def imshow_unscaled(target, return_im=False):
np_target = target
np_target_scaled = np.clip(((np_target + 1) / 2.0) * 256, 0, 255)
im = np.concatenate(np_target_scaled, axis=0)
image.imshow(np.uint8(im))
if return_im:
return im
from torch.autograd import Variable
from config import MODEL_PATH
from models.model import Net, classes
from data import loadTestData
from utils.image import imshow
testset, testloader = loadTestData()
net = Net()
net.load_state_dict(torch.load(MODEL_PATH))
dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
print('Predicted: ',
' '.join('%5s' % classes[predicted[j][0]] for j in range(4)))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
targets = []
s = slice(batch_start, min(num_samples, batch_start + batch_size))
input_test = {y: ys[s],
z: zs[s],
truncation: trunc}
out_input_test = sess.run(outputs_orig, input_test)
for i in range(a.shape[0]):
target_fn, mask_out = get_target_np(out_input_test, a[i])
alpha_val_for_graph = np.ones((zs[s].shape[0], Nsliders)) * np.log(a[i])
best_inputs = {z: zs[s], y: ys[s], truncation: trunc, alpha: alpha_val_for_graph, target: target_fn, mask: mask_out}
best_im_out = sess.run(transformed_output, best_inputs)
# collect images
ims.append(np.uint8(np.clip(((best_im_out + 1) / 2.0) * 256, 0, 255)))
targets.append(np.uint8(np.clip(((target_fn + 1) / 2.0) * 256, 0, 255)))
im_stack = np.concatenate(targets + ims).astype(np.uint8)
imshow(imgrid(im_stack, cols = len(a)))
# plot losses
import matplotlib.pyplot as plt
plt.plot(loss_vals)
plt.xlabel('num samples, lr{}'.format(lr))
plt.ylabel('Loss')
plt.show()