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more eval fixes

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This commit is contained in:
Jade Choghari
2025-11-16 11:22:05 +01:00
parent cde2e24d79
commit 589788e760
2 changed files with 51 additions and 40 deletions
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src/lerobot/scripts/lerobot_eval.py
+49 -39
View File
@@ -45,7 +45,17 @@ Note that in both examples, the repo/folder should contain at least `config.json
You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py
"""
import sys
import os
# ABSOLUTE PATH TO YOUR PROJECT ROOT
PROJECT_ROOT = "/home/jade_choghari/robot/lerobot"
# Add root to sys.path BEFORE any imports
if PROJECT_ROOT not in sys.path:
sys.path.insert(0, PROJECT_ROOT)
from xvla.models.modeling_xvla import XVLA
from xvla.models.processing_xvla import XVLAProcessor
import concurrent.futures as cf
import json
import logging
@@ -155,14 +165,11 @@ def rollout(
disable=inside_slurm(), # we dont want progress bar when we use slurm, since it clutters the logs
leave=False,
)
from transformers import AutoProcessor, AutoModel
model = AutoModel.from_pretrained(
"2toINF/X-VLA-WidowX",
trust_remote_code=True,
device="cuda"
)
model = XVLA.from_pretrained("/raid/jade/models/xvla-libero")
model.eval()
model.to("cuda")
processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", num_views=2, trust_remote_code=True)
processor = XVLAProcessor.from_pretrained("/raid/jade/models/xvla-libero", num_views=2)
from collections import deque
action_queue = deque(maxlen=30)
@@ -174,40 +181,43 @@ def rollout(
all_observations.append(deepcopy(observation))
# Infer "task" from attributes of environments.
observation[f"observation.images.image"] = observation[f"observation.images.image"] * 255
observation[f"observation.images.image2"] = observation[f"observation.images.image2"] * 255
# TODO: works with SyncVectorEnv but not AsyncVectorEnv
observation = add_envs_task(env, observation)
inputs = processor([observation[f"observation.images.image"], observation[f"observation.images.image2"]], observation["task"], do_rescale=False)
inputs = processor([observation[f"observation.images.image"], observation[f"observation.images.image2"]], observation["task"])
observation = preprocessor(observation)
observation["observation.images.image"] = inputs["image_input"][:, 0, ...].to("cuda")
observation["observation.images.image2"] = inputs["image_input"][:, 1, ...].to("cuda")
observation["observation.language.tokens"] = inputs["input_ids"].to("cuda")
inputs_1 = policy._build_model_inputs(observation)
for k in inputs.keys() & inputs_1.keys(): # intersection of keys
a = inputs[k].to("cuda")
b = inputs_1[k].to("cuda")
# (Pdb) inputs.keys()
# dict_keys(['input_ids', 'image_input', 'image_mask', 'proprio', 'domain_id'])
# image_input should be torch.Size([1, 2, 3, 224, 224])
img0 = observation["observation.images.image"] # [1, 3, 224, 224]
img1 = observation["observation.images.image2"] # [1, 3, 224, 224]
img0 = img0.unsqueeze(1) # [1, 1, 3, 224, 224]
img1 = img1.unsqueeze(1) # [1, 1, 3, 224, 224]
obs = {}
obs['input_ids'] = observation["observation.language.tokens"].to("cuda")
obs['image_input'] = torch.cat([img0, img1], dim=1).to("cuda")
obs['domain_id'] = torch.tensor([int(3)], dtype=torch.long).to("cuda")
obs['proprio'] = observation["observation.state"].to("cuda")
obs['image_mask'] = inputs["image_mask"].to("cuda")
print(f"\n🔎 Key: {k}")
with torch.inference_mode():
action_1 = policy.select_action(observation).to("cpu").numpy()
if len(action_queue) == 0:
action = model.generate_actions(**obs, steps=10) # shape (1, 30, 20)
actions_np = action.detach().cpu().numpy()
# add each timestep as (1, 20)
for t in range(actions_np.shape[1]):
act_t = actions_np[:, t, :]
action_queue.append(act_t)
action = action_queue.popleft()
# Check shape
print(" shape:", a.shape, b.shape)
# Check if close
if torch.allclose(a, b, atol=1e-5, rtol=1e-5):
print(" ✔️ tensors are equal (allclose)")
else:
action = action_queue.popleft()
diff = torch.abs(a - b)
print(" ❌ tensors differ")
print(" max diff:", diff.max().item())
print(" mean diff:", diff.mean().item())
breakpoint()
with torch.inference_mode():
action = policy.select_action(observation).to("cpu").numpy()
# if len(action_queue) == 0:
# action = model.generate_actions(**inputs_1, steps=10) # shape (1, 30, 20)
# actions_np = action.detach().cpu().numpy()
# # add each timestep as (1, 20)
# for t in range(actions_np.shape[1]):
# act_t = actions_np[:, t, :]
# action_queue.append(act_t)
# action = action_queue.popleft()
# else:
# action = action_queue.popleft()
# action = postprocessor(action)
# breakpoint()
# .to("cpu").numpy()
@@ -216,10 +226,10 @@ def rollout(
target_act = action[:, 9:10]
action_numpy = np.concatenate([target_eef, target_axis, target_act], axis=-1)
target_eef_1 = action_1[:, :3]
target_axis_1 = Rotate6D_to_AxisAngle(action_1[:, 3:9])
target_act_1 = action_1[:, 9:10]
action_numpy_1 = np.concatenate([target_eef_1, target_axis_1, target_act_1], axis=-1)
# target_eef_1 = action_1[:, :3]
# target_axis_1 = Rotate6D_to_AxisAngle(action_1[:, 3:9])
# target_act_1 = action_1[:, 9:10]
# action_numpy_1 = np.concatenate([target_eef_1, target_axis_1, target_act_1], axis=-1)
breakpoint()
# Convert to CPU / numpy.
test_3.py
+2 -1
View File
@@ -68,6 +68,7 @@ inputs.update({
"domain_id": domain_id.to("cuda"),
})
breakpoint()
for k in inputs.keys() & inputs_1.keys(): # intersection of keys
a = inputs[k]
b = inputs_1[k].to("cuda")
@@ -100,7 +101,7 @@ action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().num
action_1 = policy.model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
#np all close
print(np.allclose(action, action_1, atol=1e-4, rtol=1e-4))
print(np.allclose(action, action_1, atol=1e-2, rtol=1e-2))
print("max diff:", np.max(np.abs(action - action_1)))
print("mean diff:", np.mean(np.abs(action - action_1)))
breakpoint()