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""" This module contains utilities to capture and save intermediate layer-outputs of a model """
import re
from typing import List, Dict, Tuple, Union
import numpy as np
import tensorflow as tf
from aimet_common.utils import AimetLogger
from aimet_common.layer_output_utils import SaveInputOutput, save_layer_output_names
from aimet_tensorflow.common.connectedgraph import ConnectedGraph
from aimet_tensorflow.quantsim import QuantizationSimModel
from aimet_tensorflow.utils.common import create_input_feed_dict
logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.LayerOutputs)
[docs]class LayerOutputUtil:
""" Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim) """
def __init__(self, session: tf.compat.v1.Session, starting_op_names: List[str], output_op_names: List[str],
dir_path: str):
"""
Constructor for LayerOutputUtil.
:param session: Session containing the model whose layer-outputs are needed.
:param starting_op_names: List of starting op names of the model.
:param output_op_names: List of output op names of the model.
:param dir_path: Directory wherein layer-outputs will be saved.
"""
self.session = session
self.starting_op_names = starting_op_names
# Utility to capture layer-outputs
self.layer_output = LayerOutput(session=session, starting_op_names=starting_op_names,
output_op_names=output_op_names, dir_path=dir_path)
# Identify the axis-layout used for representing an image tensor
axis_layout = 'NHWC' if tf.keras.backend.image_data_format() == 'channels_last' else 'NCHW'
# Utility to save model inputs and their corresponding layer-outputs
self.save_input_output = SaveInputOutput(dir_path, axis_layout)
[docs] def generate_layer_outputs(self, input_batch: Union[np.ndarray, List[np.ndarray], Tuple[np.ndarray]]):
"""
This method captures output of every layer of a model & saves the inputs and corresponding layer-outputs to disk.
:param input_batch: Batch of inputs for which we want to obtain layer-outputs.
:return: None
"""
logger.info("Generating layer-outputs for %d input instances", len(input_batch))
feed_dict = create_input_feed_dict(self.session.graph, self.starting_op_names, input_batch)
# Obtain layer-output name to output dictionary
layer_output_batch_dict = self.layer_output.get_outputs(feed_dict)
# Save inputs and layer-outputs
self.save_input_output.save(input_batch, layer_output_batch_dict)
logger.info('Layer-outputs generated for %d input instances', len(input_batch))
class LayerOutput:
"""
This class creates a layer-output name to layer-output dictionary. The layer-output names are as per the AIMET exported
tensorflow model.
"""
def __init__(self, session: tf.compat.v1.Session, starting_op_names: List[str], output_op_names: List[str], dir_path: str):
"""
Constructor - It initializes few lists that are required for capturing and naming layer-outputs.
:param session: Session containing TF model.
:param starting_op_names: List of starting op names of the model.
:param output_op_names: List of output op names of the model.
"""
self.session = session
self.activation_tensor_names, self.activation_tensors = LayerOutput.get_activation_tensor_info(
session, starting_op_names, output_op_names)
# Save activation tensor names which are in topological order of model graph. This order can be used while comparing layer-outputs.
save_layer_output_names(self.activation_tensor_names, dir_path)
def get_outputs(self, feed_dict: Dict) -> Dict[str, np.ndarray]:
"""
This function creates layer-output name to layer-output dictionary. The layer-output names are as per the AIMET
exported TF model.
:param feed_dict: input tensor to input batch map
:return: layer-output name to layer-output dictionary
"""
act_outputs = self.session.run(self.activation_tensors, feed_dict=feed_dict)
return dict(zip(self.activation_tensor_names, act_outputs))
@staticmethod
def get_activation_tensor_info(session: tf.compat.v1.Session, starting_op_names: List[str], output_op_names: List[str]) -> Tuple[List, List]:
"""
This function fetches the activation tensors and its names from the given TF model. These activation tensors contain
the layer-outputs of the given TF model.
:param session: Session containing TF model.
:param starting_op_names: List of starting op names of the model.
:param output_op_names: List of output op names of the model.
:return: activation_tensor_names, activation_tensors
"""
connected_graph = ConnectedGraph(session.graph, starting_op_names, output_op_names)
# pylint: disable=protected-access
activation_op_names = QuantizationSimModel._get_ops_to_quantize_activations_for(session.graph, connected_graph)
# Get activation quantization ops
activation_quant_op_names = [op_name for op_name in activation_op_names if op_name.endswith('_quantized')]
# If activation quant ops are present then capture only their tensors
if activation_quant_op_names:
activation_op_names = activation_quant_op_names
activation_tensor_names = []
activation_tensors = []
for activation_op_name in activation_op_names:
activation_op = session.graph.get_operation_by_name(activation_op_name)
for output in activation_op.outputs:
activation_tensor_names.append(output.name)
activation_tensors.append(output)
# Update activation tensor names by removing 'quantized:' string and replacing '/' with '_'.
activation_tensor_names = [re.sub(r'\W+', "_", name.replace('quantized:', '')) for name in activation_tensor_names]
return activation_tensor_names, activation_tensors