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import matplotlib.pyplot as plt
from torchvision import transforms
def plot_incorrect_preds(incorrect, classes, num_imgs):
import random
# num_imgs is a multiple of 5
assert num_imgs % 5 == 0
assert len(incorrect) >= num_imgs
incorrect_inds = random.sample(range(len(incorrect)), num_imgs)
# incorrect (data, target, pred, output)
fig = plt.figure(figsize=(10, num_imgs // 2))
plt.suptitle("Target | Predicted Label")
for i in range(num_imgs):
curr_incorrect = incorrect[incorrect_inds[i]]
plt.subplot(num_imgs // 5, 5, i + 1, aspect="auto")
# unnormalize = T.Normalize((-mean / std).tolist(), (1.0 / std).tolist())
unnormalized = transforms.Normalize(
(-1.98947368, -1.98436214, -1.71072797), (4.048583, 4.11522634, 3.83141762)
)(curr_incorrect[0])
plt.imshow(transforms.ToPILImage()(unnormalized))
plt.title(
f"{classes[curr_incorrect[1].item()]}|{classes[curr_incorrect[2].item()]}",
# fontsize=8,
)
plt.xticks([])
plt.yticks([])
plt.tight_layout()
return fig
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