "blocks.9.gamma_1", "blocks.9.gamma_2", "blocks.9.attn.weight", "blocks.9.attn.bias", "blocks.9.mlp.fc1.weight", "blo
cks.9.mlp.fc1.bias", "blocks.9.mlp.fc2.weight", "blocks.9.mlp.fc2.bias", "blocks.10.gamma_1", "blocks.10.gamma_2", "blocks.10.attn.weight", "blocks.10.attn.bias", "blocks.10.mlp.fc1.weight", "
blocks.10.mlp.fc1.bias", "blocks.10.mlp.fc2.weight", "blocks.10.mlp.fc2.bias", "blocks.11.gamma_1", "blocks.11.gamma_2", "blocks.11.attn.weight", "blocks.11.attn.bias", "blocks.11.mlp.fc1.weig
ht", "blocks.11.mlp.fc1.bias", "blocks.11.mlp.fc2.weight", "blocks.11.mlp.fc2.bias". 

created time in a month

issue openeddeepmind/deepmind-research

Perceiver IO Training Script on Language

Hi,

Similar to https://github.com/deepmind/deepmind-research/issues/281,

do you have any plan to relase the training script for language model?

Thanks in advance.

created time in a month

issue openedXiaodongsuper/M5Product_dataset

Code Relasing date

Hi,

Thanks for your awesome work!

I noticed that the Data Toolkits will be open very soon in the webpage.

I was wondering would you mind sharing a more specific date? In a week or a month?

Thanks in advance.

created time in a month

issue commentShoufaChen/CycleMLP

some questions about the code

Closed as no response for a long time. Feel free to reopen or open a new issue if you have further questions.

qdd1234

comment created time in 2 months