def test(net, img, hyperparams):
"""
Test a model on a specific image
"""
net.eval()
patch_size = hyperparams["patch_size"]
center_pixel = hyperparams["center_pixel"]
batch_size, device = hyperparams["batch_size"], hyperparams["device"]
n_classes = hyperparams["n_classes"]
kwargs = {
"step": hyperparams["test_stride"],
"window_size": (patch_size, patch_size),
}
probs = np.zeros(img.shape[:2] + (n_classes,))
iterations = count_sliding_window(img, **kwargs) // batch_size
for batch in tqdm(
grouper(batch_size, sliding_window(img, **kwargs)),
total=(iterations),
desc="Inference on the image",
):
with torch.no_grad():
if patch_size == 1:
data = [b[0][0, 0] for b in batch]
data = np.copy(data)
data = torch.from_numpy(data)
else:
data = [b[0] for b in batch]
data = np.copy(data)
data = data.transpose(0, 3, 1, 2)
data = torch.from_numpy(data)
data = data.unsqueeze(1)
indices = [b[1:] for b in batch]
data = data.to(device)
output = net(data)
if isinstance(output, tuple):
output = output[0]
output = output.to("cpu")
if patch_size == 1 or center_pixel:
output = output.numpy()
else:
output = np.transpose(output.numpy(), (0, 2, 3, 1))
for (x, y, w, h), out in zip(indices, output):
if center_pixel:
probs[x + w // 2, y + h // 2] += out
else:
probs[x : x + w, y : y + h] += out
return probs
def test(net, img, hyperparams):
"""
Test a model on a specific image
"""
net.eval()
patch_size = hyperparams['patch_size']
center_pixel = hyperparams['center_pixel']
batch_size, device = hyperparams['batch_size'], hyperparams['device']
n_classes = hyperparams['n_classes']
kwargs = {
'step': hyperparams['test_stride'],
'window_size': (patch_size, patch_size)
}
probs = np.zeros(img.shape[:2] + (n_classes, ))
iterations = count_sliding_window(img, **kwargs) // batch_size
for batch in tqdm(grouper(batch_size, sliding_window(img, **kwargs)),
total=(iterations),
desc="Inference on the image"):
with torch.no_grad():
if patch_size == 1:
data = [b[0][0, 0] for b in batch]
data = np.copy(data)
data = torch.from_numpy(data)
else:
data = [b[0] for b in batch]
data = np.copy(data)
data = data.transpose(0, 3, 1, 2)
data = torch.from_numpy(data)
# data = data.unsqueeze(1) # 3DConv时执行
indices = [b[1:] for b in batch]
data = data.to(device)
output = net(data)
if isinstance(output, tuple):
output = output[0]
output = output.to('cpu') # 将cpu 改为 cuda
if patch_size == 1 or center_pixel:
output = output.numpy()
else:
output = np.transpose(output.numpy(), (0, 2, 3, 1))
for (x, y, w, h), out in zip(indices, output):
if center_pixel:
# probs[x, y] += out
probs[x + w // 2, y + h // 2] += out
# probs[x:x + w, y:y + h] += out
else:
probs[x:x + w, y:y + h] += out
return probs
def test(net, img, args):
"""
Test a model on a specific image
"""
net.eval()
patch_size = args.patch_size
center_pixel = args.center_pixel
batch_size, device = args.batch_size, torch.device(args.device)
n_classes = args.n_classes
kwargs = {
'step': args.test_stride,
'window_size': (patch_size, patch_size)
}
probs = np.zeros(img.shape[:2] + (n_classes, ))
iterations = utils.count_sliding_window(img, **kwargs) // batch_size
for batch in tqdm(utils.grouper(batch_size,
utils.sliding_window(img, **kwargs)),
total=(iterations),
desc="Inference on the image"):
with torch.no_grad():
if patch_size == 1:
data = [b[0][0, 0] for b in batch]
data = np.copy(data)
data = torch.from_numpy(data)
else:
data = [b[0] for b in batch]
data = np.copy(data)
data = data.transpose(0, 3, 1, 2)
data = torch.from_numpy(data)
data = data.unsqueeze(1)
indices = [b[1:] for b in batch]
data = data.to(device)
output = net(data)
if isinstance(output, tuple):
output = output[0]
output = output.to('cpu')
if patch_size == 1 or center_pixel:
output = output.numpy()
else:
output = np.transpose(output.numpy(), (0, 2, 3, 1))
for (x, y, w, h), out in zip(indices, output):
if center_pixel:
probs[x + w // 2, y + h // 2] += out
else:
probs[x:x + w, y:y + h] += out
return probs
def test(net,
test_ids,
all=False,
stride=None,
batch_size=None,
window_size=None):
