def test_pytorch_model_preprocessing_shape_change():
import torch
import torch.nn as nn
num_classes = 1000
bounds = (0, 255)
channels = num_classes
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
def forward(self, x):
x = torch.mean(x, 3)
x = torch.mean(x, 2)
logits = x
return logits
model = Net()
model1 = PyTorchModel(model, bounds=bounds, num_classes=num_classes)
def preprocessing2(x):
if x.ndim == 3:
x = np.transpose(x, axes=(2, 0, 1))
elif x.ndim == 4:
x = np.transpose(x, axes=(0, 3, 1, 2))
def grad(dmdp):
assert dmdp.ndim == 3
dmdx = np.transpose(dmdp, axes=(1, 2, 0))
return dmdx
return x, grad
model2 = PyTorchModel(model,
bounds=bounds,
num_classes=num_classes,
preprocessing=preprocessing2)
np.random.seed(22)
test_images_nhwc = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_images_nchw = np.transpose(test_images_nhwc, (0, 3, 1, 2))
p1 = model1.batch_predictions(test_images_nchw)
p2 = model2.batch_predictions(test_images_nhwc)
assert np.all(p1 == p2)
p1 = model1.predictions(test_images_nchw[0])
p2 = model2.predictions(test_images_nhwc[0])
assert np.all(p1 == p2)
g1 = model1.gradient(test_images_nchw[0], 3)
assert g1.ndim == 3
g1 = np.transpose(g1, (1, 2, 0))
g2 = model2.gradient(test_images_nhwc[0], 3)
np.testing.assert_array_almost_equal(g1, g2)
def test_pytorch_model_preprocessing():
import torch
import torch.nn as nn
num_classes = 1000
bounds = (0, 255)
channels = num_classes
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
def forward(self, x):
x = torch.mean(x, 3)
x = torch.mean(x, 2)
logits = x
return logits
model = Net()
preprocessing = (np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1)
model1 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes)
model2 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes,
preprocessing=preprocessing)
model3 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes)
np.random.seed(22)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(
p1 - p1.max(),
p3 - p3.max(),
decimal=5)
def test_pytorch_model_preprocessing():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
def forward(self, x):
x = torch.mean(x, 3)
x = torch.squeeze(x, dim=3)
x = torch.mean(x, 2)
x = torch.squeeze(x, dim=2)
logits = x
return logits
model = Net()
def preprocess_fn(x):
# modify x in-place
x /= 2
return x
model1 = PyTorchModel(model,
bounds=bounds,
num_classes=num_classes,
cuda=False)
model2 = PyTorchModel(model,
bounds=bounds,
num_classes=num_classes,
cuda=False,
preprocess_fn=preprocess_fn)
model3 = PyTorchModel(model,
bounds=bounds,
num_classes=num_classes,
cuda=False)
np.random.seed(22)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(p1 - p1.max(),
p3 - p3.max(),
decimal=5)
def test_pytorch_model_preprocessing():
import torch
import torch.nn as nn
num_classes = 1000
bounds = (0, 255)
channels = num_classes
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
def forward(self, x):
x = torch.mean(x, 3)
x = torch.mean(x, 2)
logits = x
return logits
model = Net()
preprocessing = (np.arange(num_classes)[:, None, None],
np.random.uniform(size=(channels, 5, 5)) + 1)
model1 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes,
cuda=False)
model2 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes,
cuda=False,
preprocessing=preprocessing)
model3 = PyTorchModel(
model,
bounds=bounds,
num_classes=num_classes,
cuda=False)
np.random.seed(22)
test_images = np.random.rand(2, channels, 5, 5).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(
p1 - p1.max(),
p3 - p3.max(),
decimal=5)