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import torch |
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import matplotlib.pyplot as plt |
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import numpy as np |
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def pred_label(model, img): |
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model = model.to('cpu') |
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img = img.unsqueeze(0) |
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logits = model(img) |
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pred_probab = torch.nn.Softmax(dim=1)(logits) |
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y_pred = pred_probab.argmax(1) |
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return y_pred |
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def save_image(img, title, count): |
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fig, ax = plt.subplots() |
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imgplot = ax.imshow(img, interpolation='bicubic') |
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ax.spines['top'].set_visible(False) |
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ax.spines['left'].set_visible(False) |
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ax.spines['bottom'].set_visible(False) |
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ax.spines['right'].set_visible(False) |
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ax.set_xticks([]) |
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ax.set_yticks([]) |
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plt.title(title) |
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plt.savefig('./viz/img' + str(count)) |
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def observations(model, testloader): |
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for imgs, labels in testloader: |
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images = [imgs[0].permute(1, 2, 0), |
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imgs[1].permute(1, 2, 0), |
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imgs[2].permute(1, 2, 0), |
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imgs[3].permute(1, 2, 0), |
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imgs[4].permute(1, 2, 0)] |
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pred_label1 = pred_label(model, imgs[0]).item() |
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pred_label2 = pred_label(model, imgs[1]).item() |
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pred_label3 = pred_label(model, imgs[2]).item() |
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pred_label4 = pred_label(model, imgs[3]).item() |
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pred_label5 = pred_label(model, imgs[4]).item() |
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titles = ["Pred: {}, Actual: {}".format(pred_label1, labels[0]), |
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"Pred: {}, Actual: {}".format(pred_label2, labels[1]), |
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"Pred: {}, Actual: {}".format(pred_label3, labels[2]), |
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"Pred: {}, Actual: {}".format(pred_label4, labels[3]), |
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"Pred: {}, Actual: {}".format(pred_label5, labels[4])] |
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count = 1 |
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for image, title in zip(images, titles): |
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save_image(image, title, count) |
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count += 1 |
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break |
