def test_shape_operations(self):
# concatenate
xval = np.random.random((4, 3))
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random((4, 2))
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
zth = KTH.eval(KTH.concatenate([xth, yth], axis=-1))
ztf = KTF.eval(KTF.concatenate([xtf, ytf], axis=-1))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
check_single_tensor_operation('reshape', (4, 2), shape=(8, 1))
check_single_tensor_operation('permute_dimensions', (4, 2, 3),
pattern=(2, 0, 1))
check_single_tensor_operation('repeat', (4, 1), n=3)
check_single_tensor_operation('flatten', (4, 1))
check_single_tensor_operation('expand_dims', (4, 3), dim=-1)
check_single_tensor_operation('expand_dims', (4, 3, 2), dim=1)
check_single_tensor_operation('squeeze', (4, 3, 1), axis=2)
check_single_tensor_operation('squeeze', (4, 1, 1), axis=1)
check_composed_tensor_operations('reshape', {'shape': (4, 3, 1, 1)},
'squeeze', {'axis': 2},
(4, 3, 1, 1))
python类expand_dims()的实例源码
def render(self, mode='human', close=False):#*args, **kwargs):
env = self.conopt_env
if close:
if 'viewer' in env.__dict__:
env.viewer.close()
del env.viewer
else:
img = env.world.model.render(np.expand_dims(env.x, 0))[0]
if mode == 'human':
#import cv2
#img = cv2.resize(img, (50, 50))
if not 'viewer' in env.__dict__:
from gym.envs.classic_control.rendering import SimpleImageViewer
env.viewer = SimpleImageViewer()
env.viewer.imshow(img)
return img
else:
return img
def readimg(img_path):
img = misc.imread(img_path, mode='RGB')
img = misc.imresize(img, (160, 160))
img = facenet.prewhiten(img)
img = np.expand_dims(img, axis=0)
return img
def get_embedding(img_path):
img = misc.imread(img_path, mode='RGB')
# judge alignment
aligned = align.align(160, img, [0, 0, img.shape[1], img.shape[0]], landmarkIndices=landmarkIndices)
img = facenet.prewhiten(img)
img = np.expand_dims(img, axis=0)
aligned = facenet.prewhiten(aligned)
aligned = np.expand_dims(aligned, axis=0)
# Run forward pass to calculate embeddings
feed_dict = {images_placeholder: img, phase_train_placeholder: False}
emb = sess.run(embeddings, feed_dict=feed_dict)
# Run forward pass to calculate embeddings
feed_dict_aligned = {images_placeholder: aligned, phase_train_placeholder: False}
emb_aligned = sess.run(embeddings, feed_dict=feed_dict_aligned)
return emb.ravel(), emb_aligned.ravel()
# # for test
# import os
# from time import time
# def main(dir_path):
# img_all = os.listdir(dir_path)
# for f in img_all:
# start = time()
# embedding_result = get_embedding(os.path.join(dir_path, f))
# print time() - start
# print embedding_result
#
# main('./data')
def zscore(a, axis=0, ddof=0):
a = np.asanyarray(a)
mns = a.mean(axis=axis)
sstd = a.std(axis=axis, ddof=ddof)
if axis and mns.ndim < a.ndim:
res = (((a - np.expand_dims(mns, axis=axis)) /
np.expand_dims(sstd,axis=axis)))
else:
res = (a - mns) / sstd
return np.nan_to_num(res)
def is_catastrophe(self, obs):
X = np.expand_dims(obs, axis=0)
X = np.reshape(X, [X.shape[0], -1])
return self.classifier.predict(X)[0]
def load_features_episode(self, episode):
features = {}
observations = [frame.observation for frame in episode.frames if frame.has_action()]
features['observation'] = np.concatenate(
[np.expand_dims(observation, axis=0) for observation in observations], axis=0)
images = [frame.image for frame in episode.frames if frame.has_action()]
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in images], axis=0)
actions = [frame.get_proposed_action() for frame in episode.frames if frame.has_action()]
features['action'] = np.expand_dims(np.array(actions), axis=1)
features['index'] = np.array(
[i for i, frame in enumerate(episode.frames) if frame.has_action()])
return features
def load_features_incident_records(self, incident_records):
original_images = []
actions = []
for block_record_file in incident_records:
with open(block_record_file, 'rb') as f:
block_record = pickle.load(f)
original_images.append(block_record.obs)
actions.append(block_record.action)
features = {}
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in original_images], axis=0)
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, original_images
def process_image(image, hparams):
desired_shape = hparams.image_shape
if hparams.image_crop_region is not None:
image = image[hparams.image_crop_region[0][0]:hparams.image_crop_region[0][1],
hparams.image_crop_region[1][0]:hparams.image_crop_region[1][1]]
if not tuple(image.shape) == tuple(desired_shape):
image = cv2.resize(image, (desired_shape[1], desired_shape[0]))
assert tuple(image.shape) == tuple(desired_shape), "{}, {}".format(image.shape, desired_shape)
