fix Unet global_cond_dim to use state dim, not action dim #278
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alexander-soare
approved these changes
Jun 17, 2024
| @@ -165,7 +165,7 @@ def __init__(self, config: DiffusionConfig): | |||
| num_images = len([k for k in config.input_shapes if k.startswith("observation.image")]) | |||
| self.unet = DiffusionConditionalUnet1d( | |||
| config, | |||
| global_cond_dim=(config.output_shapes["action"][0] + self.rgb_encoder.feature_dim * num_images) | |||
| global_cond_dim=(config.input_shapes["observation.state"][0] + self.rgb_encoder.feature_dim * num_images) | |||
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This change should fix the linting test
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| global_cond_dim=(config.input_shapes["observation.state"][0] + self.rgb_encoder.feature_dim * num_images) | |
| global_cond_dim=( | |
| config.input_shapes["observation.state"][0] + self.rgb_encoder.feature_dim * num_images | |
| ) |
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@alexander-soare Actually, I just fixed linting problem on my forked repo.
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This PR fixes global_cond_dim of Diffusion's unet when dim of observation.state != dim of action
This change is