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train | CycleGANTrainer.on_epoch_end | Put the various losses in the recorder. | fastai/vision/cyclegan.py | def on_epoch_end(self, last_metrics, **kwargs):
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train | CSVLogger.on_epoch_end | Add a line with `epoch` number, `smooth_loss` and `last_metrics`. | fastai/callbacks/csv_logger.py | def on_epoch_end(self, epoch: int, smooth_loss: Tensor, last_metrics: MetricsList, **kwargs: Any) -> bool:
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train | get_master | Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32. | fastai/callbacks/fp16.py | def get_master(layer_groups:ModuleList, flat_master:bool=False) -> Tuple[List[List[Tensor]], List[List[Tensor]]]:
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train | model_g2master_g | Copy the `model_params` gradients to `master_params` for the optimizer step. | fastai/callbacks/fp16.py | def model_g2master_g(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
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train | master2model | Copy `master_params` to `model_params`. | fastai/callbacks/fp16.py | def master2model(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
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train | MixedPrecision.on_train_begin | Prepare the master model. | fastai/callbacks/fp16.py | def on_train_begin(self, **kwargs:Any)->None:
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train | MixedPrecision.on_backward_begin | Scale gradients up by `self.loss_scale` to prevent underflow. | fastai/callbacks/fp16.py | def on_backward_begin(self, last_loss:Rank0Tensor, **kwargs:Any) -> Rank0Tensor:
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train | MixedPrecision.on_backward_end | Convert the gradients back to FP32 and divide them by the scale. | fastai/callbacks/fp16.py | def on_backward_end(self, **kwargs:Any)->None:
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train | MixedPrecision.on_step_end | Update the params from master to model and zero grad. | fastai/callbacks/fp16.py | def on_step_end(self, **kwargs:Any)->None:
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train | scale_min | Scale the image so that the smallest axis is of size targ.
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targ (int): target size
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train | zoom_cv | Zoom the center of image x by a factor of z+1 while retaining the original image size and proportion. | old/fastai/transforms.py | def zoom_cv(x,z):
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train | stretch_cv | Stretches image x horizontally by sr+1, and vertically by sc+1 while retaining the original image size and proportion. | old/fastai/transforms.py | def stretch_cv(x,sr,sc,interpolation=cv2.INTER_AREA):
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train | dihedral | Perform any of 8 permutations of 90-degrees rotations or flips for image x. | old/fastai/transforms.py | def dihedral(x, dih):
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train | lighting | Adjust image balance and contrast | old/fastai/transforms.py | def lighting(im, b, c):
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train | no_crop | Return a squared resized image | old/fastai/transforms.py | def no_crop(im, min_sz=None, interpolation=cv2.INTER_AREA):
""" Return a squared resized image """
r,c,*_ = im.shape
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train | center_crop | Return a center crop of an image | old/fastai/transforms.py | def center_crop(im, min_sz=None):
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r,c,*_ = im.shape
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train | googlenet_resize | Randomly crop an image with an aspect ratio and returns a squared resized image of size targ
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train | cutout | Cut out n_holes number of square holes of size length in image at random locations. Holes may overlap. | old/fastai/transforms.py | def cutout(im, n_holes, length):
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mask = np.ones((r, c), np.int32)
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train | scale_to | Calculate dimension of an image during scaling with aspect ratio | old/fastai/transforms.py | def scale_to(x, ratio, targ):
'''Calculate dimension of an image during scaling with aspect ratio'''
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train | crop | crop image into a square of size sz, | old/fastai/transforms.py | def crop(im, r, c, sz):
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return im[r:r+sz, c:c+sz] | def crop(im, r, c, sz):
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crop image into a square of size sz,
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train | to_bb | Convert mask YY to a bounding box, assumes 0 as background nonzero object | old/fastai/transforms.py | def to_bb(YY, y="deprecated"):
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cols,rows = np.nonzero(YY)
if len(cols)==0: return np.zeros(4, dtype=np.float32)
top_row = np.min(rows)
left_col = np.min(cols)
bottom_row = np.max(rows)
right_col = np.max(cols)
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train | coords2px | Transforming coordinates to pixels.