return np.expand_dims(image.astype(np.float32) / 256.0, axis=0)
def label(self, features, episode):
labels = np.array(
[self._has_label(frame) for frame in episode.frames if frame.has_action()])
actions = [
frame.get_action(self.action_type) for frame in episode.frames if frame.has_action()
]
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, labels
def load_features_episode(self, episode):
features = {}
observations = [frame.observation for frame in episode.frames if frame.has_action()]
features['observation'] = np.concatenate(
[np.expand_dims(observation, axis=0) for observation in observations], axis=0)
images = [frame.image for frame in episode.frames if frame.has_action()]
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in images], axis=0)
actions = [frame.get_proposed_action() for frame in episode.frames if frame.has_action()]
features['action'] = np.expand_dims(np.array(actions), axis=1)
features['index'] = np.array(
[i for i, frame in enumerate(episode.frames) if frame.has_action()])
return features
def load_features_incident_records(self, incident_records):
original_images = []
actions = []
for block_record_file in incident_records:
with open(block_record_file, 'rb') as f:
block_record = pickle.load(f)
original_images.append(block_record.obs)
actions.append(block_record.action)
features = {}
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in original_images], axis=0)
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, original_images
def process_image(image, hparams):
desired_shape = hparams.image_shape
if hparams.image_crop_region is not None:
image = image[hparams.image_crop_region[0][0]:hparams.image_crop_region[0][1],
hparams.image_crop_region[1][0]:hparams.image_crop_region[1][1]]
if not tuple(image.shape) == tuple(desired_shape):
image = cv2.resize(image, (desired_shape[1], desired_shape[0]))
assert tuple(image.shape) == tuple(desired_shape), "{}, {}".format(image.shape, desired_shape)
return np.expand_dims(image.astype(np.float32) / 256.0, axis=0)
def label(self, features, episode):
labels = np.array(
[self._has_label(frame) for frame in episode.frames if frame.has_action()])
actions = [
frame.get_action(self.action_type) for frame in episode.frames if frame.has_action()
]
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, labels
def is_catastrophe(self, obs):
X = np.expand_dims(obs, axis=0)
X = np.reshape(X, [X.shape[0], -1])
return self.classifier.predict(X)[0]
def load_features_incident_records(self, incident_records):
original_images = []
actions = []
for block_record_file in incident_records:
with open(block_record_file, 'rb') as f:
block_record = pickle.load(f)
original_images.append(block_record.obs)
actions.append(block_record.action)
features = {}
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in original_images], axis=0)
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, original_images
def process_image(image, hparams):
desired_shape = hparams.image_shape
if hparams.image_crop_region is not None:
image = image[hparams.image_crop_region[0][0]:hparams.image_crop_region[0][1],
hparams.image_crop_region[1][0]:hparams.image_crop_region[1][1]]
if not tuple(image.shape) == tuple(desired_shape):
image = cv2.resize(image, (desired_shape[1], desired_shape[0]))
assert tuple(image.shape) == tuple(desired_shape), "{}, {}".format(image.shape, desired_shape)
return np.expand_dims(image.astype(np.float32) / 256.0, axis=0)
def label(self, features, episode):
labels = np.array(
[self._has_label(frame) for frame in episode.frames if frame.has_action()])
actions = [
frame.get_action(self.action_type) for frame in episode.frames if frame.has_action()
]
features['action'] = np.expand_dims(np.array(actions), axis=1)
return features, labels
def is_catastrophe(self, obs):
X = np.expand_dims(obs, axis=0)
X = np.reshape(X, [X.shape[0], -1])
return self.classifier.predict(X)[0]
def load_features_episode(self, episode):
features = {}
observations = [frame.observation for frame in episode.frames if frame.has_action()]
features['observation'] = np.concatenate(
[np.expand_dims(observation, axis=0) for observation in observations], axis=0)
images = [frame.image for frame in episode.frames if frame.has_action()]
features['image'] = np.concatenate(
[process_image(image, self.hparams) for image in images], axis=0)
actions = [frame.get_proposed_action() for frame in episode.frames if frame.has_action()]
features['action'] = np.expand_dims(np.array(actions), axis=1)
features['index'] = np.array(
[i for i, frame in enumerate(episode.frames) if frame.has_action()])
return features
