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42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
import torch
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import struct
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def print_cpp_vector(vector, name="expected"):
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print("std::vector<float> " + name + " = {", end="")
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for i in range(len(vector)):
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if i != 0:
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print(", ", end="")
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print(str(round(vector[i].item(), 5)) + "f", end="")
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print("};")
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def export_model_weights(model: torch.nn.Module, filename):
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with open(filename, 'wb') as f:
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header = ""
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offset = 0
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for name, param in model.named_parameters():
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if 'weight' not in name and 'bias' not in name:
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continue
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tensor_values = param.flatten().tolist()
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tensor_bytes = struct.pack('f' * len(tensor_values), *tensor_values)
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tensor_size = param.numel()
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header += f"{name},{tensor_size},{offset}\n"
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offset += len(tensor_bytes)
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f.write(tensor_bytes)
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f.seek(0)
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f.write(struct.pack('q', len(header)))
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f.write(header.encode('utf-8'))
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def print_model_parameters(model: torch.nn.Module):
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for name, param in model.named_parameters():
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print(name, param.numel()) |