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train | compose | Apply a collection of transformation functions :fns: to images | old/fastai/transforms.py | def compose(im, y, fns):
""" Apply a collection of transformation functions :fns: to images """
for fn in fns:
#pdb.set_trace()
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im, y =fn(im, y)
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train | image_gen | Generate a standard set of transformations
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normalizer :
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denorm :
image denormalizing function
sz :
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tfms :
iterable collection of transformation functions
max_zoom : floa... | old/fastai/transforms.py | def image_gen(normalizer, denorm, sz, tfms=None, max_zoom=None, pad=0, crop_type=None,
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"""
Generate a standard set of transformations
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---------
normalizer :
image normalizing function
denorm :
... | def image_gen(normalizer, denorm, sz, tfms=None, max_zoom=None, pad=0, crop_type=None,
tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, scale=None):
"""
Generate a standard set of transformations
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---------
normalizer :
image normalizing function
denorm :
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train | tfms_from_stats | Given the statistics of the training image sets, returns separate training and validation transform functions | old/fastai/transforms.py | def tfms_from_stats(stats, sz, aug_tfms=None, max_zoom=None, pad=0, crop_type=CropType.RANDOM,
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train | tfms_from_model | Returns separate transformers of images for training and validation.
Transformers are constructed according to the image statistics given by the model. (See tfms_from_stats)
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f_model: model, pretrained or not pretrained | old/fastai/transforms.py | def tfms_from_model(f_model, sz, aug_tfms=None, max_zoom=None, pad=0, crop_type=CropType.RANDOM,
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train | get_image_files | Return list of files in `c` that are images. `check_ext` will filter to `image_extensions`. | fastai/vision/data.py | def get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False)->FilePathList:
"Return list of files in `c` that are images. `check_ext` will filter to `image_extensions`."
return get_files(c, extensions=(image_extensions if check_ext else None), recurse=recurse) | def get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False)->FilePathList:
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train | get_annotations | Open a COCO style json in `fname` and returns the lists of filenames (with maybe `prefix`) and labelled bboxes. | fastai/vision/data.py | def get_annotations(fname, prefix=None):
"Open a COCO style json in `fname` and returns the lists of filenames (with maybe `prefix`) and labelled bboxes."
annot_dict = json.load(open(fname))
id2images, id2bboxes, id2cats = {}, collections.defaultdict(list), collections.defaultdict(list)
classes = {}
... | def get_annotations(fname, prefix=None):
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annot_dict = json.load(open(fname))
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train | bb_pad_collate | Function that collect `samples` of labelled bboxes and adds padding with `pad_idx`. | fastai/vision/data.py | def bb_pad_collate(samples:BatchSamples, pad_idx:int=0) -> Tuple[FloatTensor, Tuple[LongTensor, LongTensor]]:
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train | normalize | Normalize `x` with `mean` and `std`. | fastai/vision/data.py | def normalize(x:TensorImage, mean:FloatTensor,std:FloatTensor)->TensorImage:
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train | denormalize | Denormalize `x` with `mean` and `std`. | fastai/vision/data.py | def denormalize(x:TensorImage, mean:FloatTensor,std:FloatTensor, do_x:bool=True)->TensorImage:
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train | _normalize_batch | `b` = `x`,`y` - normalize `x` array of imgs and `do_y` optionally `y`. | fastai/vision/data.py | def _normalize_batch(b:Tuple[Tensor,Tensor], mean:FloatTensor, std:FloatTensor, do_x:bool=True, do_y:bool=False)->Tuple[Tensor,Tensor]:
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train | normalize_funcs | Create normalize/denormalize func using `mean` and `std`, can specify `do_y` and `device`. | fastai/vision/data.py | def normalize_funcs(mean:FloatTensor, std:FloatTensor, do_x:bool=True, do_y:bool=False)->Tuple[Callable,Callable]:
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train | channel_view | Make channel the first axis of `x` and flatten remaining axes | fastai/vision/data.py | def channel_view(x:Tensor)->Tensor:
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train | download_images | Download images listed in text file `urls` to path `dest`, at most `max_pics` | fastai/vision/data.py | def download_images(urls:Collection[str], dest:PathOrStr, max_pics:int=1000, max_workers:int=8, timeout=4):
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urls = open(urls).read().strip().split("\n")[:max_pics]
dest = Path(dest)
dest.mkdir(exist_ok=True)
parallel(par... | def download_images(urls:Collection[str], dest:PathOrStr, max_pics:int=1000, max_workers:int=8, timeout=4):
