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ConvertCirtorch2TFLite.py
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import os
import shutil
import sys
sys.path.append('/media/anlab/data-2tb/ANLAB_THUY/ImageSearcher/cnnimageretrieval-pytorch')
import cv2
import numpy as np
import onnx
import torch
import tensorflow as tf
from PIL import Image
from torchvision.models import *
from onnx_tf.backend import prepare
import torch.nn as nn
import torch.quantization
from extract_cnn import *
import onnxruntime as ort
import onnx
from torchvision.transforms import functional as F
from torch.utils.model_zoo import load_url
from cirtorch.layers.pooling import MAC, SPoC, GeM, GeMmp, RMAC, Rpool
from cirtorch.networks.imageretrievalnet import init_network, extract_vectors, extract_vectors_by_arrays , extract_vectors_by_arrays2 , extract_db_array
from cirtorch.utils.general import get_data_root
class Network(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model.cpu()
self.mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
self.std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
def forward(self,x):
x = x.permute(0, 3, 1, 2)
x1 = F.resize(x, (300, 300))
out1 = self.model(x1)
reshaped_tensor1 = out1.view(1, 2048)
x2 = F.resize(x, (400, 400))
out2 = self.model(x2)
reshaped_tensor2 = out2.view(1, 2048)
return (reshaped_tensor2 + reshaped_tensor1) / 2
if __name__ == '__main__':
PRETRAINED = {
'rSfM120k-tl-resnet50-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/retrieval-SfM-120k/rSfM120k-tl-resnet50-gem-w-97bf910.pth',
'rSfM120k-tl-resnet101-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/retrieval-SfM-120k/rSfM120k-tl-resnet101-gem-w-a155e54.pth',
'rSfM120k-tl-resnet152-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/retrieval-SfM-120k/rSfM120k-tl-resnet152-gem-w-f39cada.pth',
'gl18-tl-resnet50-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/gl18/gl18-tl-resnet50-gem-w-83fdc30.pth',
'gl18-tl-resnet101-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/gl18/gl18-tl-resnet101-gem-w-a4d43db.pth',
'gl18-tl-resnet152-gem-w': 'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/gl18/gl18-tl-resnet152-gem-w-21278d5.pth',
}
useRmac=False
transform_ratio = 300
network = 'rSfM120k-tl-resnet101-gem-w'
state = load_url(PRETRAINED[network], model_dir=os.path.join(get_data_root(), 'networks'))
net_params = {}
net_params['architecture'] = state['meta']['architecture']
net_params['pooling'] = state['meta']['pooling']
net_params['local_whitening'] = state['meta'].get('local_whitening', False)
net_params['regional'] = state['meta'].get('regional', False)
net_params['whitening'] = state['meta'].get('whitening', True)
net_params['mean'] = state['meta']['mean']
net_params['std'] = state['meta']['std']
net_params['pretrained'] = False
net = init_network(net_params)
net.load_state_dict(state['state_dict'])
if useRmac:
net.pool = RMAC(3)
net.cuda()
net.eval()
test_model = Network(net)
test_model.eval()
img = cv2.imread("/media/anlab/0e731fe3-5959-4d40-8958-e9f6296b38cb/home/anlab/songuyen/label_aLong/prj_label/lashinbang-server/convert_model/image/CDE_BK-1_close.jpg")
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
x = cv2.resize(img, (400, 400))
tensor_img = torch.from_numpy(x).float()
tensor_img = tensor_img.unsqueeze(0)
onnx_path = "1910_400x400_300x300_cirtorch_fl32_resnet101_4D_WithoutNorm.onnx"
# convert to onnx model
torch_out = test_model(tensor_img)
torch.onnx.export(test_model,
tensor_img,
onnx_path,
verbose=True,
input_names=["images"],
output_names=["outputs"],
export_params=True,
opset_version = 10
)
# Checker
onnx_model = onnx.load( onnx_path)
onnx.checker.check_model(onnx_model)
# convert onnx to tf
tf_path = 'cirtorch_tf_1910_400x400_300x300_resnet101_WithoutNorm'
onnx_model = onnx.load(onnx_path)
onnx.checker.check_model(onnx_model)
tf_rep = prepare(onnx_model) #Prepare TF representation
tf_rep.export_graph(tf_path) #Export the model
# convert to tf lite
tf_lite_path = '1910_400x400_300x300_cirtorch_fl16_resnet101_WithoutNorm.tflite'
converter = tf.lite.TFLiteConverter.from_saved_model(tf_path)
# If want Optimize convert float16
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
converter.target_spec.supported_types = [tf.float16]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# done convert float16
tflite_model = converter.convert()
with open(tf_lite_path, 'wb') as f:
f.write(tflite_model)
#test model tflite
tflite_model_path = tf_lite_path
# Load the TFLite model and allocate tensors
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model on random input data
input_shape = input_details[0]['shape']
# print(x.shape)
# input_data = np.array(x, dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], tensor_img)
interpreter.invoke()
# get_tensor() returns a copy of the tensor data
# use tensor() in order to get a pointer to the tensor
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)