Ajout WideResNet (A tester) pour comparaison a PBA

higher/model.py

@@ -3,6 +3,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+## Basic CNN ##
 class LeNet(nn.Module):
     def __init__(self, num_inp, num_out):
         super(LeNet, self).__init__()
@@ -49,3 +50,91 @@ class LeNet(nn.Module):
 
     def __str__(self):
         return "LeNet"
+
+## Wide ResNet ##
+#https://github.com/xternalz/WideResNet-pytorch/blob/master/wideresnet.py
+#https://github.com/arcelien/pba/blob/master/pba/wrn.py
+class BasicBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
+        super(BasicBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_planes)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
+                               padding=1, bias=False)
+        self.droprate = dropRate
+        self.equalInOut = (in_planes == out_planes)
+        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
+                               padding=0, bias=False) or None
+    def forward(self, x):
+        if not self.equalInOut:
+            x = self.relu1(self.bn1(x))
+        else:
+            out = self.relu1(self.bn1(x))
+        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
+        if self.droprate > 0:
+            out = F.dropout(out, p=self.droprate, training=self.training)
+        out = self.conv2(out)
+        return torch.add(x if self.equalInOut else self.convShortcut(x), out)
+
+class NetworkBlock(nn.Module):
+    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
+        super(NetworkBlock, self).__init__()
+        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
+    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
+        layers = []
+        for i in range(int(nb_layers)):
+            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
+        return nn.Sequential(*layers)
+    def forward(self, x):
+        return self.layer(x)
+
+class WideResNet(nn.Module):
+    #def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
+    def __init__(self, num_classes, wrn_size, depth=28, dropRate=0.0):
+        super(WideResNet, self).__init__()
+
+        kernel_size = wrn_size
+        filter_size = 3
+        nChannels = [min(kernel_size, 16), kernel_size, kernel_size * 2, kernel_size * 4]
+        strides = [1, 2, 2]  # stride for each resblock
+
+        #nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
+        assert((depth - 4) % 6 == 0)
+        n = (depth - 4) / 6
+        block = BasicBlock
+        # 1st conv before any network block
+        self.conv1 = nn.Conv2d(filter_size, nChannels[0], kernel_size=3, stride=1,
+                               padding=1, bias=False)
+        # 1st block
+        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, strides[0], dropRate)
+        # 2nd block
+        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, strides[1], dropRate)
+        # 3rd block
+        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, strides[2], dropRate)
+        # global average pooling and classifier
+        self.bn1 = nn.BatchNorm2d(nChannels[3])
+        self.relu = nn.ReLU(inplace=True)
+        self.fc = nn.Linear(nChannels[3], num_classes)
+        self.nChannels = nChannels[3]
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.bias.data.zero_()
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.block1(out)
+        out = self.block2(out)
+        out = self.block3(out)
+        out = self.relu(self.bn1(out))
+        out = F.avg_pool2d(out, 8)
+        out = out.view(-1, self.nChannels)
+        return self.fc(out)

higher/test_dataug.py

@@ -783,8 +783,8 @@ if __name__ == "__main__":
     #### TF number tests ####
     #'''
     res_folder="res/TF_nb_tests/"
-    epochs= 200
+    epochs= 100
-    inner_its = [0, 10]
+    inner_its = [10]
     dataug_epoch_starts= [0]
     TF_nb = [len(TF.TF_dict)] #range(1,len(TF.TF_dict)+1)
     N_seq_TF= [1, 2, 3, 4]
@@ -808,7 +808,7 @@ if __name__ == "__main__":
                     aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, N_TF=n_tf, mix_dist=0.0), LeNet(3,10)).to(device)
                     print(str(aug_model), 'on', device_name)
                     #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
-                    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=10)
+                    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)
 
                     ####
                     plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))