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Function is used to get a message from the iopub channel.
Timeout is None by default
When timeout is reached | def get_message(self, stream, timeout=None):
"""
Function is used to get a message from the iopub channel.
Timeout is None by default
When timeout is reached
"""
try:
if stream == 'iopub':
msg = self.kc.get_iopub_msg(timeout=timeout)
elif stream == 'shell':
msg = self.kc.get_shell_msg(timeout=timeout)
else:
raise ValueError('Invalid stream specified: "%s"' % stream)
except Empty:
logger.debug('Kernel: Timeout waiting for message on %s', stream)
raise
logger.debug("Kernel message (%s):\n%s", stream, pformat(msg))
return msg |
Executes a string of python code in cell input.
We do not allow the kernel to make requests to the stdin
this is the norm for notebooks
Function returns a unique message id of the reply from
the kernel. | def execute_cell_input(self, cell_input, allow_stdin=None):
"""
Executes a string of python code in cell input.
We do not allow the kernel to make requests to the stdin
this is the norm for notebooks
Function returns a unique message id of the reply from
the kernel.
"""
if cell_input:
logger.debug('Executing cell: "%s"...', cell_input.splitlines()[0][:40])
else:
logger.debug('Executing empty cell')
return self.kc.execute(cell_input, allow_stdin=allow_stdin, stop_on_error=False) |
Continuously poll the kernel 'shell' stream for messages until:
- It receives an 'execute_reply' status for the given message id
- The timeout is reached awaiting a message, in which case
a `Queue.Empty` exception will be raised. | def await_reply(self, msg_id, timeout=None):
"""
Continuously poll the kernel 'shell' stream for messages until:
- It receives an 'execute_reply' status for the given message id
- The timeout is reached awaiting a message, in which case
a `Queue.Empty` exception will be raised.
"""
while True:
msg = self.get_message(stream='shell', timeout=timeout)
# Is this the message we are waiting for?
if msg['parent_header'].get('msg_id') == msg_id:
if msg['content']['status'] == 'aborted':
# This should not occur!
raise RuntimeError('Kernel aborted execution request')
return |
Poll the iopub stream until an idle message is received for the given parent ID | def await_idle(self, parent_id, timeout):
"""Poll the iopub stream until an idle message is received for the given parent ID"""
while True:
# Get a message from the kernel iopub channel
msg = self.get_message(timeout=timeout, stream='iopub') # raises Empty on timeout!
if msg['parent_header'].get('msg_id') != parent_id:
continue
if msg['msg_type'] == 'status':
if msg['content']['execution_state'] == 'idle':
break |
Instructs the kernel process to stop channels
and the kernel manager to then shutdown the process. | def stop(self):
"""
Instructs the kernel process to stop channels
and the kernel manager to then shutdown the process.
"""
logger.debug('Stopping kernel')
self.kc.stop_channels()
self.km.shutdown_kernel(now=True)
del self.km |
Get a list of index values for Validation set from a dataset
Arguments:
n : int, Total number of elements in the data set.
cv_idx : int, starting index [idx_start = cv_idx*int(val_pct*n)]
val_pct : (int, float), validation set percentage
seed : seed value for RandomState
Returns:
list of indexes | def get_cv_idxs(n, cv_idx=0, val_pct=0.2, seed=42):
""" Get a list of index values for Validation set from a dataset
Arguments:
n : int, Total number of elements in the data set.
cv_idx : int, starting index [idx_start = cv_idx*int(val_pct*n)]
val_pct : (int, float), validation set percentage
seed : seed value for RandomState
Returns:
list of indexes
"""
np.random.seed(seed)
n_val = int(val_pct*n)
idx_start = cv_idx*n_val
idxs = np.random.permutation(n)
return idxs[idx_start:idx_start+n_val] |
Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ. | def resize_img(fname, targ, path, new_path, fn=None):
"""
Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ.
"""
if fn is None:
fn = resize_fn(targ)
dest = os.path.join(path_for(path, new_path, targ), fname)
if os.path.exists(dest): return
im = Image.open(os.path.join(path, fname)).convert('RGB')
os.makedirs(os.path.split(dest)[0], exist_ok=True)
fn(im).save(dest) |
Enlarge or shrink a set of images in the same directory to scale, such that the smaller of the height or width dimension is equal to targ.
Note:
-- This function is multithreaded for efficiency.
-- When destination file or folder already exist, function exists without raising an error. | def resize_imgs(fnames, targ, path, new_path, resume=True, fn=None):
"""
Enlarge or shrink a set of images in the same directory to scale, such that the smaller of the height or width dimension is equal to targ.
Note:
-- This function is multithreaded for efficiency.
-- When destination file or folder already exist, function exists without raising an error.
"""
target_path = path_for(path, new_path, targ)
if resume:
subdirs = {os.path.dirname(p) for p in fnames}
subdirs = {s for s in subdirs if os.path.exists(os.path.join(target_path, s))}
already_resized_fnames = set()
for subdir in subdirs:
files = [os.path.join(subdir, file) for file in os.listdir(os.path.join(target_path, subdir))]
already_resized_fnames.update(set(files))
original_fnames = set(fnames)
fnames = list(original_fnames - already_resized_fnames)
errors = {}
def safely_process(fname):
try:
resize_img(fname, targ, path, new_path, fn=fn)
except Exception as ex:
errors[fname] = str(ex)
if len(fnames) > 0:
with ThreadPoolExecutor(num_cpus()) as e:
ims = e.map(lambda fname: safely_process(fname), fnames)
for _ in tqdm(ims, total=len(fnames), leave=False): pass
if errors:
print('Some images failed to process:')
print(json.dumps(errors, indent=2))
return os.path.join(path,new_path,str(targ)) |
Returns a list of relative file paths to `path` for all files within `folder` | def read_dir(path, folder):
""" Returns a list of relative file paths to `path` for all files within `folder` """
full_path = os.path.join(path, folder)
fnames = glob(f"{full_path}/*.*")
directories = glob(f"{full_path}/*/")
if any(fnames):
return [os.path.relpath(f,path) for f in fnames]
elif any(directories):
raise FileNotFoundError("{} has subdirectories but contains no files. Is your directory structure is correct?".format(full_path))
else:
raise FileNotFoundError("{} folder doesn't exist or is empty".format(full_path)) |
Fetches name of all files in path in long form, and labels associated by extrapolation of directory names. | def read_dirs(path, folder):
'''
Fetches name of all files in path in long form, and labels associated by extrapolation of directory names.
'''
lbls, fnames, all_lbls = [], [], []
full_path = os.path.join(path, folder)
for lbl in sorted(os.listdir(full_path)):
if lbl not in ('.ipynb_checkpoints','.DS_Store'):
all_lbls.append(lbl)
for fname in os.listdir(os.path.join(full_path, lbl)):
if fname not in ('.DS_Store'):
fnames.append(os.path.join(folder, lbl, fname))
lbls.append(lbl)
return fnames, lbls, all_lbls |
one hot encoding by index. Returns array of length c, where all entries are 0, except for the indecies in ids | def n_hot(ids, c):
'''
one hot encoding by index. Returns array of length c, where all entries are 0, except for the indecies in ids
'''
res = np.zeros((c,), dtype=np.float32)
res[ids] = 1
return res |
Returns the filenames and labels for a folder within a path
Returns:
-------
fnames: a list of the filenames within `folder`
all_lbls: a list of all of the labels in `folder`, where the # of labels is determined by the # of directories within `folder`
lbl_arr: a numpy array of the label indices in `all_lbls` | def folder_source(path, folder):
"""
Returns the filenames and labels for a folder within a path
Returns:
-------
fnames: a list of the filenames within `folder`
all_lbls: a list of all of the labels in `folder`, where the # of labels is determined by the # of directories within `folder`
lbl_arr: a numpy array of the label indices in `all_lbls`
"""
fnames, lbls, all_lbls = read_dirs(path, folder)
lbl2idx = {lbl:idx for idx,lbl in enumerate(all_lbls)}
idxs = [lbl2idx[lbl] for lbl in lbls]
lbl_arr = np.array(idxs, dtype=int)
return fnames, lbl_arr, all_lbls |
Parse filenames and label sets from a CSV file.
