import numpy as np
import pandas as pd
data_test = pd.read_csv('test3.csv')
data_test = np.array(data_test.iloc[:, 1:])
print(data_test)
data_test = data_test.reshape(data_test.shape[0], 1, 28, 28)
data_test = data_test.astype('float32')
data_test /= 255
argmax = lambda res: np.argmax(res.reshape(10))
cases = []
labels = []
cases.append(data_test) # Append the image to list of images to process
labels.append(0) # Append the correct answer to compare later
# Create a runtime engine from plan file using TensorRT Lite API
engine_single = Engine(PLAN="test_engine.engine",
postprocessors={"dense_2/Softmax": argmax})
results = []
for image in cases:
result = engine_single.infer(image) # Single function for inference
results.append(result)
print(results)
import os
import time
import glob
import cv2
import scipy
import matplotlib.pyplot as plt
from tensorrt.lite import Engine
from tensorrt.infer import LogSeverity
import tensorrt
def imsave(image, path):
return scipy.misc.imsave(path, image)
engine_single = Engine(PLAN="dncnn.engine")
GT_data_dir = 'data/Test/Set12'
GT_file_list = sorted(glob.glob(GT_data_dir + '/*.png'))
print("TensorRT Testing...")
for i in range(len(GT_file_list)):
GT_img = cv2.imread(GT_file_list[i], 0) / 255
GT_img_reshape = GT_img.reshape(1, GT_img.shape[0], GT_img.shape[1], 1)
noise = np.random.normal(0, 25 / 255.0, GT_img_reshape.shape)
test_img = GT_img_reshape + noise
print(test_img.shape)
test_img_transpose = np.transpose(test_img, [0, 3, 1, 2])
start_time2 = time.time()
result = engine_single.infer(test_img_transpose) # 这是个list类型的输出
def load_TRT_engine(plan):
engine = Engine(PLAN=plan, postprocessors={"dense_2/Softmax": analyze})
return engine
#Arguments to create lite engine
network = {
"framework": "tf", #Source framework
"path": DATA + "/resnet50/resnet50-infer-5.pb", #Path to frozen model
"input_nodes": {
"input": (3, 224, 224)
}, #Dictionary of input nodes and their associated dimensions
"output_nodes": ["GPU_0/tower_0/Softmax"], #List of output nodes
"logger_severity": LogSeverity.INFO, #Debugging info
"postprocessors": {
"GPU_0/tower_0/Softmax": analyze
}
} #Postprocessor function table
engine = Engine(**network)
#Web service
app = Flask(__name__)
@app.route("/classify", methods=["POST"])
def json_classify():
if request.method == 'POST':
img = Image.open(request.files['file'])
#Format image to Numpy CHW and run inference, get the results of the single output node
results = engine.infer(image_to_np_CHW(img))[0]
#Retrive the results created by the post processor callback
top_class_label, top5 = results[0], results[1]
#Format data for JSON
def load_TRT_engine(plan):
engine = Engine(PLAN=plan, postprocessors={"fc2/Relu": analyze})
return engine
def load_TRT_engine(plan):
if os.path.exists(plan):
engine = Engine(PLAN=plan, postprocessors={"dense_2/Softmax": analyze})
return engine
else:
print('{} not exist.'.format(plan))
# This step performs (1) Tensor fusion (2) Reduced precision
# (3) Target autotuning (4) Tensor memory management
engine = trt.utils.uff_to_trt_engine(G_LOGGER,
uff_model,
parser,
1,
1<<20,
trt.infer.DataType.FLOAT) #FP32 FLOAT, FP16 HALF
# Serialize TensorRT engine to a file for when you are ready to deploy your model.
trt.utils.write_engine_to_file(engine_name,
engine.serialize())
# Create a runtime engine from plan file using TensorRT Lite API
engine_single = Engine(PLAN=engine_name,
postprocessors={output_node_name:analyze})
#Image processing
args = iCap.parse_args()
cap = iCap.open_cam_onboard(args.image_width, args.image_height)
if not cap.isOpened():
sys.exit('Failed to open camera!')
for i in range(0, 1500):
abc, vid = cap.read()
#vid = load_and_preprocess_images()
if vid.shape[0] != input_width:
if vid.shape[1] != input_height: