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import torch.nn as nn
from torch.nn import functional as F
import torchvision.transforms as transforms
from equi_diffpo.model.detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer
import IPython
e = IPython.embed
class ACTPolicy(nn.Module):
def __init__(self, args_override):
super().__init__()
model, optimizer = build_ACT_model_and_optimizer(args_override)
self.model = model # CVAE decoder
self.optimizer = optimizer
self.kl_weight = args_override['kl_weight']
print(f'KL Weight {self.kl_weight}')
def __call__(self, qpos, image, actions=None, is_pad=None):
env_state = None
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
image = normalize(image)
if actions is not None: # training time
actions = actions[:, :self.model.num_queries]
is_pad = is_pad[:, :self.model.num_queries]
a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad)
total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar)
loss_dict = dict()
all_l1 = F.l1_loss(actions, a_hat, reduction='none')
l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean()
loss_dict['l1'] = l1
loss_dict['kl'] = total_kld[0]
loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight
return loss_dict
else: # inference time
a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior
return a_hat
def configure_optimizers(self):
return self.optimizer
class CNNMLPPolicy(nn.Module):
def __init__(self, args_override):
super().__init__()
model, optimizer = build_CNNMLP_model_and_optimizer(args_override)
self.model = model # decoder
self.optimizer = optimizer
def __call__(self, qpos, image, actions=None, is_pad=None):
env_state = None # TODO
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
image = normalize(image)
if actions is not None: # training time
actions = actions[:, 0]
a_hat = self.model(qpos, image, env_state, actions)
mse = F.mse_loss(actions, a_hat)
loss_dict = dict()
loss_dict['mse'] = mse
loss_dict['loss'] = loss_dict['mse']
return loss_dict
else: # inference time
a_hat = self.model(qpos, image, env_state) # no action, sample from prior
return a_hat
def configure_optimizers(self):
return self.optimizer
def kl_divergence(mu, logvar):
batch_size = mu.size(0)
assert batch_size != 0
if mu.data.ndimension() == 4:
mu = mu.view(mu.size(0), mu.size(1))
if logvar.data.ndimension() == 4:
logvar = logvar.view(logvar.size(0), logvar.size(1))
klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp())
total_kld = klds.sum(1).mean(0, True)
dimension_wise_kld = klds.mean(0)
mean_kld = klds.mean(1).mean(0, True)
return total_kld, dimension_wise_kld, mean_kld
from equi_diffpo.policy.base_image_policy import BaseImagePolicy
from equi_diffpo.model.common.normalizer import LinearNormalizer
from equi_diffpo.model.common.rotation_transformer import RotationTransformer
from equi_diffpo.common.pytorch_util import dict_apply
import torch
from typing import Dict, Tuple
import numpy as np
class ACTPolicyWrapper(BaseImagePolicy):
def __init__(self,
shape_meta: dict,
max_timesteps: int,
temporal_agg: bool,
n_envs: int,
horizon: int=10,
):
super().__init__()
action_dim = 10
lr = 5e-5
lr_backbone = 5e-5
chunk_size = horizon
kl_weight = 10
hidden_dim = 512
dim_feedforward = 3200
backbone = 'resnet18'
enc_layers = 4
dec_layers = 7
nheads = 8
policy_config = {'lr': lr,
'num_queries': chunk_size,
'kl_weight': kl_weight,
'hidden_dim': hidden_dim,
'dim_feedforward': dim_feedforward,
'lr_backbone': lr_backbone,
'backbone': backbone,
'enc_layers': enc_layers,
'dec_layers': dec_layers,
'nheads': nheads,
'camera_names': ['agentview_image', 'robot0_eye_in_hand_image'],
"weight_decay": 1e-4,
"dilation": False,
"position_embedding": "sine",
"dropout": 0.1,
"pre_norm": False,
"masks": False,
}
self.model = ACTPolicy(policy_config)
self.optimizer = self.model.configure_optimizers()
self.normalizer = LinearNormalizer()
self.quat_to_sixd = RotationTransformer('quaternion', 'rotation_6d')
self.num_queries = policy_config['num_queries']
self.query_frequency = 1
self.temporal_agg = temporal_agg
self.max_timesteps = max_timesteps
self.action_dim = action_dim
self.n_envs = n_envs
self.all_time_actions = torch.zeros([self.n_envs, self.max_timesteps, self.max_timesteps+self.num_queries, self.action_dim]).to(self.device)
self.t = 0
def set_normalizer(self, normalizer: LinearNormalizer):
self.normalizer.load_state_dict(normalizer.state_dict())
def to(self,*args,**kwargs):
device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
if device is not None:
self.model.device = device
super().to(*args,**kwargs)
def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
# nobs_dict = self.normalizer(obs_dict)
nobs_dict = dict_apply(obs_dict, lambda x: x[:,0,...])
