code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def calculate_fid_given_paths(paths, batch_size, cuda, dims):
"""Calculates the FID of two paths"""
for p in paths:
if not os.path.exists(p):
raise RuntimeError('Invalid path: %s' % p)
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx])
if cuda:
... | Calculates the FID of two paths | calculate_fid_given_paths | python | OFA-Sys/OFA | run_scripts/image_gen/fid_score.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/fid_score.py | Apache-2.0 |
def inception_score(imgs, cuda=True, batch_size=32, resize=False, splits=1):
"""Computes the inception score of the generated images imgs
imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1]
cuda -- whether or not to run on GPU
batch_size -- batch size for feeding into Inception... | Computes the inception score of the generated images imgs
imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1]
cuda -- whether or not to run on GPU
batch_size -- batch size for feeding into Inception v3
splits -- number of splits
| inception_score | python | OFA-Sys/OFA | run_scripts/image_gen/inception_score.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/inception_score.py | Apache-2.0 |
def __init__(self,
output_blocks=[DEFAULT_BLOCK_INDEX],
resize_input=True,
normalize_input=True,
requires_grad=False):
"""Build pretrained InceptionV3
Parameters
----------
output_blocks : list of int
Indice... | Build pretrained InceptionV3
Parameters
----------
output_blocks : list of int
Indices of blocks to return features of. Possible values are:
- 0: corresponds to output of first max pooling
- 1: corresponds to output of second max pooling
... | __init__ | python | OFA-Sys/OFA | run_scripts/image_gen/eval_utils/inceptionV3.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/eval_utils/inceptionV3.py | Apache-2.0 |
def forward(self, inp):
"""Get Inception feature maps
Parameters
----------
inp : torch.autograd.Variable
Input tensor of shape Bx3xHxW. Values are expected to be in
range (0, 1)
Returns
-------
List of torch.autograd.Variable, correspond... | Get Inception feature maps
Parameters
----------
inp : torch.autograd.Variable
Input tensor of shape Bx3xHxW. Values are expected to be in
range (0, 1)
Returns
-------
List of torch.autograd.Variable, corresponding to the selected output
... | forward | python | OFA-Sys/OFA | run_scripts/image_gen/eval_utils/inceptionV3.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/eval_utils/inceptionV3.py | Apache-2.0 |
def insert(self, word):
"""
Inserts a word into the trie.
:type word: str
:rtype: void
"""
node = self.root
for chars in word:
child = node.data.get(chars)
if not child:
node.data[chars] = TrieNode()
node = node.... |
Inserts a word into the trie.
:type word: str
:rtype: void
| insert | python | OFA-Sys/OFA | utils/text2phone.py | https://github.com/OFA-Sys/OFA/blob/master/utils/text2phone.py | Apache-2.0 |
def search(self, word):
"""
Returns if the word is in the trie.
:type word: str
:rtype: bool
"""
node = self.root
for chars in word:
node = node.data.get(chars)
if not node:
return False
return node.is_word |
Returns if the word is in the trie.
:type word: str
:rtype: bool
| search | python | OFA-Sys/OFA | utils/text2phone.py | https://github.com/OFA-Sys/OFA/blob/master/utils/text2phone.py | Apache-2.0 |
def startsWith(self, prefix):
"""
Returns if there is any word in the trie that starts with the given prefix.
:type prefix: str
:rtype: bool
"""
node = self.root
for chars in prefix:
node = node.data.get(chars)
if not node:
... |
Returns if there is any word in the trie that starts with the given prefix.
:type prefix: str
:rtype: bool
| startsWith | python | OFA-Sys/OFA | utils/text2phone.py | https://github.com/OFA-Sys/OFA/blob/master/utils/text2phone.py | Apache-2.0 |
def get_start(self, prefix):
"""
Returns words started with prefix
:param prefix:
:return: words (list)
"""
def get_key(pre, pre_node):
word_list = []
if pre_node.is_word:
word_list.append(pre)
for x in pre_node.d... |
Returns words started with prefix
:param prefix:
:return: words (list)
| get_start | python | OFA-Sys/OFA | utils/text2phone.py | https://github.com/OFA-Sys/OFA/blob/master/utils/text2phone.py | Apache-2.0 |
def autocontrast_func(img, cutoff=0):
'''
same output as PIL.ImageOps.autocontrast
'''
n_bins = 256
def tune_channel(ch):
n = ch.size
cut = cutoff * n // 100
if cut == 0:
high, low = ch.max(), ch.min()
else:
hist = cv2.calcHist([ch], [0], ... |
same output as PIL.ImageOps.autocontrast
| autocontrast_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def equalize_func(img):
'''
same output as PIL.ImageOps.equalize
PIL's implementation is different from cv2.equalize
'''
n_bins = 256
def tune_channel(ch):
hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
non_zero_hist = hist[hist != 0].reshape(-1)
ste... |
same output as PIL.ImageOps.equalize
PIL's implementation is different from cv2.equalize
| equalize_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def rotate_func(img, degree, fill=(0, 0, 0)):
'''
like PIL, rotate by degree, not radians
'''
H, W = img.shape[0], img.shape[1]
center = W / 2, H / 2
M = cv2.getRotationMatrix2D(center, degree, 1)
out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
return out |
like PIL, rotate by degree, not radians
| rotate_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def solarize_func(img, thresh=128):
'''
same output as PIL.ImageOps.posterize
'''
table = np.array([el if el < thresh else 255 - el for el in range(256)])
table = table.clip(0, 255).astype(np.uint8)
out = table[img]
return out |
same output as PIL.ImageOps.posterize
| solarize_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def color_func(img, factor):
'''
same output as PIL.ImageEnhance.Color
'''
## implementation according to PIL definition, quite slow
# degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
# out = blend(degenerate, img, factor)
# M = (
# np.eye(3) * factor
... |
same output as PIL.ImageEnhance.Color
| color_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def contrast_func(img, factor):
"""
same output as PIL.ImageEnhance.Contrast
"""
mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
table = np.array([(
el - mean) * factor + mean
for el in range(256)
]).clip(0, 255).astype(np.uint8)
out = table[img... |
same output as PIL.ImageEnhance.Contrast
| contrast_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def sharpness_func(img, factor):
'''
