From 4b903a5804ca57104dfc5fc18584f0d514b7d0ba Mon Sep 17 00:00:00 2001 From: Alexander Suslov Date: Tue, 19 Dec 2023 16:59:07 +0400 Subject: [PATCH] [PT] Constant graph (#2316) ### Changes Introduced model parameters tracing ![image](https://github.com/openvinotoolkit/nncf/assets/3229971/9eb4d341-832a-42d6-a3d5-e4722abf712d) ### Reason for changes General approach to access and change weights in PyTorch model. ### Related tickets ref: 119585 ### Tests test_graphs.py test_compressed_graph.py test_graph_building.py test_nncf_network.py test_tracing_context.py --- nncf/common/graph/definitions.py | 2 + nncf/common/graph/layer_attributes.py | 9 + nncf/common/graph/operator_metatypes.py | 11 + nncf/torch/dynamic_graph/context.py | 43 +- nncf/torch/dynamic_graph/graph.py | 16 +- nncf/torch/dynamic_graph/graph_tracer.py | 10 +- .../layer_attributes_handlers.py | 12 +- nncf/torch/dynamic_graph/patch_pytorch.py | 19 +- nncf/torch/dynamic_graph/trace_functions.py | 132 +- nncf/torch/dynamic_graph/trace_tensor.py | 202 +- nncf/torch/dynamic_graph/wrappers.py | 78 +- nncf/torch/graph/graph_builder.py | 40 +- nncf/torch/graph/operator_metatypes.py | 8 + nncf/torch/model_creation.py | 18 +- nncf/torch/nncf_network.py | 63 +- nncf/torch/utils.py | 4 +- .../alexnet_with_parameters_tracing.dot | 75 + .../densenet121_with_parameters_tracing.dot | 1631 +++++++++++++++++ .../inception_v3_with_parameters_tracing.dot | 1288 +++++++++++++ .../inception_with_parameters_tracing.dot | 974 ++++++++++ .../lenet_with_parameters_tracing.dot | 45 + .../lstm_bi_seq_with_parameters_tracing.dot | 103 ++ ...stm_bi_stacked_with_parameters_tracing.dot | 200 ++ .../lstm_cell_with_parameters_tracing.dot | 38 + .../lstm_uni_seq_with_parameters_tracing.dot | 56 + ...tm_uni_stacked_with_parameters_tracing.dot | 105 ++ .../mobilenet_v2_with_parameters_tracing.dot | 633 +++++++ ...lenet_v3_small_with_parameters_tracing.dot | 614 +++++++ .../pnasnetb_with_parameters_tracing.dot | 1183 ++++++++++++ .../preresnet50_with_parameters_tracing.dot | 655 +++++++ .../resnet18_with_parameters_tracing.dot | 271 +++ .../resnet50_with_parameters_tracing.dot | 689 +++++++ ...esnext29_32x4d_with_parameters_tracing.dot | 404 ++++ .../senet18_with_parameters_tracing.dot | 419 +++++ .../shufflenetv2_with_parameters_tracing.dot | 841 +++++++++ .../shuflenet_g2_with_parameters_tracing.dot | 781 ++++++++ .../squeezenet1_0_with_parameters_tracing.dot | 245 +++ .../squeezenet1_1_with_parameters_tracing.dot | 245 +++ ...sr_small_model_with_parameters_tracing.dot | 126 ++ .../ssd_mobilenet_with_parameters_tracing.dot | 587 ++++++ .../ssd_vgg_with_parameters_tracing.dot | 523 ++++++ .../original/unet_with_parameters_tracing.dot | 311 ++++ .../vgg16_with_parameters_tracing.dot | 203 ++ tests/torch/ptq/test_graphs.py | 2 +- tests/torch/test_compressed_graph.py | 20 +- tests/torch/test_graph_building.py | 5 +- tests/torch/test_nncf_network.py | 39 + tests/torch/test_tracing_context.py | 12 +- 48 files changed, 13802 insertions(+), 188 deletions(-) create mode 100644 tests/torch/data/reference_graphs/original/alexnet_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/densenet121_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/inception_v3_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/inception_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lenet_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lstm_bi_seq_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lstm_bi_stacked_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lstm_cell_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lstm_uni_seq_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/lstm_uni_stacked_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/mobilenet_v2_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/mobilenet_v3_small_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/pnasnetb_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/preresnet50_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/resnet18_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/resnet50_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/resnext29_32x4d_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/senet18_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/shufflenetv2_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/shuflenet_g2_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/squeezenet1_0_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/squeezenet1_1_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/sr_small_model_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/ssd_mobilenet_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/ssd_vgg_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/unet_with_parameters_tracing.dot create mode 100644 tests/torch/data/reference_graphs/original/vgg16_with_parameters_tracing.dot diff --git a/nncf/common/graph/definitions.py b/nncf/common/graph/definitions.py index 2602e1e700b..c88f077dd9b 100644 --- a/nncf/common/graph/definitions.py +++ b/nncf/common/graph/definitions.py @@ -11,8 +11,10 @@ MODEL_INPUT_OP_NAME = "nncf_model_input" MODEL_OUTPUT_OP_NAME = "nncf_model_output" +MODEL_CONST_OP_NAME = "nncf_model_const" class NNCFGraphNodeType: INPUT_NODE = MODEL_INPUT_OP_NAME OUTPUT_NODE = MODEL_OUTPUT_OP_NAME + CONST_NODE = MODEL_CONST_OP_NAME diff --git a/nncf/common/graph/layer_attributes.py b/nncf/common/graph/layer_attributes.py index b809d2583c1..56644680d4f 100644 --- a/nncf/common/graph/layer_attributes.py +++ b/nncf/common/graph/layer_attributes.py @@ -268,3 +268,12 @@ class ConvertDtypeLayerAttributes(BaseLayerAttributes): src_dtype: Any dst_dtype: Any + + +@dataclass +class ParameterLayerAttributes(BaseLayerAttributes): + """ + :param name: Parameter name. + """ + + name: str diff --git a/nncf/common/graph/operator_metatypes.py b/nncf/common/graph/operator_metatypes.py index 185f8151251..d726b8e6104 100644 --- a/nncf/common/graph/operator_metatypes.py +++ b/nncf/common/graph/operator_metatypes.py @@ -127,6 +127,7 @@ def get_operator_metatype_by_op_name(self, op_name: str) -> Type[OperatorMetatyp NOOP_METATYPES = Registry("noop_metatypes") INPUT_NOOP_METATYPES = Registry("input_noop_metatypes") OUTPUT_NOOP_METATYPES = Registry("output_noop_metatypes") +CONST_NOOP_METATYPES = Registry("const_noop_metatypes") class UnknownMetatype(OperatorMetatype): @@ -175,3 +176,13 @@ class OutputNoopMetatype(OperatorMetatype): @classmethod def get_all_aliases(cls) -> List[str]: return [NNCFGraphNodeType.OUTPUT_NODE] + + +@NOOP_METATYPES.register() +@CONST_NOOP_METATYPES.register() +class ConstNoopMetatype(OperatorMetatype): + name = "const_noop" + + @classmethod + def get_all_aliases(cls) -> List[str]: + return [NNCFGraphNodeType.CONST_NODE] diff --git a/nncf/torch/dynamic_graph/context.py b/nncf/torch/dynamic_graph/context.py index f279fea49c9..bf2f6e2a138 100644 --- a/nncf/torch/dynamic_graph/context.py +++ b/nncf/torch/dynamic_graph/context.py @@ -14,7 +14,7 @@ from collections import defaultdict from collections import deque from contextlib import contextmanager -from typing import Callable, DefaultDict, List, Optional +from typing import Callable, DefaultDict, List, Optional, Union import torch @@ -31,7 +31,7 @@ from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.scope import ScopeElement from nncf.torch.dynamic_graph.trace_tensor import TensorMeta -from nncf.torch.dynamic_graph.trace_tensor import TracedTensor +from nncf.torch.dynamic_graph.trace_tensor import TracedTensorMixin class ThreadLocalGlobalContext(threading.local): @@ -67,12 +67,14 @@ def reset(self): self.scopes = [] self.module_call_stack = [] self.in_operator = False + self.in_parameter_trace = False self.num_nested_hooks = 0 self.base_module_replica = None self.operator_counters = {} self.node_call_tracker = {} self.traced_tensor_weakrefs = [] self.nested_contexts_stack = [] + self.processed_parameters = {} class CopySafeThreadingVars: @@ -132,7 +134,7 @@ def __exit__(self, *args): previous_context = self._threading.thread_local.nested_contexts_stack.pop(-1) for traced_tensor_weakref in self._threading.thread_local.traced_tensor_weakrefs: tt = traced_tensor_weakref() - if tt is None or not isinstance(tt, TracedTensor): + if tt is None or not isinstance(tt, TracedTensorMixin): continue if previous_context is None: tt.strip() @@ -163,17 +165,36 @@ def find_operator_node( def register_global_buffer(self, name: str, buffer): self.global_buffer_store[name] = buffer - def register_traced_tensor(self, tt: TracedTensor): + def register_traced_tensor(self, tt: torch.Tensor): """ Registers a weak reference to a traced tensor in the context so that in case the block under context retains a reference to an intermediate tensor somewhere, - the context can mark this traced tensor reference as "expired" tracing-wise upon context - exit. - :param tt: A TracedTensor to be registered. + the context strips this traced tensor upon context exit. + + :param tt: A tensor with TracedTensorMixin tracing capabilities to be registered. """ wr = weakref.ref(tt) self._threading.thread_local.traced_tensor_weakrefs.append(wr) + def register_processed_parameter(self, param_name: str, tensor: torch.Tensor) -> None: + """ + Registers the processed parameter in the context to avoid double calculation of hooks + for the same parameters. + + :param param_name: The parameter name. + :param tensor: The processed parameter. + """ + self._threading.thread_local.processed_parameters[param_name] = tensor + + def get_processed_parameter(self, param_name: str) -> Union[torch.Tensor, None]: + """ + Rerturn the processed parameter by name. + + :param param_name: The parameter name. + :return: The processed parameter by name if found, otherwise None. + """ + return self._threading.thread_local.processed_parameters.get(param_name, None) + def maybe_add_node( self, inputs: OperatorInput, @@ -339,6 +360,14 @@ def in_operator(self): def in_operator(self, val): self._threading.thread_local.in_operator = val + @property + def in_parameter_trace(self): + return self._threading.thread_local.in_parameter_trace + + @in_parameter_trace.setter + def in_parameter_trace(self, val): + self._threading.thread_local.in_parameter_trace = val + @property def module_call_stack(self) -> List[torch.nn.Module]: return self._threading.thread_local.module_call_stack diff --git a/nncf/torch/dynamic_graph/graph.py b/nncf/torch/dynamic_graph/graph.py index fc20d9a9771..158deb3b78e 100644 --- a/nncf/torch/dynamic_graph/graph.py +++ b/nncf/torch/dynamic_graph/graph.py @@ -23,6 +23,7 @@ from nncf.torch.dynamic_graph.operation_address import OperationAddress from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.trace_tensor import TensorMeta +from nncf.torch.dynamic_graph.trace_tensor import TracedParameter from nncf.torch.dynamic_graph.trace_tensor import TracedTensor @@ -401,7 +402,7 @@ def save_first_iteration_node(self, inputs: OperatorInput, node: DynamicGraphNod It finds and saves "starting" points of iteration for further matching with them on next iteration, instead of adding new nodes for each iteration. "Starting" points of iteration are nodes * that have at least one input node, which is outside of iteration scope - * or whose all inputs are not TracedTensor + * or whose all inputs are not tensors with TracedTensorMixin tracing capabilities. """ op_exec_context = node.op_exec_context name = str(node) @@ -414,17 +415,24 @@ def save_first_iteration_node(self, inputs: OperatorInput, node: DynamicGraphNod has_input_outside_iteration = False untraced_tensor_inputs = [] traced_tensor_inputs = [] + traced_parameter_inputs = [] non_tensor_inputs = [] for i in inputs: input_obj = i.getter() if isinstance(input_obj, Tensor): - if not isinstance(input_obj, TracedTensor): - untraced_tensor_inputs.append(input_obj) - else: + if isinstance(input_obj, TracedTensor): traced_tensor_inputs.append(input_obj) + elif isinstance(input_obj, TracedParameter): + traced_parameter_inputs.append(input_obj) + else: + untraced_tensor_inputs.append(input_obj) else: non_tensor_inputs.append(input_obj) + for i in traced_parameter_inputs: + if i.tensor_meta is not None: + traced_tensor_inputs.append(i) + for i in traced_tensor_inputs: creator_id = i.tensor_meta.creator_id creator_node = self.get_node_by_id(creator_id) diff --git a/nncf/torch/dynamic_graph/graph_tracer.py b/nncf/torch/dynamic_graph/graph_tracer.py index 89880beda81..dc7d10ea5b4 100644 --- a/nncf/torch/dynamic_graph/graph_tracer.py +++ b/nncf/torch/dynamic_graph/graph_tracer.py @@ -19,6 +19,7 @@ from nncf.torch.dynamic_graph.io_handling import FillerInputInfo from nncf.torch.dynamic_graph.io_handling import LoaderInputInfo from nncf.torch.dynamic_graph.io_handling import ModelInputInfo +from nncf.torch.dynamic_graph.wrappers import wrap_parameters from nncf.torch.utils import get_model_device from nncf.torch.utils import is_multidevice @@ -28,7 +29,11 @@ def __init__(self, custom_forward_fn: Callable[[torch.nn.Module], Any]): self.custom_forward_fn = custom_forward_fn def trace_graph( - self, model: torch.nn.Module, context_to_use: Optional[TracingContext] = None, as_eval: bool = False + self, + model: torch.nn.Module, + context_to_use: Optional[TracingContext] = None, + as_eval: bool = False, + trace_parameters: bool = False, ) -> DynamicGraph: sd = deepcopy(model.state_dict()) @@ -41,6 +46,9 @@ def trace_graph( with context_to_use as _ctx: _ctx.base_module_thread_local_replica = model with torch.no_grad(): + if trace_parameters: + wrap_parameters(model) + if as_eval: with training_mode_switcher(model, is_training=False): self.custom_forward_fn(model) diff --git a/nncf/torch/dynamic_graph/layer_attributes_handlers.py b/nncf/torch/dynamic_graph/layer_attributes_handlers.py index 6cbf6a71c69..da9ad9254bc 100644 --- a/nncf/torch/dynamic_graph/layer_attributes_handlers.py +++ b/nncf/torch/dynamic_graph/layer_attributes_handlers.py @@ -28,9 +28,11 @@ from nncf.common.graph.layer_attributes import MultipleInputLayerAttributes from nncf.common.graph.layer_attributes import MultipleOutputLayerAttributes from nncf.common.graph.layer_attributes import PadLayerAttributes +from nncf.common.graph.layer_attributes import ParameterLayerAttributes from nncf.common.graph.layer_attributes import PermuteLayerAttributes from nncf.common.graph.layer_attributes import ReshapeLayerAttributes from nncf.common.graph.layer_attributes import TransposeLayerAttributes +from nncf.common.graph.operator_metatypes import ConstNoopMetatype from nncf.common.graph.utils import get_split_axis from nncf.torch.graph.operator_metatypes import PTCatMetatype from nncf.torch.graph.operator_metatypes import PTGroupNormMetatype @@ -49,8 +51,9 @@ GETITEM_OP_NAMES = ["__getitem__"] PAD_OP_NAMES = PTPadMetatype.get_all_aliases() CONCAT_OP_NAMES = PTCatMetatype.get_all_aliases() +CONST_OP_NAMES = ConstNoopMetatype.get_all_aliases() OP_NAMES_REQUIRING_ATTRS_FROM_ARGS_KWARGS = list( - TRANSPOSE_OP_NAMES + PERMUTE_OP_NAMES + GETITEM_OP_NAMES + PAD_OP_NAMES + CONCAT_OP_NAMES + TRANSPOSE_OP_NAMES + PERMUTE_OP_NAMES + GETITEM_OP_NAMES + PAD_OP_NAMES + CONCAT_OP_NAMES + CONST_OP_NAMES ) @@ -121,6 +124,8 @@ def get_layer_attributes_from_args_and_kwargs(op_name: str, args, kwargs) -> Bas layer_attrs = _get_pad_attrs_from_args_kwargs(args, kwargs) elif op_name in CONCAT_OP_NAMES: layer_attrs = _get_concat_attrs_from_args_kwargs(args, kwargs) + elif op_name in CONST_OP_NAMES: + layer_attrs = _get_const_attrs_from_args_kwargs(args, kwargs) return layer_attrs @@ -180,3 +185,8 @@ def _get_kwargs_shifted(args_names, args, kwargs, shift=1): for idx, arg_name in enumerate(args_names): res_kwargs[arg_name] = kwargs[arg_name] if arg_name in kwargs else args[idx + shift] return res_kwargs + + +def _get_const_attrs_from_args_kwargs(args, _) -> ParameterLayerAttributes: + name = getattr(args[0], "name", "Unknown") + return ParameterLayerAttributes(name) diff --git a/nncf/torch/dynamic_graph/patch_pytorch.py b/nncf/torch/dynamic_graph/patch_pytorch.py index b5abc5dee57..8c3acea9eec 100644 --- a/nncf/torch/dynamic_graph/patch_pytorch.py +++ b/nncf/torch/dynamic_graph/patch_pytorch.py @@ -12,7 +12,7 @@ import functools import inspect from contextlib import contextmanager -from typing import List +from typing import List, Tuple import torch import torch.utils.cpp_extension @@ -25,19 +25,20 @@ from nncf.common.utils.api_marker import api from nncf.torch.dynamic_graph.structs import NamespaceTarget from nncf.torch.dynamic_graph.structs import PatchedOperatorInfo +from nncf.torch.dynamic_graph.trace_tensor import TracedParameter from nncf.torch.dynamic_graph.trace_tensor import TracedTensor from nncf.torch.dynamic_graph.wrappers import ignore_scope from nncf.torch.dynamic_graph.wrappers import wrap_module_call from nncf.torch.dynamic_graph.wrappers import wrap_operator -def get_namespace_to_patch(namespace_target: NamespaceTarget) -> object: +def get_namespaces_to_patch(namespace_target: NamespaceTarget) -> Tuple[object, ...]: if namespace_target == NamespaceTarget.TORCH_NN_FUNCTIONAL: - return torch.nn.functional + return (torch.nn.functional,) if namespace_target == NamespaceTarget.TORCH_TENSOR: - return TracedTensor + return (TracedTensor, TracedParameter) if namespace_target == NamespaceTarget.TORCH: - return torch + return (torch,) raise RuntimeError("{} namespace wasn't found in {}".format(namespace_target, NamespaceTarget)) @@ -366,8 +367,8 @@ def patch_torch_operators(): for namespace, function_names in functions_to_patch.items(): for function_name in function_names: op_info = PatchedOperatorInfo(function_name, namespace) - patched_namespace = get_namespace_to_patch(namespace) - patch_namespace_opname(patched_namespace, op_info) + for patched_namespace in get_namespaces_to_patch(namespace): + patch_namespace_opname(patched_namespace, op_info) # Patch operators without tracing so that # both they and any internal calls to otherwise traced functions do not appear into the model graph. @@ -375,8 +376,8 @@ def patch_torch_operators(): for namespace, function_names in functions_to_patch_without_tracing.items(): for function_name in function_names: op_info = PatchedOperatorInfo(function_name, namespace, skip_trace=True) - patched_namespace = get_namespace_to_patch(namespace) - patch_namespace_opname(patched_namespace, op_info) + for patched_namespace in get_namespaces_to_patch(namespace): + patch_namespace_opname(patched_namespace, op_info) # Patch __repr__ twice in 'torch.Tensor' and 'TracedTensor'. # This is done to not add operations behind print() operator for the both TracedTensor and torch.Tensor. diff --git a/nncf/torch/dynamic_graph/trace_functions.py b/nncf/torch/dynamic_graph/trace_functions.py index 81b80626b46..3bf3621976a 100644 --- a/nncf/torch/dynamic_graph/trace_functions.py +++ b/nncf/torch/dynamic_graph/trace_functions.py @@ -10,12 +10,48 @@ # limitations under the License. from copy import deepcopy -from typing import Callable +from typing import Callable, Dict, Iterable, List, Optional, Tuple, TypeVar -from torch import Tensor +import numpy as np +import torch +from nncf import nncf_logger +from nncf.common.graph.layer_attributes import Dtype +from nncf.torch.dynamic_graph.context import TracingContext +from nncf.torch.dynamic_graph.graph import DynamicGraphNode +from nncf.torch.dynamic_graph.op_input_processing import OperatorInput +from nncf.torch.dynamic_graph.trace_tensor import TensorMeta from nncf.torch.dynamic_graph.trace_tensor import TracedTensor -from nncf.torch.dynamic_graph.trace_tensor import flatten_args +from nncf.torch.dynamic_graph.trace_tensor import TracedTensorMixin +from nncf.torch.nested_objects_traversal import objwalk + +TensorOrTupleOrList = TypeVar("TensorOrTupleOrList", List[torch.Tensor], Tuple[torch.Tensor], torch.Tensor) + + +def is_iterable(item): + non_iterable_types = (str, bytes, bytearray, torch.Tensor, np.ndarray) + + return isinstance(item, Iterable) and not isinstance(item, non_iterable_types) + + +def flatten(items): + it = items.items() if hasattr(items, "items") else iter(items) + for item in it: + if is_iterable(item): + for i in flatten(item): + yield i + else: + yield item + + +def flatten_args(args, kwargs): + return list(flatten(args)) + list(flatten(kwargs)) + + +def get_dtype(x: torch.Tensor) -> Dtype: + if x.dtype in [torch.float, torch.float16, torch.bfloat16, torch.float32, torch.float64]: + return Dtype.FLOAT + return Dtype.INTEGER def forward_trace_only(operator: Callable, *args, **kwargs): @@ -33,7 +69,7 @@ def forward_trace_only(operator: Callable, *args, **kwargs): input_traced_tensor_indices = [i for i in range(len(fargs)) if isinstance(fargs[i], TracedTensor)] if isinstance(result, (list, tuple)): - output_tensors_to_be_traced_indices = [i for i in range(len(result)) if isinstance(result[i], Tensor)] + output_tensors_to_be_traced_indices = [i for i in range(len(result)) if isinstance(result[i], torch.Tensor)] was_tuple = isinstance(result, tuple) result = list(result) @@ -70,3 +106,91 @@ def forward_trace_only(operator: Callable, *args, **kwargs): return TracedTensor.from_torch_tensor(result, forwarded_meta) # No traced tensors in input, return a usual torch.Tensor as well return result + + +def trace_tensor(x: torch.Tensor, port_id: int, node: DynamicGraphNode, ctx: TracingContext) -> torch.Tensor: + """ + Dynamically turn torch.Tensor instance into tensor with tracing capabilities instance using TracedTensorMixin. + + :param x: The input Tensor. + :param node: A node in DynamicGraph associated with the function that produced the input tensor + :param ctx: The resulting tensors will be registered within this TracingContext instance + to be stipped on context exit, which is required to correctly process situations when a traced model + retains intermediate tensor values. + :return: The resulting tensor with the TracedTensorMixin interface. + """ + if not isinstance(x, torch.Tensor): + nncf_logger.debug(f"Could not find tensors to trace in operator output: {x}") + return x + + meta = None + if node is not None: + meta = TensorMeta(node.node_id, port_id, x.shape, get_dtype(x)) + + if isinstance(x, TracedTensorMixin): + tt = x + tt.tensor_meta = meta + else: + tt = TracedTensor.from_torch_tensor(x, meta) + ctx.register_traced_tensor(tt) + return tt + + +def trace_tensors( + operator_output: TensorOrTupleOrList, node: "DynamicGraphNode", ctx: "TracingContext" +) -> TensorOrTupleOrList: + """ + Dynamically turn torch.Tensor instances in `operator_output` into tensor with tracing capabilities instances + using TracedTensorMixin. `operator_output` is presumed to be the output of a model operation (function call) + associated with `node`. + + :param operator_output: The output of an NNCF-wrapped function executed in a model object. + :param node: A node in DynamicGraph associated with the function that produced `operator_output` + :param ctx: The resulting tensors will be registered within this TracingContext instance + to be stipped on context exit, which is required to correctly process situations when a traced model + retains intermediate tensor values. + :return: Same structure as `operator_output`, but with torch.Tensor entries turned into tensor + with tracing capabilities instance using TracedTensorMixin. + """ + + if isinstance(operator_output, (list, tuple)): + output_ = [] + for i, x in enumerate(operator_output): + tt = trace_tensor(x, i, node, ctx) + output_.append(tt) + return operator_output.