This method expects that the csv file at path :fn: has two columns. If it
has a header, :skip_header: should be set to True. The labels in the
label set are expected to be space separated.
Arguments:
fn: Path to a CSV file.
skip_header: A boolean flag indicating whether to skip the header.
Returns:
a two-tuple of (
image filenames,
a dictionary of filenames and corresponding labels
)
.
:param cat_separator: the separator for the categories column | def parse_csv_labels(fn, skip_header=True, cat_separator = ' '):
"""Parse filenames and label sets from a CSV file.
This method expects that the csv file at path :fn: has two columns. If it
has a header, :skip_header: should be set to True. The labels in the
label set are expected to be space separated.
Arguments:
fn: Path to a CSV file.
skip_header: A boolean flag indicating whether to skip the header.
Returns:
a two-tuple of (
image filenames,
a dictionary of filenames and corresponding labels
)
.
:param cat_separator: the separator for the categories column
"""
df = pd.read_csv(fn, index_col=0, header=0 if skip_header else None, dtype=str)
fnames = df.index.values
df.iloc[:,0] = df.iloc[:,0].str.split(cat_separator)
return fnames, list(df.to_dict().values())[0] |
True if the fn points to a DICOM image | def isdicom(fn):
'''True if the fn points to a DICOM image'''
fn = str(fn)
if fn.endswith('.dcm'):
return True
# Dicom signature from the dicom spec.
with open(fn,'rb') as fh:
fh.seek(0x80)
return fh.read(4)==b'DICM' |
Opens an image using OpenCV given the file path.
Arguments:
fn: the file path of the image
Returns:
The image in RGB format as numpy array of floats normalized to range between 0.0 - 1.0 | def open_image(fn):
""" Opens an image using OpenCV given the file path.
Arguments:
fn: the file path of the image
Returns:
The image in RGB format as numpy array of floats normalized to range between 0.0 - 1.0
"""
flags = cv2.IMREAD_UNCHANGED+cv2.IMREAD_ANYDEPTH+cv2.IMREAD_ANYCOLOR
if not os.path.exists(fn) and not str(fn).startswith("http"):
raise OSError('No such file or directory: {}'.format(fn))
elif os.path.isdir(fn) and not str(fn).startswith("http"):
raise OSError('Is a directory: {}'.format(fn))
elif isdicom(fn):
slice = pydicom.read_file(fn)
if slice.PhotometricInterpretation.startswith('MONOCHROME'):
# Make a fake RGB image
im = np.stack([slice.pixel_array]*3,-1)
return im / ((1 << slice.BitsStored)-1)
else:
# No support for RGB yet, as it involves various color spaces.
# It shouldn't be too difficult to add though, if needed.
raise OSError('Unsupported DICOM image with PhotometricInterpretation=={}'.format(slice.PhotometricInterpretation))
else:
#res = np.array(Image.open(fn), dtype=np.float32)/255
#if len(res.shape)==2: res = np.repeat(res[...,None],3,2)
#return res
try:
if str(fn).startswith("http"):
req = urllib.urlopen(str(fn))
image = np.asarray(bytearray(req.read()), dtype="uint8")
im = cv2.imdecode(image, flags).astype(np.float32)/255
else:
im = cv2.imread(str(fn), flags).astype(np.float32)/255
if im is None: raise OSError(f'File not recognized by opencv: {fn}')
return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
except Exception as e:
raise OSError('Error handling image at: {}'.format(fn)) from e |
Split each array passed as *a, to a pair of arrays like this (elements selected by idxs, the remaining elements)
This can be used to split multiple arrays containing training data to validation and training set.
:param idxs [int]: list of indexes selected
:param a list: list of np.array, each array should have same amount of elements in the first dimension
:return: list of tuples, each containing a split of corresponding array from *a.
First element of each tuple is an array composed from elements selected by idxs,
second element is an array of remaining elements. | def split_by_idx(idxs, *a):
"""
Split each array passed as *a, to a pair of arrays like this (elements selected by idxs, the remaining elements)
This can be used to split multiple arrays containing training data to validation and training set.
:param idxs [int]: list of indexes selected
:param a list: list of np.array, each array should have same amount of elements in the first dimension
:return: list of tuples, each containing a split of corresponding array from *a.
First element of each tuple is an array composed from elements selected by idxs,
second element is an array of remaining elements.
"""
mask = np.zeros(len(a[0]),dtype=bool)
mask[np.array(idxs)] = True
return [(o[mask],o[~mask]) for o in a] |
resize all images in the dataset and save them to `new_path`
Arguments:
targ (int): the target size
new_path (string): the new folder to save the images
resume (bool): if true (default), allow resuming a partial resize operation by checking for the existence
of individual images rather than the existence of the directory
fn (function): custom resizing function Img -> Img | def resize_imgs(self, targ, new_path, resume=True, fn=None):
"""
resize all images in the dataset and save them to `new_path`
Arguments:
targ (int): the target size
new_path (string): the new folder to save the images
resume (bool): if true (default), allow resuming a partial resize operation by checking for the existence
of individual images rather than the existence of the directory
fn (function): custom resizing function Img -> Img
"""
dest = resize_imgs(self.fnames, targ, self.path, new_path, resume, fn)
return self.__class__(self.fnames, self.y, self.transform, dest) |
Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz) | def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
if len(arr.shape)==3: arr = arr[None]
return self.transform.denorm(np.rollaxis(arr,1,4)) |
Return a copy of this dataset resized | def resized(self, dl, targ, new_path, resume = True, fn=None):
"""
Return a copy of this dataset resized
"""
return dl.dataset.resize_imgs(targ, new_path, resume=resume, fn=fn) if dl else None |
Resizes all the images in the train, valid, test folders to a given size.
Arguments:
targ_sz (int): the target size
new_path (str): the path to save the resized images (default tmp)
resume (bool): if True, check for images in the DataSet that haven't been resized yet (useful if a previous resize
operation was aborted)
fn (function): optional custom resizing function | def resize(self, targ_sz, new_path='tmp', resume=True, fn=None):
"""
Resizes all the images in the train, valid, test folders to a given size.
Arguments:
targ_sz (int): the target size
new_path (str): the path to save the resized images (default tmp)
resume (bool): if True, check for images in the DataSet that haven't been resized yet (useful if a previous resize
operation was aborted)
fn (function): optional custom resizing function
"""
new_ds = []
dls = [self.trn_dl,self.val_dl,self.fix_dl,self.aug_dl]
if self.test_dl: dls += [self.test_dl, self.test_aug_dl]
else: dls += [None,None]
t = tqdm_notebook(dls)
for dl in t: new_ds.append(self.resized(dl, targ_sz, new_path, resume, fn))
t.close()
return self.__class__(new_ds[0].path, new_ds, self.bs, self.num_workers, self.classes) |
Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
trn: a tuple of training data matrix and target label/classification array (e.g. `trn=(x,y)` where `x` has the
shape of `(5000, 784)` and `y` has the shape of `(5000,)`)
val: a tuple of validation data matrix and target label/classification array.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
classes: a list of all labels/classifications
num_workers: a number of workers
test: a matrix of test data (the shape should match `trn[0]`)
Returns:
ImageClassifierData | def from_arrays(cls, path, trn, val, bs=64, tfms=(None,None), classes=None, num_workers=4, test=None, continuous=False):
""" Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
trn: a tuple of training data matrix and target label/classification array (e.g. `trn=(x,y)` where `x` has the
shape of `(5000, 784)` and `y` has the shape of `(5000,)`)
val: a tuple of validation data matrix and target label/classification array.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
classes: a list of all labels/classifications
num_workers: a number of workers
test: a matrix of test data (the shape should match `trn[0]`)
Returns:
ImageClassifierData
"""
f = ArraysIndexRegressionDataset if continuous else ArraysIndexDataset
datasets = cls.get_ds(f, trn, val, tfms, test=test)
return cls(path, datasets, bs, num_workers, classes=classes) |
Read in images and their labels given as sub-folder names
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
trn_name: a name of the folder that contains training images.