"Download images listed in text file `urls` to path `dest`, at most `max_pics`"
urls = open(urls).read().strip().split("\n")[:max_pics]
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train | resize_to | Size to resize to, to hit `targ_sz` at same aspect ratio, in PIL coords (i.e w*h) | fastai/vision/data.py | def resize_to(img, targ_sz:int, use_min:bool=False):
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w,h = img.size
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w,h = img.size
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train | verify_image | Check if the image in `file` exists, maybe resize it and copy it in `dest`. | fastai/vision/data.py | def verify_image(file:Path, idx:int, delete:bool, max_size:Union[int,Tuple[int,int]]=None, dest:Path=None, n_channels:int=3,
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train | verify_images | Check if the images in `path` aren't broken, maybe resize them and copy it in `dest`. | fastai/vision/data.py | def verify_images(path:PathOrStr, delete:bool=True, max_workers:int=4, max_size:Union[int]=None, recurse:bool=False,
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train | _ll_pre_transform | Call `train_tfm` and `valid_tfm` after opening image, before converting from `PIL.Image` | fastai/vision/data.py | def _ll_pre_transform(self, train_tfm:List[Callable], valid_tfm:List[Callable]):
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self.train.x.after_open = compose(train_tfm)
self.valid.x.after_open = compose(valid_tfm)
return self | def _ll_pre_transform(self, train_tfm:List[Callable], valid_tfm:List[Callable]):
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self.train.x.after_open = compose(train_tfm)
self.valid.x.after_open = compose(valid_tfm)
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train | _db_pre_transform | Call `train_tfm` and `valid_tfm` after opening image, before converting from `PIL.Image` | fastai/vision/data.py | def _db_pre_transform(self, train_tfm:List[Callable], valid_tfm:List[Callable]):
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self.train_ds.x.after_open = compose(train_tfm)
self.valid_ds.x.after_open = compose(valid_tfm)
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self.train_ds.x.after_open = compose(train_tfm)
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train | _presize | Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform | fastai/vision/data.py | def _presize(self, size:int, val_xtra_size:int=32, scale:Tuple[float]=(0.08, 1.0), ratio:Tuple[float]=(0.75, 4./3.),
interpolation:int=2):
"Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform"
return self.pre_transform(
tvt.RandomResizedCrop(size, sc... | def _presize(self, size:int, val_xtra_size:int=32, scale:Tuple[float]=(0.08, 1.0), ratio:Tuple[float]=(0.75, 4./3.),
interpolation:int=2):
"Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform"
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train | ImageDataBunch.create_from_ll | Create an `ImageDataBunch` from `LabelLists` `lls` with potential `ds_tfms`. | fastai/vision/data.py | def create_from_ll(cls, lls:LabelLists, bs:int=64, val_bs:int=None, ds_tfms:Optional[TfmList]=None,
num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None,
test:Optional[PathOrStr]=None, collate_fn:Callable=data_collate, size:int=None, no_che... | def create_from_ll(cls, lls:LabelLists, bs:int=64, val_bs:int=None, ds_tfms:Optional[TfmList]=None,
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train | ImageDataBunch.from_folder | Create from imagenet style dataset in `path` with `train`,`valid`,`test` subfolders (or provide `valid_pct`). | fastai/vision/data.py | def from_folder(cls, path:PathOrStr, train:PathOrStr='train', valid:PathOrStr='valid',
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train | ImageDataBunch.from_df | Create from a `DataFrame` `df`. | fastai/vision/data.py | def from_df(cls, path:PathOrStr, df:pd.DataFrame, folder:PathOrStr=None, label_delim:str=None, valid_pct:float=0.2,
fn_col:IntsOrStrs=0, label_col:IntsOrStrs=1, suffix:str='', **kwargs:Any)->'ImageDataBunch':
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train | ImageDataBunch.from_csv | Create from a csv file in `path/csv_labels`. | fastai/vision/data.py | def from_csv(cls, path:PathOrStr, folder:PathOrStr=None, label_delim:str=None, csv_labels:PathOrStr='labels.csv',
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train | ImageDataBunch.from_lists | Create from list of `fnames` in `path`. | fastai/vision/data.py | def from_lists(cls, path:PathOrStr, fnames:FilePathList, labels:Collection[str], valid_pct:float=0.2,
item_cls:Callable=None, **kwargs):
"Create from list of `fnames` in `path`."
item_cls = ifnone(item_cls, ImageList)
fname2label = {f:l for (f,l) in zip(fnames, labels)}
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"Create from list of `fnames` in `path`."
item_cls = ifnone(item_cls, ImageList)
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train | ImageDataBunch.from_name_func | Create from list of `fnames` in `path` with `label_func`. | fastai/vision/data.py | def from_name_func(cls, path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, **kwargs):
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src = ImageList(fnames, path=path).split_by_rand_pct(valid_pct)
return cls.create_from_ll(src.label_from_func(label_func),... | def from_name_func(cls, path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, **kwargs):
"Create from list of `fnames` in `path` with `label_func`."
