def test_pool_1_port_through_quantize(self):
graph = build_graph(nodes,
edges, {'mul': {
'fuse_up_to_quantize_ports': [0]
}},
nodes_with_edges_only=True)
graph.stage = 'middle'
FakeQuantizeFuse().find_and_replace_pattern(graph)
graph_ref = build_graph(nodes, edges_ref_1, nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
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)