val_name: a name of the folder that contains validation images.
test_name: a name of the folder that contains test images.
num_workers: number of workers
Returns:
ImageClassifierData | def from_paths(cls, path, bs=64, tfms=(None,None), trn_name='train', val_name='valid', test_name=None, test_with_labels=False, num_workers=8):
""" Read in images and their labels given as sub-folder names
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
trn_name: a name of the folder that contains training images.
val_name: a name of the folder that contains validation images.
test_name: a name of the folder that contains test images.
num_workers: number of workers
Returns:
ImageClassifierData
"""
assert not(tfms[0] is None or tfms[1] is None), "please provide transformations for your train and validation sets"
trn,val = [folder_source(path, o) for o in (trn_name, val_name)]
if test_name:
test = folder_source(path, test_name) if test_with_labels else read_dir(path, test_name)
else: test = None
datasets = cls.get_ds(FilesIndexArrayDataset, trn, val, tfms, path=path, test=test)
return cls(path, datasets, bs, num_workers, classes=trn[2]) |
Read in images and their labels given as a CSV file.
This method should be used when training image labels are given in an CSV file as opposed to
sub-directories with label names.
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
csv_fname: a name of the CSV file which contains target labels.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`.
If None, default arguments to get_cv_idxs are used.
suffix: suffix to add to image names in CSV file (sometimes CSV only contains the file name without file
extension e.g. '.jpg' - in which case, you can set suffix as '.jpg')
test_name: a name of the folder which contains test images.
continuous: if True, the data set is used to train regression models. If False, it is used
to train classification models.
skip_header: skip the first row of the CSV file.
num_workers: number of workers
cat_separator: Labels category separator
Returns:
ImageClassifierData | def from_csv(cls, path, folder, csv_fname, bs=64, tfms=(None,None),
val_idxs=None, suffix='', test_name=None, continuous=False, skip_header=True, num_workers=8, cat_separator=' '):
""" Read in images and their labels given as a CSV file.
This method should be used when training image labels are given in an CSV file as opposed to
sub-directories with label names.
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
csv_fname: a name of the CSV file which contains target labels.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`.
If None, default arguments to get_cv_idxs are used.
suffix: suffix to add to image names in CSV file (sometimes CSV only contains the file name without file
extension e.g. '.jpg' - in which case, you can set suffix as '.jpg')
test_name: a name of the folder which contains test images.
continuous: if True, the data set is used to train regression models. If False, it is used
to train classification models.
skip_header: skip the first row of the CSV file.
num_workers: number of workers
cat_separator: Labels category separator
Returns:
ImageClassifierData
"""
assert not (tfms[0] is None or tfms[1] is None), "please provide transformations for your train and validation sets"
assert not (os.path.isabs(folder)), "folder needs to be a relative path"
fnames,y,classes = csv_source(folder, csv_fname, skip_header, suffix, continuous=continuous, cat_separator=cat_separator)
return cls.from_names_and_array(path, fnames, y, classes, val_idxs, test_name,
num_workers=num_workers, suffix=suffix, tfms=tfms, bs=bs, continuous=continuous) |
Read in images given a sub-folder and their labels given a numpy array
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
y: numpy array which contains target labels ordered by filenames.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`.
If None, default arguments to get_cv_idxs are used.
test_name: a name of the folder which contains test images.
num_workers: number of workers
Returns:
ImageClassifierData | def from_path_and_array(cls, path, folder, y, classes=None, val_idxs=None, test_name=None,
num_workers=8, tfms=(None,None), bs=64):
""" Read in images given a sub-folder and their labels given a numpy array
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
y: numpy array which contains target labels ordered by filenames.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`.
If None, default arguments to get_cv_idxs are used.
test_name: a name of the folder which contains test images.
num_workers: number of workers
Returns:
ImageClassifierData
"""
assert not (tfms[0] is None or tfms[1] is None), "please provide transformations for your train and validation sets"
assert not (os.path.isabs(folder)), "folder needs to be a relative path"
fnames = np.core.defchararray.add(f'{folder}/', sorted(os.listdir(f'{path}{folder}')))
return cls.from_names_and_array(path, fnames, y, classes, val_idxs, test_name,
num_workers=num_workers, tfms=tfms, bs=bs) |
Is the code running in the ipython environment (jupyter including) | def is_in_ipython():
"Is the code running in the ipython environment (jupyter including)"
program_name = os.path.basename(os.getenv('_', ''))
if ('jupyter-notebook' in program_name or # jupyter-notebook
'ipython' in program_name or # ipython
'JPY_PARENT_PID' in os.environ): # ipython-notebook
return True
else:
return False |
Free traceback from references to locals() in each frame to avoid circular reference leading to gc.collect() unable to reclaim memory | def get_ref_free_exc_info():
"Free traceback from references to locals() in each frame to avoid circular reference leading to gc.collect() unable to reclaim memory"
type, val, tb = sys.exc_info()
traceback.clear_frames(tb)
return (type, val, tb) |
Reclaim GPU RAM if CUDA out of memory happened, or execution was interrupted | def gpu_mem_restore(func):
"Reclaim GPU RAM if CUDA out of memory happened, or execution was interrupted"
@functools.wraps(func)
def wrapper(*args, **kwargs):
tb_clear_frames = os.environ.get('FASTAI_TB_CLEAR_FRAMES', None)
if not IS_IN_IPYTHON or tb_clear_frames=="0":
return func(*args, **kwargs)
try:
return func(*args, **kwargs)
except Exception as e:
if ("CUDA out of memory" in str(e) or
"device-side assert triggered" in str(e) or
tb_clear_frames == "1"):
type, val, tb = get_ref_free_exc_info() # must!
gc.collect()
if "device-side assert triggered" in str(e):
warn("""When 'device-side assert triggered' error happens, it's not possible to recover and you must restart the kernel to continue. Use os.environ['CUDA_LAUNCH_BLOCKING']="1" before restarting to debug""")
raise type(val).with_traceback(tb) from None
else: raise # re-raises the exact last exception
return wrapper |
Fits a model
Arguments:
model (model): any pytorch module
net = to_gpu(net)
data (ModelData): see ModelData class and subclasses (can be a list)
opts: an optimizer. Example: optim.Adam.
If n_epochs is a list, it needs to be the layer_optimizer to get the optimizer as it changes.
n_epochs(int or list): number of epochs (or list of number of epochs)
crit: loss function to optimize. Example: F.cross_entropy | def fit(model, data, n_epochs, opt, crit, metrics=None, callbacks=None, stepper=Stepper,
swa_model=None, swa_start=None, swa_eval_freq=None, visualize=False, **kwargs):
""" Fits a model
Arguments:
model (model): any pytorch module
net = to_gpu(net)
data (ModelData): see ModelData class and subclasses (can be a list)
opts: an optimizer. Example: optim.Adam.
