Create pretrained_model_reloaded_th.py

pretrained_model_reloaded_th.py

@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+
+from functools import reduce
+from torch.autograd import Variable
+
+class LambdaBase(nn.Sequential):
+    def __init__(self, fn, *args):
+        super(LambdaBase, self).__init__(*args)
+        self.lambda_func = fn
+
+    def forward_prepare(self, input):
+        output = []
+        for module in self._modules.values():
+            output.append(module(input))
+        return output if output else input
+
+class Lambda(LambdaBase):
+    def forward(self, input):
+        return self.lambda_func(self.forward_prepare(input))
+
+class LambdaMap(LambdaBase):
+    def forward(self, input):
+        return list(map(self.lambda_func,self.forward_prepare(input)))
+
+class LambdaReduce(LambdaBase):
+    def forward(self, input):
+        return reduce(self.lambda_func,self.forward_prepare(input))
+
+def get_model(load_weights = True):
+    # alphabet seems to be fine:
+    """
+    https://github.com/davek44/Basset/tree/master/src/dna_io.py#L145-L148
+    seq = seq.replace('A','0')
+    seq = seq.replace('C','1')
+    seq = seq.replace('G','2')
+    seq = seq.replace('T','3')
+    """
+    pretrained_model_reloaded_th = nn.Sequential( # Sequential,
+        nn.Conv2d(4,300,(19, 1)),
+        nn.BatchNorm2d(300),
+        nn.ReLU(),
+        nn.MaxPool2d((3, 1),(3, 1)),
+        nn.Conv2d(300,200,(11, 1)),
+        nn.BatchNorm2d(200),
+        nn.ReLU(),
+        nn.MaxPool2d((4, 1),(4, 1)),
+        nn.Conv2d(200,200,(7, 1)),
+        nn.BatchNorm2d(200),
+        nn.ReLU(),
+        nn.MaxPool2d((4, 1),(4, 1)),
+        Lambda(lambda x: x.view(x.size(0),-1)), # Reshape,
+        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(2000,1000)), # Linear,
+        nn.BatchNorm1d(1000,1e-05,0.1,True),#BatchNorm1d,
+        nn.ReLU(),
+        nn.Dropout(0.3),
+        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1000,1000)), # Linear,
+        nn.BatchNorm1d(1000,1e-05,0.1,True),#BatchNorm1d,
+        nn.ReLU(),
+        nn.Dropout(0.3),
+        nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1000,164)), # Linear,
+        nn.Sigmoid(),
+    )
+    if load_weights:
+        sd = torch.load('model_files/pretrained_model_reloaded_th.pth')
+        pretrained_model_reloaded_th.load_state_dict(sd)
+    return  pretrained_model_reloaded_th
+
+model = get_model(load_weights = False)
+