def build_net(graph: Graph):
try:
if not hasattr(os.environ, 'GLOG_minloglevel'):
os.environ['GLOG_minloglevel'] = '2'
import caffe
log.info('Partial inference via the framework is available')
except ImportError:
log.warning(
'pyCaffe is not available. Partial inference via the framework is not '
+ 'possible')
return
try:
net = caffe.Net(graph.proto_path, graph.caffemodel_path, caffe.TEST)
except Exception as err:
raise Error(
'Error happened while constructing caffe.Net in the Caffe fallback function: {}. '
+ refer_to_faq_msg(12), str(err)) from err
inputs_node_name = find_inputs(graph)
reshape_flag = False
for i in inputs_node_name:
new_input_shape = graph.node[i]['shape'].astype(int)
top_node = get_node_top(graph, i)
caffe_shape = list(net.blobs[top_node].shape)
if not np.all(caffe_shape == new_input_shape):
net.blobs[top_node].reshape(*[int(x) for x in new_input_shape])
reshape_flag = True
if reshape_flag:
net.reshape()
try:
net.forward()
except KeyError as err:
log.error('Error happened in Caffe net.forward: {}.'.format(str(err)))
log.error(
'It may point to the known bug in pycaffe when top and name of the layer do not match.'
)
log.error('Please make sure that the latest pycaffe is used.')
raise Error(
'Cannot infer shapes due to exception in Caffe: {}. ' +
refer_to_faq_msg(13), str(err)) from err
except Exception as err:
raise Error(
'Cannot infer shapes in Caffe net.forward due to exception: {}.' +
refer_to_faq_msg(13), str(err)) from err
graph.__setattr__('caffe_net', net)
def find_and_replace_pattern(self, graph: Graph):
from mo.front.caffe import loader
argv = graph.graph['cmd_params']
original_shapes = graph.graph['original_shapes']
caffe_pb2 = graph.graph['caffe_pb2']
input_names = find_inputs(graph)
mf = []
try:
if argv.mean_file and len(original_shapes) == 1:
mf = loader.parse_mean(argv.mean_file, original_shapes[input_names[0]],
argv.mean_file_offsets, caffe_pb2)
elif argv.mean_file:
raise Error('Mean file for topologies with multiple inputs is not supported. ' +
refer_to_faq_msg(9))
except ValueError as e:
raise Error('Cannot load or process mean file: value error {}. ' +
refer_to_faq_msg(10), str(e)) from e
graph.graph['mf'] = mf
graph.graph['input_names'] = input_names
def driver(argv: argparse.Namespace,
proto_file_name: str,
model_file_name: str,
output_model_name: str,
output_dir: str,
caffe_proto_path: str,
mean_file: str = "",
mean_file_offsets: tuple = None,
custom_layers_mapping_path: str = None):
log_step(argv.steps, 'LOAD')
meta_info = get_meta_info(argv)
caffe_pb2 = loader.import_caffe_pb2(caffe_proto_path)
proto, model = loader.load_caffe_proto_model(caffe_pb2, proto_file_name,
model_file_name)
update_extractors_with_extensions(
caffe_type_extractors, argv.disable_omitting_optional if hasattr(
argv, 'disable_omitting_optional') else False,
argv.disable_flattening_optional_params if hasattr(
argv, 'disable_flattening_optional_params') else False)
try:
graph, original_shapes = loader.caffe_pb_to_nx(proto, model)
except ValueError as e:
raise Error(
'Invalid prototxt file: value error {}. ' + refer_to_faq_msg(11),
str(e)) from e
log.debug("After caffe_pb_to_nx")
graph.print_graph_stat()
graph.check_empty_graph('load_caffe_proto_model')
graph.__setattr__('proto_path', proto_file_name)
graph.__setattr__('caffemodel_path', model_file_name)
graph.__setattr__('name',
getattr(proto, 'name', None) or output_model_name)
graph.graph['layout'] = 'NCHW'
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'caffe'
if graph.graph['cmd_params'].generate_experimental_IR_V10:
version = 10
else:
version = 6
graph.graph[
'ir_version'] = 2 if argv.generate_deprecated_IR_V2 else version
custom_layers_map = custom_layers_mapping.load_layers_xml(
custom_layers_mapping_path)
custom_layers_mapping.update_extractors(
caffe_type_extractors,
custom_layers_map, argv.disable_omitting_optional if hasattr(
argv, 'disable_omitting_optional') else False,
argv.enable_flattening_nested_params if hasattr(
argv, 'enable_flattening_nested_params') else False)
extract_node_attrs(
