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ibc_dfo_lowdim_policy.py
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ibc_dfo_lowdim_policy.py
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from typing import Dict, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusion_policy.model.common.normalizer import LinearNormalizer
from diffusion_policy.policy.base_lowdim_policy import BaseLowdimPolicy
class IbcDfoLowdimPolicy(BaseLowdimPolicy):
def __init__(self,
horizon,
obs_dim,
action_dim,
n_action_steps,
n_obs_steps,
dropout=0.1,
train_n_neg=128,
pred_n_iter=5,
pred_n_samples=16384,
kevin_inference=False,
andy_train=False
):
super().__init__()
in_action_channels = action_dim * n_action_steps
in_obs_channels = obs_dim * n_obs_steps
in_channels = in_action_channels + in_obs_channels
mid_channels = 1024
out_channels = 1
self.dense0 = nn.Linear(in_features=in_channels, out_features=mid_channels)
self.drop0 = nn.Dropout(dropout)
self.dense1 = nn.Linear(in_features=mid_channels, out_features=mid_channels)
self.drop1 = nn.Dropout(dropout)
self.dense2 = nn.Linear(in_features=mid_channels, out_features=mid_channels)
self.drop2 = nn.Dropout(dropout)
self.dense3 = nn.Linear(in_features=mid_channels, out_features=mid_channels)
self.drop3 = nn.Dropout(dropout)
self.dense4 = nn.Linear(in_features=mid_channels, out_features=out_channels)
self.normalizer = LinearNormalizer()
self.train_n_neg = train_n_neg
self.pred_n_iter = pred_n_iter
self.pred_n_samples = pred_n_samples
self.obs_dim = obs_dim
self.action_dim = action_dim
self.n_obs_steps = n_obs_steps
self.n_action_steps = n_action_steps
self.horizon = horizon
self.kevin_inference = kevin_inference
self.andy_train = andy_train
def forward(self, obs, action):
B, N, Ta, Da = action.shape
B, To, Do = obs.shape
s = obs.reshape(B,1,-1).expand(-1,N,-1)
x = torch.cat([s, action.reshape(B,N,-1)], dim=-1).reshape(B*N,-1)
x = self.drop0(torch.relu(self.dense0(x)))
x = self.drop1(torch.relu(self.dense1(x)))
x = self.drop2(torch.relu(self.dense2(x)))
x = self.drop3(torch.relu(self.dense3(x)))
x = self.dense4(x)
x = x.reshape(B,N)
return x
# ========= inference ============
def predict_action(self, obs_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
obs_dict: must include "obs" key
result: must include "action" key
"""
assert 'obs' in obs_dict
assert 'past_action' not in obs_dict # not implemented yet
nobs = self.normalizer['obs'].normalize(obs_dict['obs'])
B, _, Do = nobs.shape
To = self.n_obs_steps
assert Do == self.obs_dim
T = self.horizon
Da = self.action_dim
Ta = self.n_action_steps
# only take necessary obs
this_obs = nobs[:,:To]
naction_stats = self.get_naction_stats()
# first sample
action_dist = torch.distributions.Uniform(
low=naction_stats['min'],
high=naction_stats['max']
)
samples = action_dist.sample((B, self.pred_n_samples, Ta)).to(
dtype=this_obs.dtype)
# (B, N, Ta, Da)
if self.kevin_inference:
# kevin's implementation
noise_scale = 3e-2
for i in range(self.pred_n_iter):
# Compute energies.
logits = self.forward(this_obs, samples)
probs = F.softmax(logits, dim=-1)
# Resample with replacement.
idxs = torch.multinomial(probs, self.pred_n_samples, replacement=True)
samples = samples[torch.arange(samples.size(0)).unsqueeze(-1), idxs]
# Add noise and clip to target bounds.
samples = samples + torch.randn_like(samples) * noise_scale
samples = samples.clamp(min=naction_stats['min'], max=naction_stats['max'])
# Return target with highest probability.
logits = self.forward(this_obs, samples)
probs = F.softmax(logits, dim=-1)
best_idxs = probs.argmax(dim=-1)
acts_n = samples[torch.arange(samples.size(0)), best_idxs, :]
else:
# andy's implementation
zero = torch.tensor(0, device=self.device)
resample_std = torch.tensor(3e-2, device=self.device)
for i in range(self.pred_n_iter):
# Forward pass.
logits = self.forward(this_obs, samples) # (B, N)
prob = torch.softmax(logits, dim=-1)
if i < (self.pred_n_iter - 1):
idxs = torch.multinomial(prob, self.pred_n_samples, replacement=True)
samples = samples[torch.arange(samples.size(0)).unsqueeze(-1), idxs]
samples += torch.normal(zero, resample_std, size=samples.shape, device=self.device)
# Return one sample per x in batch.
idxs = torch.multinomial(prob, num_samples=1, replacement=True)
acts_n = samples[torch.arange(samples.size(0)).unsqueeze(-1), idxs].squeeze(1)
action = self.normalizer['action'].unnormalize(acts_n)
result = {
'action': action
}
return result
# ========= training ============
def set_normalizer(self, normalizer: LinearNormalizer):
self.normalizer.load_state_dict(normalizer.state_dict())
def compute_loss(self, batch):
# normalize input
assert 'valid_mask' not in batch
nbatch = self.normalizer.normalize(batch)
nobs = nbatch['obs']
naction = nbatch['action']
# shapes
Do = self.obs_dim
Da = self.action_dim
To = self.n_obs_steps
Ta = self.n_action_steps
T = self.horizon
B = naction.shape[0]
this_obs = nobs[:,:To]
start = To - 1
end = start + Ta
this_action = naction[:,start:end]
# Small additive noise to true positives.
this_action += torch.normal(mean=0, std=1e-4,
size=this_action.shape,
dtype=this_action.dtype,
device=this_action.device)
# Sample negatives: (B, train_n_neg, Ta, Da)
naction_stats = self.get_naction_stats()
action_dist = torch.distributions.Uniform(
low=naction_stats['min'],
high=naction_stats['max']
)
samples = action_dist.sample((B, self.train_n_neg, Ta)).to(
dtype=this_action.dtype)
action_samples = torch.cat([
this_action.unsqueeze(1), samples], dim=1)
# (B, train_n_neg+1, Ta, Da)
if self.andy_train:
# Get onehot labels
labels = torch.zeros(action_samples.shape[:2],
dtype=this_action.dtype, device=this_action.device)
labels[:,0] = 1
logits = self.forward(this_obs, action_samples)
# (B, N)
logits = torch.log_softmax(logits, dim=-1)
loss = -torch.mean(torch.sum(logits * labels, axis=-1))
else:
labels = torch.zeros((B,),dtype=torch.int64, device=this_action.device)
# training
logits = self.forward(this_obs, action_samples)
loss = F.cross_entropy(logits, labels)
return loss
def get_naction_stats(self):
Da = self.action_dim
naction_stats = self.normalizer['action'].get_output_stats()
repeated_stats = dict()
for key, value in naction_stats.items():
assert len(value.shape) == 1
n_repeats = Da // value.shape[0]
assert value.shape[0] * n_repeats == Da
repeated_stats[key] = value.repeat(n_repeats)
return repeated_stats