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train | ImageDataBunch.from_name_re | Create from list of `fnames` in `path` with re expression `pat`. | fastai/vision/data.py | def from_name_re(cls, path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, **kwargs):
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pat = re.compile(pat)
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res = pat.search(st... | def from_name_re(cls, path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, **kwargs):
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train | ImageDataBunch.single_from_classes | Create an empty `ImageDataBunch` in `path` with `classes`. Typically used for inference. | fastai/vision/data.py | def single_from_classes(path:Union[Path, str], classes:Collection[str], ds_tfms:TfmList=None, **kwargs):
"Create an empty `ImageDataBunch` in `path` with `classes`. Typically used for inference."
warn("""This method is deprecated and will be removed in a future version, use `load_learner` after
... | def single_from_classes(path:Union[Path, str], classes:Collection[str], ds_tfms:TfmList=None, **kwargs):
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warn("""This method is deprecated and will be removed in a future version, use `load_learner` after
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train | ImageDataBunch.batch_stats | Grab a batch of data and call reduction function `func` per channel | fastai/vision/data.py | def batch_stats(self, funcs:Collection[Callable]=None, ds_type:DatasetType=DatasetType.Train)->Tensor:
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funcs = ifnone(funcs, [torch.mean,torch.std])
x = self.one_batch(ds_type=ds_type, denorm=False)[0].cpu()
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train | ImageDataBunch.normalize | Add normalize transform using `stats` (defaults to `DataBunch.batch_stats`) | fastai/vision/data.py | def normalize(self, stats:Collection[Tensor]=None, do_x:bool=True, do_y:bool=False)->None:
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if getattr(self,'norm',False): raise Exception('Can not call normalize twice')
if stats is None: self.stats = self.batch_stats(... | def normalize(self, stats:Collection[Tensor]=None, do_x:bool=True, do_y:bool=False)->None:
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train | ImageList.open | Open image in `fn`, subclass and overwrite for custom behavior. | fastai/vision/data.py | def open(self, fn):
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return open_image(fn, convert_mode=self.convert_mode, after_open=self.after_open) | def open(self, fn):
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train | ImageList.from_folder | Get the list of files in `path` that have an image suffix. `recurse` determines if we search subfolders. | fastai/vision/data.py | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=None, **kwargs)->ItemList:
"Get the list of files in `path` that have an image suffix. `recurse` determines if we search subfolders."
extensions = ifnone(extensions, image_extensions)
return super().from_folder(path=path, extens... | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=None, **kwargs)->ItemList:
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train | ImageList.from_df | Get the filenames in `cols` of `df` with `folder` in front of them, `suffix` at the end. | fastai/vision/data.py | def from_df(cls, df:DataFrame, path:PathOrStr, cols:IntsOrStrs=0, folder:PathOrStr=None, suffix:str='', **kwargs)->'ItemList':
"Get the filenames in `cols` of `df` with `folder` in front of them, `suffix` at the end."
suffix = suffix or ''
res = super().from_df(df, path=path, cols=cols, **kwargs... | def from_df(cls, df:DataFrame, path:PathOrStr, cols:IntsOrStrs=0, folder:PathOrStr=None, suffix:str='', **kwargs)->'ItemList':
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train | ImageList.from_csv | Get the filenames in `path/csv_name` opened with `header`. | fastai/vision/data.py | def from_csv(cls, path:PathOrStr, csv_name:str, header:str='infer', **kwargs)->'ItemList':
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path = Path(path)
df = pd.read_csv(path/csv_name, header=header)
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path = Path(path)
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train | ImageList.show_xys | Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`. | fastai/vision/data.py | def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
"Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`."
rows = int(np.ceil(math.sqrt(len(xs))))
axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize)
for x,y,ax in zip(xs, ys,... | def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
"Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`."