graph, lambda node: caffe_extractor(
node, check_for_duplicates(caffe_type_extractors)))
# --------------------------------- LOAD END ------------------------------------------------------
log_step(argv.steps, 'FRONT')
class_registration.apply_replacements(
graph, class_registration.ClassType.FRONT_REPLACER)
log_step(argv.steps, 'MIDDLE')
class_registration.apply_replacements(
graph, class_registration.ClassType.MIDDLE_REPLACER)
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
mark_unfused_nodes(graph, argv.finegrain_fusing)
# need this pass even without fusing to convert scale with 2 inputs
convert_scale_shift_to_mul_add(graph)
graph_clean_up(graph)
if not argv.disable_fusing:
convert_bn_to_mul_add(graph)
graph_clean_up(graph)
fuse_mul_add_sequence(graph)
graph_clean_up(graph)
fuse_linear_ops(graph)
graph_clean_up(graph)
if not argv.disable_resnet_optimization:
stride_optimization(graph)
convert_muladd_to_scaleshift(graph)
convert_matmul_to_fully_connected(graph)
batch_norm_fuse(graph)
convert_add_or_mul_to_scaleshift(graph) # scale = 1
graph_clean_up(graph)
log.debug("After graph_cleanup")
graph.print_graph_stat()
if argv.reverse_input_channels:
reverse_input_channels(graph)
if argv.move_to_preprocess:
move_scaleshift_to_preprocess(graph)
graph_clean_up(graph)
FuseReshapesSequence().find_and_replace_pattern(graph)
RemoveRedundantReshapes().find_and_replace_pattern(graph)
input_names = find_inputs(graph)
mf = []
try:
if mean_file and len(original_shapes) == 1:
mf = loader.parse_mean(mean_file, original_shapes[input_names[0]],
mean_file_offsets, caffe_pb2)
elif mean_file:
raise Error(
'Mean file for topologies with multiple inputs is not supported. '
+ refer_to_faq_msg(9))
except ValueError as e:
raise Error(
'Cannot load or process mean file: value error {}. ' +
refer_to_faq_msg(10), str(e)) from e
merge_nodes_permutations(graph)
permute_data_nodes_attrs(graph)
permute_op_nodes_attrs(graph)
graph_clean_up(graph)
log_step(argv.steps, 'BACK')
class_registration.apply_replacements(
graph, class_registration.ClassType.BACK_REPLACER)
remove_const_ops(graph)
CreateConstNodesReplacement().find_and_replace_pattern(graph)
remove_output_ops(graph)
log_step(argv.steps, 'EMIT')
prepare_emit_ir(graph=graph,
data_type=argv.data_type,
output_dir=output_dir,
output_model_name=output_model_name,
mean_data=mf,
input_names=input_names,
meta_info=meta_info)
return 0
def driver(argv: argparse.Namespace,
proto_file_name: str,
model_file_name: str,
output_model_name: str,
outputs: list,
output_dir: str,
scale: float,
user_shapes: [None, list, np.array] = None,
mean_scale_values: [dict, list] = (),
mean_file: str = "",
mean_file_offsets: tuple = None,
custom_layers_mapping_path: str = None):
meta_info = get_meta_info(argv)
FusePermutesSequence.enabled = False
proto, model = loader.load_caffe_proto_model(proto_file_name,
model_file_name)
update_extractors_with_extensions(
caffe_type_extractors, argv.disable_omitting_optional if hasattr(
argv, 'disable_omitting_optional') else False,
argv.disable_flattening_optional_params if hasattr(
argv, 'disable_flattening_optional_params') else False)
try:
graph, original_shapes = loader.caffe_pb_to_nx(proto, model)
except ValueError as e:
raise Error(
'Invalid prototxt file: value error {}. ' + refer_to_faq_msg(11),
str(e)) from e
log.debug("After caffe_pb_to_nx")
print_graph_stat(graph)
check_empty_graph(graph, 'load_caffe_proto_model')
graph.__setattr__('proto_path', proto_file_name)
graph.__setattr__('caffemodel_path', model_file_name)
graph.__setattr__('name',
getattr(proto, 'name', None) or output_model_name)
graph.graph['layout'] = 'NCHW'
graph.graph['cmd_params'] = argv
graph.graph['fw'] = 'caffe'
graph.graph['ir_version'] = 2 if argv.generate_deprecated_IR_V2 else 4
extract_node_attrs(graph, lambda node: (True, common_caffe_fields(node)))
log.debug("After adding specific nodes for outputs")
print_graph_stat(graph)
custom_layers_map = custom_layers_mapping.load_layers_xml(
custom_layers_mapping_path)
custom_layers_mapping.update_extractors(