If n_epochs is a list, it needs to be the layer_optimizer to get the optimizer as it changes.
n_epochs(int or list): number of epochs (or list of number of epochs)
crit: loss function to optimize. Example: F.cross_entropy
"""
seq_first = kwargs.pop('seq_first', False)
all_val = kwargs.pop('all_val', False)
get_ep_vals = kwargs.pop('get_ep_vals', False)
validate_skip = kwargs.pop('validate_skip', 0)
metrics = metrics or []
callbacks = callbacks or []
avg_mom=0.98
batch_num,avg_loss=0,0.
for cb in callbacks: cb.on_train_begin()
names = ["epoch", "trn_loss", "val_loss"] + [f.__name__ for f in metrics]
if swa_model is not None:
swa_names = ['swa_loss'] + [f'swa_{f.__name__}' for f in metrics]
names += swa_names
# will use this to call evaluate later
swa_stepper = stepper(swa_model, None, crit, **kwargs)
layout = "{!s:10} " * len(names)
if not isinstance(n_epochs, Iterable): n_epochs=[n_epochs]
if not isinstance(data, Iterable): data = [data]
if len(data) == 1: data = data * len(n_epochs)
for cb in callbacks: cb.on_phase_begin()
model_stepper = stepper(model, opt.opt if hasattr(opt,'opt') else opt, crit, **kwargs)
ep_vals = collections.OrderedDict()
tot_epochs = int(np.ceil(np.array(n_epochs).sum()))
cnt_phases = np.array([ep * len(dat.trn_dl) for (ep,dat) in zip(n_epochs,data)]).cumsum()
phase = 0
for epoch in tnrange(tot_epochs, desc='Epoch'):
if phase >= len(n_epochs): break #Sometimes cumulated errors make this append.
model_stepper.reset(True)
cur_data = data[phase]
if hasattr(cur_data, 'trn_sampler'): cur_data.trn_sampler.set_epoch(epoch)
if hasattr(cur_data, 'val_sampler'): cur_data.val_sampler.set_epoch(epoch)
num_batch = len(cur_data.trn_dl)
t = tqdm(iter(cur_data.trn_dl), leave=False, total=num_batch, miniters=0)
if all_val: val_iter = IterBatch(cur_data.val_dl)
for (*x,y) in t:
batch_num += 1
for cb in callbacks: cb.on_batch_begin()
loss = model_stepper.step(V(x),V(y), epoch)
avg_loss = avg_loss * avg_mom + loss * (1-avg_mom)
debias_loss = avg_loss / (1 - avg_mom**batch_num)
t.set_postfix(loss=debias_loss, refresh=False)
stop=False
los = debias_loss if not all_val else [debias_loss] + validate_next(model_stepper,metrics, val_iter)
for cb in callbacks: stop = stop or cb.on_batch_end(los)
if stop: return
if batch_num >= cnt_phases[phase]:
for cb in callbacks: cb.on_phase_end()
phase += 1
if phase >= len(n_epochs):
t.close()
break
for cb in callbacks: cb.on_phase_begin()
if isinstance(opt, LayerOptimizer): model_stepper.opt = opt.opt
if cur_data != data[phase]:
t.close()
break
if not all_val:
vals = validate(model_stepper, cur_data.val_dl, metrics, epoch, seq_first=seq_first, validate_skip = validate_skip)
stop=False
for cb in callbacks: stop = stop or cb.on_epoch_end(vals)
if swa_model is not None:
if (epoch + 1) >= swa_start and ((epoch + 1 - swa_start) % swa_eval_freq == 0 or epoch == tot_epochs - 1):
fix_batchnorm(swa_model, cur_data.trn_dl)
swa_vals = validate(swa_stepper, cur_data.val_dl, metrics, epoch, validate_skip = validate_skip)
vals += swa_vals
if epoch > 0:
print_stats(epoch, [debias_loss] + vals, visualize, prev_val)
else:
print(layout.format(*names))
print_stats(epoch, [debias_loss] + vals, visualize)
prev_val = [debias_loss] + vals
ep_vals = append_stats(ep_vals, epoch, [debias_loss] + vals)
if stop: break
for cb in callbacks: cb.on_train_end()
if get_ep_vals: return vals, ep_vals
else: return vals |
Computes the loss on the next minibatch of the validation set. | def validate_next(stepper, metrics, val_iter):
"""Computes the loss on the next minibatch of the validation set."""
stepper.reset(False)
with no_grad_context():
(*x,y) = val_iter.next()
preds,l = stepper.evaluate(VV(x), VV(y))
res = [delistify(to_np(l))]
res += [f(datafy(preds), datafy(y)) for f in metrics]
stepper.reset(True)
return res |
Create link to documentation. | def link_type(arg_type, arg_name=None, include_bt:bool=True):
"Create link to documentation."
arg_name = arg_name or fn_name(arg_type)
if include_bt: arg_name = code_esc(arg_name)
if belongs_to_module(arg_type, 'torch') and ('Tensor' not in arg_name): return f'[{arg_name}]({get_pytorch_link(arg_type)})'
if is_fastai_class(arg_type): return f'[{arg_name}]({get_fn_link(arg_type)})'
return arg_name |
Check if `t` belongs to `module_name`. | def belongs_to_module(t, module_name):
"Check if `t` belongs to `module_name`."
if hasattr(t, '__func__'): return belongs_to_module(t.__func__, module_name)
if not inspect.getmodule(t): return False
return inspect.getmodule(t).__name__.startswith(module_name) |
Formats function param to `param1:Type=val`. Font weights: param1=bold, val=bold+italic | def format_param(p):
"Formats function param to `param1:Type=val`. Font weights: param1=bold, val=bold+italic"
arg_prefix = arg_prefixes.get(p.kind, '') # asterisk prefix for *args and **kwargs
res = f"**{arg_prefix}{code_esc(p.name)}**"
if hasattr(p, 'annotation') and p.annotation != p.empty: res += f':{anno_repr(p.annotation)}'
if p.default != p.empty:
default = getattr(p.default, 'func', p.default)
default = getattr(default, '__name__', default)
res += f'=***`{repr(default)}`***'
return res |
Format and link `func` definition to show in documentation | def format_ft_def(func, full_name:str=None)->str:
"Format and link `func` definition to show in documentation"
sig = inspect.signature(func)
name = f'<code>{full_name or func.__name__}</code>'
fmt_params = [format_param(param) for name,param
in sig.parameters.items() if name not in ('self','cls')]
arg_str = f"({', '.join(fmt_params)})"
if sig.return_annotation and (sig.return_annotation != sig.empty): arg_str += f" → {anno_repr(sig.return_annotation)}"
if is_fastai_class(type(func)): arg_str += f" :: {link_type(type(func))}"
f_name = f"<code>class</code> {name}" if inspect.isclass(func) else name
return f'{f_name}',f'{name}{arg_str}' |
Formatted enum documentation. | def get_enum_doc(elt, full_name:str)->str:
"Formatted enum documentation."
vals = ', '.join(elt.__members__.keys())
return f'{code_esc(full_name)}',f'<code>Enum</code> = [{vals}]' |
Class definition. | def get_cls_doc(elt, full_name:str)->str:
"Class definition."
parent_class = inspect.getclasstree([elt])[-1][0][1][0]
name,args = format_ft_def(elt, full_name)
if parent_class != object: args += f' :: {link_type(parent_class, include_bt=True)}'
return name,args |
Show documentation for element `elt`. Supported types: class, Callable, and enum. | def show_doc(elt, doc_string:bool=True, full_name:str=None, arg_comments:dict=None, title_level=None, alt_doc_string:str='',
ignore_warn:bool=False, markdown=True, show_tests=True):
"Show documentation for element `elt`. Supported types: class, Callable, and enum."
