def test_native_convolution(tmpdir):
# this test needs native binary convolution library built with halide.
if not C.contrib.netopt.native_convolve_function_registered:
pytest.skip()
z = _create_convolution_model()
binz = qc.convert_to_binary_convolution(z, _filter)
# save and load to transfer the model to CPU device as native binary
# convolution does not run on GPU yet.
model_file = str(tmpdir / ('binary_model.cmf'))
binz.save(model_file)
eval_device = C.cpu()
model = C.Function.load(model_file, device=eval_device)
# convert to native halide implementation.
native_binz = qc.convert_to_native_binary_convolution(model)
functions = C.logging.graph.depth_first_search(
native_binz, (lambda x : type(x) == C.Function and x.op_name =='BinaryConvolveOp') , depth = 0)
assert(len(functions) == 3)
img_data = np.reshape(dat, (1, 1, 28, 28))
res = native_binz.eval(img_data, device=eval_device)
assert(len(res) > 0) # evaluation should work with the new model.
def test_native_convolution(tmpdir):
# this test needs native binary convolution library built with halide.
if not C.contrib.netopt.native_convolve_function_registered:
pytest.skip()
z = _create_convolution_model()
binz = qc.convert_to_binary_convolution(z, _filter)
# save and load to transfer the model to CPU device as native binary
# convolution does not run on GPU yet.
model_file = str(tmpdir / ('binary_model.cmf'))
binz.save(model_file)
eval_device = C.cpu()
model = C.Function.load(model_file, device=eval_device)
# convert to native halide implementation.
native_binz = qc.convert_to_native_binary_convolution(model)
functions = C.logging.graph.depth_first_search(
native_binz, (lambda x : type(x) == C.Function and x.op_name =='BinaryConvolveOp') , depth = 0)
assert(len(functions) == 3)
img_data = np.reshape(dat, (1, 1, 28, 28))
res = native_binz.eval(img_data, device=eval_device)
assert(len(res) > 0) # evaluation should work with the new model.
def test_binarization():
z = _create_convolution_model_with_skip_level_links()
binz = qc.convert_to_binary_convolution(z)
blocks = C.logging.graph.depth_first_search(
binz, (lambda x : type(x) == C.Function and x.is_block and x.op_name =='BinaryConvolution') , depth = 0)
assert(len(blocks) == 4) # all convolution blocks should be converted.
binz = qc.convert_to_binary_convolution(z, _filter)
blocks = C.logging.graph.depth_first_search(
binz, (lambda x : type(x) == C.Function and x.is_block and x.op_name =='BinaryConvolution') , depth = 0)
assert(len(blocks) == 3) # now only three of them should be converted.
assert(all(b.op_name != 'first_convo' for b in blocks))
