39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import pytest
|
|
param = pytest.mark.parametrize
|
|
import torch
|
|
|
|
@param('pred_orig_latent', (False, True))
|
|
def test_e2e(
|
|
pred_orig_latent
|
|
):
|
|
from dreamer4.dreamer4 import VideoTokenizer, DynamicsModel
|
|
|
|
tokenizer = VideoTokenizer(512, dim_latent = 32, patch_size = 32)
|
|
x = torch.randn(2, 3, 4, 256, 256)
|
|
|
|
loss = tokenizer(x)
|
|
assert loss.numel() == 1
|
|
|
|
latents = tokenizer(x, return_latents = True)
|
|
assert latents.shape[-1] == 32
|
|
|
|
dynamics = DynamicsModel(512, dim_latent = 32, num_signal_levels = 500, num_step_sizes = 32, pred_orig_latent = pred_orig_latent)
|
|
|
|
signal_levels = torch.randint(0, 500, (2, 4))
|
|
step_sizes = torch.randint(0, 32, (2, 4))
|
|
|
|
flow_loss = dynamics(latents, signal_levels = signal_levels, step_sizes = step_sizes)
|
|
assert flow_loss.numel() == 1
|
|
|
|
def test_symexp_two_hot():
|
|
import torch
|
|
from dreamer4.dreamer4 import SymExpTwoHot
|
|
|
|
two_hot_encoder = SymExpTwoHot((-3., 3.), 20)
|
|
values = torch.randn((10))
|
|
|
|
encoded = two_hot_encoder(values)
|
|
recon_values = two_hot_encoder.logits_to_scalar_value(encoded)
|
|
|
|
assert torch.allclose(recon_values, values, atol = 1e-6)
|