backup wip
This commit is contained in:
Alexander Soare
parent
2b928eedd4
commit
65ef8c30d0
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@ -130,27 +130,87 @@ def make_offline_buffer(
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# (Pdb) stats['observation']['state']['mean']
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# (Pdb) stats['observation']['state']['mean']
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# tensor([-0.0071, -0.6293, 1.0351, -0.0517, -0.4642, -0.0754, 0.4751, -0.0373,
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# tensor([-0.0071, -0.6293, 1.0351, -0.0517, -0.4642, -0.0754, 0.4751, -0.0373,
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# -0.3324, 0.9034, -0.2258, -0.3127, -0.2412, 0.6866])
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# -0.3324, 0.9034, -0.2258, -0.3127, -0.2412, 0.6866])
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stats['observation', 'state', 'mean'] = torch.tensor([-0.00740268, -0.63187766, 1.0356655 , -0.05027218, -0.46199223,
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stats["observation", "state", "mean"] = torch.tensor(
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-0.07467502, 0.47467607, -0.03615446, -0.33203387, 0.9038929 ,
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[
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-0.22060776, -0.31011587, -0.23484458, 0.6842416 ])
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-0.00740268,
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-0.63187766,
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1.0356655,
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-0.05027218,
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-0.46199223,
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-0.07467502,
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0.47467607,
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-0.03615446,
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-0.33203387,
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0.9038929,
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-0.22060776,
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-0.31011587,
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-0.23484458,
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0.6842416,
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]
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)
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# (Pdb) stats['observation']['state']['std']
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# (Pdb) stats['observation']['state']['std']
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# tensor([0.0022, 0.0520, 0.0291, 0.0092, 0.0267, 0.0145, 0.0563, 0.0179, 0.0494,
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# tensor([0.0022, 0.0520, 0.0291, 0.0092, 0.0267, 0.0145, 0.0563, 0.0179, 0.0494,
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# 0.0326, 0.0476, 0.0535, 0.0956, 0.0513])
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# 0.0326, 0.0476, 0.0535, 0.0956, 0.0513])
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stats['observation', 'state', 'std'] = torch.tensor([0.01219023, 0.2975381 , 0.16728032, 0.04733803, 0.1486037 ,
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stats["observation", "state", "std"] = torch.tensor(
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0.08788499, 0.31752336, 0.1049916 , 0.27933604, 0.18094037,
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[
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0.26604933, 0.30466506, 0.5298686 , 0.25505227])
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0.01219023,
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0.2975381,
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0.16728032,
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0.04733803,
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0.1486037,
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0.08788499,
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0.31752336,
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0.1049916,
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0.27933604,
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0.18094037,
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0.26604933,
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0.30466506,
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0.5298686,
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0.25505227,
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]
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)
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# (Pdb) stats['action']['mean']
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# (Pdb) stats['action']['mean']
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# tensor([-0.0075, -0.6346, 1.0353, -0.0465, -0.4686, -0.0738, 0.3723, -0.0396,
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# tensor([-0.0075, -0.6346, 1.0353, -0.0465, -0.4686, -0.0738, 0.3723, -0.0396,
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# -0.3184, 0.8991, -0.2065, -0.3182, -0.2338, 0.5593])
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# -0.3184, 0.8991, -0.2065, -0.3182, -0.2338, 0.5593])
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stats['action']['mean'] = torch.tensor([-0.00756444, -0.6281845 , 1.0312834 , -0.04664314, -0.47211358,
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stats["action"]["mean"] = torch.tensor(
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-0.074527 , 0.37389806, -0.03718753, -0.3261143 , 0.8997205 ,
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[
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-0.21371077, -0.31840396, -0.23360962, 0.551947])
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-0.00756444,
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-0.6281845,
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1.0312834,
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-0.04664314,
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-0.47211358,
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-0.074527,
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0.37389806,
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-0.03718753,
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-0.3261143,
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0.8997205,
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-0.21371077,
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-0.31840396,
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-0.23360962,
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0.551947,
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]
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)
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# (Pdb) stats['action']['std']
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# (Pdb) stats['action']['std']
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# tensor([0.0023, 0.0514, 0.0290, 0.0086, 0.0263, 0.0143, 0.0593, 0.0185, 0.0510,
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# tensor([0.0023, 0.0514, 0.0290, 0.0086, 0.0263, 0.0143, 0.0593, 0.0185, 0.0510,
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# 0.0328, 0.0478, 0.0531, 0.0945, 0.0794])
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# 0.0328, 0.0478, 0.0531, 0.0945, 0.0794])
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stats['action']['std'] = torch.tensor([0.01252818, 0.2957442 , 0.16701928, 0.04584508, 0.14833844,
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stats["action"]["std"] = torch.tensor(
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0.08763024, 0.30665937, 0.10600077, 0.27572668, 0.1805853 ,
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[
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0.26304692, 0.30708534, 0.5305411 , 0.38381037])
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0.01252818,
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0.2957442,
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0.16701928,
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0.04584508,
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0.14833844,
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0.08763024,
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0.30665937,
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0.10600077,
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0.27572668,
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0.1805853,
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0.26304692,
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0.30708534,
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0.5305411,
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0.38381037,
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]
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)
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transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
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transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
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offline_buffer.set_transform(transforms)
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offline_buffer.set_transform(transforms)
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@ -2,7 +2,6 @@ import numpy as np
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import torch
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import torch
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from torch import nn
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from torch import nn
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from torch.autograd import Variable
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from torch.autograd import Variable
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from transformers import DetrForObjectDetection
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from .backbone import build_backbone
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from .backbone import build_backbone
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from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer
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from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer
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@ -134,7 +133,9 @@ class ActionChunkingTransformer(nn.Module):
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pos_embed = self.pos_table.clone().detach()
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pos_embed = self.pos_table.clone().detach()
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pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim)
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pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim)
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# query model
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# query model
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vae_encoder_output = self.vae_encoder(vae_encoder_input, pos=pos_embed) # , src_key_padding_mask=is_pad)
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vae_encoder_output = self.vae_encoder(
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vae_encoder_input, pos=pos_embed
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) # , src_key_padding_mask=is_pad)
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vae_encoder_output = vae_encoder_output[0] # take cls output only
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vae_encoder_output = vae_encoder_output[0] # take cls output only
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latent_info = self.latent_proj(vae_encoder_output)
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latent_info = self.latent_proj(vae_encoder_output)
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mu = latent_info[:, : self.latent_dim]
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mu = latent_info[:, : self.latent_dim]
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