迁移学习：如何将预训练CNN当成特征提取器

{
 "model"           : "mobilenet",
 "weights"         : "imagenet",
 "include_top"     : false,
"train_path"      : "dataset/train",
 "test_path"    : "dataset/test",
 "features_path"   : "output/flowers_17/mobilenet/features.h5",
 "labels_path"     : "output/flowers_17/mobilenet/labels.h5",
 "results"         : "output/flowers_17/mobilenet/results.txt",
 "classifier_path" : "output/flowers_17/mobilenet/classifier.pickle",
 "model_path"   : "output/flowers_17/mobilenet/model",

"test_size"       : 0.10,
 "seed"            : 9,
 "num_classes"     : 17
}

{
 “model” : “inceptionv3”,
 “weights” : “imagenet”,
 “include_top” : false,
“train_path” : “dataset/train”,
 “test_path” : “dataset/test”,
 “features_path” : “output/flowers_17/inceptionv3/features.h5”,
 “labels_path” : “output/flowers_17/inceptionv3/labels.h5”,
 “results” : “output/flowers_17/inceptionv3/results.txt”,
 “classifier_path” : “output/flowers_17/inceptionv3/classifier.pickle”,
 “model_path” : “output/flowers_17/inceptionv3/model”,

“test_size” : 0.10,
 “seed” : 9,
 “num_classes” : 4
}

# filter warnings
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
# keras imports
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.applications.xception import Xception, preprocess_input
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.applications.mobilenet import MobileNet, preprocess_input
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing import image
from keras.models import Model
from keras.models import model_from_json
from keras.layers import Input

# other imports
from sklearn.preprocessing import LabelEncoder
import numpy as np
import glob
import cv2
import h5py
import os
import json
import datetime
import time
# load the user configs
with open('conf/conf.json') as f:    
 config = json.load(f)
# config variables
model_name   = config["model"]
weights   = config["weights"]
include_top  = config["include_top"]
train_path   = config["train_path"]
features_path  = config["features_path"]
labels_path  = config["labels_path"]
test_size   = config["test_size"]
results   = config["results"]
model_path   = config["model_path"]
# start time
print ("[STATUS] start time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))
start = time.time()

# create the pretrained models
# check for pretrained weight usage or not
# check for top layers to be included or not
if model_name == "vgg16":
 base_model = VGG16(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
 image_size = (224, 224)
elif model_name == "vgg19":
 base_model = VGG19(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
 image_size = (224, 224)
elif model_name == "resnet50":
 base_model = ResNet50(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('flatten').output)
 image_size = (224, 224)
elif model_name == "inceptionv3":
 base_model = InceptionV3(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (299, 299)
elif model_name == "inceptionresnetv2":
 base_model = InceptionResNetV2(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (299, 299)
elif model_name == "mobilenet":
 base_model = MobileNet(include_top=include_top, weights=weights, input_tensor=Input(shape=(224,224,3)), input_shape=(224,224,3))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (224, 224)
elif model_name == "xception":
 base_model = Xception(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
 image_size = (299, 299)
else:
 base_model = None

print ("[INFO] successfully loaded base model and model...")
# path to training dataset
train_labels = os.listdir(train_path)
# encode the labels
print ("[INFO] encoding labels...")
le = LabelEncoder()
le.fit([tl for tl in train_labels])
# variables to hold features and labels
features = []
labels   = []
# loop over all the labels in the folder
count = 1
for i, label in enumerate(train_labels):
 cur_path = train_path + "/" + label
 count = 1
 for image_path in glob.glob(cur_path + "/*.jpg"):
  img = image.load_img(image_path, target_size=image_size)
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  x = preprocess_input(x)
  feature = model.predict(x)
  flat = feature.flatten()
  features.append(flat)
  labels.append(label)
  print ("[INFO] processed - " + str(count))
  count += 1
 print ("[INFO] completed label - " + label)
# encode the labels using LabelEncoder
le = LabelEncoder()
le_labels = le.fit_transform(labels)
# get the shape of training labels
print ("[STATUS] training labels: {}".format(le_labels))
print ("[STATUS] training labels shape: {}".format(le_labels.shape))
# save features and labels
h5f_data = h5py.File(features_path, 'w')
h5f_data.create_dataset('dataset_1', data=np.array(features))
h5f_label = h5py.File(labels_path, 'w')
h5f_label.create_dataset('dataset_1', data=np.array(le_labels))
h5f_data.close()
h5f_label.close()
# save model and weights
model_json = model.to_json()
with open(model_path + str(test_size) + ".json", "w") as json_file:
 json_file.write(model_json)
# save weights
model.save_weights(model_path + str(test_size) + ".h5")
print("[STATUS] saved model and weights to disk..")
print ("[STATUS] features and labels saved..")
# end time
end = time.time()
print ("[STATUS] end time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))