# Default params
if stride is None:
stride = cfg.WINDOW_WIDTH
if batch_size is None:
batch_size = cfg.BATCH_SIZE
if window_size is None:
window_size = cfg.WINDOW_SIZE
# Use the network on the test set
test_images = (
1 / 255.0 *
np.asarray(io.imread(cfg.DATA_FOLDER.format(id)), dtype='float32')
for id in test_ids)
test_labels = (np.asarray(io.imread(cfg.LABEL_FOLDER.format(id)),
dtype='uint8') for id in test_ids)
eroded_labels = (convert_from_color(io.imread(
cfg.ERODED_FOLDER.format(id))) for id in test_ids)
all_preds = []
all_gts = []
# Switch the network to inference mode
net.eval()
# Start a loop to get image, ground truth and eroded ground truth
for img, gt, gt_e in tqdm(zip(test_images, test_labels, eroded_labels),
total=len(test_ids),
leave=False):
# container for predection
pred = np.zeros(img.shape[:2] + (cfg.N_CLASSES, ))
total = count_sliding_window(img, step=stride,
window_size=window_size) // batch_size
print "Total windows in image: {}".format(total)
for i, coords in enumerate(
tqdm(grouper(
batch_size,
sliding_window(img, step=stride, window_size=window_size)),
total=total,
leave=False)):
# Display in progress results
print "{} of {} done....".format(i, total)
"""if i > 0 and total > 10 and i % int(10 * total / 100) == 0:
_pred = np.argmax(pred, axis=-1)
fig = plt.figure()
fig.add_subplot(1,3,1)
plt.imshow(np.asarray(255 * img, dtype='uint8'))
fig.add_subplot(1,3,2)
plt.imshow(convert_to_color(_pred))
fig.add_subplot(1,3,3)
plt.imshow(gt)
# clear_output()
plt.show()"""
# Build the tensor
image_patches = [
np.copy(img[x:x + w, y:y + h]).transpose((2, 0, 1))
for x, y, w, h in coords
]
image_patches = np.asarray(image_patches)
image_patches = Variable(torch.from_numpy(image_patches).cuda(),
volatile=True)
# Do the inference
outs = net(image_patches)
outs = outs.data.cpu().numpy()
# Fill in the results array
for out, (x, y, w, h) in zip(outs, coords):
out = out.transpose((1, 2, 0))
pred[x:x + w, y:y + h] += out
del (outs)
pred = np.argmax(pred, axis=-1)
# Display the result
# clear_output()
# fig = plt.figure()
# fig.add_subplot(1,3,1)
# plt.imshow(np.asarray(255 * img, dtype='uint8'))
# fig.add_subplot(1,3,2)
# plt.imshow(convert_to_color(pred))
# fig.add_subplot(1,3,3)
# plt.imshow(gt)
# plt.show()
all_preds.append(pred)
all_gts.append(gt_e)
# clear_output()
# Compute some metrics
metrics(pred.ravel(), gt_e.ravel())
accuracy = metrics(
np.concatenate([p.ravel() for p in all_preds]),
np.concatenate([p.ravel() for p in all_gts]).ravel())
if all:
return accuracy, all_preds, all_gts
else:
return accuracy
def pred_and_display(net,
test_ids,
stride=None,
batch_size=None,
window_size=None):
# Default params
if stride is None:
stride = cfg.WINDOW_WIDTH
if batch_size is None:
batch_size = cfg.BATCH_SIZE
if window_size is None:
window_size = cfg.WINDOW_SIZE
test_images = (
1 / 255.0 *
np.asarray(io.imread(cfg.DATA_FOLDER.format(id)), dtype='float32')
for id in test_ids)
all_preds = []
net.eval()
for img in tqdm(test_images, total=len(test_ids), leave=False):
pred = np.zeros(img.shape[:2] + (cfg.N_CLASSES, ))
total = count_sliding_window(img, step=stride,
window_size=window_size) // batch_size
# print total
# exit(0)
for i, coords in enumerate(
tqdm(grouper(
batch_size,
sliding_window(img, step=stride, window_size=window_size)),
total=total,
leave=False)):
image_patches = [
np.copy(img[x:x + w, y:y + h]).transpose((2, 0, 1))
for x, y, w, h in coords
]
image_patches = np.asarray(image_patches)
image_patches = Variable(torch.from_numpy(image_patches).cuda(),
volatile=True)
# Do the inference
outs = net(image_patches)
outs = outs.data.cpu().numpy()
for out, (x, y, w, h) in zip(outs, coords):
out = out.transpose((1, 2, 0))
pred[x:x + w, y:y + h] += out
del (outs)
pred = np.argmax(pred, axis=-1)
# Display the result
# clear_output()
fig = plt.figure()
fig.add_subplot(1, 2, 1)
plt.imshow(np.asarray(255 * img, dtype='uint8'))
fig.add_subplot(1, 2, 2)
plt.imshow(convert_to_color(pred))
plt.show()
all_preds.append(pred)
return all_preds
