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This commit is contained in:
Thomas Wolf
2024-06-18 11:26:49 +02:00
parent b72d574891
commit cd9ace20b6
2 changed files with 212 additions and 8 deletions
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198
tests/test_policies.py
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@@ -204,17 +204,33 @@ def test_normalize(insert_temporal_dim):
input_shapes = {
"observation.image": [3, 96, 96],
"observation.state": [10],
"action_test_std": [1],
"action_test_min_max": [1],
"action_test_std_cap": [2],
"action_test_min_max_cap": [2],
}
output_shapes = {
"action": [5],
"action_test_std": [1],
"action_test_min_max": [1],
"action_test_std_cap": [2],
"action_test_min_max_cap": [2],
}
normalize_input_modes = {
"observation.image": "mean_std",
"observation.state": "min_max",
"action_test_std": "mean_std",
"action_test_min_max": "min_max",
"action_test_std_cap": "mean_std",
"action_test_min_max_cap": "min_max",
}
unnormalize_output_modes = {
"action": "min_max",
"action_test_std": "mean_std",
"action_test_min_max": "min_max",
"action_test_std_cap": "mean_std",
"action_test_min_max_cap": "min_max",
}
dataset_stats = {
@@ -236,15 +252,43 @@ def test_normalize(insert_temporal_dim):
"min": torch.randn(5),
"max": torch.randn(5),
},
"action_test_std": {
"mean": torch.ones(1) * 2,
"std": torch.ones(1) * 0.2,
},
"action_test_min_max": {
"min": torch.ones(1) * 1,
"max": torch.ones(1) * 3,
},
"action_test_std_cap": {
"mean": torch.ones(2) * 2,
"std": torch.ones(2) * 0.2,
},
"action_test_min_max_cap": {
"min": torch.ones(2) * 1.0,
"max": torch.ones(2) * 3.0,
},
}
# Set some values to 0 to test the case where the std is 0 - for max we set it to min
dataset_stats["action_test_std_cap"]["std"][1] = 0.0
dataset_stats["action_test_min_max_cap"]["max"][1] = dataset_stats["action_test_min_max_cap"]["min"][1]
bsize = 2
input_batch = {
"observation.image": torch.randn(bsize, 3, 96, 96),
"observation.state": torch.randn(bsize, 10),
"action_test_std": torch.ones(bsize, 1) * 2.5,
"action_test_min_max": torch.ones(bsize, 1) * 2.5,
"action_test_std_cap": torch.ones(bsize, 2) * 2.5,
"action_test_min_max_cap": torch.ones(bsize, 2) * 2.5,
}
output_batch = {
"action": torch.randn(bsize, 5),
"action_test_std": torch.ones(bsize, 1) * 2.5,
"action_test_min_max": torch.ones(bsize, 1) * 2.5,
"action_test_std_cap": torch.ones(bsize, 2) * 2.5,
"action_test_min_max_cap": torch.ones(bsize, 2) * 2.5,
}
if insert_temporal_dim:
@@ -263,8 +307,158 @@ def test_normalize(insert_temporal_dim):
normalize(input_batch)
# test with stats
normalize = Normalize(input_shapes, normalize_input_modes, stats=dataset_stats)
normalize(input_batch)
std_epsilon = 1e-2
normalize = Normalize(input_shapes, normalize_input_modes, stats=dataset_stats, std_epsilon=std_epsilon)
# check that the stats are correctly set including the min capping
assert torch.isclose(
normalize.buffer_action_test_std.mean, dataset_stats["action_test_std"]["mean"], rtol=0.1, atol=1e-7
).all()
assert torch.isclose(
normalize.buffer_action_test_std.std, dataset_stats["action_test_std"]["std"], rtol=0.1, atol=1e-7
).all()
assert torch.isclose(
normalize.buffer_action_test_min_max.min,
dataset_stats["action_test_min_max"]["min"],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalize.buffer_action_test_min_max.max,
dataset_stats["action_test_min_max"]["max"],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalize.buffer_action_test_std_cap.std[0],
dataset_stats["action_test_std_cap"]["std"][0],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalize.buffer_action_test_std_cap.std[1], torch.ones(1) * std_epsilon, rtol=0.1, atol=1e-7
).all()
assert torch.isclose(
normalize.buffer_action_test_min_max_cap.max[0] - normalize.buffer_action_test_min_max_cap.min[0],
dataset_stats["action_test_min_max_cap"]["max"][0]
- dataset_stats["action_test_min_max_cap"]["min"][0],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalize.buffer_action_test_min_max_cap.max[1] - normalize.buffer_action_test_min_max_cap.min[1],
torch.ones(1) * std_epsilon,
rtol=0.1,
atol=1e-7,
).all()
normalized_output = normalize(input_batch)
# check that the normalization is correct
assert torch.isclose(
normalized_output["action_test_std"],
(input_batch["action_test_std"] - dataset_stats["action_test_std"]["mean"])
/ dataset_stats["action_test_std"]["std"],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_min_max"],
(input_batch["action_test_min_max"] - dataset_stats["action_test_min_max"]["min"])
/ (dataset_stats["action_test_min_max"]["max"] - dataset_stats["action_test_min_max"]["min"])
* 2
- 1,
rtol=0.1,
atol=1e-7,
).all()
if insert_temporal_dim:
assert torch.isclose(
normalized_output["action_test_std_cap"][0, 0, 0],
(input_batch["action_test_std_cap"][0, 0, 0] - dataset_stats["action_test_std_cap"]["mean"][0])
/ dataset_stats["action_test_std_cap"]["std"][0],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_std_cap"][0, 0, 1],
(input_batch["action_test_std_cap"][0, 0, 1] - dataset_stats["action_test_std_cap"]["mean"][1])
/ std_epsilon,
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_min_max_cap"][0, 0, 0],
(
input_batch["action_test_min_max_cap"][0, 0, 0]
- dataset_stats["action_test_min_max_cap"]["min"][0]
)
/ (
dataset_stats["action_test_min_max_cap"]["max"][0]
- dataset_stats["action_test_min_max_cap"]["min"][0]
)
* 2
- 1,
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_min_max_cap"][0, 0, 1],
(
input_batch["action_test_min_max_cap"][0, 0, 1]
- dataset_stats["action_test_min_max_cap"]["min"][1]
)
/ std_epsilon
* 2
- 1,
rtol=0.1,
atol=1e-7,
).all()
else:
assert torch.isclose(
normalized_output["action_test_std_cap"][0, 0],
(input_batch["action_test_std_cap"][0, 0] - dataset_stats["action_test_std_cap"]["mean"][0])
/ dataset_stats["action_test_std_cap"]["std"][0],
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_std_cap"][0, 1],
(input_batch["action_test_std_cap"][0, 1] - dataset_stats["action_test_std_cap"]["mean"][1])
/ std_epsilon,
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_min_max_cap"][0, 0],
(
input_batch["action_test_min_max_cap"][0, 0]
- dataset_stats["action_test_min_max_cap"]["min"][0]
)
/ (
dataset_stats["action_test_min_max_cap"]["max"][0]
- dataset_stats["action_test_min_max_cap"]["min"][0]
)
* 2
- 1,
rtol=0.1,
atol=1e-7,
).all()
assert torch.isclose(
normalized_output["action_test_min_max_cap"][0, 1],
(
input_batch["action_test_min_max_cap"][0, 1]
- dataset_stats["action_test_min_max_cap"]["min"][1]
)
/ std_epsilon
* 2
- 1,
rtol=0.1,
atol=1e-7,
).all()
# test loading pretrained models
new_normalize = Normalize(input_shapes, normalize_input_modes, stats=None)