arg_comments = ifnone(arg_comments, {})
anchor_id = get_anchor(elt)
elt = getattr(elt, '__func__', elt)
full_name = full_name or fn_name(elt)
if inspect.isclass(elt):
if is_enum(elt.__class__): name,args = get_enum_doc(elt, full_name)
else: name,args = get_cls_doc(elt, full_name)
elif isinstance(elt, Callable): name,args = format_ft_def(elt, full_name)
else: raise Exception(f'doc definition not supported for {full_name}')
source_link = get_function_source(elt) if is_fastai_class(elt) else ""
test_link, test_modal = get_pytest_html(elt, anchor_id=anchor_id) if show_tests else ('', '')
title_level = ifnone(title_level, 2 if inspect.isclass(elt) else 4)
doc = f'<h{title_level} id="{anchor_id}" class="doc_header">{name}{source_link}{test_link}</h{title_level}>'
doc += f'\n\n> {args}\n\n'
doc += f'{test_modal}'
if doc_string and (inspect.getdoc(elt) or arg_comments):
doc += format_docstring(elt, arg_comments, alt_doc_string, ignore_warn) + ' '
if markdown: display(Markdown(doc))
else: return doc |
Show `show_doc` info in preview window along with link to full docs. | def doc(elt):
"Show `show_doc` info in preview window along with link to full docs."
global use_relative_links
use_relative_links = False
elt = getattr(elt, '__func__', elt)
md = show_doc(elt, markdown=False)
if is_fastai_class(elt):
md += f'\n\n<a href="{get_fn_link(elt)}" target="_blank" rel="noreferrer noopener">Show in docs</a>'
output = HTMLExporter().markdown2html(md)
use_relative_links = True
if IS_IN_COLAB: get_ipython().run_cell_magic(u'html', u'', output)
else:
try: page.page({'text/html': output})
except: display(Markdown(md)) |
Merge and format the docstring definition with `arg_comments` and `alt_doc_string`. | def format_docstring(elt, arg_comments:dict={}, alt_doc_string:str='', ignore_warn:bool=False)->str:
"Merge and format the docstring definition with `arg_comments` and `alt_doc_string`."
parsed = ""
doc = parse_docstring(inspect.getdoc(elt))
description = alt_doc_string or f"{doc['short_description']} {doc['long_description']}"
if description: parsed += f'\n\n{link_docstring(inspect.getmodule(elt), description)}'
resolved_comments = {**doc.get('comments', {}), **arg_comments} # arg_comments takes priority
args = inspect.getfullargspec(elt).args if not is_enum(elt.__class__) else elt.__members__.keys()
if resolved_comments: parsed += '\n'
for a in resolved_comments:
parsed += f'\n- *{a}*: {resolved_comments[a]}'
if a not in args and not ignore_warn: warn(f'Doc arg mismatch: {a}')
return_comment = arg_comments.get('return') or doc.get('return')
if return_comment: parsed += f'\n\n*return*: {return_comment}'
return parsed |
Search `docstring` for backticks and attempt to link those functions to respective documentation. | def link_docstring(modules, docstring:str, overwrite:bool=False)->str:
"Search `docstring` for backticks and attempt to link those functions to respective documentation."
mods = listify(modules)
for mod in mods: _modvars.update(mod.__dict__) # concat all module definitions
return re.sub(BT_REGEX, replace_link, docstring) |
Attempt to resolve keywords such as Learner.lr_find. `match_last` starts matching from last component. | def find_elt(modvars, keyword, match_last=False):
"Attempt to resolve keywords such as Learner.lr_find. `match_last` starts matching from last component."
keyword = strip_fastai(keyword)
if keyword in modvars: return modvars[keyword]
comps = keyword.split('.')
comp_elt = modvars.get(comps[0])
if hasattr(comp_elt, '__dict__'): return find_elt(comp_elt.__dict__, '.'.join(comps[1:]), match_last=match_last) |
Return module from `mod_name`. | def import_mod(mod_name:str, ignore_errors=False):
"Return module from `mod_name`."
splits = str.split(mod_name, '.')
try:
if len(splits) > 1 : mod = importlib.import_module('.' + '.'.join(splits[1:]), splits[0])
else: mod = importlib.import_module(mod_name)
return mod
except:
if not ignore_errors: print(f"Module {mod_name} doesn't exist.") |
Show documentation for `ft_name`, see `show_doc`. | def show_doc_from_name(mod_name, ft_name:str, doc_string:bool=True, arg_comments:dict={}, alt_doc_string:str=''):
"Show documentation for `ft_name`, see `show_doc`."
mod = import_mod(mod_name)
splits = str.split(ft_name, '.')
assert hasattr(mod, splits[0]), print(f"Module {mod_name} doesn't have a function named {splits[0]}.")
elt = getattr(mod, splits[0])
for i,split in enumerate(splits[1:]):
assert hasattr(elt, split), print(f"Class {'.'.join(splits[:i+1])} doesn't have a function named {split}.")
elt = getattr(elt, split)
show_doc(elt, doc_string, ft_name, arg_comments, alt_doc_string) |
Return all the functions of module `mod`. | def get_ft_names(mod, include_inner=False)->List[str]:
"Return all the functions of module `mod`."
# If the module has an attribute __all__, it picks those.
# Otherwise, it returns all the functions defined inside a module.
fn_names = []
for elt_name in get_exports(mod):
elt = getattr(mod,elt_name)
#This removes the files imported from elsewhere
try: fname = inspect.getfile(elt)
except: continue
if mod.__file__.endswith('__init__.py'):
if inspect.ismodule(elt): fn_names.append(elt_name)
else: continue
else:
if (fname != mod.__file__): continue
if inspect.isclass(elt) or inspect.isfunction(elt): fn_names.append(elt_name)
else: continue
if include_inner and inspect.isclass(elt) and not is_enum(elt.__class__):
fn_names.extend(get_inner_fts(elt))
return fn_names |
List the inner functions of a class. | def get_inner_fts(elt)->List[str]:
"List the inner functions of a class."
fts = []
for ft_name in elt.__dict__.keys():
if ft_name.startswith('_'): continue
ft = getattr(elt, ft_name)
if inspect.isfunction(ft): fts.append(f'{elt.__name__}.{ft_name}')
if inspect.ismethod(ft): fts.append(f'{elt.__name__}.{ft_name}')
if inspect.isclass(ft): fts += [f'{elt.__name__}.{n}' for n in get_inner_fts(ft)]
return fts |
Display table of contents for given `mod_name`. | def get_module_toc(mod_name):
"Display table of contents for given `mod_name`."
mod = import_mod(mod_name)
ft_names = mod.__all__ if hasattr(mod,'__all__') else get_ft_names(mod)
ft_names.sort(key = str.lower)
tabmat = ''
for ft_name in ft_names:
tabmat += f'- [{ft_name}](#{ft_name})\n'
elt = getattr(mod, ft_name)
if inspect.isclass(elt) and not is_enum(elt.__class__):
in_ft_names = get_inner_fts(elt)
for name in in_ft_names:
tabmat += f' - [{name}](#{name})\n'
display(Markdown(tabmat)) |
Return function link to notebook documentation of `ft`. Private functions link to source code | def get_fn_link(ft)->str:
"Return function link to notebook documentation of `ft`. Private functions link to source code"
ft = getattr(ft, '__func__', ft)
anchor = strip_fastai(get_anchor(ft))
module_name = strip_fastai(get_module_name(ft))
base = '' if use_relative_links else FASTAI_DOCS
return f'{base}/{module_name}.html#{anchor}' |
Returns link to pytorch docs of `ft`. | def get_pytorch_link(ft)->str:
"Returns link to pytorch docs of `ft`."
