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infere_ovino22.1.py~
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infere_ovino22.1.py~
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"""
"""
import os, sys
import math, random, cv2
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
#import matplotlib.pyplot as plt
import numpy as np
from matplotlib import pyplot as plt
import torchvision
#from openvino.runtime import Core
from openvino.runtime import Core, PartialShape
#from PIL import Image
from PIL import Image, ImageFilter
if 1:
if 1: # add for my test
# int8 model via onnx runtime
# quantize/dequantize linear layers
model = "model.test-int8-Alex.onnx-rt-int8.cut.onnx"
# int8 model via nncf package ovino22.1
# fake-quantized layer
#model = "model.test-int8-Alex.ovino-nncrf-int8.ovino22.1_mo.cut" + ".xml"
# image
#fig = "emphasizedImage.tif"
fig = "Accordion-842.jpg"
np_save_name = "save_np_ovino22.1_" + fig + "_" + model
inmodel = "sample-model/"+model
baseImg = "sample-image/" + fig
input_size = (2048, 2048)
press_size = (255, 255)
normalize_GoogleOpenImage = torchvision.transforms.Normalize(
mean=[0.4900, 0.4252, 0.3741],
std=[0.2741, 0.2673, 0.2739])
transform_resize_totensor_donorm = torchvision.transforms.Compose([
torchvision.transforms.Resize(input_size[0],interpolation=torchvision.transforms.InterpolationMode.BILINEAR),
#torchvision.transforms.Resize(input_size[0],interpolation=torchvision.transforms.InterpolationMode.NEAREST),
#torchvision.transforms.Resize(input_size[0],interpolation=torchvision.transforms.InterpolationMode.BICUBIC),
torchvision.transforms.ToTensor(),
normalize_GoogleOpenImage,
])
def get_input_image(fname, cname):
print("\n>>>>> get_input_image")
if 1: # use PIL, faster than cv2
#image = Image.open(fname)
image = Image.open(fname).convert('RGB')
# do transform and add batch size
# w/ normal process
input_image = transform_resize_totensor_donorm( image )
input_image = np.expand_dims(input_image, 0)
print("\ninput image = \n", input_image[0:1,0:1,0:1,0:9], "\n\n")
return input_image
def get_cutting_model(inmodel):
print("\n>>>>> CALLED get_cutting_model()")
core = Core()
ir_model = core.read_model(model=inmodel)
#ir_model.add_outputs([("cv1/WithoutBiases", 0)])
#print(f"ir_model = {ir_model}")
#print(f"len(ir_model.inputs) = {len(ir_model.inputs)}")
if 1:
#ir_input_layer = list(ir_model.inputs.keys())[0]
ir_input_layer1 = next(iter(ir_model.inputs))
ir_output_layer = next(iter(ir_model.outputs))
print(f"ir_input_layer1 = {ir_input_layer1.any_name}")
print(f"ir_output_layer = {ir_output_layer.any_name}")
print("\n>>>>> ORIGINAL MODEL")
print(f"input shape: {ir_input_layer1.shape}")
print(f"output shape: {ir_output_layer.shape}")
new_shape = PartialShape([1, 3, input_size[0], input_size[1]])
ir_model.reshape({ir_input_layer1.any_name: new_shape})
ir_compiled_model = core.compile_model(model=ir_model, device_name="CPU")
print("\n>>>>> RESHAPED MODEL")
print(f"model input shape: {ir_input_layer1.shape}")
print(f"compiled_model input shape: {ir_compiled_model.input(index=0).shape}")
print(f"compiled_model output shape: {ir_output_layer.shape}")
return ir_model, ir_compiled_model
def get_feature_pixel(ir_model, ir_compiled_model, input_image, name, draw):
print("\n>>>>> CALLED: get_feature_pixel()")
print(f"ir_model = {ir_model}")
if 1: # 1 input
#ir_input_layer = next(iter(ir_model.inputs))
input0Name = ir_model.input().get_any_name()
input_data = {input0Name:input_image}
method = cv2.INTER_NEAREST
#method = cv2.INTER_LINEAR
#method = cv2.INTER_CUBIC
#method = cv2.INTER_LANCZOS4
#method = cv2.INTER_AREA
# do synchronous inference
res = ir_compiled_model.infer_new_request(input_data)
# TypeError: Inputs should be either list or dict! Current type: <class 'numpy.ndarray'>
#print(type(res))
# <class 'dict'>
#print(res)
matrix_list = list(res.values())
matrix_len = len(matrix_list)
if 1: # N output
print(">>> this is N output")
feature_list = [] # prepare list
for i in range( matrix_len ):
matrix = np.squeeze(matrix_list[i]) # remove batch size since this is a single oeration:q
nite = matrix.shape[0] # n feature map
#print(f"conv {i} features: {nite}, shape: {matrix.shape}")
for j in range( nite ): # iteration over feature map
fmap = matrix[j, :, :]
resized_fmap = cv2.resize(src=fmap, dsize=(press_size)).astype(np.float32)
#resized_fmap = cv2.resize(src=fmap, dsize=(press_size), interpolation=method).astype(np.float32)
feature_list.append(resized_fmap)
#print(f"{type(resized_fmap)}, {resized_fmap.dtype}")
# <class 'numpy.ndarray'>, float32
matrix = np.asarray(feature_list)
#print("feature_list : ", matrix.shape, matrix.shape[0])
# feature_list : (256, 255, 255) 256
if 1: # add for my output
print(matrix.shape)
print("\noutput features = \n", matrix[0:1,0:1,0:9], "\n\n")
fig = plt.figure("")
plt.imshow(matrix[0])
#plt.show()
np.save(np_save_name, matrix)
plt.show()
return matrix
# 1. get IR model
ir_model, ir_compiled_model = get_cutting_model(inmodel)
# 2. get images,
input_base_img = get_input_image(baseImg, "imgB")
#
# 3. get feature map for base, and image1, and image2
#
matrixBase = get_feature_pixel(ir_model, ir_compiled_model, input_base_img, "base_img", 1)