__class__(output_) + return trace_tensor(operator_output, 0, node, ctx) + + +def make_tensor_metas(inputs: OperatorInput) -> List[Optional[TensorMeta]]: + """ + Produces TensorMeta data for each torch.Tensor or TracedTensorMixin in `inputs`. + + :param inputs: An OperatorInput representation of input arguments to an operation in the traced model. + :return: A list of TensorMeta objects, one for every torch.Tensor or TracedTensorMixin object in `inputs` in the + order of item enumeration in `inputs`. + """ + tensor_metas = [] + for i, node_input_index_entry in enumerate(inputs): + node_input = node_input_index_entry.getter() + if isinstance(node_input, TracedTensorMixin): + tensor_metas.append(node_input.tensor_meta) + elif isinstance(node_input, torch.Tensor): + meta = TensorMeta(None, i, node_input.shape, get_dtype(node_input)) + tensor_metas.append(meta) + else: + tensor_metas.append(None) + return tensor_metas + + +def strip_traced_tensors(args: Tuple, kwargs: Dict) -> Tuple[Tuple, Dict]: + """ + Required to guard against new forward calls on tensors that have already passed + through NNCF's forward once and got turned into TracedTensors by reference access. + """ + is_traced_tensor_predicate = lambda x: isinstance(x, TracedTensorMixin) + strip_traced_tensor = lambda x: x.strip() + + args = objwalk(args, is_traced_tensor_predicate, strip_traced_tensor) + kwargs = objwalk(kwargs, is_traced_tensor_predicate, strip_traced_tensor) + return args, kwargs diff --git a/nncf/torch/dynamic_graph/trace_tensor.py b/nncf/torch/dynamic_graph/trace_tensor.py index 83682a065fa..f825a9c90a2 100644 --- a/nncf/torch/dynamic_graph/trace_tensor.py +++ b/nncf/torch/dynamic_graph/trace_tensor.py @@ -8,15 +8,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, Iterable, List, Optional, Tuple, TypeVar, Union +from typing import List, Optional, Tuple, Union -import numpy as np import torch -from nncf import nncf_logger from nncf.common.graph.layer_attributes import Dtype -from nncf.torch.dynamic_graph.op_input_processing import OperatorInput -from nncf.torch.nested_objects_traversal import objwalk class TensorMeta: @@ -25,13 +21,18 @@ def default_comparator(lhs: "TensorMeta", rhs: "TensorMeta"): return lhs.index == rhs.index and lhs.creator_id == rhs.creator_id and lhs.shape[1:] == rhs.shape[1:] def __init__( - self, creator_id: int, index: int, shape: Union[List[int], Tuple[torch.Tensor, ...]], dtype: Dtype = Dtype.FLOAT + self, + creator_id: Union[int, None], + index: int, + shape: Union[List[int], Tuple[torch.Tensor, ...]], + dtype: Dtype = Dtype.FLOAT, ): """ :param creator_id: An ID of the node in DynamicGraph that corresponds to an operation that created the tensor. :param index: The index of this tensor in the creator operation's output. :param shape: The shape of the tensor. + :param dtype: The type of the tensor """ self.creator_id = creator_id self.index = index @@ -50,36 +51,73 @@ def __str__(self): return "C{}_I{}_".format(self.creator_id, self.index) + "S" + "x".join([str(s) for s in self.shape]) -class TracedTensor(torch.Tensor): +class TracedTensorMixin: """ - When tracing a torch model, intermediate tensors will be dynamically turned into - instances of this class to be able to store additional data required for establishing - relation between tensor producer and consumer operations. + A mixin class providing tracing capabilities to PyTorch tensors. + + This class provides interfaces for patching a given torch tensor and associating it with the provided tensor_meta. """ - @staticmethod - def from_torch_tensor(tensor: torch.Tensor, tensor_meta: TensorMeta) -> "TracedTensor": + TENSOR_META = "tensor_meta" + ORIGINAL_CLASS = "original_class" + _TRACING_ATTRS = "_tracing_attrs" + + @property + def tensor_meta(self): + return self.tracing_attrs[TracedTensorMixin.TENSOR_META] + + @tensor_meta.setter + def tensor_meta(self, value: Union[None, TensorMeta]): + self.tracing_attrs[TracedTensorMixin.TENSOR_META] = value + + @property + def tracing_attrs(self): + if not hasattr(self, TracedTensorMixin._TRACING_ATTRS): + self._tracing_attrs = {} + return self._tracing_attrs + + @classmethod + def patch(cls, tensor: torch.Tensor, tensor_meta: Optional[TensorMeta] = None) -> "TracedTensorMixin": """ - Creates a TracedTensor by patching a given torch.Tensor, associating it with the provided tensor_meta. + Patches a tensor with the TracedTensorMixin interface and associates it with the provided tensor_meta. - :param tensor: The input torch.Tensor. + :param tensor: The input tensor. :param tensor_meta: The metadata associated with the tensor. - :return: The resulting TracedTensor. + :return: The patched ternsor. """ - tensor.tensor_meta = tensor_meta - if not isinstance(tensor, TracedTensor): - tensor.original_class = tensor.__class__ - tensor.__class__ = TracedTensor + if not isinstance(tensor, TracedTensorMixin): + original_class = tensor.__class__ + tensor.__class__ = cls + tensor.tracing_attrs[TracedTensorMixin.ORIGINAL_CLASS] = original_class + tensor.tensor_meta = tensor_meta return tensor def strip(self) -> None: """ Reverts the tensor to its original class by removing tracing attributes. """ - self.__class__ = self.original_class - delattr(self, "tensor_meta") - delattr(self, "original_class") + self.__class__ = self.tracing_attrs[TracedTensorMixin.ORIGINAL_CLASS] + delattr(self, TracedTensorMixin._TRACING_ATTRS) + + +class TracedTensor(torch.Tensor, TracedTensorMixin): + """ + When tracing a torch model, intermediate tensors will be dynamically turned into + instances of this class to be able to store additional data required for establishing + relation between tensor producer and consumer operations. + """ + + @staticmethod + def from_torch_tensor(tensor: torch.Tensor, tensor_meta: Optional[TensorMeta] = None) -> "TracedTensor": + """ + Creates a TracedTensor by patching a given torch.Tensor, associating it with the provided tensor_meta. + + :param tensor: The input torch.Tensor. + :param tensor_meta: The metadata associated with the tensor. + :return: The resulting TracedTensor. + """ + return TracedTensor.patch(tensor, tensor_meta) def as_subclass(self, cls: "TracedTensor") -> "TracedTensor": """ @@ -96,99 +134,43 @@ def as_subclass(self, cls: "TracedTensor") -> "TracedTensor": __torch_function__ = torch._C._disabled_torch_function_impl -def is_iterable(item): - non_iterable_types = (str, bytes, bytearray, torch.Tensor, np.ndarray) - - return isinstance(item, Iterable) and not isinstance(item, non_iterable_types) - - -def flatten(items): - it = items.items() if hasattr(items, "items") else iter(items) - for item in it: - if is_iterable(item): - for i in flatten(item): - yield i - else: - yield item - +class TracedParameter(torch.nn.Parameter, TracedTensorMixin): + """ + When tracing a torch model, model parameters will be dynamically turned into + instances of this class to be able to store additional data required for tracing parameters + across operations. + """ -def flatten_args(args, kwargs): - return list(flatten(args)) + list(flatten(kwargs)) + NAME = "name" + @property + def name(self): + return self.tracing_attrs[TracedParameter.NAME] -def get_dtype(x: torch.Tensor) -> Dtype: - if x.dtype in [torch.float, torch.float16, torch.float32, torch.float64]: - return Dtype.FLOAT - return Dtype.INTEGER + @staticmethod + def from_torch_parameter(tensor: torch.nn.Parameter, name: str) -> "TracedParameter": + """ + Creates a TracedParameter by patching a given torch.nn.Parameter, associating it + with the provided parameter name. + :param tensor: The input torch.nn.Parameter. + :param name: The parameter name. + :return: The resulting TracedParameter. + """ + TracedParameter.patch(tensor) + tensor.tracing_attrs[TracedParameter.NAME] = name + return tensor -TensorOrTupleOrList = TypeVar("TensorOrTupleOrList", List[torch.Tensor], Tuple[torch.Tensor], torch.Tensor) + def as_subclass(self, cls: "TracedParameter") -> "TracedParameter": + """ + Required for PyTorch 1.7.0 compatibility - the handle_torch_function and __torch_function__ + API in general calls this after a wrapped function call; need to preserve the tensor_meta extensions + """ + return self -def trace_tensors( - operator_output: TensorOrTupleOrList, node: "DynamicGraphNode", ctx: "TracingContext" = None # noqa: F821 -) -> TensorOrTupleOrList: - """ - Dynamically turn torch.Tensor instances in `operator_output` into TracedTensor instances. `operator_output` is - presumed to be the output of a model operation (function call) associated with `node`. - :param operator_output: The output of an NNCF-wrapped function executed in a model object. - :param node: A node in DynamicGraph associated with the function that produced `operator_output` - :param ctx: If supplied, the resulting tensors will be registered within this TracingContext instance - to be marked as expired on context exit, which is required to correctly process situations when a traced model - retains intermediate tensor values. - :return: Same structure as `operator_output`, but with torch.Tensor entries turned into TracedTensors. - """ - if isinstance(operator_output, (list, tuple)): - output_ = [] - for i, x in enumerate(operator_output): - meta = None - if node is not None: - meta = TensorMeta(node.node_id, i, x.shape, get_dtype(x)) - tt = TracedTensor.from_torch_tensor(x, meta) - if ctx is not None: - ctx.register_traced_tensor(tt) - output_.append(tt) - return operator_output.__class__(output_) - if isinstance(operator_output, torch.Tensor): - meta = None - if node is not None: - meta = TensorMeta(node.node_id, 0, operator_output.shape, get_dtype(operator_output)) - tt = TracedTensor.from_torch_tensor(operator_output, meta) - if ctx is not None: - ctx.register_traced_tensor(tt) - return tt - nncf_logger.debug(f"Could not find tensors to trace in operator output: {operator_output}") - return operator_output - - -def make_tensor_metas(inputs: OperatorInput) -> List[Optional[TensorMeta]]: - """ - Produces TensorMeta data for each torch.Tensor or TracedTensor in `inputs`. - :param inputs: An OperatorInput representation of input arguments to an operation in the traced model. - :return: A list of TensorMeta objects, one for every torch.Tensor or TracedTensor object in `inputs` in the - order of item enumeration in `inputs`. - """ - tensor_metas = [] - for i, node_input_index_entry in enumerate(inputs): - node_input = node_input_index_entry.getter() - if isinstance(node_input, TracedTensor): - tensor_metas.append(node_input.tensor_meta) - elif isinstance(node_input, torch.Tensor) and not isinstance(node_input, TracedTensor): - meta = TensorMeta(None, i, node_input.shape) - tensor_metas.append(meta) - else: - tensor_metas.append(None) - return tensor_metas - - -def strip_traced_tensors(args: Tuple, kwargs: Dict) -> Tuple[Tuple, Dict]: - """ - Required to guard against new forward calls on tensors that have already passed - through NNCF's forward once and got turned into TracedTensors by reference access. - """ - is_traced_tensor_predicate = lambda x: isinstance(x, TracedTensor) - strip_traced_tensor = lambda x: x.strip() + # NOTE: This disables the __torch_function__ API altogether when using NNCF. + # TODO: make NNCF utilize the __torch_function__ API instead. - args = objwalk(args, is_traced_tensor_predicate, strip_traced_tensor) - kwargs = objwalk(kwargs, is_traced_tensor_predicate, strip_traced_tensor) - return args, kwargs + if hasattr(torch._C, "_disabled_torch_function_impl"): + __torch_function__ = torch._C._disabled_torch_function_impl diff --git a/nncf/torch/dynamic_graph/wrappers.py b/nncf/torch/dynamic_graph/wrappers.py index 144f5d68238..f083737c3e1 100644 --- a/nncf/torch/dynamic_graph/wrappers.py +++ b/nncf/torch/dynamic_graph/wrappers.py @@ -12,8 +12,10 @@ from copy import deepcopy from typing import Callable, List, Tuple +import torch from torch.nn import DataParallel +from nncf.common.graph.definitions import MODEL_CONST_OP_NAME from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.logging import nncf_logger from nncf.common.utils.debug import is_debug @@ -23,9 +25,13 @@ from nncf.torch.dynamic_graph.layer_attributes_handlers import get_layer_attributes_from_args_and_kwargs from nncf.torch.dynamic_graph.layer_attributes_handlers import get_layer_attributes_from_module from nncf.torch.dynamic_graph.op_input_processing import OperatorInput +from nncf.torch.dynamic_graph.operation_address import OperationAddress +from nncf.torch.dynamic_graph.structs import NamespaceTarget from nncf.torch.dynamic_graph.structs import PatchedOperatorInfo -from nncf.torch.dynamic_graph.trace_tensor import make_tensor_metas -from nncf.torch.dynamic_graph.trace_tensor import trace_tensors +from nncf.torch.dynamic_graph.trace_functions import forward_trace_only +from nncf.torch.dynamic_graph.trace_functions import make_tensor_metas +from nncf.torch.dynamic_graph.trace_functions import trace_tensors +from nncf.torch.dynamic_graph.trace_tensor import TracedParameter from nncf.torch.layer_utils import _NNCFModuleMixin from nncf.torch.layers import ITERATION_MODULES from nncf.torch.return_types import maybe_unwrap_from_torch_return_type @@ -83,8 +89,6 @@ def wrapped(*args, **kwargs): if operator_info.skip_trace: result = operator(*args, **kwargs) elif ctx.is_forwarding: - from nncf.torch.dynamic_graph.trace_functions import forward_trace_only - result = forward_trace_only(operator, *args, **kwargs) else: op_name = operator_info.name @@ -161,16 +165,17 @@ def wrapped(self, *args, **kwargs): def _execute_op( - op_address: "OperationAddress", # noqa: F821 - operator_info: "PatchedOperatorInfo", + op_address: OperationAddress, + operator_info: PatchedOperatorInfo, operator: Callable, - ctx: "TracingContext", + ctx: TracingContext, *args, **kwargs, ): op_name = operator_info.name op_input = OperatorInput(list(args), kwargs) + op_input = _process_parameters(op_input, ctx) processed_input = ctx.execute_pre_hooks(op_address, op_input) args = tuple(processed_input.op_args) kwargs = processed_input.op_kwargs @@ -216,3 +221,62 @@ def _collect_module_attrs_and_ignored_algorithms( elif op_name in OP_NAMES_REQUIRING_ATTRS_FROM_ARGS_KWARGS: layer_attrs = get_layer_attributes_from_args_and_kwargs(op_name, args, kwargs) return layer_attrs, ignored_algos + + +@functools.partial(wrap_operator, operator_info=PatchedOperatorInfo(MODEL_CONST_OP_NAME, NamespaceTarget.EXTERNAL)) +def process_parameter_fn(x: torch.nn.Parameter) -> torch.nn.Parameter: + """ + The identity binding function to trace and apply hooks to parameters. + + :param x: A parameter. + :return: A parameter. + """ + return x + + +def _process_parameters(operator_inputs: OperatorInput, ctx: TracingContext) -> OperatorInput: + """ + Process model parameters into operator inputs applying registered hooks to them. The function guarantees + that the parameter is processed once. + + :param operator_inputs: The operator inputs. + :param ctx: The compression context. + :return: The operator inputs with processed parameters. + """ + if ctx.in_parameter_trace: + return operator_inputs + + in_op = getattr(ctx, "in_operator", False) + ctx.in_operator = False + + for idx in range(len(operator_inputs)): + traced_parameter = operator_inputs[idx] + if not isinstance(traced_parameter, TracedParameter): + continue + + processed_parameter = ctx.get_processed_parameter(traced_parameter.name) + if processed_parameter is not None: + operator_inputs[idx] = processed_parameter + continue + + in_parameter_trace = getattr(ctx, "in_parameter_trace", False) + ctx.in_parameter_trace = True + processed_parameter = process_parameter_fn(traced_parameter) + operator_inputs[idx] = processed_parameter + ctx.register_processed_parameter(traced_parameter.name, processed_parameter) + ctx.in_parameter_trace = in_parameter_trace + + ctx.in_operator = in_op + return operator_inputs + + +def wrap_parameters(model: torch.nn.Module): + """ + Wrap model parameters inplace by adding tracing capabilities. + + :param model: A model. + """ + ctx = get_current_context() + for name, param in model.named_parameters(): + tt = TracedParameter.from_torch_parameter(param, name) + ctx.register_traced_tensor(tt) diff --git a/nncf/torch/graph/graph_builder.py b/nncf/torch/graph/graph_builder.py index e241608640e..0412a2449fc 100644 --- a/nncf/torch/graph/graph_builder.py +++ b/nncf/torch/graph/graph_builder.py @@ -13,6 +13,7 @@ import torch +from nncf.common.graph.operator_metatypes import CONST_NOOP_METATYPES from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.torch.dynamic_graph.context import TracingContext from nncf.torch.dynamic_graph.graph import DynamicGraph @@ -32,14 +33,41 @@ def build_dynamic_graph( model: torch.nn.Module, context_to_use: Optional[TracingContext] = None, as_eval: bool = False, + trace_parameters: bool = False, ) -> DynamicGraph: + """ + Builds DynamicGraph from the given Torch model. + + :param model: Model to build DynamicGraph from. + :param context_to_use: Tracing context to use during the DynamicGraph building. Creates new tracing context + if context to use is not specified. + :param as_eval: Should given model be switched to eval mode before the graph tracing or not. Default is False. + :param trace_parameters: Whether trace model parameters during the DynamicGraph building or not. + Default is False. + :return: DynamicGraph constructed from given model. + """ tracer = GraphTracer(self.custom_forward_fn) - return tracer.trace_graph(model, context_to_use, as_eval) + return tracer.trace_graph(model, context_to_use, as_eval, trace_parameters) def build_graph( - self, model: torch.nn.Module, context_to_use: Optional[TracingContext] = None, as_eval: bool = False + self, + model: torch.nn.Module, + context_to_use: Optional[TracingContext] = None, + as_eval: bool = False, + trace_parameters: bool = False, ) -> PTNNCFGraph: - dynamic_graph = self.build_dynamic_graph(model, context_to_use, as_eval) + """ + Builds PTNNCFGraph representation from the given Torch model. + + :param model: Model to build PTNNCFGraph from. + :param context_to_use: Tracing context to use during the PTNNCFGraph building. Creates new tracing context + if context to use is not specified. + :param as_eval: Should given model be switched to eval mode before the graph tracing or not. Default is False. + :param trace_parameters: Whether trace model parameters during the PTNNCFGraph building or not. + Default is False. + :return: PTNNCFGraph constructed from given model. + """ + dynamic_graph = self.build_dynamic_graph(model, context_to_use, as_eval, trace_parameters) return GraphConverter.convert(dynamic_graph) @@ -77,8 +105,12 @@ def convert(dynamic_graph: DynamicGraph) -> PTNNCFGraph: is_shared = len(module_id_vs_sorted_scopes_map[dynamic_graph_node.calling_module_id]) > 1 canonical_scope = module_id_vs_sorted_scopes_map[dynamic_graph_node.calling_module_id][0] + node_name = str(op_address) + if metatype in CONST_NOOP_METATYPES: + node_name = dynamic_graph_node.layer_attributes.name + nncf_graph.add_nncf_node( - node_name=str(op_address), + node_name=node_name, node_type=op_address.operator_name, node_metatype=metatype, layer_attributes=dynamic_graph_node.layer_attributes, diff --git a/nncf/torch/graph/operator_metatypes.py b/nncf/torch/graph/operator_metatypes.py index 695f6cb4173..324b0d7d48c 100644 --- a/nncf/torch/graph/operator_metatypes.py +++ b/nncf/torch/graph/operator_metatypes.py @@ -15,6 +15,7 @@ from nncf.common.graph.definitions import NNCFGraphNodeType from nncf.common.graph.layer_attributes import BaseLayerAttributes from nncf.common.graph.layer_attributes import ConvolutionLayerAttributes +from nncf.common.graph.operator_metatypes import CONST_NOOP_METATYPES from nncf.common.graph.operator_metatypes import INPUT_NOOP_METATYPES from nncf.common.graph.operator_metatypes import NOOP_METATYPES from nncf.common.graph.operator_metatypes import OUTPUT_NOOP_METATYPES @@ -147,6 +148,13 @@ class PTOutputNoopMetatype(PTOperatorMetatype): external_op_names = [name, NNCFGraphNodeType.OUTPUT_NODE] +@PT_OPERATOR_METATYPES.register() +@CONST_NOOP_METATYPES.register() +class PTConstNoopMetatype(PTOperatorMetatype): + name = "const_noop" + external_op_names = [name, NNCFGraphNodeType.CONST_NODE] + + @PT_OPERATOR_METATYPES.register() @NOOP_METATYPES.register() class PTNoopMetatype(PTOperatorMetatype): diff --git a/nncf/torch/model_creation.py b/nncf/torch/model_creation.py index 0e04c804f3e..29beecf274c 100644 --- a/nncf/torch/model_creation.py +++ b/nncf/torch/model_creation.py @@ -312,22 +312,32 @@ def create_compression_algorithm_builder_from_algo_names( return builder -def wrap_model(model: torch.nn.Module, example_input: Any) -> NNCFNetwork: +def wrap_model( + model: torch.nn.Module, + example_input: Any, + replace_modules: bool = True, + trace_parameters: bool = False, +) -> NNCFNetwork: """ Wraps a PyTorch model to the NNCFNetwork class. This function dynamically extends the instance of PyTorch model with NNCF-enabling functionality. :param model: PyTorch model. - :example_input: An example input that will be used for model tracing. A tuple is interpreted as an example input - of a set of non keyword arguments, and a dict as an example input of a set of keywords arguments. + :param example_input: An example input that will be used for model tracing. A tuple is interpreted + as an example input of a set of non keyword arguments, and a dict as an example input of a set + of keywords arguments. + :param replace_modules: Whether to replace model modules with NNCF modules. Default is True. + :param trace_parameters: Whether to trace model parameters. Default is False. :return: A model wrapped by NNCFNetwork. """ input_info = ExampleInputInfo.from_example_input(example_input) with training_mode_switcher(model, is_training=False): - nncf_network = NNCFNetwork(model, input_info=input_info) + nncf_network = NNCFNetwork( + model, input_info=input_info, replace_modules=replace_modules, trace_parameters=trace_parameters + ) nncf_network.nncf.get_tracing_context().disable_trace_dynamic_graph() return nncf_network diff --git a/nncf/torch/nncf_network.py b/nncf/torch/nncf_network.py index 4eae4785f45..84c08404d51 100644 --- a/nncf/torch/nncf_network.py +++ b/nncf/torch/nncf_network.py @@ -53,8 +53,9 @@ from nncf.torch.dynamic_graph.patch_pytorch import ORIGINAL_CALL from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.scope_access import get_module_by_scope -from nncf.torch.dynamic_graph.trace_tensor import strip_traced_tensors +from nncf.torch.dynamic_graph.trace_functions import strip_traced_tensors from nncf.torch.dynamic_graph.wrappers import wrap_module_call +from nncf.torch.dynamic_graph.wrappers import wrap_parameters from nncf.torch.external_hook import EXTERNAL_OP_STORAGE_NAME from nncf.torch.graph.graph import PTNNCFGraph from nncf.torch.graph.graph_builder import GraphBuilder @@ -211,9 +212,14 @@ def __init__( ignored_scopes: List[str] = None, target_scopes: List[str] = None, wrap_outputs_fn: WrapOutputsFnType = None, + replace_modules: bool = True, + trace_parameters: bool = False, ): super().__init__() + self.replace_modules = replace_modules + self.trace_parameters = trace_parameters + # Need this in order not to register owning module as sub-module of NNCFInterface and thus # avoid circular references object.__setattr__(self, "__model_ref", model) @@ -266,10 +272,11 @@ def __init__( with_input_tracing=True, with_output_tracing=True ) - eval_op_scopes = self._collect_eval_op_scopes(model, _orig_graph_build_forward_fn) + if self.replace_modules: + eval_op_scopes = self._collect_eval_op_scopes(model, _orig_graph_build_forward_fn) - # all modules called in eval mode should be replaced prior to graph building - self._replace_modules_by_nncf_modules(model, eval_op_scopes) + # all modules called in eval mode should be replaced prior to graph building + self._replace_modules_by_nncf_modules(model, eval_op_scopes) _orig_context = TracingContext() @@ -282,7 +289,7 @@ def __init__( self._original_graphs_pair = model.nncf._original_graphs_pair else: original_dynamic_graph = GraphTracer(_orig_graph_build_forward_fn).trace_graph( - model, _orig_context, as_eval=True + model, _orig_context, as_eval=True, trace_parameters=self.trace_parameters ) original_graph = GraphConverter.convert(original_dynamic_graph) self._original_graphs_pair = PTGraphPair(dynamic_graph=original_dynamic_graph, nncf_graph=original_graph) @@ -496,7 +503,9 @@ def rebuild_graph(self, *input_args): builder = GraphBuilder(dummy_forward_fn) with training_mode_switcher(self._model_ref, is_training=False): - compressed_traced_graph = builder.build_dynamic_graph(self._model_ref, self._compressed_context) + compressed_traced_graph = builder.build_dynamic_graph( + self._model_ref, self._compressed_context, trace_parameters=self.trace_parameters + ) compressed_graph = GraphConverter.convert(compressed_traced_graph) self._compressed_graphs_pair = PTGraphPair( dynamic_graph=compressed_traced_graph, nncf_graph=compressed_graph @@ -789,32 +798,37 @@ def __call__( ignored_scopes: List[str] = None, target_scopes: List[str] = None, wrap_outputs_fn: WrapOutputsFnType = None, + replace_modules: bool = True, + trace_parameters: bool = False, ) -> "NNCFNetwork": """ This function plays the role of a "constructor" call in the `nncf_network = NNCFNetwork(original_model, ...)` syntax. *_scopes arguments are to be passed as string representation of either `nncf.common.graph.graph.NNCFNodeName` or `nncf.torch.dynamic_graph.scope.Scope` objects. :param original_model: The original model object to be extended with NNCF functionality. - :param input_info: A list of descriptors of each tensor input to the model. Will be used to properly generate - dummy inputs during internal forward calls of the original model for purposes of control flow graph building. + :param input_infos: A list of descriptors of each tensor input to the model. Will be used to properly generate + dummy inputs during internal forward calls of the original model for purposes of control flow graph + building. :param dummy_forward_fn: A function to be called instead of the model's original forward function during - control flow graph building. - :param wrap_inputs_fn: A user-defined function that will be called with the model's forward arguments at each - call of the NNCFNetwork object and within which the `nncf.torch.dynamic_graph.io_handling.nncf_model_input` - function is expected to be called upon each tensor among the arguments that is to be treated as an input tensor - to the model, thus overriding `input_infos`. + control flow graph building. + :param wrap_inputs_fn: A user-defined function that will be called with the model's forward arguments + at each call of the NNCFNetwork object and within which the + `nncf.torch.dynamic_graph.io_handling.nncf_model_input` function is expected to be called upon each tensor + among the arguments that is to be treated as an input tensor to the model, thus overriding `input_infos`. :param scopes_without_shape_matching: A list of scopes in the model in which the activation tensor shapes will - not be considered for purposes of scope matching - this helps handle RNN-like cases. - :param ignored_scopes: A list of scopes in the model for which NNCF handling should not be applied. Functions as - a "denylist". If left unspecified, nothing will be ignored. + not be considered for purposes of scope matching - this helps handle RNN-like cases. + :param ignored_scopes: A list of scopes in the model for which NNCF handling should not be applied. + Functions as a "denylist". If left unspecified, nothing will be ignored. :param target_scopes: A list of scopes in the model for which NNCF handling should be applied. Functions as - an "allowlist". If left unspecified, everything will be targeted. + an "allowlist". If left unspecified, everything will be targeted. :param wrap_outputs_fn: Same as `wrap_inputs_fn`, but for marking model outputs with - `nncf.torch.dynamic_graph.io_handling.nncf_model_output` calls. + `nncf.torch.dynamic_graph.io_handling.nncf_model_output` calls. + :param replace_modules: Whether to replace model modules with NNCF modules. Default is True. + :param trace_parameters: Whether to trace model parameters. Default is False. :return: The same object as passed in `original_model`, but with internal modules extended/replaced for - purposes of further NNCF compression, and its class dynamically extended with the `NNCFNetwork` as a base class. - The object will pass both isinstance(retval, original_model.