implemented resnets and transfer learning

labml_nn/resnets/utils/utils.py

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+#!/bin/python
+
+import torch
+import torchvision
+import torchvision.transforms as transforms
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.model_selection import KFold
+from torch.utils.data.sampler import SubsetRandomSampler
+
+
+
+# Plot the loss of multiple runs together
+def PlotLosses(losses, titles, save=None):
+    fig = plt.figure()
+    fig.set_size_inches(14, 22)
+    # Plot results on 3 subgraphs
+    # subplot integers:
+    #       nrows
+    #       ncols
+    #       index
+    sublplot_str_start = "" + str(len(losses)) + "1"
+
+    for i in range(len(losses)):
+        subplot = sublplot_str_start + str(i+1)
+        loss = losses[i]
+        title = titles[i]
+
+        ax = plt.subplot(int(subplot))
+        ax.plot(range(len(loss)), loss)
+        ax.set_xlabel("Epoch")
+        ax.set_title(title)
+        ax.set_ylabel("Loss")
+
+    # Save Figure
+    if save:
+    	plt.savefig(save)
+    else:
+    	plt.show()
+
+
+
+def ClassSpecificTestCifar10(net, testdata, device=None):
+    classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+    class_correct = list(0. for i in range(10))
+    class_total = list(0. for i in range(10))
+    with torch.no_grad():
+        for data in testdata:
+            if device:
+                images, labels = data[0].to(device), data[1].to(device)
+            else:
+                images, labels = data
+
+            outputs = net(images)
+            _, predicted = torch.max(outputs, 1)
+            c = (predicted == labels).squeeze()
+            for i in range(4):
+                label = labels[i]
+                class_correct[label] += c[i].item()
+                class_total[label] += 1
+
+    # Print out
+    for i in range(10):
+        print('Accuracy of %5s : %2d %%' % (
+            classes[i], 100 * class_correct[i] / class_total[i]))
+
+
+
+def GetActivation(name="relu"):
+    if name == "relu":
+        return nn.ReLU()
+    elif name == "leakyrelu":
+        return nn.LeakyReLU()
+    elif name == "Sigmoid":
+        return nn.Sigmoid()
+    elif name == "Tanh":
+        return nn.Tanh()
+    elif name == "Identity":
+        return nn.Identity()
+    else:
+        return nn.ReLU()