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