The differences the this result and PIL are all on the 4 boundaries, the center
areas are same
'''
kernel = np.ones((3, 3), dtype=np.float32)
kernel[1][1] = 5
kernel /= 13
degenerate = cv2.filter2D(img, -1, kernel)
if factor == 0.0:
out = ... |
The differences the this result and PIL are all on the 4 boundaries, the center
areas are same
| sharpness_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def translate_x_func(img, offset, fill=(0, 0, 0)):
'''
same output as PIL.Image.transform
'''
H, W = img.shape[0], img.shape[1]
M = np.float32([[1, 0, -offset], [0, 1, 0]])
out = cv2.warpAffine(img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR).astype(np.uint8)
return out |
same output as PIL.Image.transform
| translate_x_func | python | OFA-Sys/OFA | utils/vision_helper.py | https://github.com/OFA-Sys/OFA/blob/master/utils/vision_helper.py | Apache-2.0 |
def __init__(self, n=4, df="corpus"):
"""
Initialize the CIDEr scoring function
: param n (int): n-gram size
: param df (string): specifies where to get the IDF values from
takes values 'corpus', 'coco-train'
: return: None
"""
# set cider to s... |
Initialize the CIDEr scoring function
: param n (int): n-gram size
: param df (string): specifies where to get the IDF values from
takes values 'corpus', 'coco-train'
: return: None
| __init__ | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider.py | Apache-2.0 |
def compute_score(self, gts, res):
"""
Main function to compute CIDEr score
: param gts (dict) : {image:tokenized reference sentence}
: param res (dict) : {image:tokenized candidate sentence}
: return: cider (float) : computed CIDEr score for the corpus
"""
# c... |
Main function to compute CIDEr score
: param gts (dict) : {image:tokenized reference sentence}
: param res (dict) : {image:tokenized candidate sentence}
: return: cider (float) : computed CIDEr score for the corpus
| compute_score | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider.py | Apache-2.0 |
def precook(s, n=4, out=False):
"""
Takes a string as input and returns an object that can be given to
either cook_refs or cook_test. This is optional: cook_refs and cook_test
can take string arguments as well.
:param s: string : sentence to be converted into ngrams
:param n: int : number of ... |
Takes a string as input and returns an object that can be given to
either cook_refs or cook_test. This is optional: cook_refs and cook_test
can take string arguments as well.
:param s: string : sentence to be converted into ngrams
:param n: int : number of ngrams for which representation is calc... | precook | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def cook_append(self, test, refs):
'''called by constructor and __iadd__ to avoid creating new instances.'''
if refs is not None:
self.crefs.append(cook_refs(refs))
if test is not None:
self.ctest.append(cook_test(test)) ## N.B.: -1
else:
... | called by constructor and __iadd__ to avoid creating new instances. | cook_append | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def __iadd__(self, other):
'''add an instance (e.g., from another sentence).'''
if type(other) is tuple:
## avoid creating new CiderScorer instances
self.cook_append(other[0], other[1])
else:
self.ctest.extend(other.ctest)
self.crefs.extend(other.... | add an instance (e.g., from another sentence). | __iadd__ | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def compute_doc_freq(self):
'''
Compute term frequency for reference data.
This will be used to compute idf (inverse document frequency later)
The term frequency is stored in the object
:return: None
'''
for refs in self.crefs:
# refs, k ref captions o... |
Compute term frequency for reference data.
This will be used to compute idf (inverse document frequency later)
The term frequency is stored in the object
:return: None
| compute_doc_freq | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def counts2vec(cnts):
"""
Function maps counts of ngram to vector of tfidf weights.
The function returns vec, an array of dictionary that store mapping of n-gram and tf-idf weights.
The n-th entry of array denotes length of n-grams.
:param cnts:
:r... |
Function maps counts of ngram to vector of tfidf weights.
The function returns vec, an array of dictionary that store mapping of n-gram and tf-idf weights.
The n-th entry of array denotes length of n-grams.
:param cnts:
:return: vec (array of dict), norm (arr... | counts2vec | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def sim(vec_hyp, vec_ref, norm_hyp, norm_ref, length_hyp, length_ref):
'''
Compute the cosine similarity of two vectors.
:param vec_hyp: array of dictionary for vector corresponding to hypothesis
:param vec_ref: array of dictionary for vector corresponding to reference
... |
Compute the cosine similarity of two vectors.
:param vec_hyp: array of dictionary for vector corresponding to hypothesis
:param vec_ref: array of dictionary for vector corresponding to reference
:param norm_hyp: array of float for vector corresponding to hypothesis
... | sim | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/cider/cider_scorer.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/cider/cider_scorer.py | Apache-2.0 |
def compute_score(self, gts, res):
"""
Main function to compute CIDEr score
:param hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence>
ref_for_image (dict) : dictionary with key <image> and value <tokenized reference sentenc... |
Main function to compute CIDEr score
:param hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence>
ref_for_image (dict) : dictionary with key <image> and value <tokenized reference sentence>
:return: cider (float) : computed C... | compute_score | python | OFA-Sys/OFA | utils/cider/pyciderevalcap/ciderD/ciderD.py | https://github.com/OFA-Sys/OFA/blob/master/utils/cider/pyciderevalcap/ciderD/ciderD.py | Apache-2.0 |
def datagen(filter_files, time_len=1, batch_size=256, ignore_goods=False):
"""
Parameters:
-----------
leads : bool, should we use all x, y and speed radar leads? default is false, uses only x
"""
global first
assert time_len > 0
filter_names = sorted(filter_files)
logger.info("Loading {} hdf5 bucket... |
Parameters:
-----------
leads : bool, should we use all x, y and speed radar leads? default is false, uses only x
| datagen | python | commaai/research | dask_generator.py | https://github.com/commaai/research/blob/master/dask_generator.py | BSD-3-Clause |
def send_arrays(socket, arrays, stop=False):
"""Send NumPy arrays using the buffer interface and some metadata.
