def preprocess_data(self, images, is_training):
"""Preprocesses raw images for either training or inference.
Args:
images: A 4-D float32 `Tensor` holding images to preprocess.
is_training: Boolean, whether or not we're in training.
Returns:
data_preprocessed: data after the preprocessor.
"""
config = self._config
height = config.data.height
width = config.data.width
min_scale = config.data.augmentation.minscale
max_scale = config.data.augmentation.maxscale
p_scale_up = config.data.augmentation.proportion_scaled_up
aug_color = config.data.augmentation.color
fast_mode = config.data.augmentation.fast_mode
crop_strategy = config.data.preprocessing.eval_cropping
preprocessed_images = preprocessing.preprocess_images(
images, is_training, height, width,
min_scale, max_scale, p_scale_up,
aug_color=aug_color, fast_mode=fast_mode,
crop_strategy=crop_strategy)
return preprocessed_images
def preprocess_data(self, images, is_training):
"""Preprocesses raw images for either training or inference.
Args:
images: A 4-D float32 `Tensor` holding images to preprocess.
is_training: Boolean, whether or not we're in training.
Returns:
data_preprocessed: data after the preprocessor.
"""
config = self._config
height = config.data.height
width = config.data.width
min_scale = config.data.augmentation.minscale
max_scale = config.data.augmentation.maxscale
p_scale_up = config.data.augmentation.proportion_scaled_up
aug_color = config.data.augmentation.color
fast_mode = config.data.augmentation.fast_mode
crop_strategy = config.data.preprocessing.eval_cropping
preprocessed_images = preprocessing.preprocess_images(
images, is_training, height, width,
min_scale, max_scale, p_scale_up,
aug_color=aug_color, fast_mode=fast_mode,
crop_strategy=crop_strategy)
return preprocessed_images
def preprocess(self, inputs):
"""preprocessing.
Outputs of this function can be passed to loss or postprocess functions.
Args:
preprocessed_inputs: A float32 tensor with shape [batch_size,
height, width, num_channels] representing a batch of images.
Returns:
prediction_dict: A dictionary holding prediction tensors to be
passed to the Loss or Postprocess functions.
"""
preprocessed_inputs = preprocessing.preprocess_images(
inputs,
self._default_image_size,
self._default_image_size,
resize_side_min=self._fixed_resize_side,
is_training=self._is_training,
border_expand=False,
normalize=False,
preserving_aspect_ratio_resize=False)
preprocessed_inputs = tf.cast(preprocessed_inputs,
tf.float32) #tf.cast()数据类型转化
return preprocessed_inputs
def preprocess(self, inputs):
preprocessed_inputs = preprocessing.preprocess_images(
inputs,
self._default_image_size,
self._default_image_size,
resize_side_min=self._fixed_resize_side,
is_training=self._is_training,
border_expand=True,
normalize=False,
preserving_aspect_ratio_resize=False)
preprocessed_inputs = tf.cast(preprocessed_inputs, tf.float32)
return preprocessed_inputs
def load_batch(dataset, batch_size, height, width, num_classes):
data_provider = slim.dataset_data_provider.DatasetDataProvider(dataset)
image, label = data_provider.get(['image', 'label'])
# Border expand and resize
image = preprocessing.preprocess_images(image,
output_height=height,
output_width=width,
border_expand=True,
normalize=True)
inputs, labels = tf.train.batch([image, label],
batch_size=batch_size,
allow_smaller_final_batch=True)
labels = slim.one_hot_encoding(labels, num_classes)
return inputs, labels
def preprocess(self, inputs):
"""
利用preprocessing.py中的数据处理函数对输入数据进行处理
输入 [batch_size,height, width, num_channels] a batch of images.
输出 tensors [batch_size,height, width, num_channels]
"""
# 调用批处理函数进行处理
with tf.variable_scope('Preprocess'):
preprocessed_inputs = preprocessing.preprocess_images(
inputs, self._default_image_size, self._default_image_size,
resize_side_min=self._fixed_resize_side,
is_training=self._is_training,
border_expand=False, normalize=False,
preserving_aspect_ratio_resize=False)
# 转化数据类型为 float32
preprocessed_inputs = tf.cast(preprocessed_inputs, tf.float32)
return preprocessed_inputs
# Loading the dataset
path = "../"
x_train, y_train, x_validation, y_validation, classes = filehandler.import_dataset(
path)
# Example of a picture
"""index = 20
plt.imshow(x_train[index])
plt.show()
print("y = " + str(y_train[:, index]) + ", it's a '" + classes[numpy.squeeze(y_train[:, index])].decode("utf-8") + "' picture.")
"""
# preprocessing the dataset
train_flat, validation_flat = preprocessing.preprocess_images(x_train, x_validation)
# creating our model
input_shape = train_flat.shape[0]
log_regression = log_reg.LogisticRegression(input_shape)
# training our model
epochs = 300
learning_rate = 0.001
# if the probability is below 0.3 or above 0.7, the sample is "correctly predicted"
recognition_threshold = 0.3
history = log_regression.train(train_flat, y_train, validation_flat, y_validation, epochs, learning_rate, recognition_threshold)