--- a
+++ b/old/Pytorch-Covid.py
@@ -0,0 +1,57 @@
+## The code below gives you Flatten and the double Adaptive Pooling (from fastai), plus
+## a viable head. You must fill the number of FC's nodes manually through the myhead function
+from torch import Tensor
+from torch import nn
+import torch
+import torchvision
+import logging as log
+from typing import Optional # required for "Optional[type]"
+
+class Flatten(nn.Module):
+    "Flatten `x` to a single dimension, often used at the end of a model. `full` for rank-1 tensor"
+    def __init__(self, full:bool=False):
+        super().__init__()
+        self.full = full
+
+    def forward(self, x):
+        return x.view(-1) if self.full else x.view(x.size(0), -1)
+
+class AdaptiveConcatPool2d(nn.Module):
+    "Layer that concats `AdaptiveAvgPool2d` and `AdaptiveMaxPool2d`." # from pytorch
+    def __init__(self, sz:Optional[int]=None): 
+        "Output will be 2*sz or 2 if sz is None"
+        super().__init__()
+        self.output_size = sz or 1
+        self.ap = nn.AdaptiveAvgPool2d(self.output_size)
+        self.mp = nn.AdaptiveMaxPool2d(self.output_size)
+    def forward(self, x): return torch.cat([self.mp(x), self.ap(x)], 1)
+    
+def myhead(nf, nc):
+    return \
+    nn.Sequential(        # the dropout is needed otherwise you cannot load the weights
+            AdaptiveConcatPool2d(),
+            Flatten(),
+            nn.BatchNorm1d(nf,eps=1e-05,momentum=0.1,affine=True,track_running_stats=True),
+            nn.Dropout(p=0.25,inplace=False),
+            nn.Linear(nf, 512,bias=True),
+            nn.ReLU(True),
+            nn.BatchNorm1d(512,eps=1e-05,momentum=0.1,affine=True,track_running_stats=True),
+            nn.Dropout(p=0.5,inplace=False),
+            nn.Linear(512, nc,bias=True),
+        )
+
+
+my_model=torchvision.models.resnet50() 
+modules=list(my_model.children())
+modules.pop(-1) 
+modules.pop(-1) 
+temp=nn.Sequential(nn.Sequential(*modules))
+tempchildren=list(temp.children()) 
+#append the special fastai head
+#Configured according to Model Architecture
+
+tempchildren.append(myhead(4096,2))
+model_r50=nn.Sequential(*tempchildren)
+
+state = torch.load(path+'Corona_model_stage5.pth')
+model_r50.load_state_dict(state['model'])
\ No newline at end of file