Parameters
----------
socket : :class:`zmq.Socket`
The socket to send data over.
arrays : list
A list of :class:`numpy.ndarray` to transfer.
stop : bool, optional
Instead of sending a serie... | Send NumPy arrays using the buffer interface and some metadata.
Parameters
----------
socket : :class:`zmq.Socket`
The socket to send data over.
arrays : list
A list of :class:`numpy.ndarray` to transfer.
stop : bool, optional
Instead of sending a series of NumPy arrays, send a JSON object
with a sin... | send_arrays | python | commaai/research | server.py | https://github.com/commaai/research/blob/master/server.py | BSD-3-Clause |
def recv_arrays(socket):
"""Receive a list of NumPy arrays.
Parameters
----------
socket : :class:`zmq.Socket`
The socket to receive the arrays on.
Returns
-------
list
A list of :class:`numpy.ndarray` objects.
Raises
------
StopIteration
If the first JSON object received contains the key `... | Receive a list of NumPy arrays.
Parameters
----------
socket : :class:`zmq.Socket`
The socket to receive the arrays on.
Returns
-------
list
A list of :class:`numpy.ndarray` objects.
Raises
------
StopIteration
If the first JSON object received contains the key `stop`,
signifying that the s... | recv_arrays | python | commaai/research | server.py | https://github.com/commaai/research/blob/master/server.py | BSD-3-Clause |
def client_generator(port=5557, host="localhost", hwm=20):
"""Generator in client side should extend this generator
Parameters
----------
port : int
hwm : int, optional
The `ZeroMQ high-water mark (HWM)
<http://zguide.zeromq.org/page:all#High-Water-Marks>`_ on the
sending socket. Increasing this incre... | Generator in client side should extend this generator
Parameters
----------
port : int
hwm : int, optional
The `ZeroMQ high-water mark (HWM)
<http://zguide.zeromq.org/page:all#High-Water-Marks>`_ on the
sending socket. Increasing this increases the buffer, which can be
useful if your data preprocessin... | client_generator | python | commaai/research | server.py | https://github.com/commaai/research/blob/master/server.py | BSD-3-Clause |
def start_server(data_stream, port=5557, hwm=20):
"""Start a data processing server.
This command starts a server in the current process that performs the
actual data processing (by retrieving data from the given data stream).
It also starts a second process, the broker, which mediates between the
server and... | Start a data processing server.
This command starts a server in the current process that performs the
actual data processing (by retrieving data from the given data stream).
It also starts a second process, the broker, which mediates between the
server and the client. The broker also keeps a buffer of batches ... | start_server | python | commaai/research | server.py | https://github.com/commaai/research/blob/master/server.py | BSD-3-Clause |
def train_model(name, g_train, d_train, sampler, generator, samples_per_epoch, nb_epoch,
z_dim=100, verbose=1, callbacks=[],
validation_data=None, nb_val_samples=None,
saver=None):
"""
Main training loop.
modified from Keras fit_generator
"""
self = {}... |
Main training loop.
modified from Keras fit_generator
| train_model | python | commaai/research | train_generative_model.py | https://github.com/commaai/research/blob/master/train_generative_model.py | BSD-3-Clause |
def dec_conv2d_block(x, name, f, k, bias=True, bn=False, stddev=0.02, data_format="NCHW", activation_fn=tf.nn.elu):
"""Decoding 2D conv block: chain 2 convolutions with"""
with tf.variable_scope(name):
if data_format == "NHWC":
n_in = x.get_shape()[-1]
elif data_format == "NCHW":
... | Decoding 2D conv block: chain 2 convolutions with | dec_conv2d_block | python | tdeboissiere/DeepLearningImplementations | BEGAN/src/utils/layers.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/BEGAN/src/utils/layers.py | MIT |
def upsampleNN(x, name, s, data_format="NCHW"):
""" Upsample image data by a factor s with NN interpolation"""
with tf.variable_scope(name):
# Transpose before resize
if data_format == "NCHW":
x = tf.transpose(x, [0, 2, 3, 1])
# Resize
x_shape = x.get_shape().as_lis... | Upsample image data by a factor s with NN interpolation | upsampleNN | python | tdeboissiere/DeepLearningImplementations | BEGAN/src/utils/layers.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/BEGAN/src/utils/layers.py | MIT |
def enc_conv2d_block(x, name, f, k, bias=True, bn=False, stddev=0.02, data_format="NCHW", activation_fn=tf.nn.elu, downsampling=True):
"""Encoding 2D conv block: chain 2 convolutions with x2 downsampling by default"""
with tf.variable_scope(name):
if data_format == "NHWC":
n_in = x.get_shap... | Encoding 2D conv block: chain 2 convolutions with x2 downsampling by default | enc_conv2d_block | python | tdeboissiere/DeepLearningImplementations | BEGAN/src/utils/layers.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/BEGAN/src/utils/layers.py | MIT |
def format_image(img_path, size):
"""
Load img with opencv and reshape
"""
img_color = cv2.imread(img_path)
img_color = img_color[:, :, ::-1]
img_black = cv2.imread(img_path, 0)
img_color = cv2.resize(img_color, (size, size), interpolation=cv2.INTER_AREA)
img_black = cv2.resize(img_bla... |
Load img with opencv and reshape
| format_image | python | tdeboissiere/DeepLearningImplementations | Colorful/src/data/make_dataset.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/data/make_dataset.py | MIT |
def build_HDF5(size=64):
"""
Gather the data in a single HDF5 file.