name = ft.__name__
ext = '.html'
if name == 'device': return f'{PYTORCH_DOCS}tensor_attributes{ext}#torch-device'
if name == 'Tensor': return f'{PYTORCH_DOCS}tensors{ext}#torch-tensor'
if name.startswith('torchvision'):
doc_path = get_module_name(ft).replace('.', '/')
if inspect.ismodule(ft): name = name.replace('.', '-')
return f'{PYTORCH_DOCS}{doc_path}{ext}#{name}'
if name.startswith('torch.nn') and inspect.ismodule(ft): # nn.functional is special case
nn_link = name.replace('.', '-')
return f'{PYTORCH_DOCS}nn{ext}#{nn_link}'
paths = get_module_name(ft).split('.')
if len(paths) == 1: return f'{PYTORCH_DOCS}{paths[0]}{ext}#{paths[0]}.{name}'
offset = 1 if paths[1] == 'utils' else 0 # utils is a pytorch special case
doc_path = paths[1+offset]
if inspect.ismodule(ft): return f'{PYTORCH_DOCS}{doc_path}{ext}#module-{name}'
fnlink = '.'.join(paths[:(2+offset)]+[name])
return f'{PYTORCH_DOCS}{doc_path}{ext}#{fnlink}' |
Returns github link for given file | def get_source_link(file, line, display_text="[source]", **kwargs)->str:
"Returns github link for given file"
link = f"{SOURCE_URL}{file}#L{line}"
if display_text is None: return link
return f'<a href="{link}" class="source_link" style="float:right">{display_text}</a>' |
Returns link to `ft` in source code. | def get_function_source(ft, **kwargs)->str:
"Returns link to `ft` in source code."
try: line = inspect.getsourcelines(ft)[1]
except Exception: return ''
mod_path = get_module_name(ft).replace('.', '/') + '.py'
return get_source_link(mod_path, line, **kwargs) |
Look through the cell source for comments which affect nbval's behaviour
Yield an iterable of ``(MARKER_TYPE, True)``. | def find_comment_markers(cellsource):
"""Look through the cell source for comments which affect nbval's behaviour
Yield an iterable of ``(MARKER_TYPE, True)``.
"""
found = {}
for line in cellsource.splitlines():
line = line.strip()
if line.startswith('#'):
# print("Found comment in '{}'".format(line))
comment = line.lstrip('#').strip()
if comment in comment_markers:
# print("Found marker {}".format(comment))
marker = comment_markers[comment]
if not isinstance(marker, tuple):
# If not an explicit tuple ('option', True/False),
# imply ('option', True)
marker = (marker, True)
marker_type = marker[0]
if marker_type in found:
warnings.warn(
"Conflicting comment markers found, using the latest: "
" %s VS %s" %
(found[marker_type], comment))
found[marker_type] = comment
yield marker |
Merge all stream outputs with shared names into single streams
to ensure deterministic outputs.
Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed | def coalesce_streams(outputs):
"""
Merge all stream outputs with shared names into single streams
to ensure deterministic outputs.
Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed
"""
if not outputs:
return outputs
new_outputs = []
streams = {}
for output in outputs:
if (output.output_type == 'stream'):
if output.name in streams:
streams[output.name].text += output.text
else:
new_outputs.append(output)
streams[output.name] = output
else:
new_outputs.append(output)
# process \r and \b characters
for output in streams.values():
old = output.text
while len(output.text) < len(old):
old = output.text
# Cancel out anything-but-newline followed by backspace
output.text = backspace_pat.sub('', output.text)
# Replace all carriage returns not followed by newline
output.text = carriagereturn_pat.sub('', output.text)
return new_outputs |
Trim and hash base64 strings | def _trim_base64(s):
"""Trim and hash base64 strings"""
if len(s) > 64 and _base64.match(s.replace('\n', '')):
h = hash_string(s)
s = '%s...<snip base64, md5=%s...>' % (s[:8], h[:16])
return s |
Intent each line with indent | def _indent(s, indent=' '):
"""Intent each line with indent"""
if isinstance(s, six.string_types):
return '\n'.join(('%s%s' % (indent, line) for line in s.splitlines()))
return s |
Called by pytest to setup the collector cells in .
Here we start a kernel and setup the sanitize patterns. | def setup(self):
"""
Called by pytest to setup the collector cells in .
Here we start a kernel and setup the sanitize patterns.
"""
if self.parent.config.option.current_env:
kernel_name = CURRENT_ENV_KERNEL_NAME
else:
kernel_name = self.nb.metadata.get(
'kernelspec', {}).get('name', 'python')
self.kernel = RunningKernel(kernel_name, str(self.fspath.dirname))
self.setup_sanitize_files()
if getattr(self.parent.config.option, 'cov_source', None):
setup_coverage(self.parent.config, self.kernel, getattr(self, "fspath", None)) |
For each of the sanitize files that were specified as command line options
load the contents of the file into the sanitise patterns dictionary. | def setup_sanitize_files(self):
"""
For each of the sanitize files that were specified as command line options
load the contents of the file into the sanitise patterns dictionary.
"""
for fname in self.get_sanitize_files():
with open(fname, 'r') as f:
self.sanitize_patterns.update(get_sanitize_patterns(f.read())) |
Return list of all sanitize files provided by the user on the command line.
N.B.: We only support one sanitize file at the moment, but
this is likely to change in the future | def get_sanitize_files(self):
"""
Return list of all sanitize files provided by the user on the command line.
N.B.: We only support one sanitize file at the moment, but
this is likely to change in the future
"""
if self.parent.config.option.sanitize_with is not None:
return [self.parent.config.option.sanitize_with]
else:
return [] |
Gets a message from the iopub channel of the notebook kernel. | def get_kernel_message(self, timeout=None, stream='iopub'):
"""
Gets a message from the iopub channel of the notebook kernel.
"""
return self.kernel.get_message(stream, timeout=timeout) |
The collect function is required by pytest and is used to yield pytest
Item objects. We specify an Item for each code cell in the notebook. | def collect(self):
"""
The collect function is required by pytest and is used to yield pytest
Item objects. We specify an Item for each code cell in the notebook.
"""
self.nb = nbformat.read(str(self.fspath), as_version=4)
# Start the cell count
cell_num = 0
# Iterate over the cells in the notebook
for cell in self.nb.cells:
# Skip the cells that have text, headings or related stuff
# Only test code cells
if cell.cell_type == 'code':