__class__) and isinstance(retval, NNCFNetwork) - checks. + purposes of further NNCF compression, and its class dynamically extended with the `NNCFNetwork` + as a base class. The object will pass both isinstance(retval, original_model.__class__) and + isinstance(retval, NNCFNetwork) checks. """ original_class = original_model.__class__ original_model._nncf = NNCFNetworkInterface( @@ -826,6 +840,8 @@ def __call__( ignored_scopes, target_scopes, wrap_outputs_fn, + replace_modules, + trace_parameters, ) # The new class will also have an adjusted metaclass to avoid a "metaclass conflict" upon # class creation @@ -946,6 +962,11 @@ def forward(self, *args, **kwargs): with self.nncf._compressed_context as ctx: ctx.base_module_thread_local_replica = self + + # add tracing capabilities to model parameters + if self.nncf.trace_parameters: + wrap_parameters(self) + args, kwargs = replicate_same_tensors((args, kwargs)) if not self.nncf._in_user_dummy_forward: # If a user supplies own dummy forward, he is responsible for diff --git a/nncf/torch/utils.py b/nncf/torch/utils.py index 546e0172f70..94610cacce2 100644 --- a/nncf/torch/utils.py +++ b/nncf/torch/utils.py @@ -26,7 +26,7 @@ from nncf.common.scopes import matches_any from nncf.torch.dynamic_graph.scope import Scope from nncf.torch.dynamic_graph.scope import ScopeElement -from nncf.torch.dynamic_graph.trace_tensor import TracedTensor +from nncf.torch.dynamic_graph.trace_tensor import TracedTensorMixin from nncf.torch.layer_utils import _NNCFModuleMixin from nncf.torch.structures import ExecutionParameters @@ -208,7 +208,7 @@ def is_tensor(obj): def is_traced_tensor(obj): - return isinstance(obj, TracedTensor) + return isinstance(obj, TracedTensorMixin) class _ModuleState: diff --git a/tests/torch/data/reference_graphs/original/alexnet_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/alexnet_with_parameters_tracing.dot new file mode 100644 index 00000000000..6b18fab3d54 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/alexnet_with_parameters_tracing.dot @@ -0,0 +1,75 @@ +strict digraph { +"0 features.0.weight" [id=0, type=nncf_model_const]; +"1 features.0.bias" [id=1, type=nncf_model_const]; +"2 AlexNet/Sequential[features]/Conv2d[0]/conv2d_0" [id=2, type=conv2d]; +"3 AlexNet/Sequential[features]/ReLU[1]/relu__0" [id=3, type=relu_]; +"4 AlexNet/Sequential[features]/MaxPool2d[2]/max_pool2d_0" [id=4, type=max_pool2d]; +"5 features.3.weight" [id=5, type=nncf_model_const]; +"6 features.3.bias" [id=6, type=nncf_model_const]; +"7 AlexNet/Sequential[features]/Conv2d[3]/conv2d_0" [id=7, type=conv2d]; +"8 AlexNet/Sequential[features]/ReLU[4]/relu__0" [id=8, type=relu_]; +"9 AlexNet/Sequential[features]/MaxPool2d[5]/max_pool2d_0" [id=9, type=max_pool2d]; +"10 features.6.weight" [id=10, type=nncf_model_const]; +"11 features.6.bias" [id=11, type=nncf_model_const]; +"12 AlexNet/Sequential[features]/Conv2d[6]/conv2d_0" [id=12, type=conv2d]; +"13 AlexNet/Sequential[features]/ReLU[7]/relu__0" [id=13, type=relu_]; +"14 features.8.weight" [id=14, type=nncf_model_const]; +"15 features.8.bias" [id=15, type=nncf_model_const]; +"16 AlexNet/Sequential[features]/Conv2d[8]/conv2d_0" [id=16, type=conv2d]; +"17 AlexNet/Sequential[features]/ReLU[9]/relu__0" [id=17, type=relu_]; +"18 features.10.weight" [id=18, type=nncf_model_const]; +"19 features.10.bias" [id=19, type=nncf_model_const]; +"20 AlexNet/Sequential[features]/Conv2d[10]/conv2d_0" [id=20, type=conv2d]; +"21 AlexNet/Sequential[features]/ReLU[11]/relu__0" [id=21, type=relu_]; +"22 AlexNet/Sequential[features]/MaxPool2d[12]/max_pool2d_0" [id=22, type=max_pool2d]; +"23 AlexNet/view_0" [id=23, type=view]; +"24 AlexNet/Sequential[classifier]/Dropout[0]/dropout_0" [id=24, type=dropout]; +"25 classifier.1.weight" [id=25, type=nncf_model_const]; +"26 classifier.1.bias" [id=26, type=nncf_model_const]; +"27 AlexNet/Sequential[classifier]/Linear[1]/linear_0" [id=27, type=linear]; +"28 AlexNet/Sequential[classifier]/ReLU[2]/relu__0" [id=28, type=relu_]; +"29 AlexNet/Sequential[classifier]/Dropout[3]/dropout_0" [id=29, type=dropout]; +"30 classifier.4.weight" [id=30, type=nncf_model_const]; +"31 classifier.4.bias" [id=31, type=nncf_model_const]; +"32 AlexNet/Sequential[classifier]/Linear[4]/linear_0" [id=32, type=linear]; +"33 AlexNet/Sequential[classifier]/ReLU[5]/relu__0" [id=33, type=relu_]; +"34 classifier.6.weight" [id=34, type=nncf_model_const]; +"35 classifier.6.bias" [id=35, type=nncf_model_const]; +"36 AlexNet/Sequential[classifier]/Linear[6]/linear_0" [id=36, type=linear]; +"0 features.0.weight" -> "2 AlexNet/Sequential[features]/Conv2d[0]/conv2d_0"; +"1 features.0.bias" -> "2 AlexNet/Sequential[features]/Conv2d[0]/conv2d_0"; +"2 AlexNet/Sequential[features]/Conv2d[0]/conv2d_0" -> "3 AlexNet/Sequential[features]/ReLU[1]/relu__0"; +"3 AlexNet/Sequential[features]/ReLU[1]/relu__0" -> "4 AlexNet/Sequential[features]/MaxPool2d[2]/max_pool2d_0"; +"4 AlexNet/Sequential[features]/MaxPool2d[2]/max_pool2d_0" -> "7 AlexNet/Sequential[features]/Conv2d[3]/conv2d_0"; +"5 features.3.weight" -> "7 AlexNet/Sequential[features]/Conv2d[3]/conv2d_0"; +"6 features.3.bias" -> "7 AlexNet/Sequential[features]/Conv2d[3]/conv2d_0"; +"7 AlexNet/Sequential[features]/Conv2d[3]/conv2d_0" -> "8 AlexNet/Sequential[features]/ReLU[4]/relu__0"; +"8 AlexNet/Sequential[features]/ReLU[4]/relu__0" -> "9 AlexNet/Sequential[features]/MaxPool2d[5]/max_pool2d_0"; +"9 AlexNet/Sequential[features]/MaxPool2d[5]/max_pool2d_0" -> "12 AlexNet/Sequential[features]/Conv2d[6]/conv2d_0"; +"10 features.6.weight" -> "12 AlexNet/Sequential[features]/Conv2d[6]/conv2d_0"; +"11 features.6.bias" -> "12 AlexNet/Sequential[features]/Conv2d[6]/conv2d_0"; +"12 AlexNet/Sequential[features]/Conv2d[6]/conv2d_0" -> "13 AlexNet/Sequential[features]/ReLU[7]/relu__0"; +"13 AlexNet/Sequential[features]/ReLU[7]/relu__0" -> "16 AlexNet/Sequential[features]/Conv2d[8]/conv2d_0"; +"14 features.8.weight" -> "16 AlexNet/Sequential[features]/Conv2d[8]/conv2d_0"; +"15 features.8.bias" -> "16 AlexNet/Sequential[features]/Conv2d[8]/conv2d_0"; +"16 AlexNet/Sequential[features]/Conv2d[8]/conv2d_0" -> "17 AlexNet/Sequential[features]/ReLU[9]/relu__0"; +"17 AlexNet/Sequential[features]/ReLU[9]/relu__0" -> "20 AlexNet/Sequential[features]/Conv2d[10]/conv2d_0"; +"18 features.10.weight" -> "20 AlexNet/Sequential[features]/Conv2d[10]/conv2d_0"; +"19 features.10.bias" -> "20 AlexNet/Sequential[features]/Conv2d[10]/conv2d_0"; +"20 AlexNet/Sequential[features]/Conv2d[10]/conv2d_0" -> "21 AlexNet/Sequential[features]/ReLU[11]/relu__0"; +"21 AlexNet/Sequential[features]/ReLU[11]/relu__0" -> "22 AlexNet/Sequential[features]/MaxPool2d[12]/max_pool2d_0"; +"22 AlexNet/Sequential[features]/MaxPool2d[12]/max_pool2d_0" -> "23 AlexNet/view_0"; +"23 AlexNet/view_0" -> "24 AlexNet/Sequential[classifier]/Dropout[0]/dropout_0"; +"24 AlexNet/Sequential[classifier]/Dropout[0]/dropout_0" -> "27 AlexNet/Sequential[classifier]/Linear[1]/linear_0"; +"25 classifier.1.weight" -> "27 AlexNet/Sequential[classifier]/Linear[1]/linear_0"; +"26 classifier.1.bias" -> "27 AlexNet/Sequential[classifier]/Linear[1]/linear_0"; +"27 AlexNet/Sequential[classifier]/Linear[1]/linear_0" -> "28 AlexNet/Sequential[classifier]/ReLU[2]/relu__0"; +"28 AlexNet/Sequential[classifier]/ReLU[2]/relu__0" -> "29 AlexNet/Sequential[classifier]/Dropout[3]/dropout_0"; +"29 AlexNet/Sequential[classifier]/Dropout[3]/dropout_0" -> "32 AlexNet/Sequential[classifier]/Linear[4]/linear_0"; +"30 classifier.4.weight" -> "32 AlexNet/Sequential[classifier]/Linear[4]/linear_0"; +"31 classifier.4.bias" -> "32 AlexNet/Sequential[classifier]/Linear[4]/linear_0"; +"32 AlexNet/Sequential[classifier]/Linear[4]/linear_0" -> "33 AlexNet/Sequential[classifier]/ReLU[5]/relu__0"; +"33 AlexNet/Sequential[classifier]/ReLU[5]/relu__0" -> "36 AlexNet/Sequential[classifier]/Linear[6]/linear_0"; +"34 classifier.6.weight" -> "36 AlexNet/Sequential[classifier]/Linear[6]/linear_0"; +"35 classifier.6.bias" -> "36 AlexNet/Sequential[classifier]/Linear[6]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/densenet121_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/densenet121_with_parameters_tracing.dot new file mode 100644 index 00000000000..a1276b09aca --- /dev/null +++ b/tests/torch/data/reference_graphs/original/densenet121_with_parameters_tracing.dot @@ -0,0 +1,1631 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 DenseNet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 dense1.0.bn1.weight" [id=2, type=nncf_model_const]; +"3 dense1.0.bn1.bias" [id=3, type=nncf_model_const]; +"4 DenseNet/Sequential[dense1]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" [id=4, type=batch_norm]; +"5 DenseNet/Sequential[dense1]/Bottleneck[0]/relu_0" [id=5, type=relu]; +"6 dense1.0.conv1.weight" [id=6, type=nncf_model_const]; +"7 DenseNet/Sequential[dense1]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" [id=7, type=conv2d]; +"8 dense1.0.bn2.weight" [id=8, type=nncf_model_const]; +"9 dense1.0.bn2.bias" [id=9, type=nncf_model_const]; +"10 DenseNet/Sequential[dense1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" [id=10, type=batch_norm]; +"11 DenseNet/Sequential[dense1]/Bottleneck[0]/relu_1" [id=11, type=relu]; +"12 dense1.0.conv2.weight" [id=12, type=nncf_model_const]; +"13 DenseNet/Sequential[dense1]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" [id=13, type=conv2d]; +"14 DenseNet/Sequential[dense1]/Bottleneck[0]/cat_0" [id=14, type=cat]; +"15 dense1.1.bn1.weight" [id=15, type=nncf_model_const]; +"16 dense1.1.bn1.bias" [id=16, type=nncf_model_const]; +"17 DenseNet/Sequential[dense1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" [id=17, type=batch_norm]; +"18 DenseNet/Sequential[dense1]/Bottleneck[1]/relu_0" [id=18, type=relu]; +"19 dense1.1.conv1.weight" [id=19, type=nncf_model_const]; +"20 DenseNet/Sequential[dense1]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" [id=20, type=conv2d]; +"21 dense1.1.bn2.weight" [id=21, type=nncf_model_const]; +"22 dense1.1.bn2.bias" [id=22, type=nncf_model_const]; +"23 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DenseNet/Sequential[dense4]/Bottleneck[13]/BatchNorm2d[bn2]/batch_norm_0" -> "747 DenseNet/Sequential[dense4]/Bottleneck[13]/relu_1"; +"747 DenseNet/Sequential[dense4]/Bottleneck[13]/relu_1" -> "749 DenseNet/Sequential[dense4]/Bottleneck[13]/Conv2d[conv2]/conv2d_0"; +"748 dense4.13.conv2.weight" -> "749 DenseNet/Sequential[dense4]/Bottleneck[13]/Conv2d[conv2]/conv2d_0"; +"749 DenseNet/Sequential[dense4]/Bottleneck[13]/Conv2d[conv2]/conv2d_0" -> "750 DenseNet/Sequential[dense4]/Bottleneck[13]/cat_0"; +"750 DenseNet/Sequential[dense4]/Bottleneck[13]/cat_0" -> "753 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn1]/batch_norm_0"; +"750 DenseNet/Sequential[dense4]/Bottleneck[13]/cat_0" -> "763 DenseNet/Sequential[dense4]/Bottleneck[14]/cat_0"; +"751 dense4.14.bn1.weight" -> "753 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn1]/batch_norm_0"; +"752 dense4.14.bn1.bias" -> "753 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn1]/batch_norm_0"; +"753 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn1]/batch_norm_0" -> "754 DenseNet/Sequential[dense4]/Bottleneck[14]/relu_0"; +"754 DenseNet/Sequential[dense4]/Bottleneck[14]/relu_0" -> "756 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv1]/conv2d_0"; +"755 dense4.14.conv1.weight" -> "756 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv1]/conv2d_0"; +"756 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv1]/conv2d_0" -> "759 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn2]/batch_norm_0"; +"757 dense4.14.bn2.weight" -> "759 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn2]/batch_norm_0"; +"758 dense4.14.bn2.bias" -> "759 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn2]/batch_norm_0"; +"759 DenseNet/Sequential[dense4]/Bottleneck[14]/BatchNorm2d[bn2]/batch_norm_0" -> "760 DenseNet/Sequential[dense4]/Bottleneck[14]/relu_1"; +"760 DenseNet/Sequential[dense4]/Bottleneck[14]/relu_1" -> "762 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv2]/conv2d_0"; +"761 dense4.14.conv2.weight" -> "762 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv2]/conv2d_0"; +"762 DenseNet/Sequential[dense4]/Bottleneck[14]/Conv2d[conv2]/conv2d_0" -> "763 DenseNet/Sequential[dense4]/Bottleneck[14]/cat_0"; +"763 DenseNet/Sequential[dense4]/Bottleneck[14]/cat_0" -> "766 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn1]/batch_norm_0"; +"763 DenseNet/Sequential[dense4]/Bottleneck[14]/cat_0" -> "776 DenseNet/Sequential[dense4]/Bottleneck[15]/cat_0"; +"764 dense4.15.bn1.weight" -> "766 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn1]/batch_norm_0"; +"765 dense4.15.bn1.bias" -> "766 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn1]/batch_norm_0"; +"766 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn1]/batch_norm_0" -> "767 DenseNet/Sequential[dense4]/Bottleneck[15]/relu_0"; +"767 DenseNet/Sequential[dense4]/Bottleneck[15]/relu_0" -> "769 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv1]/conv2d_0"; +"768 dense4.15.conv1.weight" -> "769 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv1]/conv2d_0"; +"769 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv1]/conv2d_0" -> "772 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn2]/batch_norm_0"; +"770 dense4.15.bn2.weight" -> "772 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn2]/batch_norm_0"; +"771 dense4.15.bn2.bias" -> "772 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn2]/batch_norm_0"; +"772 DenseNet/Sequential[dense4]/Bottleneck[15]/BatchNorm2d[bn2]/batch_norm_0" -> "773 DenseNet/Sequential[dense4]/Bottleneck[15]/relu_1"; +"773 DenseNet/Sequential[dense4]/Bottleneck[15]/relu_1" -> "775 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv2]/conv2d_0"; +"774 dense4.15.conv2.weight" -> "775 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv2]/conv2d_0"; +"775 DenseNet/Sequential[dense4]/Bottleneck[15]/Conv2d[conv2]/conv2d_0" -> "776 DenseNet/Sequential[dense4]/Bottleneck[15]/cat_0"; +"776 DenseNet/Sequential[dense4]/Bottleneck[15]/cat_0" -> "779 DenseNet/BatchNorm2d[bn]/batch_norm_0"; +"777 bn.weight" -> "779 DenseNet/BatchNorm2d[bn]/batch_norm_0"; +"778 bn.bias" -> "779 DenseNet/BatchNorm2d[bn]/batch_norm_0"; +"779 DenseNet/BatchNorm2d[bn]/batch_norm_0" -> "780 DenseNet/relu_0"; +"780 DenseNet/relu_0" -> "781 DenseNet/avg_pool2d_0"; +"781 DenseNet/avg_pool2d_0" -> "782 DenseNet/view_0"; +"782 DenseNet/view_0" -> "785 DenseNet/Linear[linear]/linear_0"; +"783 linear.weight" -> "785 DenseNet/Linear[linear]/linear_0"; +"784 linear.bias" -> "785 DenseNet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/inception_v3_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/inception_v3_with_parameters_tracing.dot new file mode 100644 index 00000000000..9bd813f1701 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/inception_v3_with_parameters_tracing.dot @@ -0,0 +1,1288 @@ +strict digraph { +"0 Inception3/unsqueeze_0" [id=0, type=unsqueeze]; +"1 Inception3/__mul___0" [id=1, type=__mul__]; +"2 Inception3/__add___0" [id=2, type=__add__]; +"3 Inception3/unsqueeze_1" [id=3, type=unsqueeze]; +"4 Inception3/__mul___1" [id=4, type=__mul__]; +"5 Inception3/__add___1" [id=5, type=__add__]; +"6 Inception3/unsqueeze_2" [id=6, type=unsqueeze]; +"7 Inception3/__mul___2" [id=7, type=__mul__]; +"8 Inception3/__add___2" [id=8, type=__add__]; +"9 Inception3/cat_0" [id=9, type=cat]; +"10 Conv2d_1a_3x3.conv.weight" [id=10, type=nncf_model_const]; +"11 Inception3/BasicConv2d[Conv2d_1a_3x3]/Conv2d[conv]/conv2d_0" [id=11, type=conv2d]; +"12 Conv2d_1a_3x3.bn.weight" [id=12, type=nncf_model_const]; +"13 Conv2d_1a_3x3.bn.bias" [id=13, type=nncf_model_const]; +"14 Inception3/BasicConv2d[Conv2d_1a_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=14, type=batch_norm]; +"15 Inception3/BasicConv2d[Conv2d_1a_3x3]/relu__0" [id=15, type=relu_]; +"16 Conv2d_2a_3x3.conv.weight" [id=16, type=nncf_model_const]; +"17 Inception3/BasicConv2d[Conv2d_2a_3x3]/Conv2d[conv]/conv2d_0" [id=17, type=conv2d]; +"18 Conv2d_2a_3x3.bn.weight" [id=18, type=nncf_model_const]; +"19 Conv2d_2a_3x3.bn.bias" [id=19, type=nncf_model_const]; +"20 Inception3/BasicConv2d[Conv2d_2a_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=20, type=batch_norm]; +"21 Inception3/BasicConv2d[Conv2d_2a_3x3]/relu__0" [id=21, type=relu_]; +"22 Conv2d_2b_3x3.conv.weight" [id=22, type=nncf_model_const]; +"23 Inception3/BasicConv2d[Conv2d_2b_3x3]/Conv2d[conv]/conv2d_0" [id=23, type=conv2d]; +"24 Conv2d_2b_3x3.bn.weight" [id=24, type=nncf_model_const]; +"25 Conv2d_2b_3x3.bn.bias" [id=25, type=nncf_model_const]; +"26 Inception3/BasicConv2d[Conv2d_2b_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=26, type=batch_norm]; +"27 Inception3/BasicConv2d[Conv2d_2b_3x3]/relu__0" [id=27, type=relu_]; +"28 Inception3/max_pool2d_0" [id=28, type=max_pool2d]; +"29 Conv2d_3b_1x1.conv.weight" [id=29, type=nncf_model_const]; +"30 Inception3/BasicConv2d[Conv2d_3b_1x1]/Conv2d[conv]/conv2d_0" [id=30, type=conv2d]; +"31 Conv2d_3b_1x1.bn.weight" [id=31, type=nncf_model_const]; +"32 Conv2d_3b_1x1.bn.bias" [id=32, type=nncf_model_const]; +"33 Inception3/BasicConv2d[Conv2d_3b_1x1]/BatchNorm2d[bn]/batch_norm_0" [id=33, type=batch_norm]; +"34 Inception3/BasicConv2d[Conv2d_3b_1x1]/relu__0" [id=34, type=relu_]; +"35 Conv2d_4a_3x3.conv.weight" [id=35, type=nncf_model_const]; +"36 Inception3/BasicConv2d[Conv2d_4a_3x3]/Conv2d[conv]/conv2d_0" [id=36, type=conv2d]; +"37 Conv2d_4a_3x3.bn.weight" [id=37, type=nncf_model_const]; +"38 Conv2d_4a_3x3.bn.bias" [id=38, type=nncf_model_const]; +"39 Inception3/BasicConv2d[Conv2d_4a_3x3]/BatchNorm2d[bn]/batch_norm_0" [id=39, type=batch_norm]; +"40 Inception3/BasicConv2d[Conv2d_4a_3x3]/relu__0" [id=40, type=relu_]; +"41 Inception3/max_pool2d_1" [id=41, type=max_pool2d]; +"42 Mixed_5b.branch1x1.conv.weight" [id=42, type=nncf_model_const]; +"43 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/Conv2d[conv]/conv2d_0" [id=43, type=conv2d]; +"44 Mixed_5b.branch1x1.bn.weight" [id=44, type=nncf_model_const]; +"45 Mixed_5b.branch1x1.bn.bias" [id=45, type=nncf_model_const]; +"46 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/BatchNorm2d[bn]/batch_norm_0" [id=46, type=batch_norm]; +"47 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch1x1]/relu__0" [id=47, type=relu_]; +"48 Mixed_5b.branch5x5_1.conv.weight" [id=48, type=nncf_model_const]; +"49 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/Conv2d[conv]/conv2d_0" [id=49, type=conv2d]; +"50 Mixed_5b.branch5x5_1.bn.weight" [id=50, type=nncf_model_const]; +"51 Mixed_5b.branch5x5_1.bn.bias" [id=51, type=nncf_model_const]; +"52 Inception3/InceptionA[Mixed_5b]/BasicConv2d[branch5x5_1]/BatchNorm2d[bn]/batch_norm_0" [id=52, type=batch_norm]; +"53 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type=conv2d]; +"589 Mixed_7c.branch3x3dbl_1.bn.weight" [id=589, type=nncf_model_const]; +"590 Mixed_7c.branch3x3dbl_1.bn.bias" [id=590, type=nncf_model_const]; +"591 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/BatchNorm2d[bn]/batch_norm_0" [id=591, type=batch_norm]; +"592 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_1]/relu__0" [id=592, type=relu_]; +"593 Mixed_7c.branch3x3dbl_2.conv.weight" [id=593, type=nncf_model_const]; +"594 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/Conv2d[conv]/conv2d_0" [id=594, type=conv2d]; +"595 Mixed_7c.branch3x3dbl_2.bn.weight" [id=595, type=nncf_model_const]; +"596 Mixed_7c.branch3x3dbl_2.bn.bias" [id=596, type=nncf_model_const]; +"597 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/BatchNorm2d[bn]/batch_norm_0" [id=597, type=batch_norm]; +"598 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_2]/relu__0" [id=598, type=relu_]; +"599 Mixed_7c.branch3x3dbl_3a.conv.weight" [id=599, type=nncf_model_const]; +"600 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/Conv2d[conv]/conv2d_0" [id=600, type=conv2d]; +"601 Mixed_7c.branch3x3dbl_3a.bn.weight" [id=601, type=nncf_model_const]; +"602 Mixed_7c.branch3x3dbl_3a.bn.bias" [id=602, type=nncf_model_const]; +"603 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/BatchNorm2d[bn]/batch_norm_0" [id=603, type=batch_norm]; +"604 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3a]/relu__0" [id=604, type=relu_]; +"605 Mixed_7c.branch3x3dbl_3b.conv.weight" [id=605, type=nncf_model_const]; +"606 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/Conv2d[conv]/conv2d_0" [id=606, type=conv2d]; +"607 Mixed_7c.branch3x3dbl_3b.bn.weight" [id=607, type=nncf_model_const]; +"608 Mixed_7c.branch3x3dbl_3b.bn.bias" [id=608, type=nncf_model_const]; +"609 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0" [id=609, type=batch_norm]; +"610 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+"608 Mixed_7c.branch3x3dbl_3b.bn.bias" -> "609 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0"; +"609 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/BatchNorm2d[bn]/batch_norm_0" -> "610 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0"; +"610 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch3x3dbl_3b]/relu__0" -> "611 Inception3/InceptionE[Mixed_7c]/cat_1"; +"611 Inception3/InceptionE[Mixed_7c]/cat_1" -> "619 Inception3/InceptionE[Mixed_7c]/cat_2"; +"612 Inception3/InceptionE[Mixed_7c]/avg_pool2d_0" -> "614 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"613 Mixed_7c.branch_pool.conv.weight" -> "614 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0"; +"614 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/Conv2d[conv]/conv2d_0" -> "617 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"615 Mixed_7c.branch_pool.bn.weight" -> "617 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"616 Mixed_7c.branch_pool.bn.bias" -> "617 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0"; +"617 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/BatchNorm2d[bn]/batch_norm_0" -> "618 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0"; +"618 Inception3/InceptionE[Mixed_7c]/BasicConv2d[branch_pool]/relu__0" -> "619 Inception3/InceptionE[Mixed_7c]/cat_2"; +"619 Inception3/InceptionE[Mixed_7c]/cat_2" -> "620 Inception3/adaptive_avg_pool2d_0"; +"620 Inception3/adaptive_avg_pool2d_0" -> "621 Inception3/dropout_0"; +"621 Inception3/dropout_0" -> "622 Inception3/view_0"; +"622 Inception3/view_0" -> "625 Inception3/Linear[fc]/linear_0"; +"623 fc.weight" -> "625 Inception3/Linear[fc]/linear_0"; +"624 fc.bias" -> "625 Inception3/Linear[fc]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/inception_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/inception_with_parameters_tracing.dot new file mode 100644 index 00000000000..a398c5fffa2 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/inception_with_parameters_tracing.dot @@ -0,0 +1,974 @@ +strict digraph { +"0 pre_layers.0.weight" [id=0, type=nncf_model_const]; +"1 pre_layers.0.bias" [id=1, type=nncf_model_const]; +"2 GoogLeNet/Sequential[pre_layers]/Conv2d[0]/conv2d_0" [id=2, type=conv2d]; +"3 pre_layers.1.weight" [id=3, type=nncf_model_const]; +"4 pre_layers.1.bias" [id=4, type=nncf_model_const]; +"5 GoogLeNet/Sequential[pre_layers]/BatchNorm2d[1]/batch_norm_0" [id=5, type=batch_norm]; +"6 GoogLeNet/Sequential[pre_layers]/ReLU[2]/relu__0" [id=6, type=relu_]; +"7 a3.b1.0.weight" [id=7, type=nncf_model_const]; +"8 a3.b1.0.bias" [id=8, type=nncf_model_const]; +"9 GoogLeNet/Inception[a3]/Sequential[b1]/Conv2d[0]/conv2d_0" [id=9, 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[id=18, type=relu]; +"19 fc3.weight" [id=19, type=nncf_model_const]; +"20 fc3.bias" [id=20, type=nncf_model_const]; +"21 LeNet/Linear[fc3]/linear_0" [id=21, type=linear]; +"0 conv1.weight" -> "2 LeNet/Conv2d[conv1]/conv2d_0"; +"1 conv1.bias" -> "2 LeNet/Conv2d[conv1]/conv2d_0"; +"2 LeNet/Conv2d[conv1]/conv2d_0" -> "3 LeNet/relu_0"; +"3 LeNet/relu_0" -> "4 LeNet/max_pool2d_0"; +"4 LeNet/max_pool2d_0" -> "7 LeNet/Conv2d[conv2]/conv2d_0"; +"5 conv2.weight" -> "7 LeNet/Conv2d[conv2]/conv2d_0"; +"6 conv2.bias" -> "7 LeNet/Conv2d[conv2]/conv2d_0"; +"7 LeNet/Conv2d[conv2]/conv2d_0" -> "8 LeNet/relu_1"; +"8 LeNet/relu_1" -> "9 LeNet/max_pool2d_1"; +"9 LeNet/max_pool2d_1" -> "10 LeNet/view_0"; +"10 LeNet/view_0" -> "13 LeNet/Linear[fc1]/linear_0"; +"11 fc1.weight" -> "13 LeNet/Linear[fc1]/linear_0"; +"12 fc1.bias" -> "13 LeNet/Linear[fc1]/linear_0"; +"13 LeNet/Linear[fc1]/linear_0" -> "14 LeNet/relu_2"; +"14 LeNet/relu_2" -> "17 LeNet/Linear[fc2]/linear_0"; +"15 fc2.weight" -> "17 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NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"21 bias_ih_l0_reverse" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"23 weight_hh_l0_reverse" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"23 weight_hh_l0_reverse" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"24 bias_hh_l0_reverse" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"24 bias_hh_l0_reverse" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"41 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +} diff --git