"""
# Read evaluation file, build it if it does not exist
# In evaluation status, "0" represents training image, "1" represents
# validation image, "2" represents testing image;
d_partition = {}
with open(os.path.join(r... |
Gather the data in a single HDF5 file.
| build_HDF5 | python | tdeboissiere/DeepLearningImplementations | Colorful/src/data/make_dataset.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/data/make_dataset.py | MIT |
def check_HDF5(size=64):
"""
Plot images with landmarks to check the processing
"""
# Get hdf5 file
hdf5_file = os.path.join(data_dir, "CelebA_%s_data.h5" % size)
with h5py.File(hdf5_file, "r") as hf:
data_color = hf["training_color_data"]
data_lab = hf["training_lab_data"]
... |
Plot images with landmarks to check the processing
| check_HDF5 | python | tdeboissiere/DeepLearningImplementations | Colorful/src/data/make_dataset.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/data/make_dataset.py | MIT |
def train(**kwargs):
"""
Train model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
data_file = kwargs["data... |
Train model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
| train | python | tdeboissiere/DeepLearningImplementations | Colorful/src/model/train_colorful.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/model/train_colorful.py | MIT |
def gen_batch(self, nn_finder, nb_q, prior_factor):
""" Use multiprocessing to generate batches in parallel. """
try:
queue = multiprocessing.Queue(maxsize=self.num_cached)
# define producer (putting items into queue)
def producer():
try:
... | Use multiprocessing to generate batches in parallel. | gen_batch | python | tdeboissiere/DeepLearningImplementations | Colorful/src/utils/batch_utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/utils/batch_utils.py | MIT |
def gen_batch_in_memory(self, X, nn_finder, nb_q, prior_factor):
"""Generate batch, assuming X is loaded in memory in the main program"""
while True:
# Select idx at random for the batch
idx = np.random.choice(X.shape[0], self.batch_size, replace=False)
X_batch_colo... | Generate batch, assuming X is loaded in memory in the main program | gen_batch_in_memory | python | tdeboissiere/DeepLearningImplementations | Colorful/src/utils/batch_utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/utils/batch_utils.py | MIT |
def remove_files(files):
"""
Remove files from disk
args: files (str or list) remove all files in 'files'
"""
if isinstance(files, (list, tuple)):
for f in files:
if os.path.isfile(os.path.expanduser(f)):
os.remove(f)
elif isinstance(files, str):
if ... |
Remove files from disk
args: files (str or list) remove all files in 'files'
| remove_files | python | tdeboissiere/DeepLearningImplementations | Colorful/src/utils/general_utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/utils/general_utils.py | MIT |
def create_dir(dirs):
"""
Create directory
args: dirs (str or list) create all dirs in 'dirs'
"""
if isinstance(dirs, (list, tuple)):
for d in dirs:
if not os.path.exists(os.path.expanduser(d)):
os.makedirs(d)
elif isinstance(dirs, str):
if not os.pa... |
Create directory
args: dirs (str or list) create all dirs in 'dirs'
| create_dir | python | tdeboissiere/DeepLearningImplementations | Colorful/src/utils/general_utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Colorful/src/utils/general_utils.py | MIT |
def __init__(self, model):
"""Creates a DeconvNet instance
:param model: Keras model
:returns: deconvnet class
:rtype: deconvnet
"""
self.model = model
list_layers = self.model.layers
self.lnames = [l.name for l in list_layers]
assert len(self.ln... | Creates a DeconvNet instance
:param model: Keras model
:returns: deconvnet class
:rtype: deconvnet
| __init__ | python | tdeboissiere/DeepLearningImplementations | DeconvNet/KerasDeconv.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/KerasDeconv.py | MIT |
def get_layers(self):
"""Returns the layers of the network
:returns: returns the layers of the network
:rtype: list
"""
list_layers = self.model.layers
list_layers_name = [l.name for l in list_layers]
return list_layers_name | Returns the layers of the network
:returns: returns the layers of the network
:rtype: list
| get_layers | python | tdeboissiere/DeepLearningImplementations | DeconvNet/KerasDeconv.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/KerasDeconv.py | MIT |
def get_deconv(self, X, target_layer, feat_map=None):
""" Starts the deconvolution process
:param X: input image: the data to be deconv-ed
:param target_layer: layer in the model up to which
we want to be deconvolved
:param feat_map: # @TODO: update
:returns: deconvolved... | Starts the deconvolution process
:param X: input image: the data to be deconv-ed
:param target_layer: layer in the model up to which
we want to be deconvolved
:param feat_map: # @TODO: update
:returns: deconvolved image
:rtype: numpy array
| get_deconv | python | tdeboissiere/DeepLearningImplementations | DeconvNet/KerasDeconv.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/KerasDeconv.py | MIT |
def find_top9_mean_act(data, Dec, target_layer, feat_map, batch_size=32):
"""
Find images with highest mean activation
args: data (numpy array) the image data
shape : (n_samples, n_channels, img_dim1, img_dim2)
Dec (DeconvNet) instance of the DeconvNet class
target_layer... |
Find images with highest mean activation
args: data (numpy array) the image data
shape : (n_samples, n_channels, img_dim1, img_dim2)
Dec (DeconvNet) instance of the DeconvNet class
target_layer (str) Layer name we want to visualise
feat_map (int) index of the fi... | find_top9_mean_act | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def get_deconv_images(d_act_path, d_deconv_path, data, Dec):
"""
Deconvolve images specified in d_act. Then pickle these images
for future use
args: d_act_path (str) path to the dict that for each target layer
and for a selection of feature maps, holds the index
... |
Deconvolve images specified in d_act. Then pickle these images
for future use
args: d_act_path (str) path to the dict that for each target layer
and for a selection of feature maps, holds the index
of the top9 images activating said feature maps
... | get_deconv_images | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def format_array(arr):
"""
Utility to format array for tiled plot
args: arr (numpy array)
shape : (n_samples, n_channels, img_dim1, img_dim2)
"""
n_channels = arr.shape[1]
len_arr = arr.shape[0]
assert (n_channels == 1 or n_channels == 3), "n_channels should be 1 (Greyscale) or ... |
Utility to format array for tiled plot
args: arr (numpy array)
shape : (n_samples, n_channels, img_dim1, img_dim2)
| format_array | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def scale_to_unit_interval(ndar, eps=1e-8):
""" Scales all values in the ndarray ndar to be between 0 and 1 """
ndar = ndar.copy()
ndar -= ndar.min()
ndar *= 1.0 / (ndar.max() + eps)
return ndar | Scales all values in the ndarray ndar to be between 0 and 1 | scale_to_unit_interval | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),
scale_rows_to_unit_interval=True,
output_pixel_vals=True):
"""
Transform an array with one flattened image per row, into an array in
which images are reshaped and layed out like tiles on a flo... |
Transform an array with one flattened image per row, into an array in
which images are reshaped and layed out like tiles on a floor.
