What's wrong with my code? (Solved)

AI & Data Science Deep Learning (Training & Inference) TensorRT
#1

Provide details on the platforms you are using:
OS : Linux 16.04
GPU : GTX 1080ti
nvidia driver version : 384.130
CUDA version : 9.0
CUDNN version : 7.3.1
Python version : 3.5.2
Tensorflow version : 1.11.0
TensorRT version : 5.0.0.10

Describe the problem
I got poor accuracy when i use conv layer with keras. I thought it cause from data format.
so I set my network’s input and data format set to be CHW.
But still bad…

Blow is my code for data.
I have MNIST data(28x28 grayscale image)

def load_testdata(data_path):
images = dict()
for (path, dir, files) in os.walk(data_path):
for file in files:
ext = os.path.splitext(file)[-1]

        if ext == '.png':
            img_path = path+'/'+file
            img = Image.open(img_path)
            label = img_path.replace('/'+file, '').split('/')[-1]
            np_img = np.array(img, dtype=np.float32, order='C')
            np_img = np.expand_dims(np_img, axis=0)

            if not label in images:
                images[label] = list()
                images[label].append(np_img)
            else:
                images[label].append(np_img)

return images

def copy_image_to_host_mem(img, pagelock_memory):
img = img / 255.0
img = img.ravel()
np.copyto(pagelock_memory, img)

Engine

======================================================
def build_engine(model_file, input_names, output_names):
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
builder.max_workspace_size = GiB(3)
# builder.max_batch_size = 1
# only support CHW format
parser.register_input(input_names, (1, 28, 28))
parser.register_output(output_names)
parser.parse(model_file, network)
return builder.build_cuda_engine(network)

Network

======================================================
def create_vgg_style():
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=‘relu’,
input_shape=(1, 28, 28), padding=‘same’, data_format=‘channels_first’))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation=‘relu’, padding=‘same’))
model.add(keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(256, activation=‘relu’))
model.add(keras.layers.Dense(10, activation=‘softmax’))
model.compile(optimizer=‘adam’, loss=‘sparse_categorical_crossentropy’, metrics=[‘accuracy’])

return model

======================================================

Thanks!

#2

Hello,

can you quantify what you mean by “poor accuracy” as compared to keras inference?

Also, it’d help us debug if you can share a small repro package containing the source, model, dataset, and inference source that demonstrate the symptoms you are seeing.

Regards,
NVIDIA Enterprise Support

#3

“poor accuracy” means that the model can’t recognize the input well.
I got about 99% from keras model. but I got about 10% from TensorRT model.

I attached the files
thanks :)

I removed the attached files because I solved the problem.

#4

I solved that problem.
I use the ‘reshape function’ instead of ‘flatten layer’.

thanks :)

#5