frogermcs

LABELS_FILE = 'ImageNetLabels.txt' 
with open(LABELS_FILE) as f:
    labels = f.read().splitlines() 

frogermcs

Prediction for Golden Retriever: golden retriever 0.5627079010009766
Prediction for laptop: laptop 0.42153415083885193

frogermcs

INPUT_TENSOR = "input__0"
OUTPUT_TENSOR = "MobilenetV2__Predictions__Reshape_1__0"
PREDICTED_FEATURE_NAME = "classLabel"

# Prediction is run on CPU
coreml_output_golden = mlmodel.predict({INPUT_TENSOR: img_golden}, useCPUOnly=True)

#Prediction is run on GPU
coreml_output_laptop = mlmodel.predict({INPUT_TENSOR: img_laptop}, useCPUOnly=False)

frogermcs

img_golden = img_golden.resize([224,224], PIL.Image.ANTIALIAS)
img_laptop = img_laptop.resize([224,224], PIL.Image.ANTIALIAS)

frogermcs

# Load previously saved CoreML model of MobileNet v2
mlmodel = coremltools.models.MLModel('mobilenet_v2_1.0_224.mlmodel')

# Get spec from the model
spec = mlmodel.get_spec()
print(spec.description)

# Output 
# >> input {
# >>   name: "input__0"

frogermcs

img_laptop_url = "https://upload.wikimedia.org/wikipedia/commons/9/90/ThinkPad_X220.jpg"
img_laptop = PIL.Image.open(BytesIO(requests.get(img_laptop_url).content))
imshow(np.asarray(img_laptop))
img_golden_url = "https://upload.wikimedia.org/wikipedia/commons/9/93/Golden_Retriever_Carlos_%2810581910556%29.jpg"
img_golden = PIL.Image.open(BytesIO(requests.get(img_golden_url).content))
imshow(np.asarray(img_golden))

frogermcs

%matplotlib inline #will be useful for images preview
from matplotlib.pyplot import imshow

import tensorflow as tf
import coremltools

#For easier images processing
import numpy as np
import PIL
import requests

frogermcs

Core ML model generated. Saved at location: mobilenet_v2_1.0_224.mlmodel 

Core ML input(s): 
 [name: "input__0"
  type {
    imageType {
      width: 224
      height: 224
      colorSpace: RGB
  }

frogermcs

# Convert TensorFlow model to Core ML format

# Input model definition
IMAGE_INPUT_NAME = ["input:0"]
IMAGE_INPUT_NAME_SHAPE = {'input:0':[1,224,224,3]}
IMAGE_INPUT_SCALE = 1.0/255.0
OUTPUT_NAME = ['MobilenetV2/Predictions/Reshape_1:0']
MODEL_LABELS = 'ImageNetLabels.txt'

# Output model

frogermcs

!cat mobilenet_v2_1.0_224_info.txt

# Output
# >> Model: mobilenet_v2_1.0_224
# >> Input: input
# >> Output: MobilenetV2/Predictions/Reshape_1