def test_div_with_integer(self):
# Test where transformation should not be applied because the divisor is integer
graph = build_graph({
**regular_op_with_shaped_data('parameter', [1, 227, 227, 3], {'type': 'Parameter', 'data_type': np.int32}),
**valued_const_with_data('const', np.array([-1.], dtype=np.int32)),
**regular_op_with_shaped_data('div', None, {'op': 'Div', 'type': 'Divide', 'name': 'my_div'}),
**result()},
[
*connect('parameter:0', '0:div'),
*connect_data('const:0', '1:div'),
*connect('div', 'output'),
])
graph_ref = graph.copy()
Div().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
self.assertTrue(flag, resp)
def test_div_test_2(self):
# Test with two same inputs from one placeholder
graph = build_graph(nodes, [
*connect('placeholder_1:0', '0:div'),
*connect_data('placeholder_1:0', '1:div'),
*connect('div', 'output'),
], nodes_with_edges_only=True)
Div().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes, [
*connect('placeholder_1:0', '0:mul'),
*connect_data('placeholder_1:0', '0:reciprocal'),
*connect('minus_one', '1:reciprocal'),
*connect('reciprocal', '1:mul'),
*connect('mul', 'output'),
], nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
self.assertTrue(flag, resp)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='Multiply')[0]]['name'] == 'my_div')
def find_and_replace_pattern(self, graph: Graph):
fw = graph.graph['fw']
argv = graph.graph['cmd_params']
layout = graph.graph['layout']
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
for_graph_and_each_sub_graph_recursively(
graph,
lambda graph: mark_unfused_nodes(graph, argv.finegrain_fusing))
# Converting FusedBatchNorm layer to Mul->Add->Mul->Add sequence
# IE doesn't support batchNormInference with 4 inputs, so we have to split it to two ScaleShift
for_graph_and_each_sub_graph_recursively(graph, convert_batch_norm)
if fw == 'caffe':
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(
graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(
graph,
Div().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(
graph,
Sub().find_and_replace_pattern)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if not argv.disable_fusing:
if fw != 'caffe':
# Converting ScaleShift layer to Mul->Add
for_graph_and_each_sub_graph_recursively(
graph, convert_scale_shift_to_mul_add)
for_graph_and_each_sub_graph_recursively(
graph, lambda G: G.clean_up())
# Fusing the sequences of Mul/Add operations
for_graph_and_each_sub_graph_recursively(graph,
fuse_mul_add_sequence)
for_graph_and_each_sub_graph_recursively(graph,
lambda G: G.clean_up())
normalize_eltwise_inputs(graph)
for_graph_and_each_sub_graph_recursively(graph,
lambda G: G.clean_up())
# Fusing linear operation to Convolution
for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(graph,
lambda G: G.clean_up())
if not argv.disable_gfusing:
for_graph_and_each_sub_graph_recursively(
graph, grouped_convolutions_fusing)
for_graph_and_each_sub_graph_recursively(graph,
lambda G: G.clean_up())
if not argv.disable_fusing:
for_graph_and_each_sub_graph_recursively(
graph, fuse_linear_ops)
for_graph_and_each_sub_graph_recursively(
graph, lambda G: G.clean_up())
for_graph_and_each_sub_graph_recursively(graph,
normalize_eltwise_inputs)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if not argv.disable_fusing:
MarkNodesToFuseUpToFakeQuantize().find_and_replace_pattern(graph)
FakeQuantizeFuse().find_and_replace_pattern(graph)
AddFakeQuantizeFuse().find_and_replace_pattern(graph)
MulFakeQuantizeFuse().find_and_replace_pattern(graph)
for_graph_and_each_sub_graph_recursively(graph,
lambda G: G.clean_up())
for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
if layout != 'NHWC' and not argv.disable_resnet_optimization:
stride_optimization(graph)