def injectFaultConv2D(a, b):
"Function to call injectFault on Conv2D"
logging.debug("Calling Operator conv2D " + getArgs(a, b))
# FIXME: Make this work with any type, not just float32
res = np.float32( conv2d.conv2d(a, b) )
res = condPerturb(Ops.CONV2D, res)
if logReturn: logging.debug("\tReturning from Conv2D " + str(res) )
return res
def test(x, w, pad='SAME', stride=(1, 1)):
y = conv2d(x, w, pad=pad, stride=stride).ravel()
xx = tf.constant(x, dtype='float32')
ww = tf.constant(w, dtype='float32')
yy = tf.nn.conv2d(xx,
ww,
strides=[1, stride[0], stride[1], 1],
padding=pad)
y_tf = yy.numpy()
np.testing.assert_almost_equal(y, y_tf.ravel(), decimal=3)
def test_gradx(x, w, pad='SAME', stride=(1, 1)):
# [N, H, W, K]
y = conv2d(x, w, pad=pad, stride=stride)
dy = np.random.rand(*y.shape)
dx = conv2d_gradx(w, dy, x.shape[1:3], pad=pad, stride=stride)
# Tensorflow checks
xx = tf.constant(x, dtype='float32')
ww = tf.constant(w, dtype='float32')
dyy = tf.constant(dy, dtype='float32')
yy = tf.nn.conv2d(xx,
ww,
strides=[1, stride[0], stride[1], 1],
padding=pad)
dww = tf.squeeze(tf.gradients(yy, xx, dyy), [0])
with tf.Session() as sess:
dx_tf = dww.eval()
np.testing.assert_almost_equal(dx.ravel(), dx_tf.ravel(), decimal=3)
def test_gradx(x, w, pad='SAME', stride=(1, 1)):
# [N, H, W, K]
y = conv2d(x, w, pad=pad, stride=stride)
dy = np.random.rand(*y.shape)
dx = conv2d_gradx(w, dy, x.shape[1:3], pad=pad, stride=stride)
# Tensorflow checks
with tf.GradientTape() as tape:
xx = tf.constant(x, dtype='float32')
tape.watch(xx)
ww = tf.constant(w, dtype='float32')
dyy = tf.constant(dy, dtype='float32')
yy = tf.nn.conv2d(xx,
ww,
strides=[1, stride[0], stride[1], 1],
padding=pad)
dww = tape.gradient(yy, xx, output_gradients=dyy)
dx_tf = dww.numpy()
np.testing.assert_almost_equal(dx.ravel(), dx_tf.ravel(), decimal=3)