def _build_example(self, row, amenities, url):
""" Builds an example by building a map of Proto features.
Args:
row: Dictionary with row information and type
amenities: List of amenities of the example
url: URL of the image to include
"""
# Map features into corresponding types
feature_dict = {name: map_feature(row[name]['value'],
row[name]['type'])
for name, info in row.items()}
# Create Sparse Column for the amenities of the row
feature_dict.update({'amenities': bytes_feature(amenities)})
# Load image from url
decoded_image, height, width = self.image_from_url(url, jpeg=True)
# Add image metadata
feature_dict.update({
'image': bytes_feature(decoded_image),
'height': int64_feature(height),
'width': int64_feature(width),
'path': bytes_feature(url),
'format': bytes_feature('JPEG'),
'colorspace': bytes_feature('RGB')
})
# Build example
return tf.train.Example(features=tf.train.Features(feature=feature_dict)) # noqa
def build_examples(self, index):
row = self.features[index, :]
feature_dict = {}
for i in range(self.features.shape[1]):
feature_dict.update({str(i): float64_feature(row[i])})
feature_dict.update({'id': int64_feature(int(self.ids[index]))})
feature_dict.update({'class': int64_feature(int(self.labels[index]))})
return [
tf.train.Example(features=tf.train.Features(feature=feature_dict))
] # noqa
def build_examples(self, index):
# Get row
i, subset = index
img = self.train_data[i, ...] \
if subset in [DataMode.TRAINING, DataMode.VALIDATION] \
else self.test_data[i, ...]
label = self.train_labels[i, ...] \
if subset in [DataMode.TRAINING, DataMode.VALIDATION] \
else self.test_labels[i, ...]
# Fill features
feature_dict = {
'image': bytes_feature(self.process_image_bytes(img)),
'label': int64_feature(int(label)),
'colorspace': bytes_feature('RGB')
}
# Add wide and deep pixel features
for i in range(self.height):
for j in range(self.width):
feat_bytes = bytes_feature(str(img[i, j]))
feat_float = float64_feature(float(img[i, j]))
feature_dict.update({self._get_pixel_name(i, j): feat_bytes})
feature_dict.update(
{self._get_pixel_name(i, j) + '_num': feat_float})
return [
tf.train.Example(features=tf.train.Features(feature=feature_dict))
] # noqa
def build_examples(self, index):
feature_dict = {
'image': bytes_feature(self.process_image_bytes(self.x[index,
...])),
'label': int64_feature(int(self.y[index])),
}
return [
tf.train.Example(features=tf.train.Features(feature=feature_dict))
]
def build_examples(self, index):
# Get row
i, subset = index
i = int(i)
img = self.train_data[i, ...] \
if subset in [DataMode.TRAINING, DataMode.VALIDATION] \
else self.test_data[i, ...]
label = self.train_labels[i, ...] \
if subset in [DataMode.TRAINING, DataMode.VALIDATION] \
else self.test_labels[i, ...]
# Fill features
feature_dict = {
'image': bytes_feature(self.process_image_bytes(img)),
'label': int64_feature(int(label)),
'colorspace': bytes_feature('RGB')
}
return [tf.train.Example(features=tf.train.Features(feature=feature_dict))] # noqa