File size: 4,566 Bytes
393d3de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
import utils
import hydra
import torch
import einops
import numpy as np
from workspaces import base
from accelerate import Accelerator
from utils import get_split_idx
OBS_ELEMENT_INDICES = {
"block_translation": np.array([0, 1]),
"block2_translation": np.array([2, 3]),
"effector_translation": np.array([4, 5]),
"target_translation": np.array([6, 7]),
"target2_translation": np.array([8, 9]),
}
accelerator = Accelerator()
def calc_state_dist(a, b):
result = {}
for k, v in OBS_ELEMENT_INDICES.items():
idx = torch.Tensor(v).long()
result[k] = ((a[idx] - b[idx]) ** 2).mean()
result["total"] = ((a - b) ** 2).mean()
return result
def mean_dicts(dicts):
result = {}
for k in dicts[0].keys():
result[k] = np.mean([x[k] for x in dicts])
return result
class BlockPushMultiviewWorkspace(base.Workspace):
def __init__(self, cfg, work_dir):
super().__init__(cfg, work_dir)
def _report_result_upon_completion(self, goal_idx=None):
return {
"entered": self.env.entered,
"moved": self.env.moved,
}
def run_offline_eval(self):
train_idx, val_idx = get_split_idx(
len(self.dataset),
self.cfg.seed,
train_fraction=self.cfg.train_fraction,
)
embeddings = utils.inference.embed_trajectory_dataset(
self.encoder, self.dataset
)
embeddings = [
einops.rearrange(x, "T V E -> T (V E)") for x in embeddings
] # flatten views
states = []
# linear probe on the block/target/EE translations for diagnostics
state_subset_idx = [0, 1, 3, 4, 6, 7, 10, 11, 13, 14]
if self.accelerator.is_main_process:
states = []
actions = []
for i in range(len(self.dataset)):
T = self.dataset.get_seq_length(i)
state = self.dataset.states[i, :T]
state = state[:, state_subset_idx]
states.append(state)
actions.append(self.dataset.actions[i, :T])
embd_state_linear_probe_results = (
utils.inference.linear_probe_with_trajectory_split(
embeddings,
states,
train_idx,
val_idx,
)
)
# add prefix to keys
embd_state_linear_probe_results = {
f"embd_state_{k}": v for k, v in embd_state_linear_probe_results.items()
}
embd_action_linear_probe_results = (
utils.inference.linear_probe_with_trajectory_split(
embeddings,
actions,
train_idx,
val_idx,
)
)
embd_action_linear_probe_results = {
f"embd_action_{k}": v
for k, v in embd_action_linear_probe_results.items()
}
state_dists = []
N = 200
rng = np.random.default_rng(self.cfg.seed)
for i in range(N):
query_traj_idx = rng.choice(len(self.dataset))
query_frame_idx = rng.choice(
range(10, self.dataset.get_seq_length(query_traj_idx))
)
query_embedding = embeddings[query_traj_idx][query_frame_idx]
query_frame_state = self.dataset.states[
query_traj_idx, query_frame_idx, state_subset_idx
]
pool_embeddings = torch.cat(
[x for i, x in enumerate(embeddings) if i != query_traj_idx]
)
pool_states = torch.cat(
[x for i, x in enumerate(states) if i != query_traj_idx]
)
_, nn_idx = utils.inference.batch_knn(
query_embedding.unsqueeze(0),
pool_embeddings,
metric=utils.inference.mse,
k=1,
batch_size=1,
)
closest_frame_state = pool_states[nn_idx[0, 0]]
state_dist = calc_state_dist(query_frame_state, closest_frame_state)
state_dists.append(state_dist)
mean_state_dist = mean_dicts(state_dists)
return {
**embd_state_linear_probe_results,
**embd_action_linear_probe_results,
**mean_state_dist,
}
else:
return None
|