def testRandomization(self):
# Run 1x1 crop num_samples times in an image and ensure that one finds each
# pixel 1/num_pixels of the time.
num_samples = 1000
height = 5
width = 4
num_pixels = height * width
data = np.arange(num_pixels).reshape([height, width, 1])
x_np = np.array(data).astype(np.int32)
target_shape_np = np.array([1, 1], dtype=np.int64)
y = []
with self.test_session():
x = constant_op.constant(x_np, shape=x_np.shape)
target_shape = constant_op.constant(target_shape_np, shape=[2])
y_tf = image_ops.random_crop(x, target_shape)
for _ in xrange(num_samples):
y_np = y_tf.eval()
self.assertAllEqual(y_np.shape, [1, 1, 1])
y.extend(y_np.flatten())
# Calculate the mean and 4 * standard deviation.
mean = [num_samples / num_pixels] * num_pixels
four_stddev = 4.0 * np.sqrt(mean)
# Ensure that each entry is observed in 1/num_pixels of the samples
# within 4 standard deviations.
counts = np.bincount(y)
self.assertAllClose(counts, mean, atol=four_stddev)
def testRandomization(self):
# Run 1x1 crop num_samples times in an image and ensure that one finds each
# pixel 1/num_pixels of the time.
num_samples = 1000
height = 5
width = 4
num_pixels = height * width
data = np.arange(num_pixels).reshape([height, width, 1])
x_np = np.array(data).astype(np.int32)
target_shape_np = np.array([1, 1], dtype=np.int64)
y = []
with self.test_session():
x = constant_op.constant(x_np, shape=x_np.shape)
target_shape = constant_op.constant(target_shape_np, shape=[2])
y_tf = image_ops.random_crop(x, target_shape)
for _ in xrange(num_samples):
y_np = y_tf.eval()
self.assertAllEqual(y_np.shape, [1, 1, 1])
y.extend(y_np.flatten())
# Calculate the mean and 4 * standard deviation.
mean = [num_samples / num_pixels] * num_pixels
four_stddev = 4.0 * np.sqrt(mean)
# Ensure that each entry is observed in 1/num_pixels of the samples
# within 4 standard deviations.
counts = np.bincount(y)
self.assertAllClose(counts, mean, atol=four_stddev)
def testNoOp(self):
# No random cropping is performed since the target width and height
# are match the image dimensions.
height = 4
width = 5
x_shape = [height, width, 3]
x_np = np.arange(0, np.prod(x_shape), dtype=np.int32).reshape(x_shape)
target_shape_np = np.array([height, width], dtype=np.int64)
with self.test_session():
x = constant_op.constant(x_np, shape=x_shape)
target_shape = constant_op.constant(target_shape_np, shape=[2])
y = image_ops.random_crop(x, target_shape)
y_tf = y.eval()
self.assertAllEqual(y_tf, x_np)
def testNoOp(self):
# No random cropping is performed since the target width and height
# are match the image dimensions.
height = 4
width = 5
x_shape = [height, width, 3]
x_np = np.arange(0, np.prod(x_shape), dtype=np.int32).reshape(x_shape)
target_shape_np = np.array([height, width], dtype=np.int64)
with self.test_session():
x = constant_op.constant(x_np, shape=x_shape)
target_shape = constant_op.constant(target_shape_np, shape=[2])
y = image_ops.random_crop(x, target_shape)
y_tf = y.eval()
self.assertAllEqual(y_tf, x_np)