rows = int(np.ceil(math.sqrt(len(xs))))
axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize)
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train | ImageList.show_xyzs | Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`. | fastai/vision/data.py | def show_xyzs(self, xs, ys, zs, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
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train | ObjectCategoryProcessor.generate_classes | Generate classes from unique `items` and add `background`. | fastai/vision/data.py | def generate_classes(self, items):
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classes = super().generate_classes([o[1] for o in items])
classes = ['background'] + list(classes)
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train | ImageImageList.show_xys | Show the `xs` (inputs) and `ys`(targets) on a figure of `figsize`. | fastai/vision/data.py | def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
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train | ImageImageList.show_xyzs | Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`. | fastai/vision/data.py | def show_xyzs(self, xs, ys, zs, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
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train | gpu_mem_get | get total, used and free memory (in MBs) for gpu `id`. if `id` is not passed, currently selected torch device is used | fastai/utils/mem.py | def gpu_mem_get(id=None):
"get total, used and free memory (in MBs) for gpu `id`. if `id` is not passed, currently selected torch device is used"
if not use_gpu: return GPUMemory(0, 0, 0)
if id is None: id = torch.cuda.current_device()
try:
handle = pynvml.nvmlDeviceGetHandleByIndex(id)
... | def gpu_mem_get(id=None):
"get total, used and free memory (in MBs) for gpu `id`. if `id` is not passed, currently selected torch device is used"
if not use_gpu: return GPUMemory(0, 0, 0)
if id is None: id = torch.cuda.current_device()
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train | gpu_with_max_free_mem | get [gpu_id, its_free_ram] for the first gpu with highest available RAM | fastai/utils/mem.py | def gpu_with_max_free_mem():
"get [gpu_id, its_free_ram] for the first gpu with highest available RAM"
mem_all = gpu_mem_get_all()
if not len(mem_all): return None, 0
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id = np.argmax(free_all)
return id, free_all[id] | def gpu_with_max_free_mem():
"get [gpu_id, its_free_ram] for the first gpu with highest available RAM"
mem_all = gpu_mem_get_all()
if not len(mem_all): return None, 0
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id = np.argmax(free_all)
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train | gpu_mem_trace | A decorator that runs `GPUMemTrace` w/ report on func | fastai/utils/mem.py | def gpu_mem_trace(func):
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@functools.wraps(func)
def wrapper(*args, **kwargs):
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@functools.wraps(func)
def wrapper(*args, **kwargs):
with GPUMemTrace(ctx=func.__qualname__, on_exit_report=True):
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train | reduce_mem_usage | iterate through all the columns of a dataframe and modify the data type
to reduce memory usage. | fastai/utils/mem.py | def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
#Removed from debugging
columns = df.columns
... | def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
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columns = df.columns
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train | GPUMemTrace._get_ctx | Return ' (ctx: subctx)' or ' (ctx)' or ' (subctx)' or '' depending on this and constructor arguments | fastai/utils/mem.py | def _get_ctx(self, subctx=None):
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l = []
if self.ctx is not None: l.append(self.ctx)
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return '' if len(l) == 0 else f" ({': '.j... | def _get_ctx(self, subctx=None):
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l = []
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train | _learner_distributed | Put `learn` on distributed training with `cuda_id`. | fastai/distributed.py | def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'):
"Put `learn` on distributed training with `cuda_id`."
learn.callbacks.append(DistributedTrainer(learn, cuda_id))
learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir))
return learn | def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'):
"Put `learn` on distributed training with `cuda_id`."
learn.callbacks.append(DistributedTrainer(learn, cuda_id))
learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir))
return learn | [
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train | xresnet18 | Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | fastai/vision/models/xresnet2.py | def xresnet18(pretrained=False, **kwargs):
"""Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet18']))
... | def xresnet18(pretrained=False, **kwargs):
"""Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet18']))
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train | xresnet50_2 | Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | fastai/vision/models/xresnet2.py | def xresnet50_2(pretrained=False, **kwargs):
"""Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet50']))
... | def xresnet50_2(pretrained=False, **kwargs):
"""Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet50']))
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train | loss_batch | Calculate loss and metrics for a batch, call out to callbacks as necessary. | fastai/basic_train.py | def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
cb_handler:Optional[CallbackHandler]=None)->Tuple[Union[Tensor,int,float,str]]:
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cb_handler = ifnone(cb_handler, Ca... | def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
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"Calculate loss and metrics for a batch, call out to callbacks as necessary."
