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update testing script

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This commit is contained in:
Jade Choghari
2025-11-10 13:17:47 +01:00
parent 3cb14248a4
commit 8d9a992953
2 changed files with 51 additions and 13 deletions
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src/lerobot/policies/xvla/configuration_xvla.py
+22 -11
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@@ -129,33 +129,44 @@ class XVLAConfig(PreTrainedConfig):
config_dict["vision_config"] = { config_dict["vision_config"] = {
"model_type": "davit", "model_type": "davit",
"drop_path_rate": 0.1, "drop_path_rate": 0.1,
"patch_size": [14, 7, 7, 7], "patch_size": [7, 3, 3, 3],
"patch_stride": [4, 2, 2, 2], "patch_stride": [4, 2, 2, 2],
"patch_padding": [3, 1, 1, 1], "patch_padding": [3, 1, 1, 1],
"patch_prenorm": [False, True, True, True], "patch_prenorm": [False, True, True, True],
"enable_checkpoint": False,
"dim_embed": [256, 512, 1024, 2048], "dim_embed": [256, 512, 1024, 2048],
"num_heads": [8, 16, 32, 64], "num_heads": [8, 16, 32, 64],
"num_groups": [8, 16, 32, 64], "num_groups": [8, 16, 32, 64],
"depths": [1, 1, 9, 1], "depths": [1, 1, 9, 1],
"window_size": 12, "window_size": 12,
"projection_dim": 1024, "projection_dim": 1024,
"visual_temporal_embedding": {"type": "COSINE", "max_temporal_embeddings": 100}, "visual_temporal_embedding": {
"image_pos_embed": {"type": "learned_abs_2d", "max_pos_embeddings": 50}, "type": "COSINE",
"image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"], "max_temporal_embeddings": 100
} },
"image_pos_embed": {
"type": "learned_abs_2d",
"max_pos_embeddings": 50
},
"image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"]
}
if "text_config" not in config_dict or config_dict["text_config"] is None: if "text_config" not in config_dict or config_dict["text_config"] is None:
# Provide default text config # Provide default text config
config_dict["text_config"] = { config_dict["text_config"] = {
"model_type": "florence2_language",
"vocab_size": 51289, "vocab_size": 51289,
"activation_dropout": 0.1,
"activation_function": "gelu",
"attention_dropout": 0.1,
"d_model": 1024, "d_model": 1024,
"encoder_layers": 12, "decoder_attention_heads": 16,
"decoder_layers": 12, "decoder_layers": 12,
"encoder_attention_heads": 16, "encoder_attention_heads": 16,
"decoder_attention_heads": 16, "encoder_layers": 12,
"encoder_ffn_dim": 4096, "dropout": 0.1,
"decoder_ffn_dim": 4096, "max_position_embeddings": 4096,
} "num_hidden_layers": 12,
"num_beams": 3
}
self._florence_config_obj = Florence2Config(**config_dict) self._florence_config_obj = Florence2Config(**config_dict)
return self._florence_config_obj return self._florence_config_obj
test_xvla.py
+29 -2
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@@ -1,10 +1,37 @@
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
from lerobot.policies.factory import make_policy, make_policy_config from lerobot.policies.factory import make_policy, make_policy_config
import os
cfg = make_policy_config("xvla") cfg = make_policy_config("xvla")
dataset_id = "lerobot/svla_so101_pickplace" dataset_id = "lerobot/svla_so101_pickplace"
# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets # This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
dataset_metadata = LeRobotDatasetMetadata(dataset_id) dataset_metadata = LeRobotDatasetMetadata(dataset_id)
policy = make_policy(cfg=cfg, ds_meta=dataset_metadata) policy = make_policy(cfg=cfg, ds_meta=dataset_metadata)
print(policy)
for name, param in policy.state_dict().items():
print(name, param.shape)
# now let's load in safetensors
import safetensors.torch
from huggingface_hub import snapshot_download
cache_dir = snapshot_download(repo_id="2toINF/X-VLA-Libero", repo_type="model")
state_dict = safetensors.torch.load_file(os.path.join(cache_dir, "model.safetensors"))
# policy.load_state_dict(state_dict)
# 3. Add "model." prefix to every key
new_state_dict = {f"model.{k}": v for k, v in state_dict.items()}
keys_to_skip = [
"model.transformer.action_encoder.fc.weight",
"model.transformer.action_encoder.fc.bias",
]
new_state_dict = {k: v for k, v in new_state_dict.items() if k not in keys_to_skip}
# 4. Load into your model
missing, unexpected = policy.load_state_dict(new_state_dict, strict=False)
print("missing keys:", missing)
print()
print("unexpected keys:", unexpected)