# The cell may contain a comment indicating that its output
# should be checked or ignored. If it doesn't, use the default
# behaviour. The --nbval option checks unmarked cells.
with warnings.catch_warnings(record=True) as ws:
options = defaultdict(bool, find_metadata_tags(cell.metadata))
comment_opts = dict(find_comment_markers(cell.source))
if set(comment_opts.keys()) & set(options.keys()):
warnings.warn(
"Overlapping options from comments and metadata, "
"using options from comments: %s" %
str(set(comment_opts.keys()) & set(options.keys())))
for w in ws:
self.parent.config.warn(
"C1",
str(w.message),
'%s:Cell %d' % (
getattr(self, "fspath", None),
cell_num))
options.update(comment_opts)
options.setdefault('check', self.compare_outputs)
yield IPyNbCell('Cell ' + str(cell_num), self, cell_num,
cell, options)
# Update 'code' cell count
cell_num += 1 |
Format an output for printing | def format_output_compare(self, key, left, right):
"""Format an output for printing"""
if isinstance(left, six.string_types):
left = _trim_base64(left)
if isinstance(right, six.string_types):
right = _trim_base64(right)
cc = self.colors
self.comparison_traceback.append(
cc.OKBLUE
+ " mismatch '%s'" % key
+ cc.FAIL)
# Use comparison repr from pytest:
hook_result = self.ihook.pytest_assertrepr_compare(
config=self.config, op='==', left=left, right=right)
for new_expl in hook_result:
if new_expl:
new_expl = [' %s' % line.replace("\n", "\\n") for line in new_expl]
self.comparison_traceback.append("\n assert reference_output == test_output failed:\n")
self.comparison_traceback.extend(new_expl)
break
else:
# Fallback repr:
self.comparison_traceback.append(
" <<<<<<<<<<<< Reference output from ipynb file:"
+ cc.ENDC)
self.comparison_traceback.append(_indent(left))
self.comparison_traceback.append(
cc.FAIL
+ ' ============ disagrees with newly computed (test) output:'
+ cc.ENDC)
self.comparison_traceback.append(_indent(right))
self.comparison_traceback.append(
cc.FAIL
+ ' >>>>>>>>>>>>')
self.comparison_traceback.append(cc.ENDC) |
called when self.runtest() raises an exception. | def repr_failure(self, excinfo):
""" called when self.runtest() raises an exception. """
exc = excinfo.value
cc = self.colors
if isinstance(exc, NbCellError):
msg_items = [
cc.FAIL + "Notebook cell execution failed" + cc.ENDC]
formatstring = (
cc.OKBLUE + "Cell %d: %s\n\n" +
"Input:\n" + cc.ENDC + "%s\n")
msg_items.append(formatstring % (
exc.cell_num,
str(exc),
exc.source
))
if exc.inner_traceback:
msg_items.append((
cc.OKBLUE + "Traceback:" + cc.ENDC + "\n%s\n") %
exc.inner_traceback)
return "\n".join(msg_items)
else:
return "pytest plugin exception: %s" % str(exc) |
sanitize a string for comparison. | def sanitize(self, s):
"""sanitize a string for comparison.
"""
if not isinstance(s, six.string_types):
return s
"""
re.sub matches a regex and replaces it with another.
The regex replacements are taken from a file if the option
is passed when py.test is called. Otherwise, the strings
are not processed
"""
for regex, replace in six.iteritems(self.parent.sanitize_patterns):
s = re.sub(regex, replace, s)
return s |
Computes the outputs for several augmented inputs for TTA | def _tta_only(learn:Learner, ds_type:DatasetType=DatasetType.Valid, scale:float=1.35) -> Iterator[List[Tensor]]:
"Computes the outputs for several augmented inputs for TTA"
dl = learn.dl(ds_type)
ds = dl.dataset
old = ds.tfms
augm_tfm = [o for o in learn.data.train_ds.tfms if o.tfm not in
(crop_pad, flip_lr, dihedral, zoom)]
try:
pbar = master_bar(range(8))
for i in pbar:
row = 1 if i&1 else 0
col = 1 if i&2 else 0
flip = i&4
d = {'row_pct':row, 'col_pct':col, 'is_random':False}
tfm = [*augm_tfm, zoom(scale=scale, **d), crop_pad(**d)]
if flip: tfm.append(flip_lr(p=1.))
ds.tfms = tfm
yield get_preds(learn.model, dl, pbar=pbar, activ=_loss_func2activ(learn.loss_func))[0]
finally: ds.tfms = old |
Applies TTA to predict on `ds_type` dataset. | def _TTA(learn:Learner, beta:float=0.4, scale:float=1.35, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False) -> Tensors:
"Applies TTA to predict on `ds_type` dataset."
preds,y = learn.get_preds(ds_type)
all_preds = list(learn.tta_only(scale=scale, ds_type=ds_type))
avg_preds = torch.stack(all_preds).mean(0)
if beta is None: return preds,avg_preds,y
else:
final_preds = preds*beta + avg_preds*(1-beta)
if with_loss:
with NoneReduceOnCPU(learn.loss_func) as lf: loss = lf(final_preds, y)
return final_preds, y, loss
return final_preds, y |
Computes the f_beta between `preds` and `targets` | def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
"Computes the f_beta between `preds` and `targets`"
beta2 = beta ** 2
if sigmoid: y_pred = y_pred.sigmoid()
y_pred = (y_pred>thresh).float()
y_true = y_true.float()
TP = (y_pred*y_true).sum(dim=1)
prec = TP/(y_pred.sum(dim=1)+eps)
rec = TP/(y_true.sum(dim=1)+eps)
res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
return res.mean() |
Compute accuracy with `targs` when `input` is bs * n_classes. | def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
"Compute accuracy with `targs` when `input` is bs * n_classes."
n = targs.shape[0]
input = input.argmax(dim=-1).view(n,-1)
targs = targs.view(n,-1)
return (input==targs).float().mean() |
Compute accuracy when `y_pred` and `y_true` are the same size. | def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
"Compute accuracy when `y_pred` and `y_true` are the same size."
if sigmoid: y_pred = y_pred.sigmoid()
return ((y_pred>thresh)==y_true.byte()).float().mean() |
Computes the Top-k accuracy (target is in the top k predictions). | def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
"Computes the Top-k accuracy (target is in the top k predictions)."
input = input.topk(k=k, dim=-1)[1]
targs = targs.unsqueeze(dim=-1).expand_as(input)
return (input == targs).max(dim=-1)[0].float().mean() |
Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems. | def dice(input:Tensor, targs:Tensor, iou:bool=False)->Rank0Tensor:
"Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
n = targs.shape[0]
input = input.argmax(dim=1).view(n,-1)
targs = targs.view(n,-1)
intersect = (input * targs).sum().float()
union = (input+targs).sum().float()
if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
else: return intersect / (union-intersect+1.0) |
Exp RMSE between `pred` and `targ`. | def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Exp RMSE between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
pred, targ = torch.exp(pred), torch.exp(targ)
pct_var = (targ - pred)/targ
return torch.sqrt((pct_var**2).mean()) |
Mean absolute error between `pred` and `targ`. | def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean absolute error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return torch.abs(targ - pred).mean() |
Mean squared error between `pred` and `targ`. | def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean squared error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return F.mse_loss(pred, targ) |
Root mean squared error between `pred` and `targ`. | def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Root mean squared error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return torch.sqrt(F.mse_loss(pred, targ)) |
Mean squared logarithmic error between `pred` and `targ`. | def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean squared logarithmic error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ)) |
Explained variance between `pred` and `targ`. | def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Explained variance between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
var_pct = torch.var(targ - pred) / torch.var(targ)
return 1 - var_pct |
R2 score (coefficient of determination) between `pred` and `targ`. | def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
"R2 score (coefficient of determination) between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
u = torch.sum((targ - pred) ** 2)
d = torch.sum((targ - targ.mean()) ** 2)
return 1 - u / d |
Using trapezoid method to calculate the area under roc curve | def auc_roc_score(input:Tensor, targ:Tensor):
"Using trapezoid method to calculate the area under roc curve"
fpr, tpr = roc_curve(input, targ)
d = fpr[1:] - fpr[:-1]
sl1, sl2 = [slice(None)], [slice(None)]
sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1) |
Returns the false positive and true positive rates | def roc_curve(input:Tensor, targ:Tensor):
"Returns the false positive and true positive rates"
targ = (targ == 1)
desc_score_indices = torch.flip(input.argsort(-1), [-1])
input = input[desc_score_indices]
targ = targ[desc_score_indices]
d = input[1:] - input[:-1]
distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
fps = (1 + threshold_idxs - tps)
if tps[0] != 0 or fps[0] != 0:
fps = torch.cat((LongTensor([0]), fps))
tps = torch.cat((LongTensor([0]), tps))
fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
return fpr, tpr |
convert iterable object into numpy array | def A(*a):
"""convert iterable object into numpy array"""
return np.array(a[0]) if len(a)==1 else [np.array(o) for o in a] |
Convert numpy array into a pytorch tensor.
if Cuda is available and USE_GPU=True, store resulting tensor in GPU. | def T(a, half=False, cuda=True):
"""
Convert numpy array into a pytorch tensor.
if Cuda is available and USE_GPU=True, store resulting tensor in GPU.