This function is useful for visualizing datasets whose rows are images,
and also columns of matrices for transforming those rows
(such as the first layer of a neural... | tile_raster_images | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def plot_max_activation(d_act_path, d_deconv_path, data, target_layer, save=False):
"""
Plot original images (or cropped versions) and the deconvolution result
for images specified in d_act_path / d_dedeconv_path
args: d_act_path (str) path to the dict that for each target layer
... |
Plot original images (or cropped versions) and the deconvolution result
for images specified in d_act_path / d_dedeconv_path
args: d_act_path (str) path to the dict that for each target layer
and for a selection of feature maps, holds the index
of the top9 im... | plot_max_activation | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def plot_deconv(img_index, data, Dec, target_layer, feat_map, save=False):
"""
Plot original images (or cropped versions) and the deconvolution result
for images specified in img_index, for the target layer and feat_map
specified in the arguments
args: img_index (list/arr) array or list of index. ... |
Plot original images (or cropped versions) and the deconvolution result
for images specified in img_index, for the target layer and feat_map
specified in the arguments
args: img_index (list/arr) array or list of index. These are the indices
of the images we want to plo... | plot_deconv | python | tdeboissiere/DeepLearningImplementations | DeconvNet/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/utils.py | MIT |
def VGG_16(weights_path=None):
"""
VGG Model Keras specification
args: weights_path (str) trained weights file path
returns model (Keras model)
"""
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(Conv2D(64, (3, 3), activation='relu'))
mod... |
VGG Model Keras specification
args: weights_path (str) trained weights file path
returns model (Keras model)
| VGG_16 | python | tdeboissiere/DeepLearningImplementations | DeconvNet/VGG_deconv.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/VGG_deconv.py | MIT |
def load_model(weights_path):
"""
Load and compile VGG model
args: weights_path (str) trained weights file path
returns model (Keras model)
"""
model = VGG_16(weights_path)
model.compile(optimizer="sgd", loss='categorical_crossentropy')
return model |
Load and compile VGG model
args: weights_path (str) trained weights file path
returns model (Keras model)
| load_model | python | tdeboissiere/DeepLearningImplementations | DeconvNet/VGG_deconv.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DeconvNet/VGG_deconv.py | MIT |
def conv_factory(x, concat_axis, nb_filter,
dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 3x3Conv2D, optional dropout
:param x: Input keras network
:param concat_axis: int -- index of contatenate axis
:param nb_filter: int -- number of filters
:param dropout_rate:... | Apply BatchNorm, Relu 3x3Conv2D, optional dropout
:param x: Input keras network
:param concat_axis: int -- index of contatenate axis
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: keras network with ... | conv_factory | python | tdeboissiere/DeepLearningImplementations | DenseNet/densenet.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.py | MIT |
def transition(x, concat_axis, nb_filter,
dropout_rate=None, weight_decay=1E-4):
"""Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D
:param x: keras model
:param concat_axis: int -- index of contatenate axis
:param nb_filter: int -- number of filters
:param dropout_... | Apply BatchNorm, Relu 1x1Conv2D, optional dropout and Maxpooling2D
:param x: keras model
:param concat_axis: int -- index of contatenate axis
:param nb_filter: int -- number of filters
:param dropout_rate: int -- dropout rate
:param weight_decay: int -- weight decay factor
:returns: model
... | transition | python | tdeboissiere/DeepLearningImplementations | DenseNet/densenet.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.py | MIT |
def denseblock(x, concat_axis, nb_layers, nb_filter, growth_rate,
dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each
conv_factory is fed to subsequent ones
:param x: keras model
:param concat_axis: int -- index of contatenate axis
:param nb_layer... | Build a denseblock where the output of each
conv_factory is fed to subsequent ones
:param x: keras model
:param concat_axis: int -- index of contatenate axis
:param nb_layers: int -- the number of layers of conv_
factory to append to the model.
:param nb_filter: int -- numb... | denseblock | python | tdeboissiere/DeepLearningImplementations | DenseNet/densenet.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.py | MIT |
def denseblock_altern(x, concat_axis, nb_layers, nb_filter, growth_rate,
dropout_rate=None, weight_decay=1E-4):
"""Build a denseblock where the output of each conv_factory
is fed to subsequent ones. (Alternative of a above)
:param x: keras model
:param concat_axis: int -- index... | Build a denseblock where the output of each conv_factory
is fed to subsequent ones. (Alternative of a above)
:param x: keras model
:param concat_axis: int -- index of contatenate axis
:param nb_layers: int -- the number of layers of conv_
factory to append to the model.
