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https://github.com/huggingface/lerobot.git
synced 2026-05-16 00:59:46 +00:00
fixup! Fixup eval with real robot
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@@ -83,10 +83,17 @@ import os
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import random
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from dataclasses import dataclass, field
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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try:
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import matplotlib.pyplot as plt
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MATPLOTLIB_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_AVAILABLE = False
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plt = None
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from lerobot.configs import parser
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from lerobot.configs.default import DatasetConfig
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from lerobot.configs.policies import PreTrainedConfig
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@@ -114,6 +121,16 @@ def set_seed(seed: int):
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torch.backends.cudnn.benchmark = False
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def _check_matplotlib_available():
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"""Check if matplotlib is available, raise helpful error if not."""
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if not MATPLOTLIB_AVAILABLE:
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raise ImportError(
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"matplotlib is required for RTC debug visualizations. "
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"Please install it by running:\n"
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" uv pip install -e '.[matplotlib-dep]'"
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)
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@dataclass
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class RTCEvalConfig(HubMixin):
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"""Configuration for RTC evaluation."""
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@@ -686,6 +703,8 @@ class RTCEvaluator:
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no_rtc_actions: Final actions from non-RTC policy
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prev_chunk_left_over: Previous chunk used as ground truth
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"""
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_check_matplotlib_available()
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# Remove batch dimension if present
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rtc_actions_plot = rtc_actions.squeeze(0).cpu() if len(rtc_actions.shape) == 3 else rtc_actions.cpu()
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no_rtc_actions_plot = (
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@@ -776,6 +795,8 @@ class RTCEvaluator:
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plt.close(fig)
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def plot_tracked_data(self, rtc_tracked_steps, no_rtc_tracked_steps, prev_chunk_left_over, num_steps):
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_check_matplotlib_available()
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# Create side-by-side figures for denoising visualization
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fig_xt, axs_xt = self._create_figure("x_t Denoising: No RTC (left) vs RTC (right)")
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fig_vt, axs_vt = self._create_figure("v_t Denoising: No RTC (left) vs RTC (right)")
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@@ -573,7 +573,7 @@ def demo_cli(cfg: RTCDemoConfig):
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# The processor won't be created
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policy.init_rtc_processor()
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assert policy.name in ["smolvla"], "Only smolvla are supported for RTC"
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assert policy.name in ["smolvla", "pi05", "pi0"], "Only smolvla, pi05, and pi0 are supported for RTC"
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policy = policy.to(cfg.device)
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policy.eval()
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