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20c251e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | from __future__ import annotations
import pytest
torch = pytest.importorskip("torch")
from dovla_cil.data.schema import ActionChunk
from dovla_cil.models import devectorize_toy_action, vectorize_toy_action
from dovla_cil.models.dovla import DoVLAConfig, DoVLAModel, load_model_state
def _model_and_inputs():
config = DoVLAConfig(
obs_dim=10,
lang_dim=16,
action_dim=8,
hidden_dim=32,
action_horizon=3,
effect_dim=7,
intervention_dim=24,
)
model = DoVLAModel(config)
observation = torch.randn(4, config.obs_dim)
instructions = [
"pick the red mug",
"put the mug in the bowl",
"open the drawer",
"push the cube",
]
action = torch.randn(4, config.action_horizon, config.action_dim)
return model, config, observation, instructions, action
def test_forward_policy_works_and_shapes() -> None:
model, config, observation, instructions, _action = _model_and_inputs()
predicted = model.forward_policy(observation, instructions)
assert predicted.shape == (4, config.action_horizon, config.action_dim)
def test_forward_effect_works_and_shapes() -> None:
model, config, observation, instructions, action = _model_and_inputs()
output = model.forward_effect(observation, instructions, action)
assert output["effect_vector"].shape == (4, config.effect_dim)
assert output["success_logit"].shape == (4,)
assert output["success"].shape == (4,)
assert output["progress"].shape == (4,)
def test_forward_reward_works_and_shapes() -> None:
model, _config, observation, instructions, action = _model_and_inputs()
reward = model.forward_reward(observation, instructions, action)
regret = model.forward_regret(observation, instructions, action)
z = model.encode_intervention(observation, instructions, action)
assert reward.shape == (4,)
assert regret.shape == (4,)
assert z.shape == (4, model.config.intervention_dim)
def test_gradients_flow() -> None:
model, _config, observation, instructions, action = _model_and_inputs()
policy = model.forward_policy(observation, instructions)
effect = model.forward_effect(observation, instructions, action)
reward = model.forward_reward(observation, instructions, action)
loss = policy.square().mean() + effect["effect_vector"].square().mean() + reward.mean()
loss.backward()
grads = [
parameter.grad
for parameter in model.parameters()
if parameter.requires_grad and parameter.grad is not None
]
assert grads
assert sum(float(grad.abs().sum()) for grad in grads) > 0.0
def test_rgb_observation_encoder_drives_policy_and_field_gradients() -> None:
config = DoVLAConfig(
obs_dim=10,
lang_dim=16,
action_dim=7,
hidden_dim=32,
action_horizon=2,
effect_dim=6,
intervention_dim=24,
observation_mode="rgb",
)
model = DoVLAModel(config)
images = torch.randint(0, 256, (3, 32, 40, 3), dtype=torch.uint8)
instructions = ["push the cube", "stack the cubes", "lift the peg"]
actions = torch.randn(3, config.action_horizon, config.action_dim)
policy = model.forward_policy(images, instructions)
field = model.forward_field(images, instructions, actions)
(policy.square().mean() + field["potential"].mean()).backward()
assert policy.shape == (3, 2, 7)
assert field["effect_vector"].shape == (3, 6)
assert any(
parameter.grad is not None and float(parameter.grad.abs().sum()) > 0
for parameter in model.observation_encoder.image_net.parameters()
)
def test_toy_action_vectorize_and_devectorize() -> None:
action = ActionChunk(
representation="semantic",
horizon=2,
values=[
{"command": "move_to", "object": "red_mug"},
{"command": "push", "object": "red_mug", "dx": 0.1, "dy": -0.2},
],
skill_type="push",
)
matrix = vectorize_toy_action(action, action_dim=8, action_horizon=3)
restored = devectorize_toy_action(matrix, skill_type="push")
assert len(matrix) == 3
assert len(matrix[0]) == 8
assert restored.representation == "semantic"
assert restored.skill_type == "push"
def test_numeric_action_chunk_vectorization_preserves_simulator_controls() -> None:
action = ActionChunk(
representation="maniskill_pd_ee_delta_pose",
horizon=2,
values=[
[0.1, -0.2, 0.3, 0.4, -0.5, 0.6, 0.7],
[0.0, 0.1, -0.1, 0.2, -0.2, 0.3, -0.3],
],
skill_type="pick_cube",
)
matrix = vectorize_toy_action(action, action_dim=8, action_horizon=3)
assert matrix[0] == [0.1, -0.2, 0.3, 0.4, -0.5, 0.6, 0.7, 0.0]
assert matrix[1] == [0.0, 0.1, -0.1, 0.2, -0.2, 0.3, -0.3, 0.0]
assert matrix[2] == [0.0] * 8
def test_model_state_loader_allows_only_declared_omitted_prefixes() -> None:
model, config, _observation, _instructions, _action = _model_and_inputs()
state = model.state_dict()
prefix = "observation_encoder.net.0."
compact = {key: value for key, value in state.items() if not key.startswith(prefix)}
restored = DoVLAModel(config)
load_model_state(
restored,
{
"model_state_dict": compact,
"omitted_state_prefixes": [prefix],
},
)
invalid = dict(compact)
invalid.pop("language_encoder.net.0.weight")
with pytest.raises(RuntimeError, match="invalid_missing"):
load_model_state(
DoVLAModel(config),
{
"model_state_dict": invalid,
"omitted_state_prefixes": [prefix],
},
)
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