# organize imports
from __future__ import print_function
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np
import h5py
import os
import json
import pickle
import seaborn as sns
import matplotlib.pyplot as plt
# load the user configs
with open('conf/conf.json') as f:    
 config = json.load(f)
# config variables
test_size   = config["test_size"]
seed    = config["seed"]
features_path  = config["features_path"]
labels_path  = config["labels_path"]
results   = config["results"]
classifier_path = config["classifier_path"]
train_path   = config["train_path"]
num_classes  = config["num_classes"]
classifier_path = config["classifier_path"]
# import features and labels
h5f_data  = h5py.File(features_path, 'r')
h5f_label = h5py.File(labels_path, 'r')
features_string = h5f_data['dataset_1']
labels_string   = h5f_label['dataset_1']
features = np.array(features_string)
labels   = np.array(labels_string)
h5f_data.close()
h5f_label.close()
# verify the shape of features and labels
print ("[INFO] features shape: {}".format(features.shape))
print ("[INFO] labels shape: {}".format(labels.shape))
print ("[INFO] training started...")
# split the training and testing data
(trainData, testData, trainLabels, testLabels) = train_test_split(np.array(features),
                                                                  np.array(labels),
                                                                  test_size=test_size,
                                                                  random_state=seed)

print ("[INFO] splitted train and test data...")
print ("[INFO] train data  : {}".format(trainData.shape))
print ("[INFO] test data   : {}".format(testData.shape))
print ("[INFO] train labels: {}".format(trainLabels.shape))
print ("[INFO] test labels : {}".format(testLabels.shape))
# use logistic regression as the model
print ("[INFO] creating model...")
model = LogisticRegression(random_state=seed)
model.fit(trainData, trainLabels)
# use rank-1 and rank-5 predictions
print ("[INFO] evaluating model...")
f = open(results, "w")
rank_1 = 0
rank_5 = 0
# loop over test data
for (label, features) in zip(testLabels, testData):
 # predict the probability of each class label and
 # take the top-5 class labels
 predictions = model.predict_proba(np.atleast_2d(features))[0]
 predictions = np.argsort(predictions)[::-1][:5]
# rank-1 prediction increment
 if label == predictions[0]:
  rank_1 += 1
# rank-5 prediction increment
 if label in predictions:
  rank_5 += 1
# convert accuracies to percentages
rank_1 = (rank_1 / float(len(testLabels))) * 100
rank_5 = (rank_5 / float(len(testLabels))) * 100
# write the accuracies to file
f.write("Rank-1: {:.2f}%\n".format(rank_1))
f.write("Rank-5: {:.2f}%\n\n".format(rank_5))
# evaluate the model of test data
preds = model.predict(testData)
# write the classification report to file
f.write("{}\n".format(classification_report(testLabels, preds)))
f.close()
# dump classifier to file
print ("[INFO] saving model...")
pickle.dump(model, open(classifier_path, 'wb'))
# display the confusion matrix
print ("[INFO] confusion matrix")
# get the list of training lables
labels = sorted(list(os.listdir(train_path)))
# plot the confusion matrix
cm = confusion_matrix(testLabels, preds)
sns.heatmap(cm,
            annot=True,
            cmap="Set2")
plt.show()

# test script to preform prediction on test images inside 
# dataset/test/
#   -- image_1.jpg
#   -- image_2.jpg
#   ...
# organize imports
from __future__ import print_function
# keras imports
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.applications.xception import Xception, preprocess_input
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.applications.mobilenet import MobileNet, preprocess_input
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing import image
from keras.models import Model
from keras.models import model_from_json
from keras.layers import Input

# other imports
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import json
import pickle
import cv2
# load the user configs
with open('conf/conf.json') as f:    
 config = json.load(f)
# config variables
model_name   = config["model"]
weights   = config["weights"]
include_top  = config["include_top"]
train_path   = config["train_path"]
test_path   = config["test_path"]
features_path  = config["features_path"]
labels_path  = config["labels_path"]
test_size   = config["test_size"]
results   = config["results"]
model_path   = config["model_path"]
seed    = config["seed"]
classifier_path = config["classifier_path"]
# load the trained logistic regression classifier
print ("[INFO] loading the classifier...")
classifier = pickle.load(open(classifier_path, 'rb'))
# pretrained models needed to perform feature extraction on test data too!
if model_name == "vgg16":
 base_model = VGG16(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
 image_size = (224, 224)
elif model_name == "vgg19":
 base_model = VGG19(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
 image_size = (224, 224)
elif model_name == "resnet50":
 base_model = ResNet50(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('flatten').output)
 image_size = (224, 224)
elif model_name == "inceptionv3":
 base_model = InceptionV3(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (299, 299)
elif model_name == "inceptionresnetv2":
 base_model = InceptionResNetV2(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (299, 299)
elif model_name == "mobilenet":
 base_model = MobileNet(include_top=include_top, weights=weights, input_tensor=Input(shape=(224,224,3)), input_shape=(224,224,3))
 model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
 image_size = (224, 224)
elif model_name == "xception":
 base_model = Xception(weights=weights)
 model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
 image_size = (299, 299)
else:
 base_model = None

# get all the train labels
train_labels = os.listdir(train_path)
# get all the test images paths
test_images = os.listdir(test_path)
# loop through each image in the test data
for image_path in test_images:
 path   = test_path + "/" + image_path
 img   = image.load_img(path, target_size=image_size)
 x    = image.img_to_array(img)
 x    = np.expand_dims(x, axis=0)
 x    = preprocess_input(x)
 feature  = model.predict(x)
 flat   = feature.flatten()
 flat   = np.expand_dims(flat, axis=0)
 preds   = classifier.predict(flat)
 prediction  = train_labels[preds[0]]
 
 # perform prediction on test image
 print ("I think it is a " + train_labels[preds[0]])
 img_color = cv2.imread(path, 1)
 cv2.putText(img_color, "I think it is a " + prediction, (140,445), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
 cv2.imshow("test", img_color)

# key tracker
 key = cv2.waitKey(0) & 0xFF
 if (key == ord('q')):
  cv2.destroyAllWindows()