従来のトランスフォーメーション API からの移行#

import openvino as ov 
import torch 
import torchvision 
from torch.nn import Identity 

# Load pretrained model 
model = torchvision.models.resnet50(weights='DEFAULT') 

# input cut 
model.conv1 = Identity() 
model.bn1 = Identity() 

# output cut 
model.fc = Identity() 

# convert and compile the model 
ov_model = ov.convert_model(model, input=([-1,64,-1,-1], torch.float32)) 
compiled_model = ov.compile_model(ov_model)

import openvino as ov 
import torch 

# Example of model with multiple outputs 
class Model(torch.nn.Module): 
     def __init__(self): 
          super(Model, self).__init__() 
          self.linear1 = torch.nn.Linear(100, 200) 
          self.activation1 = torch.nn.ReLU() 
          self.linear2 = torch.nn.Linear(200, 10) 
          self.activation2 = torch.nn.Sigmoid() 

     def forward(self, x): 
          x = self.linear1(x) 
          x = self.activation1(x) 
          y = self.linear2(x) 
          y = self.activation2(y) 
          return x, y 

# New model, where some outputs are cut 
class CutModel(torch.nn.Module): 
     def __init__(self): 
          super(CutModel, self).__init__() 
          self.model = Model() 

     def forward(self, x): 
          # get first output 
          x, _ = self.model(x) 

          return x 

# Model with output cut 
cut_model = CutModel() 

# convert and compile the model 
ov_model = ov.convert_model(cut_model, input=([-1,-1,-1], torch.float32)) 
compiled_model = ov.compile_model(ov_model)

import openvino as ov 
import openvino.tools.mo as mo 

import tensorflow as tf 

def load_graph(model_path): 
     graph_def = tf.compat.v1.GraphDef() 
     with open(model_path, "rb") as f: 
          graph_def.ParseFromString(f.read()) 
     with tf.compat.v1.Graph().as_default() as graph: 
          tf.graph_util.import_graph_def(graph_def, name="") 
          return graph 

# Load TF model 
graph = load_graph("/path_to_model/HugeCTR.pb") 

# Convert the model with input and output cut 
input_name = "concat" 
output_name = "MatVec_3/Squeeze" 
ov_model = mo.convert_model(graph, input=(input_name, [-1, -1]), output=output_name) 

# Compile the model 
compiled_model = ov.compile_model(ov_model)

import openvino as ov 
import tensorflow as tf 

from tensorflow.python.tools.strip_unused_lib import strip_unused 

def load_graph(model_path): 
     graph_def = tf.compat.v1.GraphDef() 
     with open(model_path, "rb") as f: 
          graph_def.ParseFromString(f.read()) 
     with tf.compat.v1.Graph().as_default() as graph: 
          tf.graph_util.import_graph_def(graph_def, name="") 
          return graph 

# Load TF model 
graph = load_graph("/path_to_model/HugeCTR.pb") 

# Cut the model 
input_name = "concat" 
output_name = "MatVec_3/Squeeze" 
graph_def = graph.as_graph_def() 
new_graph_def = strip_unused(graph_def, [input_name], [output_name], tf.float32.as_datatype_enum) 

# Convert and compile model 
ov_model = ov.convert_model(new_graph_def, input=[-1, -1]) 
cmp_model = ov.compile_model(ov_model)

import openvino as ov 
import tensorflow_hub as hub 

import tensorflow as tf 
from tensorflow.python.framework.convert_to_constants import 
convert_variables_to_constants_v2 
from tensorflow.python.tools.strip_unused_lib import strip_unused 

# Load TF model 
model = hub.load("https://tfhub.dev/svampeatlas/vision/embedder/fungi_V2/1?tf-hub-format=compressed") 

# Convert model to GraphDef 
model_func = model.signatures["default"] 
frozen_func = convert_variables_to_constants_v2(model_func) 
graph_def = frozen_func.graph.as_graph_def() 

# Cut the model 
input_name = 'InceptionV4/InceptionV4/Conv2d_2b_3x3/Relu' 
output_name = 'InceptionV4/InceptionV4/Mixed_7c/concat' 
new_graph_def = strip_unused(graph_def, [input_name], [output_name], 
tf.float32.as_datatype_enum) 

# Convert and compile the model 
ov_model = ov.convert_model(new_graph_def) 
compiled_model = ov.compile_model(ov_model)

import openvino as ov 
import openvino.tools.mo as mo 

input_path = "/path_to_model/yolov8x.onnx" 

# Convert model and perform input and output cut 
input_name = "/model.2/Concat_output_0" 
output_name = "/model.22/Concat_3_output_0" 
ov_model = mo.convert_model(input_path, input=input_name, output=output_name) 

# Compile model 
ov.compile_model(ov_model)

import onnx 
import openvino as ov 

input_path = "/path_to_model/yolov8x.onnx" 

# Cut the model 
input_name = "/model.2/Concat_output_0" 
output_name = "/model.22/Concat_3_output_0" 
cut_model_path = "/path_to_model/yolov8x_cut.onnx" 
onnx.utils.extract_model(input_path, cut_model_path, [input_name], [output_name]) 

# Convert model 
ov_model = ov.convert_model(cut_model_path) 

# Compile model 
ov.compile_model(ov_model)