Deep Learning for humans-

How to install-

pip install inaccel-keras

FPGA Platforms-

Get the available accelerators for your platform.

U250U280

xilinx xdma_201830.2

inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/xilinx/u250/xdma_201830.2/xilinx/com/researchlabs/1.0/2mobilenet
inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/xilinx/u250/xdma_201830.2/xilinx/com/researchlabs/1.2/1resnet50

xilinx xdma_201920.3

inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/xilinx/u280/xdma_201920.3/xilinx/com/researchlabs/1.0/1resnet50
inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/xilinx/u280/xdma_201920.3/xilinx/com/researchlabs/1.1/2mobilenet

Examples-

Classify ImageNet classes with ResNet50-

Weights are downloaded automatically when instantiating a model. They are stored at ~/.keras/inaccel/models/.

import time

from inaccel.keras.applications.resnet50 import ResNet50
from inaccel.keras.preprocessing.image import ImageDataGenerator

model = ResNet50(weights='imagenet')

data = ImageDataGenerator(dtype='int8')
images = data.flow_from_directory('imagenet/', target_size=(224, 224), class_mode=None, batch_size=64)

begin = time.monotonic()
preds = model.predict(images, workers=16)
end = time.monotonic()

print('Duration for', len(preds), 'images: %.3f sec' % (end - begin))
print('FPS: %.3f' % (len(preds) / (end - begin)))

import numpy as np

from inaccel.keras.applications.resnet50 import decode_predictions, ResNet50
from inaccel.keras.preprocessing.image import load_img

model = ResNet50(weights='imagenet')

dog = load_img('data/dog.jpg', target_size=(224, 224))
dog = np.expand_dims(dog, axis=0)

elephant = load_img('data/elephant.jpg', target_size=(224, 224))
elephant = np.expand_dims(elephant, axis=0)

images = np.vstack([dog, elephant])

preds = model.predict(images)

print('Predicted:', decode_predictions(preds, top=1))

Last update: April 25, 2023