Jason
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import torch
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import torch.nn as nn
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from functools import reduce
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class LambdaBase(nn.Sequential):
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def __init__(self, fn, *args):
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super(LambdaBase, self).__init__(*args)
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self.lambda_func = fn
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def forward_prepare(self, input):
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output = []
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for module in self._modules.values():
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output.append(module(input))
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return output if output else input
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class Lambda(LambdaBase):
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def forward(self, input):
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return self.lambda_func(self.forward_prepare(input))
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class LambdaMap(LambdaBase):
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def forward(self, input):
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return list(map(self.lambda_func,self.forward_prepare(input)))
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class LambdaReduce(LambdaBase):
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def forward(self, input):
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return reduce(self.lambda_func,self.forward_prepare(input))
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def load_estimation_model(inputfilename, outputfilename, begin, end):
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with open(inputfilename, "r") as rf:
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contents = rf.read()
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contents = contents.split(",")
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data = torch.Tensor(1,350)
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name = ""
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for i in range(len(contents)):
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if i == 0:
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name = contents[i].strip()
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else:
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val = float(contents[i].strip())
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data[0][i-1] = val
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model = nn.Sequential( # Sequential,
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nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(350,1024)), # Linear,
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nn.Sigmoid(),
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nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1024,512)), # Linear,
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nn.Sigmoid(),
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nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(512,256)), # Linear,
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nn.Sigmoid(),
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nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(256,4)), # Linear,
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)
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model.load_state_dict(torch.load("em.pth"))
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my_prediction = model.forward(data)
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with open(outputfilename, "w") as wf:
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wf.write("NAME,begin,end,F1,F2,F3,F4\n")
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wf.write(name + "," + str(begin) + "," + str(end) + "," + \
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str(1000 * float(my_prediction[0][0])) + "," + str(1000 * float(my_prediction[0][1])) + "," + \
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str(1000 * float(my_prediction[0][2])) + "," + str(1000 * float(my_prediction[0][3])) + "\n")
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