a/tests/torch/data/reference_graphs/original/lstm_bi_stacked_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/lstm_bi_stacked_with_parameters_tracing.dot new file mode 100644 index 00000000000..1984c0a0d08 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/lstm_bi_stacked_with_parameters_tracing.dot @@ -0,0 +1,200 @@ +strict digraph { +"0 weight_ih_l0" [id=0, type=nncf_model_const]; +"1 bias_ih_l0" [id=1, type=nncf_model_const]; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=2, type=linear]; +"3 weight_hh_l0" [id=3, type=nncf_model_const]; +"4 bias_hh_l0" [id=4, type=nncf_model_const]; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=5, type=linear]; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" [id=6, type=__add__]; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" [id=7, type=chunk]; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=8, type=sigmoid]; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=9, type=sigmoid]; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=10, type=tanh]; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=11, type=sigmoid]; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=12, type=__mul__]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=13, type=__mul__]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=14, type=__add__]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=15, type=tanh]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=16, type=__mul__]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=17, type=linear]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=18, type=cat]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=19, type=view]; +"20 weight_ih_l0_reverse" [id=20, type=nncf_model_const]; +"21 bias_ih_l0_reverse" [id=21, type=nncf_model_const]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=22, type=linear]; +"23 weight_hh_l0_reverse" [id=23, type=nncf_model_const]; +"24 bias_hh_l0_reverse" [id=24, type=nncf_model_const]; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=25, type=linear]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=26, type=__add__]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=27, type=chunk]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=28, type=sigmoid]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=29, type=sigmoid]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=30, type=tanh]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=31, type=sigmoid]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=32, type=__mul__]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=33, type=__mul__]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=34, type=__add__]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=35, type=tanh]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=36, type=__mul__]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=37, type=linear]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=38, type=cat]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=39, type=view]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=40, type=cat]; +"41 weight_ih_l1" [id=41, type=nncf_model_const]; +"42 bias_ih_l1" [id=42, type=nncf_model_const]; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=43, type=linear]; +"44 weight_hh_l1" [id=44, type=nncf_model_const]; +"45 bias_hh_l1" [id=45, type=nncf_model_const]; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=46, type=linear]; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" [id=47, type=__add__]; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" [id=48, type=chunk]; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=49, type=sigmoid]; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=50, type=sigmoid]; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" [id=51, type=tanh]; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=52, type=sigmoid]; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" [id=53, type=__mul__]; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" [id=54, type=__mul__]; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" [id=55, type=__add__]; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" [id=56, type=tanh]; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" [id=57, type=__mul__]; +"58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=58, type=linear]; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [id=59, type=cat]; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" [id=60, type=view]; +"61 weight_ih_l1_reverse" [id=61, type=nncf_model_const]; +"62 bias_ih_l1_reverse" [id=62, type=nncf_model_const]; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=63, type=linear]; +"64 weight_hh_l1_reverse" [id=64, type=nncf_model_const]; +"65 bias_hh_l1_reverse" [id=65, type=nncf_model_const]; +"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=66, type=linear]; +"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" [id=67, type=__add__]; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" [id=68, type=chunk]; +"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=69, type=sigmoid]; +"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=70, type=sigmoid]; +"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" [id=71, type=tanh]; +"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=72, type=sigmoid]; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" [id=73, type=__mul__]; +"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" [id=74, type=__mul__]; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" [id=75, type=__add__]; +"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" [id=76, type=tanh]; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" [id=77, type=__mul__]; +"78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=78, type=linear]; +"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [id=79, type=cat]; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" [id=80, type=view]; +"81 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" [id=81, type=cat]; +"82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=82, type=cat]; +"83 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=83, type=view]; +"84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=84, type=cat]; +"85 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=85, type=view]; +"0 weight_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"1 bias_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"3 weight_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"3 weight_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"20 weight_ih_l0_reverse" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"21 bias_ih_l0_reverse" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"22 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"23 weight_hh_l0_reverse" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"23 weight_hh_l0_reverse" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"24 bias_hh_l0_reverse" -> "25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"24 bias_hh_l0_reverse" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"25 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"41 weight_ih_l1" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"42 bias_ih_l1" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"43 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"44 weight_hh_l1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"44 weight_hh_l1" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"45 bias_hh_l1" -> "46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"45 bias_hh_l1" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"46 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0"; +"47 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___0" -> "48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0"; +"48 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/chunk_0" -> "52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"49 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"50 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0"; +"51 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_0" -> "54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1"; +"52 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"53 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___0" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"54 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___1" -> "55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1"; +"55 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__add___1" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"56 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/tanh_1" -> "57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "58 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"57 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/LSTMCellForwardNNCF[cell]/__mul___2" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"59 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/cat_0" -> "60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0"; +"60 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[2]/view_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"61 weight_ih_l1_reverse" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"62 bias_ih_l1_reverse" -> "63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"63 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"64 weight_hh_l1_reverse" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"64 weight_hh_l1_reverse" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"65 bias_hh_l1_reverse" -> "66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"65 bias_hh_l1_reverse" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"66 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0"; +"67 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___0" -> "68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0"; +"68 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/chunk_0" -> "72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"69 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"70 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0"; +"71 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_0" -> "74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1"; +"72 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"73 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___0" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"74 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___1" -> "75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1"; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1"; +"75 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__add___1" -> "84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"76 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/tanh_1" -> "77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "78 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"77 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/LSTMCellForwardNNCF[cell]/__mul___2" -> "82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"79 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/cat_0" -> "80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0"; +"80 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[3]/view_0" -> "81 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"82 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "83 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"84 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "85 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +} diff --git a/tests/torch/data/reference_graphs/original/lstm_cell_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/lstm_cell_with_parameters_tracing.dot new file mode 100644 index 00000000000..9e7906c0829 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/lstm_cell_with_parameters_tracing.dot @@ -0,0 +1,38 @@ +strict digraph { +"0 weight_ih" [id=0, type=nncf_model_const]; +"1 bias_ih" [id=1, type=nncf_model_const]; +"2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=2, type=linear]; +"3 weight_hh" [id=3, type=nncf_model_const]; +"4 bias_hh" [id=4, type=nncf_model_const]; +"5 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=5, type=linear]; +"6 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0" [id=6, type=__add__]; +"7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" [id=7, type=chunk]; +"8 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=8, type=sigmoid]; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=9, type=sigmoid]; +"10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" [id=10, type=tanh]; +"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=11, type=sigmoid]; +"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" [id=12, type=__mul__]; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" [id=13, type=__mul__]; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" [id=14, type=__add__]; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" [id=15, type=tanh]; +"16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2" [id=16, type=__mul__]; +"0 weight_ih" -> "2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"1 bias_ih" -> "2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"2 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "6 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0"; +"3 weight_hh" -> "5 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh" -> "5 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"5 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "6 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0"; +"6 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___0" -> "7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0"; +"7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "8 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0"; +"7 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/chunk_0" -> "11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"8 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"9 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0"; +"10 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_0" -> "13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1"; +"11 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +"12 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___0" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"13 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___1" -> "14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1"; +"14 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__add___1" -> "15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1"; +"15 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/tanh_1" -> "16 LSTMCellNNCF/LSTMCellForwardNNCF[cell]/__mul___2"; +} diff --git a/tests/torch/data/reference_graphs/original/lstm_uni_seq_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/lstm_uni_seq_with_parameters_tracing.dot new file mode 100644 index 00000000000..4503eeed99c --- /dev/null +++ b/tests/torch/data/reference_graphs/original/lstm_uni_seq_with_parameters_tracing.dot @@ -0,0 +1,56 @@ +strict digraph { +"0 weight_ih_l0" [id=0, type=nncf_model_const]; +"1 bias_ih_l0" [id=1, type=nncf_model_const]; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=2, type=linear]; +"3 weight_hh_l0" [id=3, type=nncf_model_const]; +"4 bias_hh_l0" [id=4, type=nncf_model_const]; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=5, type=linear]; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" [id=6, type=__add__]; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" [id=7, type=chunk]; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=8, type=sigmoid]; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=9, type=sigmoid]; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=10, type=tanh]; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=11, type=sigmoid]; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=12, type=__mul__]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=13, type=__mul__]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=14, type=__add__]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=15, type=tanh]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=16, type=__mul__]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=17, type=linear]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=18, type=cat]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=19, type=view]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=20, type=cat]; +"21 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=21, type=cat]; +"22 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=22, type=view]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=23, type=cat]; +"24 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=24, type=view]; +"0 weight_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"1 bias_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"3 weight_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"3 weight_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "21 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"21 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "22 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "24 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +} diff --git a/tests/torch/data/reference_graphs/original/lstm_uni_stacked_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/lstm_uni_stacked_with_parameters_tracing.dot new file mode 100644 index 00000000000..3e2baee17ce --- /dev/null +++ b/tests/torch/data/reference_graphs/original/lstm_uni_stacked_with_parameters_tracing.dot @@ -0,0 +1,105 @@ +strict digraph { +"0 weight_ih_l0" [id=0, type=nncf_model_const]; +"1 bias_ih_l0" [id=1, type=nncf_model_const]; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=2, type=linear]; +"3 weight_hh_l0" [id=3, type=nncf_model_const]; +"4 bias_hh_l0" [id=4, type=nncf_model_const]; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=5, type=linear]; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" [id=6, type=__add__]; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" [id=7, type=chunk]; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=8, type=sigmoid]; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=9, type=sigmoid]; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" [id=10, type=tanh]; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=11, type=sigmoid]; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" [id=12, type=__mul__]; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" [id=13, type=__mul__]; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" [id=14, type=__add__]; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" [id=15, type=tanh]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" [id=16, type=__mul__]; +"17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=17, type=linear]; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [id=18, type=cat]; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" [id=19, type=view]; +"20 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" [id=20, type=cat]; +"21 weight_ih_l1" [id=21, type=nncf_model_const]; +"22 bias_ih_l1" [id=22, type=nncf_model_const]; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" [id=23, type=linear]; +"24 weight_hh_l1" [id=24, type=nncf_model_const]; +"25 bias_hh_l1" [id=25, type=nncf_model_const]; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=26, type=linear]; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" [id=27, type=__add__]; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" [id=28, type=chunk]; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" [id=29, type=sigmoid]; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" [id=30, type=sigmoid]; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" [id=31, type=tanh]; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" [id=32, type=sigmoid]; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" [id=33, type=__mul__]; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" [id=34, type=__mul__]; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" [id=35, type=__add__]; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" [id=36, type=tanh]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" [id=37, type=__mul__]; +"38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" [id=38, type=linear]; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [id=39, type=cat]; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" [id=40, type=view]; +"41 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1" [id=41, type=cat]; +"42 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" [id=42, type=cat]; +"43 NNCF_RNN/StackedRNN[rnn_impl]/view_0" [id=43, type=view]; +"44 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" [id=44, type=cat]; +"45 NNCF_RNN/StackedRNN[rnn_impl]/view_1" [id=45, type=view]; +"0 weight_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"1 bias_ih_l0" -> "2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"2 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"3 weight_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"3 weight_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"4 bias_hh_l0" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"5 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0"; +"6 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___0" -> "7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0"; +"7 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/chunk_0" -> "11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"8 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"9 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0"; +"10 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_0" -> "13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1"; +"11 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"12 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___0" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"13 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___1" -> "14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1"; +"14 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__add___1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"15 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/tanh_1" -> "16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "17 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"16 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/LSTMCellForwardNNCF[cell]/__mul___2" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"18 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/cat_0" -> "19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0"; +"19 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[0]/view_0" -> "20 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0"; +"20 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_0" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"21 weight_ih_l1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"22 bias_ih_l1" -> "23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0"; +"23 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[input_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"24 weight_hh_l1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"24 weight_hh_l1" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"25 bias_hh_l1" -> "26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"25 bias_hh_l1" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"26 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0" -> "27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0"; +"27 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___0" -> "28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0"; +"28 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/chunk_0" -> "32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2"; +"29 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"30 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_1" -> "33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0"; +"31 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_0" -> "34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1"; +"32 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/sigmoid_2" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"33 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___0" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"34 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___1" -> "35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1"; +"35 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__add___1" -> "44 NNCF_RNN/StackedRNN[rnn_impl]/cat_1"; +"36 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/tanh_1" -> "37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "38 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/Linear[hidden_linear]/linear_0"; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" [label="parallel_input_port_ids:[1, 2]"]; +"37 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/LSTMCellForwardNNCF[cell]/__mul___2" -> "42 NNCF_RNN/StackedRNN[rnn_impl]/cat_0"; +"39 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/cat_0" -> "40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0"; +"40 NNCF_RNN/StackedRNN[rnn_impl]/ModuleList[inners]/Recurrent[1]/view_0" -> "41 NNCF_RNN/StackedRNN[rnn_impl]/StackedRNNResetPoint/cat_1"; +"42 NNCF_RNN/StackedRNN[rnn_impl]/cat_0" -> "43 NNCF_RNN/StackedRNN[rnn_impl]/view_0"; +"44 NNCF_RNN/StackedRNN[rnn_impl]/cat_1" -> "45 NNCF_RNN/StackedRNN[rnn_impl]/view_1"; +} diff --git a/tests/torch/data/reference_graphs/original/mobilenet_v2_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/mobilenet_v2_with_parameters_tracing.dot new