caffe_type_extractors,
custom_layers_map, argv.disable_omitting_optional if hasattr(
argv, 'disable_omitting_optional') else False,
argv.enable_flattening_nested_params if hasattr(
argv, 'enable_flattening_nested_params') else False)
extract_node_attrs(
graph, lambda node: caffe_extractor(
node, check_for_duplicates(caffe_type_extractors)))
log.debug("After extract_node_attr")
print_graph_stat(graph)
packed_user_shapes, packed_outputs, freeze_placeholder = user_data_repack(
graph, user_shapes, outputs, argv.freeze_placeholder_with_value)
if argv.freeze_placeholder_with_value is not None:
FreezePlaceholderValue.enabled = True
FreezePlaceholderValue.replacement_dict = freeze_placeholder
class_registration.update_registration([FrontReplacementSubgraph])
output_op_nodes = add_output_ops(graph, packed_outputs)
input_op_nodes = add_input_ops(graph, packed_user_shapes, True)
override_placeholder_shapes(graph, packed_user_shapes)
override_batch(graph, argv.batch)
graph_clean_up(graph)
check_empty_graph(graph, 'add_output_ops and add_input_ops')
class_registration.apply_replacements(
graph, class_registration.ClassType.FRONT_REPLACER)
graph = create_tensor_nodes(graph)
log.debug("After create_tensor_nodes")
print_graph_stat(graph)
remove_op_nodes(graph, {'op': 'Identity'})
remove_output_ops(graph)
graph_clean_up(graph)
log.debug("After removing specific nodes for output")
print_graph_stat(graph)
# you need to pass required network outputs here
# but we don't have a way yet, so just passing all discovered sinks
mark_outputs(graph)
graph_clean_up(graph)
log.debug("After graph_cleanup")
print_graph_stat(graph)
graph = partial_infer(graph)
log.debug("After partial_infer")
print_graph_stat(graph)
check_empty_graph(graph, 'partial_infer')
duplicate_shared_weights(graph)
input_op_nodes = add_input_ops(graph, packed_user_shapes, False)
graph_clean_up(graph)
check_empty_graph(graph, 'add_input_ops')
scale_input(graph, scale)
add_mean_scale_values(graph, mean_scale_values)
log.debug("Split multi input convolutions")
convert_multi_input_conv(graph)
graph_clean_up(graph)
log.debug("After graph_cleanup")
print_graph_stat(graph)
remove_op_nodes(graph, {'op': 'Dropout'})
remove_op_nodes(graph, {'phase': 0})
graph_clean_up(graph)
class_registration.apply_replacements(
graph, class_registration.ClassType.MIDDLE_REPLACER)
mean_to_avgpool(graph)
# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
mark_unfused_nodes(graph, argv.finegrain_fusing)
#need this pass even without fusing to convert scale with 2 inputs
convert_scale_shift_to_mul_add(graph)
graph_clean_up(graph)
if not argv.disable_fusing:
convert_bn_to_mul_add(graph)
graph_clean_up(graph)
fuse_mul_add_sequence(graph)
graph_clean_up(graph)
fuse_linear_ops(graph)
graph_clean_up(graph)
if not argv.disable_resnet_optimization:
stride_optimization(graph)
convert_muladd_to_scaleshift_or_power(graph)
convert_matmul_to_fully_connected(graph)
batch_norm_fuse(graph)
convert_mul_add_to_power(graph)
convert_add_to_scaleshift(graph) # scale = 1
convert_mul_to_scaleshift(graph) # biases = 0
graph_clean_up(graph)
log.debug("After graph_cleanup")
print_graph_stat(graph)
if argv.reverse_input_channels:
reverse_input_channels(graph)
if argv.move_to_preprocess:
move_scaleshift_to_preprocess(graph)
graph_clean_up(graph)
fuse_sequence_of_reshapes(graph)
input_names = find_inputs(graph)
mf = []
try:
if mean_file and len(original_shapes) == 1:
mf = loader.parse_mean(mean_file, original_shapes[input_names[0]],
mean_file_offsets)
elif mean_file:
raise Error(
'Mean file for topologies with multiple inputs is not supported. '
+ refer_to_faq_msg(9))
except ValueError as e:
raise Error(
'Cannot load or process mean file: value error {}. ' +
refer_to_faq_msg(10), str(e)) from e
class_registration.apply_replacements(
graph, class_registration.ClassType.BACK_REPLACER)
prepare_emit_ir(graph=graph,
data_type=argv.data_type,
output_dir=output_dir,
output_model_name=output_model_name,
mean_data=mf,
input_names=input_names,
meta_info=meta_info)
return 0
