| import json |
| import os |
|
|
| import torch |
|
|
| from diffusers import UNet1DModel |
|
|
|
|
| os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True) |
| os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True) |
|
|
| os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True) |
|
|
|
|
| def unet(hor): |
| if hor == 128: |
| down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") |
| block_out_channels = (32, 128, 256) |
| up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D") |
|
|
| elif hor == 32: |
| down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") |
| block_out_channels = (32, 64, 128, 256) |
| up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D") |
| model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch") |
| state_dict = model.state_dict() |
| config = dict( |
| down_block_types=down_block_types, |
| block_out_channels=block_out_channels, |
| up_block_types=up_block_types, |
| layers_per_block=1, |
| use_timestep_embedding=True, |
| out_block_type="OutConv1DBlock", |
| norm_num_groups=8, |
| downsample_each_block=False, |
| in_channels=14, |
| out_channels=14, |
| extra_in_channels=0, |
| time_embedding_type="positional", |
| flip_sin_to_cos=False, |
| freq_shift=1, |
| sample_size=65536, |
| mid_block_type="MidResTemporalBlock1D", |
| act_fn="mish", |
| ) |
| hf_value_function = UNet1DModel(**config) |
| print(f"length of state dict: {len(state_dict.keys())}") |
| print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") |
| mapping = dict((k, hfk) for k, hfk in zip(model.state_dict().keys(), hf_value_function.state_dict().keys())) |
| for k, v in mapping.items(): |
| state_dict[v] = state_dict.pop(k) |
| hf_value_function.load_state_dict(state_dict) |
|
|
| torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin") |
| with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f: |
| json.dump(config, f) |
|
|
|
|
| def value_function(): |
| config = dict( |
| in_channels=14, |
| down_block_types=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"), |
| up_block_types=(), |
| out_block_type="ValueFunction", |
| mid_block_type="ValueFunctionMidBlock1D", |
| block_out_channels=(32, 64, 128, 256), |
| layers_per_block=1, |
| downsample_each_block=True, |
| sample_size=65536, |
| out_channels=14, |
| extra_in_channels=0, |
| time_embedding_type="positional", |
| use_timestep_embedding=True, |
| flip_sin_to_cos=False, |
| freq_shift=1, |
| norm_num_groups=8, |
| act_fn="mish", |
| ) |
|
|
| model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch") |
| state_dict = model |
| hf_value_function = UNet1DModel(**config) |
| print(f"length of state dict: {len(state_dict.keys())}") |
| print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") |
|
|
| mapping = dict((k, hfk) for k, hfk in zip(state_dict.keys(), hf_value_function.state_dict().keys())) |
| for k, v in mapping.items(): |
| state_dict[v] = state_dict.pop(k) |
|
|
| hf_value_function.load_state_dict(state_dict) |
|
|
| torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin") |
| with open("hub/hopper-medium-v2/value_function/config.json", "w") as f: |
| json.dump(config, f) |
|
|
|
|
| if __name__ == "__main__": |
| unet(32) |
| |
| value_function() |
|
|