file mode 100644 index 00000000000..292e0e059da --- /dev/null +++ b/tests/torch/data/reference_graphs/original/mobilenet_v2_with_parameters_tracing.dot @@ -0,0 +1,633 @@ +strict digraph { +"0 features.0.0.weight" [id=0, type=nncf_model_const]; +"1 MobileNetV2/Sequential[features]/ConvBNActivation[0]/Conv2d[0]/conv2d_0" [id=1, type=conv2d]; +"2 features.0.1.weight" [id=2, type=nncf_model_const]; +"3 features.0.1.bias" [id=3, type=nncf_model_const]; +"4 MobileNetV2/Sequential[features]/ConvBNActivation[0]/BatchNorm2d[1]/batch_norm_0" [id=4, type=batch_norm]; +"5 MobileNetV2/Sequential[features]/ConvBNActivation[0]/ReLU6[2]/hardtanh_0" [id=5, type=hardtanh]; +"6 features.1.conv.0.0.weight" [id=6, type=nncf_model_const]; +"7 MobileNetV2/Sequential[features]/InvertedResidual[1]/Sequential[conv]/ConvBNActivation[0]/Conv2d[0]/conv2d_0" [id=7, type=conv2d]; +"8 features.1.conv.0.1.weight" [id=8, type=nncf_model_const]; +"9 features.1.conv.0.1.bias" [id=9, type=nncf_model_const]; +"10 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PNASNet/Sequential[layer5]/CellB[4]/__add___0" -> "519 PNASNet/Sequential[layer5]/CellB[4]/relu_0"; +"519 PNASNet/Sequential[layer5]/CellB[4]/relu_0" -> "522 PNASNet/Sequential[layer5]/CellB[4]/cat_0"; +"520 PNASNet/Sequential[layer5]/CellB[4]/__add___1" -> "521 PNASNet/Sequential[layer5]/CellB[4]/relu_1"; +"521 PNASNet/Sequential[layer5]/CellB[4]/relu_1" -> "522 PNASNet/Sequential[layer5]/CellB[4]/cat_0"; +"522 PNASNet/Sequential[layer5]/CellB[4]/cat_0" -> "524 PNASNet/Sequential[layer5]/CellB[4]/Conv2d[conv2]/conv2d_0"; +"523 layer5.4.conv2.weight" -> "524 PNASNet/Sequential[layer5]/CellB[4]/Conv2d[conv2]/conv2d_0"; +"524 PNASNet/Sequential[layer5]/CellB[4]/Conv2d[conv2]/conv2d_0" -> "527 PNASNet/Sequential[layer5]/CellB[4]/BatchNorm2d[bn2]/batch_norm_0"; +"525 layer5.4.bn2.weight" -> "527 PNASNet/Sequential[layer5]/CellB[4]/BatchNorm2d[bn2]/batch_norm_0"; +"526 layer5.4.bn2.bias" -> "527 PNASNet/Sequential[layer5]/CellB[4]/BatchNorm2d[bn2]/batch_norm_0"; +"527 PNASNet/Sequential[layer5]/CellB[4]/BatchNorm2d[bn2]/batch_norm_0" -> "528 PNASNet/Sequential[layer5]/CellB[4]/relu_2"; +"528 PNASNet/Sequential[layer5]/CellB[4]/relu_2" -> "530 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv1]/Conv2d[conv1]/conv2d_0"; +"528 PNASNet/Sequential[layer5]/CellB[4]/relu_2" -> "535 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv2]/Conv2d[conv1]/conv2d_0"; +"528 PNASNet/Sequential[layer5]/CellB[4]/relu_2" -> "539 PNASNet/Sequential[layer5]/CellB[5]/max_pool2d_0"; +"528 PNASNet/Sequential[layer5]/CellB[4]/relu_2" -> "541 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/Conv2d[conv1]/conv2d_0"; +"529 layer5.5.sep_conv1.conv1.weight" -> "530 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv1]/Conv2d[conv1]/conv2d_0"; +"530 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv1]/Conv2d[conv1]/conv2d_0" -> "533 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv1]/BatchNorm2d[bn1]/batch_norm_0"; +"531 layer5.5.sep_conv1.bn1.weight" -> "533 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PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv2]/BatchNorm2d[bn1]/batch_norm_0" -> "545 PNASNet/Sequential[layer5]/CellB[5]/__add___0"; +"539 PNASNet/Sequential[layer5]/CellB[5]/max_pool2d_0" -> "547 PNASNet/Sequential[layer5]/CellB[5]/__add___1"; +"540 layer5.5.sep_conv3.conv1.weight" -> "541 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/Conv2d[conv1]/conv2d_0"; +"541 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/Conv2d[conv1]/conv2d_0" -> "544 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/BatchNorm2d[bn1]/batch_norm_0"; +"542 layer5.5.sep_conv3.bn1.weight" -> "544 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/BatchNorm2d[bn1]/batch_norm_0"; +"543 layer5.5.sep_conv3.bn1.bias" -> "544 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/BatchNorm2d[bn1]/batch_norm_0"; +"544 PNASNet/Sequential[layer5]/CellB[5]/SepConv[sep_conv3]/BatchNorm2d[bn1]/batch_norm_0" -> "547 PNASNet/Sequential[layer5]/CellB[5]/__add___1"; +"545 PNASNet/Sequential[layer5]/CellB[5]/__add___0" -> "546 PNASNet/Sequential[layer5]/CellB[5]/relu_0"; +"546 PNASNet/Sequential[layer5]/CellB[5]/relu_0" -> "549 PNASNet/Sequential[layer5]/CellB[5]/cat_0"; +"547 PNASNet/Sequential[layer5]/CellB[5]/__add___1" -> "548 PNASNet/Sequential[layer5]/CellB[5]/relu_1"; +"548 PNASNet/Sequential[layer5]/CellB[5]/relu_1" -> "549 PNASNet/Sequential[layer5]/CellB[5]/cat_0"; +"549 PNASNet/Sequential[layer5]/CellB[5]/cat_0" -> "551 PNASNet/Sequential[layer5]/CellB[5]/Conv2d[conv2]/conv2d_0"; +"550 layer5.5.conv2.weight" -> "551 PNASNet/Sequential[layer5]/CellB[5]/Conv2d[conv2]/conv2d_0"; +"551 PNASNet/Sequential[layer5]/CellB[5]/Conv2d[conv2]/conv2d_0" -> "554 PNASNet/Sequential[layer5]/CellB[5]/BatchNorm2d[bn2]/batch_norm_0"; +"552 layer5.5.bn2.weight" -> "554 PNASNet/Sequential[layer5]/CellB[5]/BatchNorm2d[bn2]/batch_norm_0"; +"553 layer5.5.bn2.bias" -> "554 PNASNet/Sequential[layer5]/CellB[5]/BatchNorm2d[bn2]/batch_norm_0"; +"554 PNASNet/Sequential[layer5]/CellB[5]/BatchNorm2d[bn2]/batch_norm_0" -> "555 PNASNet/Sequential[layer5]/CellB[5]/relu_2"; +"555 PNASNet/Sequential[layer5]/CellB[5]/relu_2" -> "556 PNASNet/avg_pool2d_0"; +"556 PNASNet/avg_pool2d_0" -> "557 PNASNet/view_0"; +"557 PNASNet/view_0" -> "560 PNASNet/Linear[linear]/linear_0"; +"558 linear.weight" -> "560 PNASNet/Linear[linear]/linear_0"; +"559 linear.bias" -> "560 PNASNet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/preresnet50_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/preresnet50_with_parameters_tracing.dot new file mode 100644 index 00000000000..6b1b91f3e6b --- /dev/null +++ b/tests/torch/data/reference_graphs/original/preresnet50_with_parameters_tracing.dot @@ -0,0 +1,655 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 PreActResNet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 layer1.0.bn1.weight" [id=2, type=nncf_model_const]; +"3 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PreActResNet/Sequential[layer4]/PreActBottleneck[1]/relu_0"; +"279 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/relu_0" -> "281 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/Conv2d[conv1]/conv2d_0"; +"280 layer4.1.conv1.weight" -> "281 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/Conv2d[conv1]/conv2d_0"; +"281 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/Conv2d[conv1]/conv2d_0" -> "284 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"282 layer4.1.bn2.weight" -> "284 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"283 layer4.1.bn2.bias" -> "284 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"284 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" -> "285 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/relu_1"; +"285 PreActResNet/Sequential[layer4]/PreActBottleneck[1]/relu_1" -> "287 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PreActResNet/Sequential[layer4]/PreActBottleneck[2]/__iadd___0"; +"313 PreActResNet/Sequential[layer4]/PreActBottleneck[2]/__iadd___0" -> "314 PreActResNet/avg_pool2d_0"; +"314 PreActResNet/avg_pool2d_0" -> "315 PreActResNet/view_0"; +"315 PreActResNet/view_0" -> "318 PreActResNet/Linear[linear]/linear_0"; +"316 linear.weight" -> "318 PreActResNet/Linear[linear]/linear_0"; +"317 linear.bias" -> "318 PreActResNet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/resnet18_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/resnet18_with_parameters_tracing.dot new file mode 100644 index 00000000000..c69fbc27677 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/resnet18_with_parameters_tracing.dot @@ -0,0 +1,271 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 ResNet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 bn1.weight" [id=2, type=nncf_model_const]; +"3 bn1.bias" [id=3, 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ResNet/Sequential[layer2]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"50 ResNet/Sequential[layer2]/BasicBlock[0]/relu_1" -> "62 ResNet/Sequential[layer2]/BasicBlock[1]/__iadd___0"; +"51 layer2.1.conv1.weight" -> "52 ResNet/Sequential[layer2]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"52 ResNet/Sequential[layer2]/BasicBlock[1]/Conv2d[conv1]/conv2d_0" -> "55 ResNet/Sequential[layer2]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"53 layer2.1.bn1.weight" -> "55 ResNet/Sequential[layer2]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"54 layer2.1.bn1.bias" -> "55 ResNet/Sequential[layer2]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"55 ResNet/Sequential[layer2]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0" -> "56 ResNet/Sequential[layer2]/BasicBlock[1]/relu_0"; +"56 ResNet/Sequential[layer2]/BasicBlock[1]/relu_0" -> "58 ResNet/Sequential[layer2]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"57 layer2.1.conv2.weight" -> "58 ResNet/Sequential[layer2]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"58 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ResNet/Sequential[layer3]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0"; +"74 ResNet/Sequential[layer3]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0" -> "80 ResNet/Sequential[layer3]/BasicBlock[0]/__iadd___0"; +"75 layer3.0.shortcut.0.weight" -> "76 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"76 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "79 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"77 layer3.0.shortcut.1.weight" -> "79 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"78 layer3.0.shortcut.1.bias" -> "79 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"79 ResNet/Sequential[layer3]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "80 ResNet/Sequential[layer3]/BasicBlock[0]/__iadd___0"; +"80 ResNet/Sequential[layer3]/BasicBlock[0]/__iadd___0" -> "81 ResNet/Sequential[layer3]/BasicBlock[0]/relu_1"; +"81 ResNet/Sequential[layer3]/BasicBlock[0]/relu_1" -> "83 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"81 ResNet/Sequential[layer3]/BasicBlock[0]/relu_1" -> "93 ResNet/Sequential[layer3]/BasicBlock[1]/__iadd___0"; +"82 layer3.1.conv1.weight" -> "83 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"83 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv1]/conv2d_0" -> "86 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"84 layer3.1.bn1.weight" -> "86 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"85 layer3.1.bn1.bias" -> "86 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"86 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0" -> "87 ResNet/Sequential[layer3]/BasicBlock[1]/relu_0"; +"87 ResNet/Sequential[layer3]/BasicBlock[1]/relu_0" -> "89 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"88 layer3.1.conv2.weight" -> "89 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"89 ResNet/Sequential[layer3]/BasicBlock[1]/Conv2d[conv2]/conv2d_0" -> "92 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"90 layer3.1.bn2.weight" -> "92 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"91 layer3.1.bn2.bias" -> "92 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"92 ResNet/Sequential[layer3]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0" -> "93 ResNet/Sequential[layer3]/BasicBlock[1]/__iadd___0"; +"93 ResNet/Sequential[layer3]/BasicBlock[1]/__iadd___0" -> "94 ResNet/Sequential[layer3]/BasicBlock[1]/relu_1"; +"94 ResNet/Sequential[layer3]/BasicBlock[1]/relu_1" -> "96 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv1]/conv2d_0"; +"94 ResNet/Sequential[layer3]/BasicBlock[1]/relu_1" -> "107 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"95 layer4.0.conv1.weight" -> "96 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv1]/conv2d_0"; +"96 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv1]/conv2d_0" -> "99 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn1]/batch_norm_0"; +"97 layer4.0.bn1.weight" -> "99 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn1]/batch_norm_0"; +"98 layer4.0.bn1.bias" -> "99 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn1]/batch_norm_0"; +"99 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn1]/batch_norm_0" -> "100 ResNet/Sequential[layer4]/BasicBlock[0]/relu_0"; +"100 ResNet/Sequential[layer4]/BasicBlock[0]/relu_0" -> "102 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv2]/conv2d_0"; +"101 layer4.0.conv2.weight" -> "102 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv2]/conv2d_0"; +"102 ResNet/Sequential[layer4]/BasicBlock[0]/Conv2d[conv2]/conv2d_0" -> "105 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0"; +"103 layer4.0.bn2.weight" -> "105 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0"; +"104 layer4.0.bn2.bias" -> "105 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0"; +"105 ResNet/Sequential[layer4]/BasicBlock[0]/BatchNorm2d[bn2]/batch_norm_0" -> "111 ResNet/Sequential[layer4]/BasicBlock[0]/__iadd___0"; +"106 layer4.0.shortcut.0.weight" -> "107 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"107 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "110 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"108 layer4.0.shortcut.1.weight" -> "110 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"109 layer4.0.shortcut.1.bias" -> "110 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"110 ResNet/Sequential[layer4]/BasicBlock[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "111 ResNet/Sequential[layer4]/BasicBlock[0]/__iadd___0"; +"111 ResNet/Sequential[layer4]/BasicBlock[0]/__iadd___0" -> "112 ResNet/Sequential[layer4]/BasicBlock[0]/relu_1"; +"112 ResNet/Sequential[layer4]/BasicBlock[0]/relu_1" -> "114 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"112 ResNet/Sequential[layer4]/BasicBlock[0]/relu_1" -> "124 ResNet/Sequential[layer4]/BasicBlock[1]/__iadd___0"; +"113 layer4.1.conv1.weight" -> "114 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv1]/conv2d_0"; +"114 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv1]/conv2d_0" -> "117 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"115 layer4.1.bn1.weight" -> "117 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"116 layer4.1.bn1.bias" -> "117 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"117 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn1]/batch_norm_0" -> "118 ResNet/Sequential[layer4]/BasicBlock[1]/relu_0"; +"118 ResNet/Sequential[layer4]/BasicBlock[1]/relu_0" -> "120 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"119 layer4.1.conv2.weight" -> "120 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv2]/conv2d_0"; +"120 ResNet/Sequential[layer4]/BasicBlock[1]/Conv2d[conv2]/conv2d_0" -> "123 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"121 layer4.1.bn2.weight" -> "123 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"122 layer4.1.bn2.bias" -> "123 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"123 ResNet/Sequential[layer4]/BasicBlock[1]/BatchNorm2d[bn2]/batch_norm_0" -> "124 ResNet/Sequential[layer4]/BasicBlock[1]/__iadd___0"; +"124 ResNet/Sequential[layer4]/BasicBlock[1]/__iadd___0" -> "125 ResNet/Sequential[layer4]/BasicBlock[1]/relu_1"; +"125 ResNet/Sequential[layer4]/BasicBlock[1]/relu_1" -> "126 ResNet/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0"; +"126 ResNet/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0" -> "127 ResNet/flatten_0"; +"127 ResNet/flatten_0" -> "130 ResNet/Linear[linear]/linear_0"; +"128 linear.weight" -> "130 ResNet/Linear[linear]/linear_0"; +"129 linear.bias" -> "130 ResNet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/resnet50_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/resnet50_with_parameters_tracing.dot new file mode 100644 index 00000000000..9980b271e83 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/resnet50_with_parameters_tracing.dot @@ -0,0 +1,689 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 ResNet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 bn1.weight" [id=2, type=nncf_model_const]; +"3 bn1.bias" [id=3, type=nncf_model_const]; +"4 ResNet/BatchNorm2d[bn1]/batch_norm_0" [id=4, type=batch_norm]; +"5 ResNet/relu_0" [id=5, type=relu]; +"6 ResNet/MaxPool2d[maxpool]/max_pool2d_0" [id=6, type=max_pool2d]; +"7 layer1.0.conv1.weight" [id=7, type=nncf_model_const]; +"8 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" [id=8, type=conv2d]; +"9 layer1.0.bn1.weight" [id=9, type=nncf_model_const]; +"10 layer1.0.bn1.bias" [id=10, type=nncf_model_const]; +"11 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" [id=11, type=batch_norm]; +"12 ResNet/Sequential[layer1]/Bottleneck[0]/relu_0" [id=12, type=relu]; +"13 layer1.0.conv2.weight" [id=13, type=nncf_model_const]; +"14 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" [id=14, type=conv2d]; +"15 layer1.0.bn2.weight" [id=15, type=nncf_model_const]; +"16 layer1.0.bn2.bias" [id=16, type=nncf_model_const]; +"17 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" [id=17, type=batch_norm]; +"18 ResNet/Sequential[layer1]/Bottleneck[0]/relu_1" [id=18, type=relu]; +"19 layer1.0.conv3.weight" [id=19, type=nncf_model_const]; +"20 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" [id=20, type=conv2d]; +"21 layer1.0.bn3.weight" [id=21, type=nncf_model_const]; +"22 layer1.0.bn3.bias" [id=22, type=nncf_model_const]; +"23 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" [id=23, type=batch_norm]; +"24 layer1.0.shortcut.0.weight" [id=24, type=nncf_model_const]; +"25 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" [id=25, type=conv2d]; +"26 layer1.0.shortcut.1.weight" [id=26, type=nncf_model_const]; +"27 layer1.0.shortcut.1.bias" [id=27, type=nncf_model_const]; +"28 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" [id=28, type=batch_norm]; +"29 ResNet/Sequential[layer1]/Bottleneck[0]/__iadd___0" [id=29, type=__iadd__]; +"30 ResNet/Sequential[layer1]/Bottleneck[0]/relu_2" [id=30, type=relu]; +"31 layer1.1.conv1.weight" [id=31, type=nncf_model_const]; +"32 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" [id=32, type=conv2d]; +"33 layer1.1.bn1.weight" [id=33, type=nncf_model_const]; +"34 layer1.1.bn1.bias" [id=34, type=nncf_model_const]; +"35 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" [id=35, type=batch_norm]; +"36 ResNet/Sequential[layer1]/Bottleneck[1]/relu_0" [id=36, type=relu]; +"37 layer1.1.conv2.weight" [id=37, type=nncf_model_const]; +"38 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" [id=38, type=conv2d]; +"39 layer1.1.bn2.weight" [id=39, type=nncf_model_const]; +"40 layer1.1.bn2.bias" [id=40, type=nncf_model_const]; +"41 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" [id=41, type=batch_norm]; +"42 ResNet/Sequential[layer1]/Bottleneck[1]/relu_1" [id=42, type=relu]; +"43 layer1.1.conv3.weight" [id=43, type=nncf_model_const]; +"44 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" [id=44, type=conv2d]; +"45 layer1.1.bn3.weight" [id=45, type=nncf_model_const]; +"46 layer1.1.bn3.bias" [id=46, type=nncf_model_const]; +"47 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" [id=47, type=batch_norm]; +"48 ResNet/Sequential[layer1]/Bottleneck[1]/__iadd___0" [id=48, type=__iadd__]; +"49 ResNet/Sequential[layer1]/Bottleneck[1]/relu_2" [id=49, type=relu]; +"50 layer1.2.conv1.weight" [id=50, type=nncf_model_const]; +"51 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" [id=51, type=conv2d]; +"52 layer1.2.bn1.weight" [id=52, type=nncf_model_const]; +"53 layer1.2.bn1.bias" [id=53, type=nncf_model_const]; +"54 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" [id=54, type=batch_norm]; +"55 ResNet/Sequential[layer1]/Bottleneck[2]/relu_0" [id=55, type=relu]; +"56 layer1.2.conv2.weight" [id=56, type=nncf_model_const]; +"57 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" [id=57, type=conv2d]; +"58 layer1.2.bn2.weight" [id=58, type=nncf_model_const]; +"59 layer1.2.bn2.bias" [id=59, type=nncf_model_const]; +"60 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" [id=60, type=batch_norm]; +"61 ResNet/Sequential[layer1]/Bottleneck[2]/relu_1" [id=61, type=relu]; +"62 layer1.2.conv3.weight" [id=62, type=nncf_model_const]; +"63 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv3]/conv2d_0" [id=63, type=conv2d]; +"64 layer1.2.bn3.weight" [id=64, type=nncf_model_const]; +"65 layer1.2.bn3.bias" [id=65, type=nncf_model_const]; +"66 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0" [id=66, type=batch_norm]; +"67 ResNet/Sequential[layer1]/Bottleneck[2]/__iadd___0" [id=67, type=__iadd__]; +"68 ResNet/Sequential[layer1]/Bottleneck[2]/relu_2" [id=68, type=relu]; +"69 layer2.0.conv1.weight" [id=69, type=nncf_model_const]; +"70 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" [id=70, type=conv2d]; +"71 layer2.0.bn1.weight" [id=71, type=nncf_model_const]; +"72 layer2.0.bn1.bias" [id=72, type=nncf_model_const]; +"73 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" [id=73, type=batch_norm]; +"74 ResNet/Sequential[layer2]/Bottleneck[0]/relu_0" [id=74, type=relu]; +"75 layer2.0.conv2.weight" [id=75, type=nncf_model_const]; +"76 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" [id=76, type=conv2d]; +"77 layer2.0.bn2.weight" [id=77, type=nncf_model_const]; +"78 layer2.0.bn2.bias" [id=78, type=nncf_model_const]; +"79 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" [id=79, type=batch_norm]; +"80 ResNet/Sequential[layer2]/Bottleneck[0]/relu_1" [id=80, type=relu]; +"81 layer2.0.conv3.weight" [id=81, type=nncf_model_const]; +"82 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" [id=82, type=conv2d]; +"83 layer2.0.bn3.weight" [id=83, type=nncf_model_const]; +"84 layer2.0.bn3.bias" [id=84, type=nncf_model_const]; +"85 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" [id=85, type=batch_norm]; +"86 layer2.0.shortcut.0.weight" [id=86, type=nncf_model_const]; +"87 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" [id=87, type=conv2d]; +"88 layer2.0.shortcut.1.weight" [id=88, type=nncf_model_const]; +"89 layer2.0.shortcut.1.bias" [id=89, type=nncf_model_const]; +"90 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" [id=90, type=batch_norm]; +"91 ResNet/Sequential[layer2]/Bottleneck[0]/__iadd___0" [id=91, type=__iadd__]; +"92 ResNet/Sequential[layer2]/Bottleneck[0]/relu_2" [id=92, type=relu]; +"93 layer2.1.conv1.weight" [id=93, type=nncf_model_const]; +"94 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" [id=94, type=conv2d]; +"95 layer2.1.bn1.weight" [id=95, type=nncf_model_const]; +"96 layer2.1.bn1.bias" [id=96, type=nncf_model_const]; +"97 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" [id=97, type=batch_norm]; +"98 ResNet/Sequential[layer2]/Bottleneck[1]/relu_0" [id=98, type=relu]; +"99 layer2.1.conv2.weight" [id=99, type=nncf_model_const]; +"100 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" [id=100, type=conv2d]; +"101 layer2.1.bn2.weight" [id=101, type=nncf_model_const]; +"102 layer2.1.bn2.bias" [id=102, type=nncf_model_const]; +"103 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" [id=103, type=batch_norm]; +"104 ResNet/Sequential[layer2]/Bottleneck[1]/relu_1" [id=104, type=relu]; +"105 layer2.1.conv3.weight" [id=105, type=nncf_model_const]; +"106 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" [id=106, type=conv2d]; +"107 layer2.1.bn3.weight" [id=107, type=nncf_model_const]; +"108 layer2.1.bn3.bias" [id=108, type=nncf_model_const]; +"109 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" [id=109, type=batch_norm]; +"110 ResNet/Sequential[layer2]/Bottleneck[1]/__iadd___0" [id=110, type=__iadd__]; +"111 ResNet/Sequential[layer2]/Bottleneck[1]/relu_2" [id=111, type=relu]; +"112 layer2.2.conv1.weight" [id=112, type=nncf_model_const]; +"113 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" [id=113, type=conv2d]; +"114 layer2.2.bn1.weight" [id=114, type=nncf_model_const]; +"115 layer2.2.bn1.bias" [id=115, type=nncf_model_const]; +"116 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" [id=116, type=batch_norm]; +"117 ResNet/Sequential[layer2]/Bottleneck[2]/relu_0" [id=117, type=relu]; +"118 layer2.2.conv2.weight" [id=118, type=nncf_model_const]; +"119 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" [id=119, type=conv2d]; +"120 layer2.2.bn2.weight" [id=120, type=nncf_model_const]; +"121 layer2.2.bn2.bias" [id=121, type=nncf_model_const]; +"122 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" [id=122, type=batch_norm]; +"123 ResNet/Sequential[layer2]/Bottleneck[2]/relu_1" [id=123, type=relu]; +"124 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ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"15 layer1.0.bn2.weight" -> "17 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"16 layer1.0.bn2.bias" -> "17 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"17 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" -> "18 ResNet/Sequential[layer1]/Bottleneck[0]/relu_1"; +"18 ResNet/Sequential[layer1]/Bottleneck[0]/relu_1" -> "20 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"19 layer1.0.conv3.weight" -> "20 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"20 ResNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" -> "23 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"21 layer1.0.bn3.weight" -> "23 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"22 layer1.0.bn3.bias" -> "23 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"23 ResNet/Sequential[layer1]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" -> "29 ResNet/Sequential[layer1]/Bottleneck[0]/__iadd___0"; +"24 layer1.0.shortcut.0.weight" -> "25 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"25 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "28 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"26 layer1.0.shortcut.1.weight" -> "28 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"27 