def outputTest(epoch, batch_idx, content, size):
model.eval()
test_loss = 0
labels = np.array([], dtype='str').reshape(0, 1)
index = 0
if not args.output_tsne:
probability = np.array([], dtype='str').reshape(0, 10)
else:
probability = np.array([], dtype='str').reshape(0, 2)
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output, fc1 = model(data)
labels = np.concatenate(
(labels, np.expand_dims(target.data.cpu().numpy(),
axis=1).astype('str')),
axis=0)
if not args.output_tsne:
probability = np.concatenate(
(probability, np.exp(output.data.cpu().numpy()).astype('str')),
axis=0)
else:
features_embedded = TSNE(n_components=2,
perplexity=40).fit_transform(
fc1.data.cpu().numpy()).astype('str')
probability = np.concatenate(
(probability, features_embedded.astype('str')), axis=0)
test_loss += F.nll_loss(
output, target, size_average=False).data[0] # sum up batch loss
#pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
index += 1
if size / args.test_batch_size >= index:
break
test_loss /= size
epochs = np.expand_dims(np.repeat(str(epoch), size), axis=1)
batchIds = np.expand_dims(np.repeat(str(batch_idx), size), axis=1)
loss = np.expand_dims(np.repeat(test_loss, size), axis=1)
path = np.expand_dims(np.array([str(i + 1) + '.png' for i in range(size)]),
axis=1)
combine = np.concatenate(
(epochs, loss, batchIds, labels, path, probability), axis=1)
return np.concatenate((content, combine), axis=0)
# dist = cdist(feas_normalized, feas_normalized)
# dist[np.arange(569), np.arange(569)] = np.nan
# plt.matshow(dist)
# plt.colorbar()
# plt.show()
embed2 = TSNE(n_components=2).fit_transform(feas_normalized)
height = 128
width = 64
embed2 = embed2 - embed2.min(axis=0)
# np.median(np.abs(np.diff(embed2, axis=0)), axis=0)
space = 64 + 16
embed2 *= space
embed2 = embed2.astype(int)
extend = np.array([
height,
width,
])
shape = tuple((embed2.max(axis=0).astype(int) + extend).tolist()) + (3, )
print('res shape', shape)
res = np.ones(shape).astype(np.uint8) * 255
for ind in range(feas.shape[0]):
img_name = feask[ind]
img = cv2.imread(img_name)
img2 = cvb.resize_keep_ar(img, height, width)
if not (img2.shape[0] <= height and img2.shape[1] <= width):
img2 = cvb.resize_keep_ar(img, width, width)