:pa... | denseblock_altern | python | tdeboissiere/DeepLearningImplementations | DenseNet/densenet.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.py | MIT |
def DenseNet(nb_classes, img_dim, depth, nb_dense_block, growth_rate,
nb_filter, dropout_rate=None, weight_decay=1E-4):
""" Build the DenseNet model
:param nb_classes: int -- number of classes
:param img_dim: tuple -- (channels, rows, columns)
:param depth: int -- how many layers
:para... | Build the DenseNet model
:param nb_classes: int -- number of classes
:param img_dim: tuple -- (channels, rows, columns)
:param depth: int -- how many layers
:param nb_dense_block: int -- number of dense blocks to add to end
:param growth_rate: int -- number of filters to add
:param nb_filter: ... | DenseNet | python | tdeboissiere/DeepLearningImplementations | DenseNet/densenet.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.py | MIT |
def run_cifar10(batch_size,
nb_epoch,
depth,
nb_dense_block,
nb_filter,
growth_rate,
dropout_rate,
learning_rate,
weight_decay,
plot_architecture):
""" Run CIFAR10 experime... | Run CIFAR10 experiments
:param batch_size: int -- batch size
:param nb_epoch: int -- number of training epochs
:param depth: int -- network depth
:param nb_dense_block: int -- number of dense blocks
:param nb_filter: int -- initial number of conv filter
:param growth_rate: int -- number of new... | run_cifar10 | python | tdeboissiere/DeepLearningImplementations | DenseNet/run_cifar10.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/run_cifar10.py | MIT |
def train(model_name, **kwargs):
"""
Train model
args: model_name (str, keras model name)
**kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
nb_epoch = kwargs["nb_epoch"]
dataset = kwargs["d... |
Train model
args: model_name (str, keras model name)
**kwargs (dict) keyword arguments that specify the model hyperparameters
| train | python | tdeboissiere/DeepLearningImplementations | Eve/train.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Eve/train.py | MIT |
def generator_upsampling(noise_dim, img_dim, bn_mode, model_name="generator_upsampling", dset="mnist"):
"""
Generator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net ... |
Generator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
| generator_upsampling | python | tdeboissiere/DeepLearningImplementations | GAN/src/model/models_GAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/GAN/src/model/models_GAN.py | MIT |
def generator_deconv(noise_dim, img_dim, bn_mode, batch_size, model_name="generator_deconv", dset="mnist"):
"""
Generator model of the DCGAN
args : nb_classes (int) number of classes
img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weigh... |
Generator model of the DCGAN
args : nb_classes (int) number of classes
img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
| generator_deconv | python | tdeboissiere/DeepLearningImplementations | GAN/src/model/models_GAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/GAN/src/model/models_GAN.py | MIT |
def DCGAN_discriminator(noise_dim, img_dim, bn_mode, model_name="DCGAN_discriminator", dset="mnist", use_mbd=False):
"""
Discriminator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN... |
Discriminator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
| DCGAN_discriminator | python | tdeboissiere/DeepLearningImplementations | GAN/src/model/models_GAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/GAN/src/model/models_GAN.py | MIT |
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]... |
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
| train | python | tdeboissiere/DeepLearningImplementations | GAN/src/model/train_GAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/GAN/src/model/train_GAN.py | MIT |
def save_image(data, data_format, e):
"""Saves a picture showing the current progress of the model"""
X_G, X_real = data
Xg = X_G[:8]
Xr = X_real[:8]
if data_format == "NHWC":
X = np.concatenate((Xg, Xr), axis=0)
list_rows = []
for i in range(int(X.shape[0] / 4)):
... | Saves a picture showing the current progress of the model | save_image | python | tdeboissiere/DeepLearningImplementations | GAN_tf/src/utils/visualization_utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/GAN_tf/src/utils/visualization_utils.py | MIT |
def cdgmm(A, B, jit=True, inplace=False):
"""This function uses the C-wrapper to use cuBLAS.
"""
A, B = A.contiguous(), B.contiguous()
if A.size()[-3:] != B.size():
raise RuntimeError('The filters are not compatible for multiplication!')
if not iscomplex(A) or not iscomplex(B):
... | This function uses the C-wrapper to use cuBLAS.
| cdgmm | python | tdeboissiere/DeepLearningImplementations | ScatteringTransform/test/utils_pytorch.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/ScatteringTransform/test/utils_pytorch.py | MIT |
def alpha_dropout(input, p=0.5, training=False):
"""Applies alpha dropout to the input.
See :class:`~torch.nn.AlphaDropout` for details.
Args:
p (float, optional): the drop probability
training (bool, optional): switch between training and evaluation mode
"""
if p < 0 or p > 1:
... | Applies alpha dropout to the input.
See :class:`~torch.nn.AlphaDropout` for details.
Args:
p (float, optional): the drop probability
training (bool, optional): switch between training and evaluation mode
| alpha_dropout | python | tdeboissiere/DeepLearningImplementations | SELU/models.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/SELU/models.py | MIT |
def train(args):
"""Train NN for eye tracking regression
Args:
args: CLI arguments
"""
# Setup experiment ID
exp_ID = "%s_depth_%s_opt_%s_drop_%s_bn_%s" % (args.model,
args.n_inner_layers + 2,
... | Train NN for eye tracking regression
Args:
args: CLI arguments
| train | python | tdeboissiere/DeepLearningImplementations | SELU/train.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/SELU/train.py | MIT |
def standard_train(args, input_var, network, tang_output):
"""Train a network normally, output the network, a function to make predictions and training losses"""
# Create data
X = utils.create_dataset(args.npts)
# Create a list of batches (a list of batch idxs splitting X in batches of size batch_size... | Train a network normally, output the network, a function to make predictions and training losses | standard_train | python | tdeboissiere/DeepLearningImplementations | Sobolev/sobolev_training.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Sobolev/sobolev_training.py | MIT |
def sobolev_train(args, input_var, network, tang_output):
"""Train a network with Sobolev, output the network, a function to make predictions and training losses"""
# Create data
X = utils.create_dataset(args.npts)
# Create a list of batches (a list of batch idxs splitting X in batches of size batch_s... | Train a network with Sobolev, output the network, a function to make predictions and training losses | sobolev_train | python | tdeboissiere/DeepLearningImplementations | Sobolev/sobolev_training.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Sobolev/sobolev_training.py | MIT |
def get_list_batches(npts, batch_size):
"""Create batches (i.e a list of index) such that an array of size npts
is split in batches of size batch_size"""