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train | get_preds | Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batches `n_batch`. | fastai/basic_train.py | def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,
activ:nn.Module=None, loss_func:OptLossFunc=None, n_batch:Optional[int]=None) -> List[Tensor]:
"Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batc... | def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,
activ:nn.Module=None, loss_func:OptLossFunc=None, n_batch:Optional[int]=None) -> List[Tensor]:
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train | validate | Calculate `loss_func` of `model` on `dl` in evaluation mode. | fastai/basic_train.py | def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
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train | train_epoch | Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`. | fastai/basic_train.py | def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
"Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`."
model.train()
for xb,yb in dl:
loss = loss_func(model(xb), yb)
loss.backward()
opt.ste... | def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
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model.train()
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train | fit | Fit the `model` on `data` and learn using `loss_func` and `opt`. | fastai/basic_train.py | def fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None)->None:
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train | load_learner | Load a `Learner` object saved with `export_state` in `path/file` with empty data, optionally add `test` and load on `cpu`. `file` can be file-like (file or buffer) | fastai/basic_train.py | def load_learner(path:PathOrStr, file:PathLikeOrBinaryStream='export.pkl', test:ItemList=None, **db_kwargs):
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train | Recorder.on_train_begin | Initialize recording status at beginning of training. | fastai/basic_train.py | def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
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self.pbar = pbar
self.names = ['epoch', 'train_loss'] if self.no_val else ['epoch', 'train_loss', 'valid_loss']
self.metrics_names = metrics_nam... | def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
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train | Recorder.on_batch_begin | Record learning rate and momentum at beginning of batch. | fastai/basic_train.py | def on_batch_begin(self, train, **kwargs:Any)->None:
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if train:
self.lrs.append(self.opt.lr)
self.moms.append(self.opt.mom) | def on_batch_begin(self, train, **kwargs:Any)->None:
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self.lrs.append(self.opt.lr)
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train | Recorder.on_backward_begin | Record the loss before any other callback has a chance to modify it. | fastai/basic_train.py | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
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self.pbar.child.comment = f'{smooth_loss:.4f}' | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
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self.losses.append(smooth_loss)
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train | Recorder.on_epoch_end | Save epoch info: num_batch, smooth_loss, metrics. | fastai/basic_train.py | def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
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self.nb_batches.append(num_batch)
if last_metrics is not None: self.val_losses.append(last_metrics[0])
... | def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
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"Save epoch info: num_batch, smooth_loss, metrics."
self.nb_batches.append(num_batch)
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train | Recorder.format_stats | Format stats before printing. | fastai/basic_train.py | def format_stats(self, stats:TensorOrNumList)->None:
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str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
if self.add_time: str_stats.append(f... | def format_stats(self, stats:TensorOrNumList)->None:
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str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
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train | Recorder.add_metric_names | Add `names` to the inner metric names. | fastai/basic_train.py | def add_metric_names(self, names):
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if hasattr(self, '_added_met_names'): self._added_met_names += names
else: self._added_met_names = names | def add_metric_names(self, names):
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train | Recorder.plot_lr | Plot learning rate, `show_moms` to include momentum. | fastai/basic_train.py | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
... | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
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train | Recorder.plot | Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient | fastai/basic_train.py | def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
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train | Recorder.plot_losses | Plot training and validation losses. | fastai/basic_train.py | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
losses = self._split_list(self.losses, skip_start, skip_end)
iterations = self._split_list(range_of(self.losses), skip_s... | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
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train | Recorder.plot_metrics | Plot metrics collected during training. | fastai/basic_train.py | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
... | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
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train | call_plac | Decorator to create a simple CLI from `func` using `plac` | fastai/script.py | def call_plac(f):
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train | QRNN.reset | If your convolutional window is greater than 1 and you save previous xs, you must reset at the beginning of each new sequence. | fastai/text/models/qrnn.py | def reset(self):
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train | start_new_kernel | Start a new kernel, and return its Manager and Client | docs_src/nbval/kernel.py | def start_new_kernel(startup_timeout=60, kernel_name='python', **kwargs):
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train | RunningKernel.get_message | Function is used to get a message from the iopub channel.
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train | RunningKernel.await_idle | Poll the iopub stream until an idle message is received for the given parent ID | docs_src/nbval/kernel.py | def await_idle(self, parent_id, timeout):
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train | RunningKernel.stop | Instructs the kernel process to stop channels
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val_pct : (int, float), validation set percentage
seed : seed value for RandomState
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n : int, Total number of elements in the data set.
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train | resize_img | Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ. | old/fastai/dataset.py | def resize_img(fname, targ, path, new_path, fn=None):
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Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ.
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if os.path.exis... | 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.exis... | [
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] | fastai/fastai | python | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/dataset.py#L34-L44 | [
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train | resize_imgs | 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. | old/fastai/dataset.py | 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 fo... | 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 fo... | [
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"subdir... | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 |
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