layer1.0.shortcut.1.bias" -> "28 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"28 ResNet/Sequential[layer1]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "29 ResNet/Sequential[layer1]/Bottleneck[0]/__iadd___0"; +"29 ResNet/Sequential[layer1]/Bottleneck[0]/__iadd___0" -> "30 ResNet/Sequential[layer1]/Bottleneck[0]/relu_2"; +"30 ResNet/Sequential[layer1]/Bottleneck[0]/relu_2" -> "32 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"30 ResNet/Sequential[layer1]/Bottleneck[0]/relu_2" -> "48 ResNet/Sequential[layer1]/Bottleneck[1]/__iadd___0"; +"31 layer1.1.conv1.weight" -> "32 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"32 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" -> "35 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"33 layer1.1.bn1.weight" -> "35 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"34 layer1.1.bn1.bias" -> "35 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"35 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" -> "36 ResNet/Sequential[layer1]/Bottleneck[1]/relu_0"; +"36 ResNet/Sequential[layer1]/Bottleneck[1]/relu_0" -> "38 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"37 layer1.1.conv2.weight" -> "38 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"38 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" -> "41 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"39 layer1.1.bn2.weight" -> "41 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"40 layer1.1.bn2.bias" -> "41 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"41 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" -> "42 ResNet/Sequential[layer1]/Bottleneck[1]/relu_1"; +"42 ResNet/Sequential[layer1]/Bottleneck[1]/relu_1" -> "44 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"43 layer1.1.conv3.weight" -> "44 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"44 ResNet/Sequential[layer1]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" -> "47 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"45 layer1.1.bn3.weight" -> "47 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"46 layer1.1.bn3.bias" -> "47 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"47 ResNet/Sequential[layer1]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" -> "48 ResNet/Sequential[layer1]/Bottleneck[1]/__iadd___0"; +"48 ResNet/Sequential[layer1]/Bottleneck[1]/__iadd___0" -> "49 ResNet/Sequential[layer1]/Bottleneck[1]/relu_2"; +"49 ResNet/Sequential[layer1]/Bottleneck[1]/relu_2" -> "51 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"49 ResNet/Sequential[layer1]/Bottleneck[1]/relu_2" -> "67 ResNet/Sequential[layer1]/Bottleneck[2]/__iadd___0"; +"50 layer1.2.conv1.weight" -> "51 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"51 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" -> "54 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"52 layer1.2.bn1.weight" -> "54 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"53 layer1.2.bn1.bias" -> "54 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"54 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" -> "55 ResNet/Sequential[layer1]/Bottleneck[2]/relu_0"; +"55 ResNet/Sequential[layer1]/Bottleneck[2]/relu_0" -> "57 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"56 layer1.2.conv2.weight" -> "57 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"57 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" -> "60 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"58 layer1.2.bn2.weight" -> "60 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"59 layer1.2.bn2.bias" -> "60 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"60 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" -> "61 ResNet/Sequential[layer1]/Bottleneck[2]/relu_1"; +"61 ResNet/Sequential[layer1]/Bottleneck[2]/relu_1" -> "63 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"62 layer1.2.conv3.weight" -> "63 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"63 ResNet/Sequential[layer1]/Bottleneck[2]/Conv2d[conv3]/conv2d_0" -> "66 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"64 layer1.2.bn3.weight" -> "66 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"65 layer1.2.bn3.bias" -> "66 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"66 ResNet/Sequential[layer1]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0" -> "67 ResNet/Sequential[layer1]/Bottleneck[2]/__iadd___0"; +"67 ResNet/Sequential[layer1]/Bottleneck[2]/__iadd___0" -> "68 ResNet/Sequential[layer1]/Bottleneck[2]/relu_2"; +"68 ResNet/Sequential[layer1]/Bottleneck[2]/relu_2" -> "70 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"68 ResNet/Sequential[layer1]/Bottleneck[2]/relu_2" -> "87 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"69 layer2.0.conv1.weight" -> "70 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"70 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" -> "73 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"71 layer2.0.bn1.weight" -> "73 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"72 layer2.0.bn1.bias" -> "73 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"73 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" -> "74 ResNet/Sequential[layer2]/Bottleneck[0]/relu_0"; +"74 ResNet/Sequential[layer2]/Bottleneck[0]/relu_0" -> "76 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"75 layer2.0.conv2.weight" -> "76 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"76 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" -> "79 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"77 layer2.0.bn2.weight" -> "79 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"78 layer2.0.bn2.bias" -> "79 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"79 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" -> "80 ResNet/Sequential[layer2]/Bottleneck[0]/relu_1"; +"80 ResNet/Sequential[layer2]/Bottleneck[0]/relu_1" -> "82 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"81 layer2.0.conv3.weight" -> "82 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"82 ResNet/Sequential[layer2]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" -> "85 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"83 layer2.0.bn3.weight" -> "85 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"84 layer2.0.bn3.bias" -> "85 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"85 ResNet/Sequential[layer2]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" -> "91 ResNet/Sequential[layer2]/Bottleneck[0]/__iadd___0"; +"86 layer2.0.shortcut.0.weight" -> "87 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"87 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "90 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"88 layer2.0.shortcut.1.weight" -> "90 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"89 layer2.0.shortcut.1.bias" -> "90 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"90 ResNet/Sequential[layer2]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "91 ResNet/Sequential[layer2]/Bottleneck[0]/__iadd___0"; +"91 ResNet/Sequential[layer2]/Bottleneck[0]/__iadd___0" -> "92 ResNet/Sequential[layer2]/Bottleneck[0]/relu_2"; +"92 ResNet/Sequential[layer2]/Bottleneck[0]/relu_2" -> "94 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"92 ResNet/Sequential[layer2]/Bottleneck[0]/relu_2" -> "110 ResNet/Sequential[layer2]/Bottleneck[1]/__iadd___0"; +"93 layer2.1.conv1.weight" -> "94 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"94 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" -> "97 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"95 layer2.1.bn1.weight" -> "97 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"96 layer2.1.bn1.bias" -> "97 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"97 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" -> "98 ResNet/Sequential[layer2]/Bottleneck[1]/relu_0"; +"98 ResNet/Sequential[layer2]/Bottleneck[1]/relu_0" -> "100 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"99 layer2.1.conv2.weight" -> "100 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"100 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" -> "103 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"101 layer2.1.bn2.weight" -> "103 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"102 layer2.1.bn2.bias" -> "103 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"103 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" -> "104 ResNet/Sequential[layer2]/Bottleneck[1]/relu_1"; +"104 ResNet/Sequential[layer2]/Bottleneck[1]/relu_1" -> "106 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"105 layer2.1.conv3.weight" -> "106 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"106 ResNet/Sequential[layer2]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" -> "109 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"107 layer2.1.bn3.weight" -> "109 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"108 layer2.1.bn3.bias" -> "109 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"109 ResNet/Sequential[layer2]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" -> "110 ResNet/Sequential[layer2]/Bottleneck[1]/__iadd___0"; +"110 ResNet/Sequential[layer2]/Bottleneck[1]/__iadd___0" -> "111 ResNet/Sequential[layer2]/Bottleneck[1]/relu_2"; +"111 ResNet/Sequential[layer2]/Bottleneck[1]/relu_2" -> "113 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"111 ResNet/Sequential[layer2]/Bottleneck[1]/relu_2" -> "129 ResNet/Sequential[layer2]/Bottleneck[2]/__iadd___0"; +"112 layer2.2.conv1.weight" -> "113 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"113 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" -> "116 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"114 layer2.2.bn1.weight" -> "116 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"115 layer2.2.bn1.bias" -> "116 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"116 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" -> "117 ResNet/Sequential[layer2]/Bottleneck[2]/relu_0"; +"117 ResNet/Sequential[layer2]/Bottleneck[2]/relu_0" -> "119 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"118 layer2.2.conv2.weight" -> "119 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"119 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" -> "122 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"120 layer2.2.bn2.weight" -> "122 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"121 layer2.2.bn2.bias" -> "122 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"122 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" -> "123 ResNet/Sequential[layer2]/Bottleneck[2]/relu_1"; +"123 ResNet/Sequential[layer2]/Bottleneck[2]/relu_1" -> "125 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"124 layer2.2.conv3.weight" -> "125 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"125 ResNet/Sequential[layer2]/Bottleneck[2]/Conv2d[conv3]/conv2d_0" -> "128 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"126 layer2.2.bn3.weight" -> "128 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"127 layer2.2.bn3.bias" -> "128 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"128 ResNet/Sequential[layer2]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0" -> "129 ResNet/Sequential[layer2]/Bottleneck[2]/__iadd___0"; +"129 ResNet/Sequential[layer2]/Bottleneck[2]/__iadd___0" -> "130 ResNet/Sequential[layer2]/Bottleneck[2]/relu_2"; +"130 ResNet/Sequential[layer2]/Bottleneck[2]/relu_2" -> "132 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv1]/conv2d_0"; +"130 ResNet/Sequential[layer2]/Bottleneck[2]/relu_2" -> "148 ResNet/Sequential[layer2]/Bottleneck[3]/__iadd___0"; +"131 layer2.3.conv1.weight" -> "132 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv1]/conv2d_0"; +"132 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv1]/conv2d_0" -> "135 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"133 layer2.3.bn1.weight" -> "135 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"134 layer2.3.bn1.bias" -> "135 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"135 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0" -> "136 ResNet/Sequential[layer2]/Bottleneck[3]/relu_0"; +"136 ResNet/Sequential[layer2]/Bottleneck[3]/relu_0" -> "138 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv2]/conv2d_0"; +"137 layer2.3.conv2.weight" -> "138 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv2]/conv2d_0"; +"138 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv2]/conv2d_0" -> "141 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"139 layer2.3.bn2.weight" -> "141 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"140 layer2.3.bn2.bias" -> "141 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"141 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0" -> "142 ResNet/Sequential[layer2]/Bottleneck[3]/relu_1"; +"142 ResNet/Sequential[layer2]/Bottleneck[3]/relu_1" -> "144 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv3]/conv2d_0"; +"143 layer2.3.conv3.weight" -> "144 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv3]/conv2d_0"; +"144 ResNet/Sequential[layer2]/Bottleneck[3]/Conv2d[conv3]/conv2d_0" -> "147 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"145 layer2.3.bn3.weight" -> "147 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"146 layer2.3.bn3.bias" -> "147 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"147 ResNet/Sequential[layer2]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0" -> "148 ResNet/Sequential[layer2]/Bottleneck[3]/__iadd___0"; +"148 ResNet/Sequential[layer2]/Bottleneck[3]/__iadd___0" -> "149 ResNet/Sequential[layer2]/Bottleneck[3]/relu_2"; +"149 ResNet/Sequential[layer2]/Bottleneck[3]/relu_2" -> "151 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"149 ResNet/Sequential[layer2]/Bottleneck[3]/relu_2" -> "168 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"150 layer3.0.conv1.weight" -> "151 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"151 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" -> "154 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"152 layer3.0.bn1.weight" -> "154 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"153 layer3.0.bn1.bias" -> "154 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"154 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" -> "155 ResNet/Sequential[layer3]/Bottleneck[0]/relu_0"; +"155 ResNet/Sequential[layer3]/Bottleneck[0]/relu_0" -> "157 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"156 layer3.0.conv2.weight" -> "157 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"157 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" -> "160 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"158 layer3.0.bn2.weight" -> "160 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"159 layer3.0.bn2.bias" -> "160 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"160 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" -> "161 ResNet/Sequential[layer3]/Bottleneck[0]/relu_1"; +"161 ResNet/Sequential[layer3]/Bottleneck[0]/relu_1" -> "163 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"162 layer3.0.conv3.weight" -> "163 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"163 ResNet/Sequential[layer3]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" -> "166 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"164 layer3.0.bn3.weight" -> "166 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"165 layer3.0.bn3.bias" -> "166 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"166 ResNet/Sequential[layer3]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" -> "172 ResNet/Sequential[layer3]/Bottleneck[0]/__iadd___0"; +"167 layer3.0.shortcut.0.weight" -> "168 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"168 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "171 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"169 layer3.0.shortcut.1.weight" -> "171 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"170 layer3.0.shortcut.1.bias" -> "171 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"171 ResNet/Sequential[layer3]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "172 ResNet/Sequential[layer3]/Bottleneck[0]/__iadd___0"; +"172 ResNet/Sequential[layer3]/Bottleneck[0]/__iadd___0" -> "173 ResNet/Sequential[layer3]/Bottleneck[0]/relu_2"; +"173 ResNet/Sequential[layer3]/Bottleneck[0]/relu_2" -> "175 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"173 ResNet/Sequential[layer3]/Bottleneck[0]/relu_2" -> "191 ResNet/Sequential[layer3]/Bottleneck[1]/__iadd___0"; +"174 layer3.1.conv1.weight" -> "175 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"175 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" -> "178 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"176 layer3.1.bn1.weight" -> "178 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"177 layer3.1.bn1.bias" -> "178 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"178 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" -> "179 ResNet/Sequential[layer3]/Bottleneck[1]/relu_0"; +"179 ResNet/Sequential[layer3]/Bottleneck[1]/relu_0" -> "181 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"180 layer3.1.conv2.weight" -> "181 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"181 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" -> "184 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"182 layer3.1.bn2.weight" -> "184 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"183 layer3.1.bn2.bias" -> "184 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"184 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" -> "185 ResNet/Sequential[layer3]/Bottleneck[1]/relu_1"; +"185 ResNet/Sequential[layer3]/Bottleneck[1]/relu_1" -> "187 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"186 layer3.1.conv3.weight" -> "187 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"187 ResNet/Sequential[layer3]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" -> "190 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"188 layer3.1.bn3.weight" -> "190 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"189 layer3.1.bn3.bias" -> "190 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"190 ResNet/Sequential[layer3]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" -> "191 ResNet/Sequential[layer3]/Bottleneck[1]/__iadd___0"; +"191 ResNet/Sequential[layer3]/Bottleneck[1]/__iadd___0" -> "192 ResNet/Sequential[layer3]/Bottleneck[1]/relu_2"; +"192 ResNet/Sequential[layer3]/Bottleneck[1]/relu_2" -> "194 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"192 ResNet/Sequential[layer3]/Bottleneck[1]/relu_2" -> "210 ResNet/Sequential[layer3]/Bottleneck[2]/__iadd___0"; +"193 layer3.2.conv1.weight" -> "194 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"194 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" -> "197 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"195 layer3.2.bn1.weight" -> "197 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"196 layer3.2.bn1.bias" -> "197 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"197 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" -> "198 ResNet/Sequential[layer3]/Bottleneck[2]/relu_0"; +"198 ResNet/Sequential[layer3]/Bottleneck[2]/relu_0" -> "200 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"199 layer3.2.conv2.weight" -> "200 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"200 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" -> "203 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"201 layer3.2.bn2.weight" -> "203 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"202 layer3.2.bn2.bias" -> "203 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"203 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" -> "204 ResNet/Sequential[layer3]/Bottleneck[2]/relu_1"; +"204 ResNet/Sequential[layer3]/Bottleneck[2]/relu_1" -> "206 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"205 layer3.2.conv3.weight" -> "206 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"206 ResNet/Sequential[layer3]/Bottleneck[2]/Conv2d[conv3]/conv2d_0" -> "209 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"207 layer3.2.bn3.weight" -> "209 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"208 layer3.2.bn3.bias" -> "209 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"209 ResNet/Sequential[layer3]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0" -> "210 ResNet/Sequential[layer3]/Bottleneck[2]/__iadd___0"; +"210 ResNet/Sequential[layer3]/Bottleneck[2]/__iadd___0" -> "211 ResNet/Sequential[layer3]/Bottleneck[2]/relu_2"; +"211 ResNet/Sequential[layer3]/Bottleneck[2]/relu_2" -> "213 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv1]/conv2d_0"; +"211 ResNet/Sequential[layer3]/Bottleneck[2]/relu_2" -> "229 ResNet/Sequential[layer3]/Bottleneck[3]/__iadd___0"; +"212 layer3.3.conv1.weight" -> "213 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv1]/conv2d_0"; +"213 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv1]/conv2d_0" -> "216 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"214 layer3.3.bn1.weight" -> "216 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"215 layer3.3.bn1.bias" -> "216 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0"; +"216 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn1]/batch_norm_0" -> "217 ResNet/Sequential[layer3]/Bottleneck[3]/relu_0"; +"217 ResNet/Sequential[layer3]/Bottleneck[3]/relu_0" -> "219 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv2]/conv2d_0"; +"218 layer3.3.conv2.weight" -> "219 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv2]/conv2d_0"; +"219 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv2]/conv2d_0" -> "222 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"220 layer3.3.bn2.weight" -> "222 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"221 layer3.3.bn2.bias" -> "222 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0"; +"222 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn2]/batch_norm_0" -> "223 ResNet/Sequential[layer3]/Bottleneck[3]/relu_1"; +"223 ResNet/Sequential[layer3]/Bottleneck[3]/relu_1" -> "225 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv3]/conv2d_0"; +"224 layer3.3.conv3.weight" -> "225 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv3]/conv2d_0"; +"225 ResNet/Sequential[layer3]/Bottleneck[3]/Conv2d[conv3]/conv2d_0" -> "228 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"226 layer3.3.bn3.weight" -> "228 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"227 layer3.3.bn3.bias" -> "228 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0"; +"228 ResNet/Sequential[layer3]/Bottleneck[3]/BatchNorm2d[bn3]/batch_norm_0" -> "229 ResNet/Sequential[layer3]/Bottleneck[3]/__iadd___0"; +"229 ResNet/Sequential[layer3]/Bottleneck[3]/__iadd___0" -> "230 ResNet/Sequential[layer3]/Bottleneck[3]/relu_2"; +"230 ResNet/Sequential[layer3]/Bottleneck[3]/relu_2" -> "232 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv1]/conv2d_0"; +"230 ResNet/Sequential[layer3]/Bottleneck[3]/relu_2" -> "248 ResNet/Sequential[layer3]/Bottleneck[4]/__iadd___0"; +"231 layer3.4.conv1.weight" -> "232 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv1]/conv2d_0"; +"232 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv1]/conv2d_0" -> "235 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn1]/batch_norm_0"; +"233 layer3.4.bn1.weight" -> "235 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn1]/batch_norm_0"; +"234 layer3.4.bn1.bias" -> "235 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn1]/batch_norm_0"; +"235 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn1]/batch_norm_0" -> "236 ResNet/Sequential[layer3]/Bottleneck[4]/relu_0"; +"236 ResNet/Sequential[layer3]/Bottleneck[4]/relu_0" -> "238 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv2]/conv2d_0"; +"237 layer3.4.conv2.weight" -> "238 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv2]/conv2d_0"; +"238 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv2]/conv2d_0" -> "241 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn2]/batch_norm_0"; +"239 layer3.4.bn2.weight" -> "241 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn2]/batch_norm_0"; +"240 layer3.4.bn2.bias" -> "241 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn2]/batch_norm_0"; +"241 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn2]/batch_norm_0" -> "242 ResNet/Sequential[layer3]/Bottleneck[4]/relu_1"; +"242 ResNet/Sequential[layer3]/Bottleneck[4]/relu_1" -> "244 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv3]/conv2d_0"; +"243 layer3.4.conv3.weight" -> "244 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv3]/conv2d_0"; +"244 ResNet/Sequential[layer3]/Bottleneck[4]/Conv2d[conv3]/conv2d_0" -> "247 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn3]/batch_norm_0"; +"245 layer3.4.bn3.weight" -> "247 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn3]/batch_norm_0"; +"246 layer3.4.bn3.bias" -> "247 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn3]/batch_norm_0"; +"247 ResNet/Sequential[layer3]/Bottleneck[4]/BatchNorm2d[bn3]/batch_norm_0" -> "248 ResNet/Sequential[layer3]/Bottleneck[4]/__iadd___0"; +"248 ResNet/Sequential[layer3]/Bottleneck[4]/__iadd___0" -> "249 ResNet/Sequential[layer3]/Bottleneck[4]/relu_2"; +"249 ResNet/Sequential[layer3]/Bottleneck[4]/relu_2" -> "251 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv1]/conv2d_0"; +"249 ResNet/Sequential[layer3]/Bottleneck[4]/relu_2" -> "267 ResNet/Sequential[layer3]/Bottleneck[5]/__iadd___0"; +"250 layer3.5.conv1.weight" -> "251 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv1]/conv2d_0"; +"251 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv1]/conv2d_0" -> "254 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn1]/batch_norm_0"; +"252 layer3.5.bn1.weight" -> "254 