# sixd = self.quat_to_sixd.forward(nobs_dict['robot0_eef_quat'])
qpos = torch.cat([nobs_dict['robot0_eef_pos'], nobs_dict['robot0_eef_quat'], nobs_dict['robot0_gripper_qpos']], dim=1)
image = torch.stack([nobs_dict['agentview_image'], nobs_dict['robot0_eye_in_hand_image']], dim=1)
if self.temporal_agg:
if self.t % self.query_frequency == 0:
all_actions = self.model(qpos, image)
self.all_actions = all_actions
else:
all_actions = self.all_actions
self.all_time_actions[:, self.t, self.t:self.t+self.num_queries] = all_actions
actions_for_curr_step = self.all_time_actions[:, :, self.t]
actions_populated = torch.all(actions_for_curr_step != 0, axis=2)
raw_actions = []
for i in range(self.n_envs):
populated_actions = actions_for_curr_step[i, actions_populated[i]]
k = 0.01
exp_weights = np.exp(-k * np.arange(len(populated_actions)))
exp_weights /= exp_weights.sum()
exp_weights = torch.from_numpy(exp_weights).to(self.device).unsqueeze(dim=1)
raw_action = (populated_actions * exp_weights).sum(dim=0, keepdim=True)
raw_actions.append(raw_action)
raw_action = torch.cat(raw_actions, dim=0)
# actions_for_curr_step = actions_for_curr_step[actions_populated].reshape(self.n_envs, -1, 10)
# # actions_for_curr_step2 = []
# # for i in range(2):
# # actions_for_curr_step2.append(actions_for_curr_step[i:i+1][actions_populated[i:i+1]])
# # actions_for_curr_step2 = torch.stack(actions_for_curr_step2, dim=0)
# k = 0.01
# exp_weights = np.exp(-k * np.arange(actions_for_curr_step.shape[1]))
# exp_weights = exp_weights / exp_weights.sum()
# exp_weights = torch.from_numpy(exp_weights).to(self.device).unsqueeze(dim=1)
# raw_action = (actions_for_curr_step * exp_weights).sum(dim=1)
action = self.normalizer['action'].unnormalize(raw_action)
# (B, Da)
result = {
'action': action[:,None,:] # (B, 1, Da)
}
else:
raw_action = self.model(qpos, image)
action = self.normalizer['action'].unnormalize(raw_action)
result = {
'action': action # (B, 1, Da)
}
self.t += 1
return result
def reset(self):
self.all_time_actions = torch.zeros([self.n_envs, self.max_timesteps, self.max_timesteps+self.num_queries, self.action_dim]).to(self.device)
self.t = 0
def compute_loss(self, batch):
# nobs_dict = self.normalizer.normalize(batch['obs'])
nobs_dict = batch['obs']
nactions = self.normalizer['action'].normalize(batch['action'])
nobs_dict = dict_apply(nobs_dict, lambda x: x[:,0,...])
# sixd = self.quat_to_sixd.forward(nobs_dict['robot0_eef_quat'])
qpos = torch.cat([nobs_dict['robot0_eef_pos'], nobs_dict['robot0_eef_quat'], nobs_dict['robot0_gripper_qpos']], dim=1)
image = torch.stack([nobs_dict['agentview_image'], nobs_dict['robot0_eye_in_hand_image']], dim=1)
forward_dict = self.model(qpos, image, nactions, torch.zeros([*nactions.shape[:2]]).bool().to(self.device))
return forward_dict['loss']
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