num_elem = npts
num_batches = num_elem / batch_size
# list_batches is a list of array. Each array contains the indeces of the batch elements.
... | Create batches (i.e a list of index) such that an array of size npts
is split in batches of size batch_size | get_list_batches | python | tdeboissiere/DeepLearningImplementations | Sobolev/utils.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/Sobolev/utils.py | MIT |
def generator_toy(noise_dim, model_name="generator_toy"):
"""
Simple MLP generator for the MoG unrolled GAN toy experiment
"""
gen_input = Input(shape=noise_dim, name="generator_input")
x = Dense(128)(gen_input)
x = Activation("tanh")(x)
x = Dense(128)(x)
x = Activation("tanh")(x)
... |
Simple MLP generator for the MoG unrolled GAN toy experiment
| generator_toy | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def discriminator_toy(model_name="discriminator_toy"):
"""
Simple MLP discriminator for the MoG unrolled GAN toy experiment
"""
disc_input = Input(shape=(2,), name="discriminator_input")
x = Dense(128)(disc_input)
x = Activation("tanh")(x)
x = Dense(128)(x)
x = Activation("tanh")(x)
... |
Simple MLP discriminator for the MoG unrolled GAN toy experiment
| discriminator_toy | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def GAN_toy(generator, discriminator, noise_dim):
"""
Simple GAN genrator + discriminator for the MoG unrolled GAN toy experiment
"""
gen_input = Input(shape=noise_dim, name="noise_input")
generated_sample = generator(gen_input)
GAN_output = discriminator(generated_sample)
GAN_toy = Model(... |
Simple GAN genrator + discriminator for the MoG unrolled GAN toy experiment
| GAN_toy | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def generator_upsampling(noise_dim, img_dim, bn_mode, model_name="generator_upsampling", dset="mnist"):
"""DCGAN generator based on Upsampling and Conv2D
Args:
noise_dim: Dimension of the noise input
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
model_name... | DCGAN generator based on Upsampling and Conv2D
Args:
noise_dim: Dimension of the noise input
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
model_name: model name (default: {"generator_upsampling"})
dset: dataset (default: {"mnist"})
Returns:
... | generator_upsampling | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def generator_deconv(noise_dim, img_dim, bn_mode, batch_size, model_name="generator_deconv", dset="mnist"):
"""DCGAN generator based on Deconv2D
Args:
noise_dim: Dimension of the noise input
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
batch_size: needed ... | DCGAN generator based on Deconv2D
Args:
noise_dim: Dimension of the noise input
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
batch_size: needed to reshape after the deconv2D
model_name: model name (default: {"generator_deconv"})
dset: dataset ... | generator_deconv | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def discriminator(img_dim, bn_mode, model_name="discriminator"):
"""DCGAN discriminator
Args:
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
model_name: model name (default: {"generator_deconv"})
Returns:
keras model
"""
if K.image_dim_orderin... | DCGAN discriminator
Args:
img_dim: dimension of the image output
bn_mode: keras batchnorm mode
model_name: model name (default: {"generator_deconv"})
Returns:
keras model
| discriminator | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def DCGAN(generator, discriminator, noise_dim, img_dim):
"""DCGAN generator + discriminator model
Args:
generator: keras generator model
discriminator: keras discriminator model
noise_dim: generator input noise dimension
img_dim: real image data dimension
Returns:
k... | DCGAN generator + discriminator model
Args:
generator: keras generator model
discriminator: keras discriminator model
noise_dim: generator input noise dimension
img_dim: real image data dimension
Returns:
keras model
| DCGAN | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/models_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/models_WGAN.py | MIT |
def train(**kwargs):
"""
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
generator = kwargs["generator"]
dset = kwargs["dset"]
img_dim = kwargs["img_dim"]
nb_epoch = kwargs["nb_epoch"]
batch... |
Train standard DCGAN model
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
| train | python | tdeboissiere/DeepLearningImplementations | WassersteinGAN/src/model/train_WGAN.py | https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/WassersteinGAN/src/model/train_WGAN.py | MIT |
def infer(self, model_input, frame=None):
"""Schedule current model input to infer, return last result"""
next_req_id = next(self._req_ids)
self.model.async_infer(model_input, next_req_id)
last_frame = self._frames[0] if self._frames else frame
self._frames.append(frame)
... | Schedule current model input to infer, return last result | infer | python | openvinotoolkit/open_model_zoo | demos/action_recognition_demo/python/action_recognition_demo/models.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/action_recognition_demo/python/action_recognition_demo/models.py | Apache-2.0 |
def fill_area(image, bottom_left, top_right, color=(0, 0, 0), alpha=1.):
"""Fills area with the specified color"""
xmin, ymax = bottom_left
xmax, ymin = top_right
image[ymin:ymax, xmin:xmax, :] = image[ymin:ymax, xmin:xmax, :] * (1 - alpha) + np.asarray(color) * alpha
return image | Fills area with the specified color | fill_area | python | openvinotoolkit/open_model_zoo | demos/action_recognition_demo/python/action_recognition_demo/result_renderer.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/action_recognition_demo/python/action_recognition_demo/result_renderer.py | Apache-2.0 |
def decode_output(probs, labels, top_k=None, label_postprocessing=None):
"""Decodes top probabilities into corresponding label names"""
top_ind = np.argsort(probs)[::-1][:top_k]
if label_postprocessing:
for k in range(top_k):
label_postprocessing[k].update(top_ind[k])
top_ind =... | Decodes top probabilities into corresponding label names | decode_output | python | openvinotoolkit/open_model_zoo | demos/action_recognition_demo/python/action_recognition_demo/result_renderer.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/action_recognition_demo/python/action_recognition_demo/result_renderer.py | Apache-2.0 |
def softmax(x, axis=None):
"""Normalizes logits to get confidence values along specified axis"""
exp = np.exp(x)
return exp / np.sum(exp, axis=axis) | Normalizes logits to get confidence values along specified axis | softmax | python | openvinotoolkit/open_model_zoo | demos/action_recognition_demo/python/action_recognition_demo/steps.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/action_recognition_demo/python/action_recognition_demo/steps.py | Apache-2.0 |
def __init__(self):
'''
An abstract Model Adapter constructor.
Reads the model from disk or other place.
''' |
An abstract Model Adapter constructor.
Reads the model from disk or other place.
| __init__ | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def load_model(self):
'''
Loads the model on the device.
''' |
Loads the model on the device.
| load_model | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def get_input_layers(self):
'''
Gets the names of model inputs and for each one creates the Metadata structure,
which contains the information about the input shape, layout, precision
in OpenVINO format, meta (optional)
Returns:
- the dict containing Metadata f... |
Gets the names of model inputs and for each one creates the Metadata structure,
which contains the information about the input shape, layout, precision
in OpenVINO format, meta (optional)
Returns:
- the dict containing Metadata for all inputs
| get_input_layers | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def get_output_layers(self):
'''
Gets the names of model outputs and for each one creates the Metadata structure,
which contains the information about the output shape, layout, precision
in OpenVINO format, meta (optional)
Returns:
- the dict containing Metadat... |
Gets the names of model outputs and for each one creates the Metadata structure,
which contains the information about the output shape, layout, precision
in OpenVINO format, meta (optional)
Returns:
- the dict containing Metadata for all outputs
| get_output_layers | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def reshape_model(self, new_shape):
'''
Reshapes the model inputs to fit the new input shape.
