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7b58366 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import torch
import numpy as np
import matplotlib.pyplot as plt
from data_loaders import load_dataset, get_dataset_sizes, get_dataloaders, get_class_names
# Visualize Model Predictions
def visualize_model(model, device, batch_size, fig_name="Predictions"):
images_so_far = 0
_fig = plt.figure(fig_name)
model.eval()
# Load data
data_set = load_dataset()
dataset_sizes = get_dataset_sizes(data_set)
class_names = get_class_names(data_set)
dataloaders = get_dataloaders(data_set, batch_size)
with torch.no_grad():
for _i, (inputs, labels) in enumerate(dataloaders["validation"]):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(batch_size // 2, 2, images_so_far)
ax.axis("off")
ax.set_title("[Pred: {}]\n[Label: {}]".format(class_names[preds[j]], class_names[labels[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == batch_size:
return
plt.show()
# Show images
def imshow(inp, title=None):
"""Display image from tensor."""
inp = inp.numpy().transpose((1, 2, 0))
# Inverse of the initial normalization operation.
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
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