fix: rewrote info logger calls to debug calls for debug related logs
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4c5122e779
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517c3134ca
2 changed files with 10 additions and 8 deletions
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@ -128,18 +128,18 @@ def create_obs_processor(
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for k, v in obs.items():
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if hasattr(v, "shape"):
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size = v.size
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logger11.info(f"[RAW] {k}: shape={v.shape}, size={size}")
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logger11.debug(f"[RAW] {k}: shape={v.shape}, size={size}")
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total += size
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else:
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logger11.info(f"[RAW] {k}: non-array")
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logger11.debug(f"[RAW] {k}: non-array")
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logger11.info(f"[RAW TOTAL FEATURES]: {total}")
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logger11.debug(f"[RAW TOTAL FEATURES]: {total}")
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output = {}
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num_agents = needed_copies # IMPORTANT: number of MLPs
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for key, arr in obs.items():
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if key not in ordered_keys or arr.size == 0:
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continue
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logger11.info(f"[INPUT] {key}: {arr.shape}")
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logger11.debug(f"[INPUT] {key}: {arr.shape}")
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if arr.ndim == 0:
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arr = arr.reshape(1)
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# -------- CENTRALIZED --------
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@ -154,7 +154,7 @@ def create_obs_processor(
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for i, agent_id in enumerate(agent_indices):
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idx = segment_indices[i]
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taken = jnp.take(arr, idx, axis=0) # (segs, ...)
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logger11.info(f"WHY {taken.shape}")
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logger11.debug(f"WHY {taken.shape}")
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# pad to 4
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pad_len = 4 - taken.shape[0]
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padded = jnp.pad(taken, [(0, pad_len)] + [(0, 0)] * (taken.ndim - 1))
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@ -182,7 +182,7 @@ def create_obs_processor(
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else:
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out = jnp.repeat(arr[None, :], num_agents, axis=0)
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logger11.info(f"[OUTPUT] {key}: {out.shape}")
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logger11.debug(f"[OUTPUT] {key}: {out.shape}")
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output[key] = out
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return output
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@ -220,8 +220,8 @@ def create_obs_processor(
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processed = _normalize_features(processed)
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processed = _split_to_agents(processed, morph_mode)
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flat = _flatten_features(processed) # (num_arms, total_feat)
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logger11.info(f"[FLATTENED FINAL] shape: {flat.shape}")
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logger11.info(f"[PER AGENT] example row 0 shape: {flat[0].shape}")
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logger11.debug(f"[FLATTENED FINAL] shape: {flat.shape}")
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logger11.debug(f"[PER AGENT] example row 0 shape: {flat[0].shape}")
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return _flatten_features(processed) # (agents, feat)
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return jax.jit(jax.vmap(_process_single))
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@ -119,6 +119,8 @@ def get_action_and_value(
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):
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hidden_sensor = sensor_apply(params["sensor_params"], x)
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hidden_critic = feature_extractor_apply(params["feature_extractor_params"], x)
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# only apply message passing in decentralized context
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if message_passer is not None:
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hidden_sensor = message_passer(params["message_passer_params"], hidden_sensor)
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