"""
if not torch.is_tensor(a):
a = np.array(np.ascontiguousarray(a))
if a.dtype in (np.int8, np.int16, np.int32, np.int64):
a = torch.LongTensor(a.astype(np.int64))
elif a.dtype in (np.float32, np.float64):
a = to_half(a) if half else torch.FloatTensor(a)
else: raise NotImplementedError(a.dtype)
if cuda: a = to_gpu(a)
return a |
equivalent to create_variable, which creates a pytorch tensor | def V_(x, requires_grad=False, volatile=False):
'''equivalent to create_variable, which creates a pytorch tensor'''
return create_variable(x, volatile=volatile, requires_grad=requires_grad) |
creates a single or a list of pytorch tensors, depending on input x. | def V(x, requires_grad=False, volatile=False):
'''creates a single or a list of pytorch tensors, depending on input x. '''
return map_over(x, lambda o: V_(o, requires_grad, volatile)) |
returns an np.array object given an input of np.array, list, tuple, torch variable or tensor. | def to_np(v):
'''returns an np.array object given an input of np.array, list, tuple, torch variable or tensor.'''
if isinstance(v, float): return np.array(v)
if isinstance(v, (np.ndarray, np.generic)): return v
if isinstance(v, (list,tuple)): return [to_np(o) for o in v]
if isinstance(v, Variable): v=v.data
if torch.cuda.is_available():
if is_half_tensor(v): v=v.float()
if isinstance(v, torch.FloatTensor): v=v.float()
return v.cpu().numpy() |
puts pytorch variable to gpu, if cuda is available and USE_GPU is set to true. | def to_gpu(x, *args, **kwargs):
'''puts pytorch variable to gpu, if cuda is available and USE_GPU is set to true. '''
return x.cuda(*args, **kwargs) if USE_GPU else x |
A generator that returns sequence pieces, seperated by indexes specified in idxs. | def split_by_idxs(seq, idxs):
'''A generator that returns sequence pieces, seperated by indexes specified in idxs. '''
last = 0
for idx in idxs:
if not (-len(seq) <= idx < len(seq)):
raise KeyError(f'Idx {idx} is out-of-bounds')
yield seq[last:idx]
last = idx
yield seq[last:] |
splits iterables a in equal parts of size sz | def partition(a, sz):
"""splits iterables a in equal parts of size sz"""
return [a[i:i+sz] for i in range(0, len(a), sz)] |
A generator that yields chunks of iterable, chunk_size at a time. | def chunk_iter(iterable, chunk_size):
'''A generator that yields chunks of iterable, chunk_size at a time. '''
while True:
chunk = []
try:
for _ in range(chunk_size): chunk.append(next(iterable))
yield chunk
except StopIteration:
if chunk: yield chunk
break |
Apply `change` in brightness of image `x`. | def _brightness(x, change:uniform):
"Apply `change` in brightness of image `x`."
return x.add_(scipy.special.logit(change)) |
Rotate image by `degrees`. | def _rotate(degrees:uniform):
"Rotate image by `degrees`."
angle = degrees * math.pi / 180
return [[cos(angle), -sin(angle), 0.],
[sin(angle), cos(angle), 0.],
[0. , 0. , 1.]] |
`sw`,`sh` scale width,height - `c`,`r` focus col,row. | def _get_zoom_mat(sw:float, sh:float, c:float, r:float)->AffineMatrix:
"`sw`,`sh` scale width,height - `c`,`r` focus col,row."
return [[sw, 0, c],
[0, sh, r],
[0, 0, 1.]] |
Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom. | def _zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
s = 1-1/scale
col_c = s * (2*col_pct - 1)
row_c = s * (2*row_pct - 1)
return _get_zoom_mat(1/scale, 1/scale, col_c, row_c) |
Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom. | def _squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
if scale <= 1:
col_c = (1-scale) * (2*col_pct - 1)
return _get_zoom_mat(scale, 1, col_c, 0.)
else:
row_c = (1-1/scale) * (2*row_pct - 1)
return _get_zoom_mat(1, 1/scale, 0., row_c) |
Replace pixels by random neighbors at `magnitude`. | def _jitter(c, magnitude:uniform):
"Replace pixels by random neighbors at `magnitude`."
c.flow.add_((torch.rand_like(c.flow)-0.5)*magnitude*2)
return c |
Flip `x` horizontally. | def _flip_lr(x):
"Flip `x` horizontally."
#return x.flip(2)
if isinstance(x, ImagePoints):
x.flow.flow[...,0] *= -1
return x
return tensor(np.ascontiguousarray(np.array(x)[...,::-1])) |
Randomly flip `x` image based on `k`. | def _dihedral(x, k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
flips=[]
if k&1: flips.append(1)
if k&2: flips.append(2)
if flips: x = torch.flip(x,flips)
if k&4: x = x.transpose(1,2)
return x.contiguous() |
Randomly flip `x` image based on `k`. | def _dihedral_affine(k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
x = -1 if k&1 else 1
y = -1 if k&2 else 1
if k&4: return [[0, x, 0.],
[y, 0, 0],
[0, 0, 1.]]
return [[x, 0, 0.],
[0, y, 0],
[0, 0, 1.]] |
Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border'). | def _pad_default(x, padding:int, mode='reflection'):
"Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border')."
mode = _pad_mode_convert[mode]
return F.pad(x[None], (padding,)*4, mode=mode)[0] |
Cut out `n_holes` number of square holes of size `length` in image at random locations. | def _cutout(x, n_holes:uniform_int=1, length:uniform_int=40):
"Cut out `n_holes` number of square holes of size `length` in image at random locations."
h,w = x.shape[1:]
for n in range(n_holes):
h_y = np.random.randint(0, h)
h_x = np.random.randint(0, w)
y1 = int(np.clip(h_y - length / 2, 0, h))
y2 = int(np.clip(h_y + length / 2, 0, h))
x1 = int(np.clip(h_x - length / 2, 0, w))
x2 = int(np.clip(h_x + length / 2, 0, w))
x[:, y1:y2, x1:x2] = 0
return x |
Randomize one of the channels of the input image | def _rgb_randomize(x, channel:int=None, thresh:float=0.3):
"Randomize one of the channels of the input image"
if channel is None: channel = np.random.randint(0, x.shape[0] - 1)
x[channel] = torch.rand(x.shape[1:]) * np.random.uniform(0, thresh)
return x |
Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop. | def _crop_default(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop."
rows,cols = tis2hw(size)
row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
row = int((x.size(1)-rows+1) * row_pct)
col = int((x.size(2)-cols+1) * col_pct)
return x[:, row:row+rows, col:col+cols].contiguous() |
Crop and pad tfm - `row_pct`,`col_pct` sets focal point. | def _crop_pad_default(x, size, padding_mode='reflection', row_pct:uniform = 0.5, col_pct:uniform = 0.5):
"Crop and pad tfm - `row_pct`,`col_pct` sets focal point."
padding_mode = _pad_mode_convert[padding_mode]
size = tis2hw(size)
if x.shape[1:] == torch.Size(size): return x
rows,cols = size
row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
if x.size(1)<rows or x.size(2)<cols:
row_pad = max((rows-x.size(1)+1)//2, 0)
col_pad = max((cols-x.size(2)+1)//2, 0)
x = F.pad(x[None], (col_pad,col_pad,row_pad,row_pad), mode=padding_mode)[0]
row = int((x.size(1)-rows+1)*row_pct)
col = int((x.size(2)-cols+1)*col_pct)
x = x[:, row:row+rows, col:col+cols]
return x.contiguous() |
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