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn1]/batch_norm_0"; +"253 layer3.5.bn1.bias" -> "254 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn1]/batch_norm_0"; +"254 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn1]/batch_norm_0" -> "255 ResNet/Sequential[layer3]/Bottleneck[5]/relu_0"; +"255 ResNet/Sequential[layer3]/Bottleneck[5]/relu_0" -> "257 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv2]/conv2d_0"; +"256 layer3.5.conv2.weight" -> "257 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv2]/conv2d_0"; +"257 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv2]/conv2d_0" -> "260 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn2]/batch_norm_0"; +"258 layer3.5.bn2.weight" -> "260 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn2]/batch_norm_0"; +"259 layer3.5.bn2.bias" -> "260 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn2]/batch_norm_0"; +"260 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn2]/batch_norm_0" -> "261 ResNet/Sequential[layer3]/Bottleneck[5]/relu_1"; +"261 ResNet/Sequential[layer3]/Bottleneck[5]/relu_1" -> "263 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv3]/conv2d_0"; +"262 layer3.5.conv3.weight" -> "263 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv3]/conv2d_0"; +"263 ResNet/Sequential[layer3]/Bottleneck[5]/Conv2d[conv3]/conv2d_0" -> "266 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn3]/batch_norm_0"; +"264 layer3.5.bn3.weight" -> "266 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn3]/batch_norm_0"; +"265 layer3.5.bn3.bias" -> "266 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn3]/batch_norm_0"; +"266 ResNet/Sequential[layer3]/Bottleneck[5]/BatchNorm2d[bn3]/batch_norm_0" -> "267 ResNet/Sequential[layer3]/Bottleneck[5]/__iadd___0"; +"267 ResNet/Sequential[layer3]/Bottleneck[5]/__iadd___0" -> "268 ResNet/Sequential[layer3]/Bottleneck[5]/relu_2"; +"268 ResNet/Sequential[layer3]/Bottleneck[5]/relu_2" -> "270 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"268 ResNet/Sequential[layer3]/Bottleneck[5]/relu_2" -> "287 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"269 layer4.0.conv1.weight" -> "270 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv1]/conv2d_0"; +"270 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" -> "273 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"271 layer4.0.bn1.weight" -> "273 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"272 layer4.0.bn1.bias" -> "273 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0"; +"273 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn1]/batch_norm_0" -> "274 ResNet/Sequential[layer4]/Bottleneck[0]/relu_0"; +"274 ResNet/Sequential[layer4]/Bottleneck[0]/relu_0" -> "276 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"275 layer4.0.conv2.weight" -> "276 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv2]/conv2d_0"; +"276 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv2]/conv2d_0" -> "279 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"277 layer4.0.bn2.weight" -> "279 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"278 layer4.0.bn2.bias" -> "279 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0"; +"279 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn2]/batch_norm_0" -> "280 ResNet/Sequential[layer4]/Bottleneck[0]/relu_1"; +"280 ResNet/Sequential[layer4]/Bottleneck[0]/relu_1" -> "282 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"281 layer4.0.conv3.weight" -> "282 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv3]/conv2d_0"; +"282 ResNet/Sequential[layer4]/Bottleneck[0]/Conv2d[conv3]/conv2d_0" -> "285 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"283 layer4.0.bn3.weight" -> "285 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"284 layer4.0.bn3.bias" -> "285 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0"; +"285 ResNet/Sequential[layer4]/Bottleneck[0]/BatchNorm2d[bn3]/batch_norm_0" -> "291 ResNet/Sequential[layer4]/Bottleneck[0]/__iadd___0"; +"286 layer4.0.shortcut.0.weight" -> "287 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"287 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "290 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"288 layer4.0.shortcut.1.weight" -> "290 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"289 layer4.0.shortcut.1.bias" -> "290 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"290 ResNet/Sequential[layer4]/Bottleneck[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "291 ResNet/Sequential[layer4]/Bottleneck[0]/__iadd___0"; +"291 ResNet/Sequential[layer4]/Bottleneck[0]/__iadd___0" -> "292 ResNet/Sequential[layer4]/Bottleneck[0]/relu_2"; +"292 ResNet/Sequential[layer4]/Bottleneck[0]/relu_2" -> "294 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"292 ResNet/Sequential[layer4]/Bottleneck[0]/relu_2" -> "310 ResNet/Sequential[layer4]/Bottleneck[1]/__iadd___0"; +"293 layer4.1.conv1.weight" -> "294 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv1]/conv2d_0"; +"294 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv1]/conv2d_0" -> "297 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"295 layer4.1.bn1.weight" -> "297 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"296 layer4.1.bn1.bias" -> "297 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0"; +"297 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn1]/batch_norm_0" -> "298 ResNet/Sequential[layer4]/Bottleneck[1]/relu_0"; +"298 ResNet/Sequential[layer4]/Bottleneck[1]/relu_0" -> "300 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"299 layer4.1.conv2.weight" -> "300 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv2]/conv2d_0"; +"300 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv2]/conv2d_0" -> "303 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"301 layer4.1.bn2.weight" -> "303 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"302 layer4.1.bn2.bias" -> "303 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0"; +"303 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn2]/batch_norm_0" -> "304 ResNet/Sequential[layer4]/Bottleneck[1]/relu_1"; +"304 ResNet/Sequential[layer4]/Bottleneck[1]/relu_1" -> "306 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"305 layer4.1.conv3.weight" -> "306 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv3]/conv2d_0"; +"306 ResNet/Sequential[layer4]/Bottleneck[1]/Conv2d[conv3]/conv2d_0" -> "309 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"307 layer4.1.bn3.weight" -> "309 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"308 layer4.1.bn3.bias" -> "309 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0"; +"309 ResNet/Sequential[layer4]/Bottleneck[1]/BatchNorm2d[bn3]/batch_norm_0" -> "310 ResNet/Sequential[layer4]/Bottleneck[1]/__iadd___0"; +"310 ResNet/Sequential[layer4]/Bottleneck[1]/__iadd___0" -> "311 ResNet/Sequential[layer4]/Bottleneck[1]/relu_2"; +"311 ResNet/Sequential[layer4]/Bottleneck[1]/relu_2" -> "313 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"311 ResNet/Sequential[layer4]/Bottleneck[1]/relu_2" -> "329 ResNet/Sequential[layer4]/Bottleneck[2]/__iadd___0"; +"312 layer4.2.conv1.weight" -> "313 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv1]/conv2d_0"; +"313 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv1]/conv2d_0" -> "316 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"314 layer4.2.bn1.weight" -> "316 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"315 layer4.2.bn1.bias" -> "316 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0"; +"316 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn1]/batch_norm_0" -> "317 ResNet/Sequential[layer4]/Bottleneck[2]/relu_0"; +"317 ResNet/Sequential[layer4]/Bottleneck[2]/relu_0" -> "319 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"318 layer4.2.conv2.weight" -> "319 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv2]/conv2d_0"; +"319 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv2]/conv2d_0" -> "322 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"320 layer4.2.bn2.weight" -> "322 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"321 layer4.2.bn2.bias" -> "322 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0"; +"322 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn2]/batch_norm_0" -> "323 ResNet/Sequential[layer4]/Bottleneck[2]/relu_1"; +"323 ResNet/Sequential[layer4]/Bottleneck[2]/relu_1" -> "325 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"324 layer4.2.conv3.weight" -> "325 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv3]/conv2d_0"; +"325 ResNet/Sequential[layer4]/Bottleneck[2]/Conv2d[conv3]/conv2d_0" -> "328 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"326 layer4.2.bn3.weight" -> "328 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"327 layer4.2.bn3.bias" -> "328 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0"; +"328 ResNet/Sequential[layer4]/Bottleneck[2]/BatchNorm2d[bn3]/batch_norm_0" -> "329 ResNet/Sequential[layer4]/Bottleneck[2]/__iadd___0"; +"329 ResNet/Sequential[layer4]/Bottleneck[2]/__iadd___0" -> "330 ResNet/Sequential[layer4]/Bottleneck[2]/relu_2"; +"330 ResNet/Sequential[layer4]/Bottleneck[2]/relu_2" -> "331 ResNet/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0"; +"331 ResNet/AdaptiveAvgPool2d[avgpool]/adaptive_avg_pool2d_0" -> "332 ResNet/flatten_0"; +"332 ResNet/flatten_0" -> "335 ResNet/Linear[linear]/linear_0"; +"333 linear.weight" -> "335 ResNet/Linear[linear]/linear_0"; +"334 linear.bias" -> "335 ResNet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/resnext29_32x4d_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/resnext29_32x4d_with_parameters_tracing.dot new file mode 100644 index 00000000000..8e27ce8c9c8 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/resnext29_32x4d_with_parameters_tracing.dot @@ -0,0 +1,404 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 ResNeXt/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 bn1.weight" [id=2, type=nncf_model_const]; +"3 bn1.bias" [id=3, type=nncf_model_const]; +"4 ResNeXt/BatchNorm2d[bn1]/batch_norm_0" [id=4, type=batch_norm]; +"5 ResNeXt/relu_0" [id=5, type=relu]; +"6 layer1.0.conv1.weight" [id=6, type=nncf_model_const]; +"7 ResNeXt/Sequential[layer1]/Block[0]/Conv2d[conv1]/conv2d_0" [id=7, type=conv2d]; +"8 layer1.0.bn1.weight" [id=8, type=nncf_model_const]; +"9 layer1.0.bn1.bias" [id=9, type=nncf_model_const]; +"10 ResNeXt/Sequential[layer1]/Block[0]/BatchNorm2d[bn1]/batch_norm_0" [id=10, type=batch_norm]; +"11 ResNeXt/Sequential[layer1]/Block[0]/relu_0" [id=11, type=relu]; +"12 layer1.0.conv2.weight" [id=12, type=nncf_model_const]; +"13 ResNeXt/Sequential[layer1]/Block[0]/Conv2d[conv2]/conv2d_0" [id=13, type=conv2d]; +"14 layer1.0.bn2.weight" [id=14, type=nncf_model_const]; +"15 layer1.0.bn2.bias" [id=15, type=nncf_model_const]; +"16 ResNeXt/Sequential[layer1]/Block[0]/BatchNorm2d[bn2]/batch_norm_0" [id=16, type=batch_norm]; +"17 ResNeXt/Sequential[layer1]/Block[0]/relu_1" [id=17, type=relu]; +"18 layer1.0.conv3.weight" [id=18, type=nncf_model_const]; +"19 ResNeXt/Sequential[layer1]/Block[0]/Conv2d[conv3]/conv2d_0" [id=19, type=conv2d]; +"20 layer1.0.bn3.weight" [id=20, type=nncf_model_const]; +"21 layer1.0.bn3.bias" [id=21, type=nncf_model_const]; +"22 ResNeXt/Sequential[layer1]/Block[0]/BatchNorm2d[bn3]/batch_norm_0" [id=22, type=batch_norm]; +"23 layer1.0.shortcut.0.weight" [id=23, type=nncf_model_const]; +"24 ResNeXt/Sequential[layer1]/Block[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" [id=24, type=conv2d]; +"25 layer1.0.shortcut.1.weight" [id=25, type=nncf_model_const]; +"26 layer1.0.shortcut.1.bias" [id=26, type=nncf_model_const]; +"27 ResNeXt/Sequential[layer1]/Block[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" [id=27, type=batch_norm]; +"28 ResNeXt/Sequential[layer1]/Block[0]/__iadd___0" [id=28, type=__iadd__]; +"29 ResNeXt/Sequential[layer1]/Block[0]/relu_2" [id=29, type=relu]; +"30 layer1.1.conv1.weight" [id=30, type=nncf_model_const]; +"31 ResNeXt/Sequential[layer1]/Block[1]/Conv2d[conv1]/conv2d_0" [id=31, type=conv2d]; +"32 layer1.1.bn1.weight" [id=32, type=nncf_model_const]; +"33 layer1.1.bn1.bias" [id=33, type=nncf_model_const]; +"34 ResNeXt/Sequential[layer1]/Block[1]/BatchNorm2d[bn1]/batch_norm_0" [id=34, type=batch_norm]; +"35 ResNeXt/Sequential[layer1]/Block[1]/relu_0" [id=35, type=relu]; +"36 layer1.1.conv2.weight" [id=36, type=nncf_model_const]; +"37 ResNeXt/Sequential[layer1]/Block[1]/Conv2d[conv2]/conv2d_0" [id=37, type=conv2d]; +"38 layer1.1.bn2.weight" [id=38, type=nncf_model_const]; +"39 layer1.1.bn2.bias" [id=39, type=nncf_model_const]; +"40 ResNeXt/Sequential[layer1]/Block[1]/BatchNorm2d[bn2]/batch_norm_0" [id=40, type=batch_norm]; +"41 ResNeXt/Sequential[layer1]/Block[1]/relu_1" [id=41, type=relu]; +"42 layer1.1.conv3.weight" [id=42, type=nncf_model_const]; +"43 ResNeXt/Sequential[layer1]/Block[1]/Conv2d[conv3]/conv2d_0" [id=43, type=conv2d]; +"44 layer1.1.bn3.weight" [id=44, type=nncf_model_const]; +"45 layer1.1.bn3.bias" [id=45, type=nncf_model_const]; +"46 ResNeXt/Sequential[layer1]/Block[1]/BatchNorm2d[bn3]/batch_norm_0" [id=46, type=batch_norm]; +"47 ResNeXt/Sequential[layer1]/Block[1]/__iadd___0" [id=47, type=__iadd__]; +"48 ResNeXt/Sequential[layer1]/Block[1]/relu_2" [id=48, type=relu]; +"49 layer1.2.conv1.weight" [id=49, type=nncf_model_const]; +"50 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layer2.1.conv1.weight" -> "93 ResNeXt/Sequential[layer2]/Block[1]/Conv2d[conv1]/conv2d_0"; +"93 ResNeXt/Sequential[layer2]/Block[1]/Conv2d[conv1]/conv2d_0" -> "96 ResNeXt/Sequential[layer2]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"94 layer2.1.bn1.weight" -> "96 ResNeXt/Sequential[layer2]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"95 layer2.1.bn1.bias" -> "96 ResNeXt/Sequential[layer2]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"96 ResNeXt/Sequential[layer2]/Block[1]/BatchNorm2d[bn1]/batch_norm_0" -> "97 ResNeXt/Sequential[layer2]/Block[1]/relu_0"; +"97 ResNeXt/Sequential[layer2]/Block[1]/relu_0" -> "99 ResNeXt/Sequential[layer2]/Block[1]/Conv2d[conv2]/conv2d_0"; +"98 layer2.1.conv2.weight" -> "99 ResNeXt/Sequential[layer2]/Block[1]/Conv2d[conv2]/conv2d_0"; +"99 ResNeXt/Sequential[layer2]/Block[1]/Conv2d[conv2]/conv2d_0" -> "102 ResNeXt/Sequential[layer2]/Block[1]/BatchNorm2d[bn2]/batch_norm_0"; +"100 layer2.1.bn2.weight" -> "102 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ResNeXt/Sequential[layer2]/Block[1]/__iadd___0" -> "110 ResNeXt/Sequential[layer2]/Block[1]/relu_2"; +"110 ResNeXt/Sequential[layer2]/Block[1]/relu_2" -> "112 ResNeXt/Sequential[layer2]/Block[2]/Conv2d[conv1]/conv2d_0"; +"110 ResNeXt/Sequential[layer2]/Block[1]/relu_2" -> "128 ResNeXt/Sequential[layer2]/Block[2]/__iadd___0"; +"111 layer2.2.conv1.weight" -> "112 ResNeXt/Sequential[layer2]/Block[2]/Conv2d[conv1]/conv2d_0"; +"112 ResNeXt/Sequential[layer2]/Block[2]/Conv2d[conv1]/conv2d_0" -> "115 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"113 layer2.2.bn1.weight" -> "115 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"114 layer2.2.bn1.bias" -> "115 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"115 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn1]/batch_norm_0" -> "116 ResNeXt/Sequential[layer2]/Block[2]/relu_0"; +"116 ResNeXt/Sequential[layer2]/Block[2]/relu_0" -> "118 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layer2.2.bn3.weight" -> "127 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn3]/batch_norm_0"; +"126 layer2.2.bn3.bias" -> "127 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn3]/batch_norm_0"; +"127 ResNeXt/Sequential[layer2]/Block[2]/BatchNorm2d[bn3]/batch_norm_0" -> "128 ResNeXt/Sequential[layer2]/Block[2]/__iadd___0"; +"128 ResNeXt/Sequential[layer2]/Block[2]/__iadd___0" -> "129 ResNeXt/Sequential[layer2]/Block[2]/relu_2"; +"129 ResNeXt/Sequential[layer2]/Block[2]/relu_2" -> "131 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv1]/conv2d_0"; +"129 ResNeXt/Sequential[layer2]/Block[2]/relu_2" -> "148 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"130 layer3.0.conv1.weight" -> "131 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv1]/conv2d_0"; +"131 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv1]/conv2d_0" -> "134 ResNeXt/Sequential[layer3]/Block[0]/BatchNorm2d[bn1]/batch_norm_0"; +"132 layer3.0.bn1.weight" -> "134 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ResNeXt/Sequential[layer3]/Block[0]/relu_1" -> "143 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv3]/conv2d_0"; +"142 layer3.0.conv3.weight" -> "143 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv3]/conv2d_0"; +"143 ResNeXt/Sequential[layer3]/Block[0]/Conv2d[conv3]/conv2d_0" -> "146 ResNeXt/Sequential[layer3]/Block[0]/BatchNorm2d[bn3]/batch_norm_0"; +"144 layer3.0.bn3.weight" -> "146 ResNeXt/Sequential[layer3]/Block[0]/BatchNorm2d[bn3]/batch_norm_0"; +"145 layer3.0.bn3.bias" -> "146 ResNeXt/Sequential[layer3]/Block[0]/BatchNorm2d[bn3]/batch_norm_0"; +"146 ResNeXt/Sequential[layer3]/Block[0]/BatchNorm2d[bn3]/batch_norm_0" -> "152 ResNeXt/Sequential[layer3]/Block[0]/__iadd___0"; +"147 layer3.0.shortcut.0.weight" -> "148 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0"; +"148 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/Conv2d[0]/conv2d_0" -> "151 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"149 layer3.0.shortcut.1.weight" -> "151 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"150 layer3.0.shortcut.1.bias" -> "151 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0"; +"151 ResNeXt/Sequential[layer3]/Block[0]/Sequential[shortcut]/BatchNorm2d[1]/batch_norm_0" -> "152 ResNeXt/Sequential[layer3]/Block[0]/__iadd___0"; +"152 ResNeXt/Sequential[layer3]/Block[0]/__iadd___0" -> "153 ResNeXt/Sequential[layer3]/Block[0]/relu_2"; +"153 ResNeXt/Sequential[layer3]/Block[0]/relu_2" -> "155 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv1]/conv2d_0"; +"153 ResNeXt/Sequential[layer3]/Block[0]/relu_2" -> "171 ResNeXt/Sequential[layer3]/Block[1]/__iadd___0"; +"154 layer3.1.conv1.weight" -> "155 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv1]/conv2d_0"; +"155 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv1]/conv2d_0" -> "158 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"156 layer3.1.bn1.weight" -> "158 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"157 layer3.1.bn1.bias" -> "158 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn1]/batch_norm_0"; +"158 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn1]/batch_norm_0" -> "159 ResNeXt/Sequential[layer3]/Block[1]/relu_0"; +"159 ResNeXt/Sequential[layer3]/Block[1]/relu_0" -> "161 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv2]/conv2d_0"; +"160 layer3.1.conv2.weight" -> "161 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv2]/conv2d_0"; +"161 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv2]/conv2d_0" -> "164 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn2]/batch_norm_0"; +"162 layer3.1.bn2.weight" -> "164 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn2]/batch_norm_0"; +"163 layer3.1.bn2.bias" -> "164 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn2]/batch_norm_0"; +"164 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn2]/batch_norm_0" -> "165 ResNeXt/Sequential[layer3]/Block[1]/relu_1"; +"165 ResNeXt/Sequential[layer3]/Block[1]/relu_1" -> "167 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv3]/conv2d_0"; +"166 layer3.1.conv3.weight" -> "167 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv3]/conv2d_0"; +"167 ResNeXt/Sequential[layer3]/Block[1]/Conv2d[conv3]/conv2d_0" -> "170 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn3]/batch_norm_0"; +"168 layer3.1.bn3.weight" -> "170 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn3]/batch_norm_0"; +"169 layer3.1.bn3.bias" -> "170 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn3]/batch_norm_0"; +"170 ResNeXt/Sequential[layer3]/Block[1]/BatchNorm2d[bn3]/batch_norm_0" -> "171 ResNeXt/Sequential[layer3]/Block[1]/__iadd___0"; +"171 ResNeXt/Sequential[layer3]/Block[1]/__iadd___0" -> "172 ResNeXt/Sequential[layer3]/Block[1]/relu_2"; +"172 ResNeXt/Sequential[layer3]/Block[1]/relu_2" -> "174 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv1]/conv2d_0"; +"172 ResNeXt/Sequential[layer3]/Block[1]/relu_2" -> "190 ResNeXt/Sequential[layer3]/Block[2]/__iadd___0"; +"173 layer3.2.conv1.weight" -> "174 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv1]/conv2d_0"; +"174 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv1]/conv2d_0" -> "177 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"175 layer3.2.bn1.weight" -> "177 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"176 layer3.2.bn1.bias" -> "177 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn1]/batch_norm_0"; +"177 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn1]/batch_norm_0" -> "178 ResNeXt/Sequential[layer3]/Block[2]/relu_0"; +"178 ResNeXt/Sequential[layer3]/Block[2]/relu_0" -> "180 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv2]/conv2d_0"; +"179 layer3.2.conv2.weight" -> "180 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv2]/conv2d_0"; +"180 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv2]/conv2d_0" -> "183 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn2]/batch_norm_0"; +"181 layer3.2.bn2.weight" -> "183 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn2]/batch_norm_0"; +"182 layer3.2.bn2.bias" -> "183 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn2]/batch_norm_0"; +"183 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn2]/batch_norm_0" -> "184 ResNeXt/Sequential[layer3]/Block[2]/relu_1"; +"184 ResNeXt/Sequential[layer3]/Block[2]/relu_1" -> "186 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv3]/conv2d_0"; +"185 layer3.2.conv3.weight" -> "186 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv3]/conv2d_0"; +"186 ResNeXt/Sequential[layer3]/Block[2]/Conv2d[conv3]/conv2d_0" -> "189 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn3]/batch_norm_0"; +"187 layer3.2.bn3.weight" -> "189 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn3]/batch_norm_0"; +"188 layer3.2.bn3.bias" -> "189 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn3]/batch_norm_0"; +"189 ResNeXt/Sequential[layer3]/Block[2]/BatchNorm2d[bn3]/batch_norm_0" -> "190 ResNeXt/Sequential[layer3]/Block[2]/__iadd___0"; +"190 ResNeXt/Sequential[layer3]/Block[2]/__iadd___0" -> "191 ResNeXt/Sequential[layer3]/Block[2]/relu_2"; +"191 ResNeXt/Sequential[layer3]/Block[2]/relu_2" -> "192 ResNeXt/avg_pool2d_0"; +"192 ResNeXt/avg_pool2d_0" -> "193 ResNeXt/view_0"; +"193 ResNeXt/view_0" -> "196 ResNeXt/Linear[linear]/linear_0"; +"194 linear.weight" -> "196 ResNeXt/Linear[linear]/linear_0"; +"195 linear.bias" -> "196 ResNeXt/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/senet18_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/senet18_with_parameters_tracing.dot new file mode 100644 index 00000000000..3b390641cb0 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/senet18_with_parameters_tracing.dot @@ -0,0 +1,419 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 SENet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 bn1.weight" [id=2, type=nncf_model_const]; +"3 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SENet/Sequential[layer4]/PreActBlock[0]/avg_pool2d_0" -> "165 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc1]/conv2d_0"; +"163 layer4.0.fc1.weight" -> "165 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc1]/conv2d_0"; +"164 layer4.0.fc1.bias" -> "165 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc1]/conv2d_0"; +"165 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc1]/conv2d_0" -> "166 SENet/Sequential[layer4]/PreActBlock[0]/relu_2"; +"166 SENet/Sequential[layer4]/PreActBlock[0]/relu_2" -> "169 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc2]/conv2d_0"; +"167 layer4.0.fc2.weight" -> "169 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc2]/conv2d_0"; +"168 layer4.0.fc2.bias" -> "169 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc2]/conv2d_0"; +"169 SENet/Sequential[layer4]/PreActBlock[0]/Conv2d[fc2]/conv2d_0" -> "170 SENet/Sequential[layer4]/PreActBlock[0]/sigmoid_0"; +"170 SENet/Sequential[layer4]/PreActBlock[0]/sigmoid_0" -> "171 SENet/Sequential[layer4]/PreActBlock[0]/__mul___0"; +"171 SENet/Sequential[layer4]/PreActBlock[0]/__mul___0" -> "172 SENet/Sequential[layer4]/PreActBlock[0]/__iadd___0"; +"172 SENet/Sequential[layer4]/PreActBlock[0]/__iadd___0" -> "175 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"172 SENet/Sequential[layer4]/PreActBlock[0]/__iadd___0" -> "195 SENet/Sequential[layer4]/PreActBlock[1]/__iadd___0"; +"173 layer4.1.bn1.weight" -> "175 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"174 layer4.1.bn1.bias" -> "175 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn1]/batch_norm_0"; +"175 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn1]/batch_norm_0" -> "176 SENet/Sequential[layer4]/PreActBlock[1]/relu_0"; +"176 SENet/Sequential[layer4]/PreActBlock[1]/relu_0" -> "178 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv1]/conv2d_0"; +"177 layer4.1.conv1.weight" -> "178 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv1]/conv2d_0"; +"178 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv1]/conv2d_0" -> "181 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"179 layer4.1.bn2.weight" -> "181 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"180 layer4.1.bn2.bias" -> "181 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn2]/batch_norm_0"; +"181 SENet/Sequential[layer4]/PreActBlock[1]/BatchNorm2d[bn2]/batch_norm_0" -> "182 SENet/Sequential[layer4]/PreActBlock[1]/relu_1"; +"182 