init

2024-04-02 23:20:57 +08:00
commit e74e9cd336
1612 changed files with 725675 additions and 0 deletions
@@ -0,0 +1,100 @@
+import json
+import os
+
+import torch
+
+from MuseVdiffusers 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 = {
+        "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(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 = {
+        "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(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)
+    # unet(128)
+    value_function()