Update app.py

app.py

@@ -5,6 +5,260 @@ import numpy as np
 import torch
 from PIL import Image
 import torchvision.transforms as transforms
+from typing import NamedTuple, List, Callable, List, Tuple, Optional
+from torch import nn
+import torch.nn.functional as F
+
+class LinData(NamedTuple):
+    in_dim : int # input dimension
+    hidden_layers : List[int] # hidden layers including the output layer
+    activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # list of activations
+    bns : List[bool] # list of bools
+    dropouts : List[Optional[float]] # list of dropouts probas
+        
+class CNNData(NamedTuple):
+    in_dim : int # input dimension
+    n_f : List[int] # num filters
+    kernel_size : List[Tuple] # kernel size [(5,5,5), (3,3,3),(3,3,3)]
+    activations : List[Optional[Callable[[torch.Tensor],torch.Tensor]]] # activation list
+    bns : List[bool] # batch normialization [True, True, False]
+    dropouts : List[Optional[float]] # # list of dropouts probas [.5,0,0]
+    #dropouts_ps : list # [0.5,.7, 0]
+    paddings : List[Optional[Tuple]] #[(0,0,0),(0,0,0), (0,0,0)]
+    strides : List[Optional[Tuple]] #[(1,1,1),(1,1,1),(1,1,1)]
+    
+    
+class NetData(NamedTuple):
+    cnn3d : CNNData
+    lin : LinData
+    
+class CNN3D_Mike(nn.Module):
+    def __init__(self, t_dim=30, img_x=256 , img_y=342, drop_p=0, fc_hidden1=256, fc_hidden2=256):
+        super(CNN3D_Mike, self).__init__()        # set video dimension
+        self.t_dim = t_dim
+        self.img_x = img_x
+        self.img_y = img_y
+        # fully connected layer hidden nodes
+        self.fc_hidden1, self.fc_hidden2 = fc_hidden1, fc_hidden2
+        self.drop_p = drop_p
+        #self.num_classes = num_classes
+        self.ch1, self.ch2 = 32, 48
+        self.k1, self.k2 = (5, 5, 5), (3, 3, 3)  # 3d kernel size
+        self.s1, self.s2 = (2, 2, 2), (2, 2, 2)  # 3d strides
+        self.pd1, self.pd2 = (0, 0, 0), (0, 0, 0)  # 3d padding        # compute conv1 & conv2 output shape
+        self.conv1_outshape = conv3D_output_size((self.t_dim, self.img_x, self.img_y), self.pd1, self.k1, self.s1)
+        self.conv2_outshape = conv3D_output_size(self.conv1_outshape, self.pd2, self.k2, self.s2)        
+        self.conv1 = nn.Conv3d(in_channels=1, out_channels=self.ch1, kernel_size=self.k1, stride=self.s1,
+                               padding=self.pd1)
+        self.bn1 = nn.BatchNorm3d(self.ch1)
+        self.conv2 = nn.Conv3d(in_channels=self.ch1, out_channels=self.ch2, kernel_size=self.k2, stride=self.s2,
+                               padding=self.pd2)
+        self.bn2 = nn.BatchNorm3d(self.ch2)
+        self.relu = nn.ReLU(inplace=True)
+        self.drop = nn.Dropout3d(self.drop_p)
+        self.pool = nn.MaxPool3d(2)
+        self.fc1 = nn.Linear(self.ch2*self.conv2_outshape[0]*self.conv2_outshape[1]*self.conv2_outshape[2],
+                             self.fc_hidden1)  # fully connected hidden layer
+        self.fc2 = nn.Linear(self.fc_hidden1, self.fc_hidden2)
+        self.fc3 = nn.Linear(self.fc_hidden2,1)  # fully connected layer, output = multi-classes 
+        
+        
+    def forward(self, x_3d):
+        # Conv 1
+        x = self.conv1(x_3d)
+       
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.drop(x)
+        # Conv 2
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+        x = self.drop(x)
+        # FC 1 and 2
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        
+        #x = F.relu(self.fc3(x))
+        #x = F.relu(self.fc3(x))
+        x = F.dropout(x, p=self.drop_p, training=self.training)
+        #x = self.fc3(x)
+        #x = F.softmax(self.fc2(x))
+        
+        x = self.fc3(x) 
+        
+        
+        
+        return x
+
+    
+
+class CNNLayers(nn.Module):
+
+    def __init__(self, args):
+    
+        super(CNNLayers, self).__init__()
+        
+        self.in_dim = args.in_dim# 1/3
+        self.n_f = args.n_f#[32,64]
+        self.kernel_size = args.kernel_size # [(5,5,5), (3,3,3)]
+        self.activations = args.activations#['relu', 'relu']
+        self.bns = args.bns #[True, True], 
+        self.dropouts = args.dropouts #[True, True]
+        #self.dropouts_ps = args.dropouts_ps#[0.5,.7]
+        self.paddings = args.paddings #[(0,0,0),(0,0,0)]
+        self.strides = args.strides # strides [(1,1,1),(1,1,1),(1,1,1)])
+        #self.poolings = args.poolings
+        
+        assert len(self.n_f) == len(self.activations) == len(self.bns) == len(self.dropouts), 'dimensions mismatch : check dimensions!'
+        
+        # generate layers seq of seq 
+        self._get_layers()
+       
+    def _get_layers(self):