SENet/Sequential[layer4]/PreActBlock[1]/relu_1" -> "184 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv2]/conv2d_0"; +"183 layer4.1.conv2.weight" -> "184 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv2]/conv2d_0"; +"184 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv2]/conv2d_0" -> "185 SENet/Sequential[layer4]/PreActBlock[1]/avg_pool2d_0"; +"184 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[conv2]/conv2d_0" -> "194 SENet/Sequential[layer4]/PreActBlock[1]/__mul___0"; +"185 SENet/Sequential[layer4]/PreActBlock[1]/avg_pool2d_0" -> "188 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc1]/conv2d_0"; +"186 layer4.1.fc1.weight" -> "188 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc1]/conv2d_0"; +"187 layer4.1.fc1.bias" -> "188 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc1]/conv2d_0"; +"188 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc1]/conv2d_0" -> "189 SENet/Sequential[layer4]/PreActBlock[1]/relu_2"; +"189 SENet/Sequential[layer4]/PreActBlock[1]/relu_2" -> "192 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc2]/conv2d_0"; +"190 layer4.1.fc2.weight" -> "192 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc2]/conv2d_0"; +"191 layer4.1.fc2.bias" -> "192 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc2]/conv2d_0"; +"192 SENet/Sequential[layer4]/PreActBlock[1]/Conv2d[fc2]/conv2d_0" -> "193 SENet/Sequential[layer4]/PreActBlock[1]/sigmoid_0"; +"193 SENet/Sequential[layer4]/PreActBlock[1]/sigmoid_0" -> "194 SENet/Sequential[layer4]/PreActBlock[1]/__mul___0"; +"194 SENet/Sequential[layer4]/PreActBlock[1]/__mul___0" -> "195 SENet/Sequential[layer4]/PreActBlock[1]/__iadd___0"; +"195 SENet/Sequential[layer4]/PreActBlock[1]/__iadd___0" -> "196 SENet/avg_pool2d_0"; +"196 SENet/avg_pool2d_0" -> "197 SENet/view_0"; +"197 SENet/view_0" -> "200 SENet/Linear[linear]/linear_0"; +"198 linear.weight" -> "200 SENet/Linear[linear]/linear_0"; +"199 linear.bias" -> "200 SENet/Linear[linear]/linear_0"; +} diff --git a/tests/torch/data/reference_graphs/original/shufflenetv2_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/shufflenetv2_with_parameters_tracing.dot new file mode 100644 index 00000000000..4fc5a16f6f0 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/shufflenetv2_with_parameters_tracing.dot @@ -0,0 +1,841 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 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a/tests/torch/data/reference_graphs/original/shuflenet_g2_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/shuflenet_g2_with_parameters_tracing.dot new file mode 100644 index 00000000000..0f8c638fbb3 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/shuflenet_g2_with_parameters_tracing.dot @@ -0,0 +1,781 @@ +strict digraph { +"0 conv1.weight" [id=0, type=nncf_model_const]; +"1 ShuffleNet/Conv2d[conv1]/conv2d_0" [id=1, type=conv2d]; +"2 bn1.weight" [id=2, type=nncf_model_const]; +"3 bn1.bias" [id=3, type=nncf_model_const]; +"4 ShuffleNet/BatchNorm2d[bn1]/batch_norm_0" [id=4, type=batch_norm]; +"5 ShuffleNet/relu_0" [id=5, type=relu]; +"6 layer1.0.conv1.weight" [id=6, type=nncf_model_const]; +"7 ShuffleNet/Sequential[layer1]/Bottleneck[0]/Conv2d[conv1]/conv2d_0" [id=7, type=conv2d]; +"8 layer1.0.bn1.weight" [id=8, type=nncf_model_const]; +"9 layer1.0.bn1.bias" [id=9, type=nncf_model_const]; +"10 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SqueezeNet/Sequential[features]/Fire[10]/ReLU[expand3x3_activation]/relu__0"; +"83 SqueezeNet/Sequential[features]/Fire[10]/ReLU[expand3x3_activation]/relu__0" -> "84 SqueezeNet/Sequential[features]/Fire[10]/cat_0"; +"84 SqueezeNet/Sequential[features]/Fire[10]/cat_0" -> "87 SqueezeNet/Sequential[features]/Fire[11]/Conv2d[squeeze]/conv2d_0"; +"85 features.11.squeeze.weight" -> "87 SqueezeNet/Sequential[features]/Fire[11]/Conv2d[squeeze]/conv2d_0"; +"86 features.11.squeeze.bias" -> "87 SqueezeNet/Sequential[features]/Fire[11]/Conv2d[squeeze]/conv2d_0"; +"87 SqueezeNet/Sequential[features]/Fire[11]/Conv2d[squeeze]/conv2d_0" -> "88 SqueezeNet/Sequential[features]/Fire[11]/ReLU[squeeze_activation]/relu__0"; +"88 SqueezeNet/Sequential[features]/Fire[11]/ReLU[squeeze_activation]/relu__0" -> "91 SqueezeNet/Sequential[features]/Fire[11]/Conv2d[expand1x1]/conv2d_0"; +"88 SqueezeNet/Sequential[features]/Fire[11]/ReLU[squeeze_activation]/relu__0" -> "95 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SqueezeNet/Sequential[features]/Fire[12]/cat_0"; +"110 SqueezeNet/Sequential[features]/Fire[12]/cat_0" -> "111 SqueezeNet/Sequential[classifier]/Dropout[0]/dropout_0"; +"111 SqueezeNet/Sequential[classifier]/Dropout[0]/dropout_0" -> "114 SqueezeNet/Sequential[classifier]/Conv2d[1]/conv2d_0"; +"112 classifier.1.weight" -> "114 SqueezeNet/Sequential[classifier]/Conv2d[1]/conv2d_0"; +"113 classifier.1.bias" -> "114 SqueezeNet/Sequential[classifier]/Conv2d[1]/conv2d_0"; +"114 SqueezeNet/Sequential[classifier]/Conv2d[1]/conv2d_0" -> "115 SqueezeNet/Sequential[classifier]/ReLU[2]/relu__0"; +"115 SqueezeNet/Sequential[classifier]/ReLU[2]/relu__0" -> "116 SqueezeNet/Sequential[classifier]/AdaptiveAvgPool2d[3]/adaptive_avg_pool2d_0"; +"116 SqueezeNet/Sequential[classifier]/AdaptiveAvgPool2d[3]/adaptive_avg_pool2d_0" -> "117 SqueezeNet/view_0"; +} diff --git a/tests/torch/data/reference_graphs/original/sr_small_model_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/sr_small_model_with_parameters_tracing.dot new file mode 100644 index 00000000000..8c087f77a4b --- /dev/null +++ b/tests/torch/data/reference_graphs/original/sr_small_model_with_parameters_tracing.dot @@ -0,0 +1,126 @@ +strict digraph { +"0 /nncf_model_input_0" [id=0, type=nncf_model_input]; +"1 /nncf_model_input_1" [id=1, type=nncf_model_input]; +"2 conv_cubic1.weight" [id=2, type=nncf_model_const]; +"3 conv_cubic1.bias" [id=3, type=nncf_model_const]; +"4 SmallModel/Conv2d[conv_cubic1]/conv2d_0" [id=4, type=conv2d]; +"5 SmallModel/ReLU[relu]/relu_0" [id=5, type=relu]; +"6 conv_cubic2.weight" [id=6, type=nncf_model_const]; +"7 conv_cubic2.bias" [id=7, type=nncf_model_const]; +"8 SmallModel/Conv2d[conv_cubic2]/conv2d_0" [id=8, type=conv2d]; +"9 SmallModel/Sigmoid[sigmoid]/sigmoid_0" [id=9, type=sigmoid]; +"10 conv_input.weight" [id=10, type=nncf_model_const]; +"11 conv_input.bias" [id=11, type=nncf_model_const]; +"12 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MobileNetSSD/SSDDetectionOutput[detection_head]/view_10" -> "283 MobileNetSSD/SSDDetectionOutput[detection_head]/cat_1"; +"282 MobileNetSSD/SSDDetectionOutput[detection_head]/view_11" -> "283 MobileNetSSD/SSDDetectionOutput[detection_head]/cat_1"; +"283 MobileNetSSD/SSDDetectionOutput[detection_head]/cat_1" -> "284 MobileNetSSD/SSDDetectionOutput[detection_head]/view_12"; +"283 MobileNetSSD/SSDDetectionOutput[detection_head]/cat_1" -> "287 MobileNetSSD/SSDDetectionOutput[detection_head]/view_14"; +"284 MobileNetSSD/SSDDetectionOutput[detection_head]/view_12" -> "285 MobileNetSSD/SSDDetectionOutput[detection_head]/softmax_0"; +} diff --git a/tests/torch/data/reference_graphs/original/ssd_vgg_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/ssd_vgg_with_parameters_tracing.dot new file mode 100644 index 00000000000..114f9087858 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/ssd_vgg_with_parameters_tracing.dot @@ -0,0 +1,523 @@ +strict digraph { +"0 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SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"246 SSD_VGG/SSDDetectionOutput[detection_head]/view_8" -> "250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"247 SSD_VGG/SSDDetectionOutput[detection_head]/view_9" -> "250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"248 SSD_VGG/SSDDetectionOutput[detection_head]/view_10" -> "250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"249 SSD_VGG/SSDDetectionOutput[detection_head]/view_11" -> "250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1"; +"250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1" -> "251 SSD_VGG/SSDDetectionOutput[detection_head]/view_12"; +"250 SSD_VGG/SSDDetectionOutput[detection_head]/cat_1" -> "254 SSD_VGG/SSDDetectionOutput[detection_head]/view_14"; +"251 SSD_VGG/SSDDetectionOutput[detection_head]/view_12" -> "252 SSD_VGG/SSDDetectionOutput[detection_head]/softmax_0"; +} diff --git a/tests/torch/data/reference_graphs/original/unet_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/unet_with_parameters_tracing.dot new file mode 100644 index 00000000000..582af8c207c --- /dev/null +++ b/tests/torch/data/reference_graphs/original/unet_with_parameters_tracing.dot @@ -0,0 +1,311 @@ +strict digraph { +"0 down_path.0.block.0.weight" [id=0, type=nncf_model_const]; +"1 down_path.0.block.0.bias" [id=1, type=nncf_model_const]; +"2 UNet/ModuleList[down_path]/UNetConvBlock[0]/Sequential[block]/Conv2d[0]/conv2d_0" [id=2, type=conv2d]; +"3 down_path.0.block.1.weight" [id=3, type=nncf_model_const]; +"4 down_path.0.block.1.bias" [id=4, type=nncf_model_const]; +"5 UNet/ModuleList[down_path]/UNetConvBlock[0]/Sequential[block]/BatchNorm2d[1]/batch_norm_0" [id=5, type=batch_norm]; +"6 UNet/ModuleList[down_path]/UNetConvBlock[0]/Sequential[block]/ReLU[2]/relu_0" [id=6, type=relu]; +"7 down_path.0.block.3.weight" [id=7, type=nncf_model_const]; +"8 down_path.0.block.3.bias" [id=8, type=nncf_model_const]; +"9 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up_path.2.conv_block.block.1.weight" -> "122 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/BatchNorm2d[1]/batch_norm_0"; +"121 up_path.2.conv_block.block.1.bias" -> "122 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/BatchNorm2d[1]/batch_norm_0"; +"122 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/BatchNorm2d[1]/batch_norm_0" -> "123 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/ReLU[2]/relu_0"; +"123 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/ReLU[2]/relu_0" -> "126 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/Conv2d[3]/conv2d_0"; +"124 up_path.2.conv_block.block.3.weight" -> "126 UNet/ModuleList[up_path]/UNetUpBlock[2]/UNetConvBlock[conv_block]/Sequential[block]/Conv2d[3]/conv2d_0"; +"125 up_path.2.conv_block.block.3.bias" -> "126 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-> "133 UNet/ModuleList[up_path]/UNetUpBlock[3]/ConvTranspose2d[up]/conv_transpose2d_0"; +"131 up_path.3.up.weight" -> "133 UNet/ModuleList[up_path]/UNetUpBlock[3]/ConvTranspose2d[up]/conv_transpose2d_0"; +"132 up_path.3.up.bias" -> "133 UNet/ModuleList[up_path]/UNetUpBlock[3]/ConvTranspose2d[up]/conv_transpose2d_0"; +"133 UNet/ModuleList[up_path]/UNetUpBlock[3]/ConvTranspose2d[up]/conv_transpose2d_0" -> "135 UNet/ModuleList[up_path]/UNetUpBlock[3]/cat_0"; +"134 UNet/ModuleList[up_path]/UNetUpBlock[3]/__getitem___0" -> "135 UNet/ModuleList[up_path]/UNetUpBlock[3]/cat_0"; +"135 UNet/ModuleList[up_path]/UNetUpBlock[3]/cat_0" -> "138 UNet/ModuleList[up_path]/UNetUpBlock[3]/UNetConvBlock[conv_block]/Sequential[block]/Conv2d[0]/conv2d_0"; +"136 up_path.3.conv_block.block.0.weight" -> "138 UNet/ModuleList[up_path]/UNetUpBlock[3]/UNetConvBlock[conv_block]/Sequential[block]/Conv2d[0]/conv2d_0"; +"137 up_path.3.conv_block.block.0.bias" -> "138 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UNet/ModuleList[up_path]/UNetUpBlock[3]/UNetConvBlock[conv_block]/Sequential[block]/BatchNorm2d[4]/batch_norm_0" -> "149 UNet/ModuleList[up_path]/UNetUpBlock[3]/UNetConvBlock[conv_block]/Sequential[block]/ReLU[5]/relu_0"; +"149 UNet/ModuleList[up_path]/UNetUpBlock[3]/UNetConvBlock[conv_block]/Sequential[block]/ReLU[5]/relu_0" -> "152 UNet/Conv2d[last]/conv2d_0"; +"150 last.weight" -> "152 UNet/Conv2d[last]/conv2d_0"; +"151 last.bias" -> "152 UNet/Conv2d[last]/conv2d_0"; +} diff --git a/tests/torch/data/reference_graphs/original/vgg16_with_parameters_tracing.dot b/tests/torch/data/reference_graphs/original/vgg16_with_parameters_tracing.dot new file mode 100644 index 00000000000..b536f1a0743 --- /dev/null +++ b/tests/torch/data/reference_graphs/original/vgg16_with_parameters_tracing.dot @@ -0,0 +1,203 @@ +strict digraph { +"0 features.0.weight" [id=0, type=nncf_model_const]; +"1 features.0.bias" [id=1, type=nncf_model_const]; +"2 VGG/Sequential[features]/Conv2d[0]/conv2d_0" [id=2, 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features.21.bias" -> "49 VGG/Sequential[features]/BatchNorm2d[21]/batch_norm_0"; +"49 VGG/Sequential[features]/BatchNorm2d[21]/batch_norm_0" -> "50 VGG/Sequential[features]/ReLU[22]/relu__0"; +"50 VGG/Sequential[features]/ReLU[22]/relu__0" -> "51 VGG/Sequential[features]/MaxPool2d[23]/max_pool2d_0"; +"51 VGG/Sequential[features]/MaxPool2d[23]/max_pool2d_0" -> "54 VGG/Sequential[features]/Conv2d[24]/conv2d_0"; +"52 features.24.weight" -> "54 VGG/Sequential[features]/Conv2d[24]/conv2d_0"; +"53 features.24.bias" -> "54 VGG/Sequential[features]/Conv2d[24]/conv2d_0"; +"54 VGG/Sequential[features]/Conv2d[24]/conv2d_0" -> "57 VGG/Sequential[features]/BatchNorm2d[25]/batch_norm_0"; +"55 features.25.weight" -> "57 VGG/Sequential[features]/BatchNorm2d[25]/batch_norm_0"; +"56 features.25.bias" -> "57 VGG/Sequential[features]/BatchNorm2d[25]/batch_norm_0"; +"57 VGG/Sequential[features]/BatchNorm2d[25]/batch_norm_0" -> "58 VGG/Sequential[features]/ReLU[26]/relu__0"; +"58 VGG/Sequential[features]/ReLU[26]/relu__0" -> "61 VGG/Sequential[features]/Conv2d[27]/conv2d_0"; +"59 features.27.weight" -> "61 VGG/Sequential[features]/Conv2d[27]/conv2d_0"; +"60 features.27.bias" -> "61 VGG/Sequential[features]/Conv2d[27]/conv2d_0"; +"61 VGG/Sequential[features]/Conv2d[27]/conv2d_0" -> "64 VGG/Sequential[features]/BatchNorm2d[28]/batch_norm_0"; +"62 features.28.weight" -> "64 VGG/Sequential[features]/BatchNorm2d[28]/batch_norm_0"; +"63 features.28.bias" -> "64 VGG/Sequential[features]/BatchNorm2d[28]/batch_norm_0"; +"64 VGG/Sequential[features]/BatchNorm2d[28]/batch_norm_0" -> "65 VGG/Sequential[features]/ReLU[29]/relu__0"; +"65 VGG/Sequential[features]/ReLU[29]/relu__0" -> "68 VGG/Sequential[features]/Conv2d[30]/conv2d_0"; +"66 features.30.weight" -> "68 VGG/Sequential[features]/Conv2d[30]/conv2d_0"; +"67 features.30.bias" -> "68 VGG/Sequential[features]/Conv2d[30]/conv2d_0"; +"68 VGG/Sequential[features]/Conv2d[30]/conv2d_0" -> "71 VGG/Sequential[features]/BatchNorm2d[31]/batch_norm_0"; +"69 features.31.weight" -> "71 VGG/Sequential[features]/BatchNorm2d[31]/batch_norm_0"; +"70 features.31.bias" -> "71 VGG/Sequential[features]/BatchNorm2d[31]/batch_norm_0"; +"71 VGG/Sequential[features]/BatchNorm2d[31]/batch_norm_0" -> "72 VGG/Sequential[features]/ReLU[32]/relu__0"; +"72 VGG/Sequential[features]/ReLU[32]/relu__0" -> "73 VGG/Sequential[features]/MaxPool2d[33]/max_pool2d_0"; +"73 VGG/Sequential[features]/MaxPool2d[33]/max_pool2d_0" -> "76 VGG/Sequential[features]/Conv2d[34]/conv2d_0"; +"74 features.34.weight" -> "76 VGG/Sequential[features]/Conv2d[34]/conv2d_0"; +"75 features.34.bias" -> "76 VGG/Sequential[features]/Conv2d[34]/conv2d_0"; +"76 VGG/Sequential[features]/Conv2d[34]/conv2d_0" -> "79 VGG/Sequential[features]/BatchNorm2d[35]/batch_norm_0"; +"77 features.35.weight" -> "79 VGG/Sequential[features]/BatchNorm2d[35]/batch_norm_0"; +"78 features.35.bias" -> "79 VGG/Sequential[features]/BatchNorm2d[35]/batch_norm_0"; +"79 VGG/Sequential[features]/BatchNorm2d[35]/batch_norm_0" -> "80 VGG/Sequential[features]/ReLU[36]/relu__0"; +"80 VGG/Sequential[features]/ReLU[36]/relu__0" -> "83 VGG/Sequential[features]/Conv2d[37]/conv2d_0"; +"81 features.37.weight" -> "83 VGG/Sequential[features]/Conv2d[37]/conv2d_0"; +"82 features.37.bias" -> "83 VGG/Sequential[features]/Conv2d[37]/conv2d_0"; +"83 VGG/Sequential[features]/Conv2d[37]/conv2d_0" -> "86 VGG/Sequential[features]/BatchNorm2d[38]/batch_norm_0"; +"84 features.38.weight" -> "86 VGG/Sequential[features]/BatchNorm2d[38]/batch_norm_0"; +"85 features.38.bias" -> "86 VGG/Sequential[features]/BatchNorm2d[38]/batch_norm_0"; +"86 VGG/Sequential[features]/BatchNorm2d[38]/batch_norm_0" -> "87 VGG/Sequential[features]/ReLU[39]/relu__0"; +"87 VGG/Sequential[features]/ReLU[39]/relu__0" -> "90 VGG/Sequential[features]/Conv2d[40]/conv2d_0"; +"88 features.40.weight" -> "90 VGG/Sequential[features]/Conv2d[40]/conv2d_0"; +"89 features.40.bias" -> "90 VGG/Sequential[features]/Conv2d[40]/conv2d_0"; +"90 VGG/Sequential[features]/Conv2d[40]/conv2d_0" -> "93 VGG/Sequential[features]/BatchNorm2d[41]/batch_norm_0"; +"91 features.41.weight" -> "93 VGG/Sequential[features]/BatchNorm2d[41]/batch_norm_0"; +"92 features.41.bias" -> "93 VGG/Sequential[features]/BatchNorm2d[41]/batch_norm_0"; +"93 VGG/Sequential[features]/BatchNorm2d[41]/batch_norm_0" -> "94 VGG/Sequential[features]/ReLU[42]/relu__0"; +"94 VGG/Sequential[features]/ReLU[42]/relu__0" -> "95 VGG/Sequential[features]/MaxPool2d[43]/max_pool2d_0"; +"95 VGG/Sequential[features]/MaxPool2d[43]/max_pool2d_0" -> "96 VGG/Sequential[features]/AvgPool2d[44]/avg_pool2d_0"; +"96 VGG/Sequential[features]/AvgPool2d[44]/avg_pool2d_0" -> "97 VGG/view_0"; +"97 VGG/view_0" -> "100 VGG/Linear[classifier]/linear_0"; +"98 classifier.weight" -> "100 VGG/Linear[classifier]/linear_0"; +"99 classifier.bias" -> "100 VGG/Linear[classifier]/linear_0"; +} diff --git a/tests/torch/ptq/test_graphs.py b/tests/torch/ptq/test_graphs.py index b688e9cf830..4aace885e10 100644 --- a/tests/torch/ptq/test_graphs.py +++ b/tests/torch/ptq/test_graphs.py @@ -102,4 +102,4 @@ def test_min_max_classification_quantized_graphs(desc: ModelDesc, quantization_p quantized_model = quantization_algorithm.apply(nncf_network, nncf_network.nncf.get_graph(), dataset=None) - check_graph(quantized_model.nncf.get_graph(), desc.dot_filename, graph_dir) + check_graph(quantized_model.nncf.get_graph(), desc.dot_filename(), graph_dir) diff --git a/tests/torch/test_compressed_graph.py b/tests/torch/test_compressed_graph.py index 70674b5cdd4..f41d35d7f07 100644 --- a/tests/torch/test_compressed_graph.py +++ b/tests/torch/test_compressed_graph.py @@ -186,8 +186,9 @@ def dummy_forward_wrapper(model_): self.wrap_inputs_fn = wrap_inputs_fn - @property - def dot_filename(self): + def dot_filename(self, trace_parameters=False): + if trace_parameters: + return self.model_name + "_with_parameters_tracing.dot" return self.model_name + ".dot" @@ -252,7 +253,8 @@ def check_model_graph(compressed_model: NNCFNetwork, ref_dot_file_name: str, ref @pytest.mark.parametrize("desc", TEST_MODELS_DESC, ids=[m.model_name for m in TEST_MODELS_DESC]) class TestModelsGraph: - def test_build_graph(self, desc: ModelDesc): + @pytest.mark.parametrize("trace_parameters", (False, True)) + def test_build_graph(self, desc: ModelDesc, trace_parameters): net = desc.model_builder() input_sample_sizes = desc.input_sample_sizes if isinstance(input_sample_sizes, tuple): @@ -263,8 +265,8 @@ def test_build_graph(self, desc: ModelDesc): if not dummy_forward_fn: dummy_forward_fn = create_dummy_forward_fn(input_info, desc.wrap_inputs_fn) graph_builder = GraphBuilder(custom_forward_fn=dummy_forward_fn) - graph = graph_builder.build_graph(net) - check_graph(graph, desc.dot_filename, "original") + graph = graph_builder.build_graph(net, trace_parameters=trace_parameters) + check_graph(graph, desc.dot_filename(trace_parameters), "original") def get_sparsifiable_modules(self, algo_name): # counts wrapped NNCF modules to ignore the ones that are called in the training mode only @@ -296,7 +298,7 @@ def test_sparse_network(self, desc: ModelDesc, algo): sparsifiable_modules = self.get_sparsifiable_modules(algo) ref_num_sparsed = len(get_all_modules_by_type(model, sparsifiable_modules)) assert ref_num_sparsed == len(compression_ctrl.sparsified_module_info) - check_model_graph(compressed_model, desc.dot_filename, algo) + check_model_graph(compressed_model, desc.dot_filename(), algo) def test_quantize_network(self, desc: ModelDesc, _case_config): model = desc.model_builder() @@ -306,7 +308,7 @@ def test_quantize_network(self, desc: ModelDesc, _case_config): compressed_model, _ = create_compressed_model_and_algo_for_test( model, config, dummy_forward_fn=desc.dummy_forward_fn, wrap_inputs_fn=desc.wrap_inputs_fn ) - check_model_graph(compressed_model, desc.dot_filename, _case_config.graph_dir) + check_model_graph(compressed_model, desc.dot_filename(), _case_config.graph_dir) def test_sparse_quantize_network(self, desc: ModelDesc): model = desc.model_builder() @@ -323,7 +325,7 @@ def test_sparse_quantize_network(self, desc: ModelDesc): ref_num_sparsed = len(get_all_modules_by_type(compressed_model, sparsifiable_modules)) assert ref_num_sparsed == len(compression_ctrl.child_ctrls[0].sparsified_module_info) - check_model_graph(compressed_model, desc.dot_filename, "quantized_rb_sparsity") + check_model_graph(compressed_model, desc.dot_filename(), "quantized_rb_sparsity") @pytest.mark.skip(reason="Sporadic failures") @@ -868,7 +870,7 @@ def test_compressed_graph_models_hw(desc, hw_config_type): sketch_graph = compressed_model.nncf.get_original_graph() potential_quantizer_graph = prepare_potential_quantizer_graph(sketch_graph, single_config_quantizer_setup) - path_to_dot = get_full_path_to_the_graph(desc.dot_filename, _case_dir(hw_config_type.value)) + path_to_dot = get_full_path_to_the_graph(desc.dot_filename(), _case_dir(hw_config_type.value)) compare_nx_graph_with_reference(potential_quantizer_graph, path_to_dot, sort_dot_graph=False) diff --git a/tests/torch/test_graph_building.py b/tests/torch/test_graph_building.py index f9315e635d2..f4039c7fa6b 100644 --- a/tests/torch/test_graph_building.py +++ b/tests/torch/test_graph_building.py @@ -40,7 +40,7 @@ from nncf.torch.dynamic_graph.io_handling import FillerInputInfo from nncf.torch.dynamic_graph.io_handling import LoaderInputInfo from nncf.torch.dynamic_graph.io_handling import ModelInputInfo -from nncf.torch.dynamic_graph.trace_tensor import trace_tensors +from nncf.torch.dynamic_graph.trace_functions import trace_tensors from nncf.torch.graph.graph_builder import GraphBuilder from nncf.torch.initialization import PTInitializingDataLoader from nncf.torch.nested_objects_traversal import objwalk @@ -633,7 +633,8 @@ def test_integer_path_marking(): def test_trace_output_with_no_tensors(): output = None - trace_tensors(output, MagicMock()) + with TracingContext() as ctx: + trace_tensors(output, MagicMock(), ctx) class ModelWithRepeatInputs(torch.nn.Module): diff --git a/tests/torch/test_nncf_network.py b/tests/torch/test_nncf_network.py index c0fcb3336bf..80e96e5eefc 100644 --- a/tests/torch/test_nncf_network.py +++ b/tests/torch/test_nncf_network.py @@ -38,6 +38,7 @@ from nncf.torch.graph.operator_metatypes import PTModuleConv2dMetatype from nncf.torch.layer_utils import _NNCFModuleMixin from nncf.torch.layers import NNCFConv2d +from nncf.torch.model_creation import wrap_model from nncf.torch.nncf_module_replacement import replace_modules_by_nncf_modules from nncf.torch.nncf_network import ExtraCompressionModuleType from nncf.torch.nncf_network import NNCFNetwork @@ -874,3 +875,41 @@ def fn_to_check_input_type(input): nncf_model.nncf.insert_at_point(insertion_point, [fn_to_check_input_type]) nncf_model.nncf.rebuild_graph() + + +class TestWhisperDecoderModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.embedding = torch.nn.Embedding(10, 3) + + def forward(self, idx): + x = self.embedding(idx) + x = x @ torch.transpose(self.embedding.weight, 0, 1) + return x + + +class ZeroHook(torch.nn.Module): + def __init__(self): + super().__init__() + self.forward_calls_counter = 0 + + def forward(self, x): + self.forward_calls_counter += 1 + return x * 0 + + +def test_insert_hook_after_parameter(): + model = TestWhisperDecoderModel() + example_input = torch.randint(0, 9, (2,)) + nncf_model = wrap_model(model, example_input, trace_parameters=True) + result = nncf_model(example_input) + + hook = ZeroHook() + node_to_op_address_mapping = nncf_model.nncf.get_node_to_op_address_mapping() + insertion_point = PTInsertionPoint(TargetType.OPERATOR_POST_HOOK, node_to_op_address_mapping["embedding.weight"], 0) + nncf_model.nncf.insert_at_point(insertion_point, [hook]) + result_with_hook = nncf_model(example_input) + + assert hook.forward_calls_counter == 1 + assert torch.sum(result.nonzero()) > 0 + assert torch.sum(result_with_hook.nonzero()) == 0 diff --git a/tests/torch/test_tracing_context.py b/tests/torch/test_tracing_context.py index d5d5a57a6ca..c10d5815184 100644 --- a/tests/torch/test_tracing_context.py +++ b/tests/torch/test_tracing_context.py @@ -13,7 +13,9 @@ from packaging import version from nncf.torch.dynamic_graph.context import TracingContext +from nncf.torch.dynamic_graph.trace_tensor import TracedParameter from nncf.torch.dynamic_graph.trace_tensor import TracedTensor +from nncf.torch.dynamic_graph.wrappers import wrap_parameters @pytest.mark.skipif( @@ -82,6 +84,7 @@ def __init__(self): super().__init__() self.conv2d = torch.nn.Conv2d(1, 1, 1) self.cached_tensor = None + self.weight = torch.nn.Parameter(torch.zeros([1, 2])) def forward(self, x): if self.cached_tensor is not None: @@ -98,11 +101,16 @@ def test_traced_tensors_are_stripped_on_context_exit(): module.train() tensor = torch.ones([1, 1, 1, 1]) with TracingContext(): + wrap_parameters(module) result = module(tensor) assert isinstance(module.cached_tensor, TracedTensor) + assert isinstance(module.weight, TracedParameter) + assert isinstance(module.conv2d.weight, TracedParameter) assert isinstance(result, TracedTensor) - assert isinstance(module.cached_tensor, torch.Tensor) - assert isinstance(result, torch.Tensor) + assert type(module.cached_tensor) == torch.Tensor + assert type(result) == torch.Tensor + assert type(module.weight) == torch.nn.Parameter + assert type(module.conv2d.weight) == torch.nn.Parameter def test_no_cross_forward_run_dependency():