Args:
- new_shape (dict): the dictionary with inputs names as keys and
list of new shape as values in the following format:
{
'input_laye... |
Reshapes the model inputs to fit the new input shape.
Args:
- new_shape (dict): the dictionary with inputs names as keys and
list of new shape as values in the following format:
{
'input_layer_name_1': [1, 128, 128, 3],
... | reshape_model | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def await_all(self):
'''
In case of asynchronous execution waits the completion of all
busy infer requests.
''' |
In case of asynchronous execution waits the completion of all
busy infer requests.
| await_all | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def await_any(self):
'''
In case of asynchronous execution waits the completion of any
busy infer request until it becomes available for the data submission.
''' |
In case of asynchronous execution waits the completion of any
busy infer request until it becomes available for the data submission.
| await_any | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/model_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/model_adapter.py | Apache-2.0 |
def from_user_layouts(input_names: set, user_layouts: dict):
'''
Create Layout for input based on user info
'''
for input_name in input_names:
if input_name in user_layouts:
return user_layouts[input_name]
return user_layouts.get('', '') |
Create Layout for input based on user info
| from_user_layouts | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/utils.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/utils.py | Apache-2.0 |
def parse_layouts(layout_string: str) -> Optional[dict]:
'''
Parse layout parameter in format "input0:NCHW,input1:NC" or "NCHW" (applied to all inputs)
'''
if not layout_string:
return None
search_string = layout_string if layout_string.rfind(':') != -1 else ":" + lay... |
Parse layout parameter in format "input0:NCHW,input1:NC" or "NCHW" (applied to all inputs)
| parse_layouts | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/adapters/utils.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/adapters/utils.py | Apache-2.0 |
def get_proposals(self, rpn_cls_prob_reshape, bbox_deltas, image_size, _feat_stride=16):
"""
Parameters
rpn_cls_prob_reshape: (H , W , Ax2), probabilities for predicted regions
bbox_deltas: (H , W , Ax4), predicted regions
image_size: a list of [image_height, image_width]
... |
Parameters
rpn_cls_prob_reshape: (H , W , Ax2), probabilities for predicted regions
bbox_deltas: (H , W , Ax4), predicted regions
image_size: a list of [image_height, image_width]
_feat_stride: the downsampling ratio of feature map to the original input image
Algorithm:
... | get_proposals | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/ctpn.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/ctpn.py | Apache-2.0 |
def __init__(self, model_adapter, configuration=None, preload=False):
'''Detection Model constructor
It extends the `ImageModel` construtor.
Args:
model_adapter (ModelAdapter): allows working with the specified executor
configuration (dict, optional): it contains values... | Detection Model constructor
It extends the `ImageModel` construtor.
Args:
model_adapter (ModelAdapter): allows working with the specified executor
configuration (dict, optional): it contains values for parameters accepted by specific
wrapper (`confidence_threshold... | __init__ | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/detection_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/detection_model.py | Apache-2.0 |
def _resize_detections(self, detections, meta):
'''Resizes detection bounding boxes according to initial image shape.
It implements image resizing depending on the set `resize_type`(see `ImageModel` for details).
Next, it applies bounding boxes clipping.
Args:
detections (L... | Resizes detection bounding boxes according to initial image shape.
It implements image resizing depending on the set `resize_type`(see `ImageModel` for details).
Next, it applies bounding boxes clipping.
Args:
detections (List[Detection]): list of detections with coordinates in nor... | _resize_detections | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/detection_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/detection_model.py | Apache-2.0 |
def __init__(self, model_adapter, configuration=None, preload=False):
'''Image model constructor
It extends the `Model` constructor.
Args:
model_adapter (ModelAdapter): allows working with the specified executor
configuration (dict, optional): it contains values for par... | Image model constructor
It extends the `Model` constructor.
Args:
model_adapter (ModelAdapter): allows working with the specified executor
configuration (dict, optional): it contains values for parameters accepted by specific
wrapper (`confidence_threshold`, `labe... | __init__ | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/image_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/image_model.py | Apache-2.0 |
def _get_inputs(self):
'''Defines the model inputs for images and additional info.
Raises:
WrapperError: if the wrapper failed to define appropriate inputs for images
Returns:
- list of inputs names for images
- list of inputs names for additional info
... | Defines the model inputs for images and additional info.
Raises:
WrapperError: if the wrapper failed to define appropriate inputs for images
Returns:
- list of inputs names for images
- list of inputs names for additional info
| _get_inputs | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/image_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/image_model.py | Apache-2.0 |
def preprocess(self, inputs):
'''Data preprocess method
It performs basic preprocessing of a single image:
- Resizes the image to fit the model input size via the defined resize type
- Normalizes the image: subtracts means, divides by scales, switch channels BGR-RGB
... | Data preprocess method
It performs basic preprocessing of a single image:
- Resizes the image to fit the model input size via the defined resize type
- Normalizes the image: subtracts means, divides by scales, switch channels BGR-RGB
- Changes the image layout according to t... | preprocess | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/image_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/image_model.py | Apache-2.0 |
def _change_layout(self, image):
'''Changes the input image layout to fit the layout of the model input layer.
Args:
inputs (ndarray): a single image as 3D array in HWC layout
Returns:
- the image with layout aligned with the model layout
'''
if self.nch... | Changes the input image layout to fit the layout of the model input layer.
Args:
inputs (ndarray): a single image as 3D array in HWC layout
Returns:
- the image with layout aligned with the model layout
| _change_layout | python | openvinotoolkit/open_model_zoo | demos/common/python/model_zoo/model_api/models/image_model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/model_zoo/model_api/models/image_model.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.