+        
+        layers =nn.ModuleList()
+        in_channels = self.in_dim
+        
+        for idx, chans in enumerate(self.n_f):
+            sub_layers = nn.ModuleList()                            
+                                        
+            sub_layers.append(nn.Conv3d(in_channels = in_channels,
+                                        out_channels = chans, #self.n_f[idx],
+                                        kernel_size = self.kernel_size[idx],
+                                        stride = self.strides[idx],
+                                        padding = self.paddings[idx]
+                                        ))
+                                        
+
+
+            if self.bns[idx] : sub_layers.append(nn.BatchNorm3d(num_features = self.n_f[idx]))
+
+            #if self.dropouts[idx] : sub_layers.append(nn.Dropout3d(p = self.dropouts_ps[idx]))
+            
+            if self.dropouts[idx] : sub_layers.append(nn.Dropout3d(p = self.dropouts[idx]))
+
+            #if self.activations[idx]  : sub_layers.append(self.__class__.get_activation(self.activations[idx]))
+            
+            if self.activations[idx]  : sub_layers.append(self.activations[idx])
+            
+            sub_layers = nn.Sequential(*sub_layers) 
+            
+            layers.append(sub_layers)
+            
+            in_channels = self.n_f[idx]
+    
+        self.layers = nn.Sequential(*layers)
+        
+        
+    @staticmethod
+    def get_activation(activation):
+        if activation == 'relu':
+            activation=nn.ReLU()
+        elif activation == 'leakyrelu':
+            activation=nn.LeakyReLU(negative_slope=0.1)
+        elif activation == 'selu':
+            activation=nn.SELU()
+        
+        return activation
+        
+        
+        
+    def forward(self, x):
+        
+        x = self.layers(x)
+        
+        return x 
+        
+        
+
+class CNN3D(nn.Module):
+
+    def __init__(self, args):
+        super(CNN3D,self).__init__()
+        # check datatype
+        if not isinstance(args, NetData):
+            raise TypeError("input must be a ParserClass")
+            
+        self.cnn3d = CNNLayers(args.cnn3d)
+
+        self.lin = LinLayers(args.lin)
+        
+        self.in_dim = args.lin.in_dim
+        
+        
+    def forward(self, x):
+        
+        # cnn 3d
+        x = self.cnn3d(x)
+        
+        x = x.view(-1, self.in_dim)
+        
+        # feedforward
+        x = self.lin(x)
+        
+        return x
+        
+        
+     
+
+class LinLayers(nn.Module):
+    
+    def __init__(self, args):
+        super(LinLayers,self).__init__()
+        
+        in_dim= args.in_dim #16,
+        hidden_layers= args.hidden_layers #[512,256,128,2],
+        activations=args.activations#[nn.LeakyReLU(0.2),nn.LeakyReLU(0.2),nn.LeakyReLU(0.2)],
+        batchnorms=args.bns#[True,True,True],
+        dropouts = args.dropouts#[None, 0.2, 0.2]
+                
+        
+        assert len(hidden_layers) == len(activations) == len(batchnorms) == len(dropouts), 'dimensions mismatch!'
+       
+        
+        layers=nn.ModuleList()
+        
+        if hidden_layers:
+            old_dim=in_dim
+            for idx,layer in enumerate(hidden_layers):
+                sub_layers = nn.ModuleList()
+                sub_layers.append(nn.Linear(old_dim,layer))
+                if batchnorms[idx] : sub_layers.append(nn.BatchNorm1d(num_features=layer))
+                if activations[idx] : sub_layers.append(activations[idx])
+                if dropouts[idx] : sub_layers.append(nn.Dropout(p=dropouts[idx]))
+                old_dim = layer
+                
+                sub_layers = nn.Sequential(*sub_layers) 
+            
+                layers.append(sub_layers)
+            
+        
+            
+        else:# for single layer
+            layers.append(nn.Linear(in_dim,out_dim)) 
+            if batchnorms : layers.append(nn.BatchNorm1d(num_features=out_dim))
+            if activations : layers.append(activations)
+            if dropouts : layers.append(nn.Dropout(p=dropouts))
+        
+        self.layers = nn.Sequential(*layers)
+            
+    
+        
+    def forward(self,x):
+    
+        x = self.layers(x)
+        
+        return x
+        
+    '''
+    def _check_dimensions(self):
+        if isinstance(self.hidden_layers,list) : 
+            assert len(self.hidden_layers)==len(self.activations)
+            assert len(self.hidden_layers)==len(self.batchnorms)
+            assert len(self.hidden_layers)==len(self.dropouts)
+    '''
 
 
 def load_model():