def test_conv(self):
for conv_op in [conv.conv2d, conv2d]:
for border_mode in ['valid', 'full']:
image_shape = (2, 2, 4, 5)
filter_shape = (2, 2, 2, 3)
image_dim = len(image_shape)
filter_dim = len(filter_shape)
input = tensor.TensorType(
theano.config.floatX,
[False] * image_dim)(name='input')
filters = tensor.TensorType(
theano.config.floatX,
[False] * filter_dim)(name='filter')
ev_input = tensor.TensorType(
theano.config.floatX,
[False] * image_dim)(name='ev_input')
ev_filters = tensor.TensorType(
theano.config.floatX,
[False] * filter_dim)(name='ev_filters')
def sym_conv2d(input, filters):
return conv_op(input, filters, border_mode=border_mode)
output = sym_conv2d(input, filters).flatten()
yv = tensor.Rop(output, [input, filters], [ev_input, ev_filters])
mode = None
if theano.config.mode == "FAST_COMPILE":
mode = "FAST_RUN"
rop_f = function([input, filters, ev_input, ev_filters],
yv, on_unused_input='ignore', mode=mode)
sy, _ = theano.scan(lambda i, y, x1, x2, v1, v2:
(tensor.grad(y[i], x1) * v1).sum() +
(tensor.grad(y[i], x2) * v2).sum(),
sequences=tensor.arange(output.shape[0]),
non_sequences=[output, input, filters,
ev_input, ev_filters],
mode=mode)
scan_f = function([input, filters, ev_input, ev_filters], sy,
on_unused_input='ignore', mode=mode)
dtype = theano.config.floatX
image_data = numpy.random.random(image_shape).astype(dtype)
filter_data = numpy.random.random(filter_shape).astype(dtype)
ev_image_data = numpy.random.random(image_shape).astype(dtype)
ev_filter_data = numpy.random.random(filter_shape).astype(dtype)
v1 = rop_f(image_data, filter_data, ev_image_data, ev_filter_data)
v2 = scan_f(image_data, filter_data, ev_image_data, ev_filter_data)
assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
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