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train | _perspective_warp | Apply warp of `magnitude` to `c`. | fastai/vision/transform.py | def _perspective_warp(c, magnitude:partial(uniform,size=8)=0, invert=False):
"Apply warp of `magnitude` to `c`."
magnitude = magnitude.view(4,2)
targ_pts = [[x+m for x,m in zip(xs, ms)] for xs, ms in zip(_orig_pts, magnitude)]
return _do_perspective_warp(c, targ_pts, invert) | def _perspective_warp(c, magnitude:partial(uniform,size=8)=0, invert=False):
"Apply warp of `magnitude` to `c`."
magnitude = magnitude.view(4,2)
targ_pts = [[x+m for x,m in zip(xs, ms)] for xs, ms in zip(_orig_pts, magnitude)]
return _do_perspective_warp(c, targ_pts, invert) | [
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train | _symmetric_warp | Apply symmetric warp of `magnitude` to `c`. | fastai/vision/transform.py | def _symmetric_warp(c, magnitude:partial(uniform,size=4)=0, invert=False):
"Apply symmetric warp of `magnitude` to `c`."
m = listify(magnitude, 4)
targ_pts = [[-1-m[3],-1-m[1]], [-1-m[2],1+m[1]], [1+m[3],-1-m[0]], [1+m[2],1+m[0]]]
return _do_perspective_warp(c, targ_pts, invert) | def _symmetric_warp(c, magnitude:partial(uniform,size=4)=0, invert=False):
"Apply symmetric warp of `magnitude` to `c`."
m = listify(magnitude, 4)
targ_pts = [[-1-m[3],-1-m[1]], [-1-m[2],1+m[1]], [1+m[3],-1-m[0]], [1+m[2],1+m[0]]]
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train | _tilt | Tilt `c` field with random `direction` and `magnitude`. | fastai/vision/transform.py | def _tilt(c, direction:uniform_int, magnitude:uniform=0, invert=False):
"Tilt `c` field with random `direction` and `magnitude`."
orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
if direction == 0: targ_pts = [[-1,-1], [-1,1], [1,-1-magnitude], [1,1+magnitude]]
elif direction == 1: targ_pts = [[-1,-1-magni... | def _tilt(c, direction:uniform_int, magnitude:uniform=0, invert=False):
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orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
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train | get_transforms | Utility func to easily create a list of flip, rotate, `zoom`, warp, lighting transforms. | fastai/vision/transform.py | def get_transforms(do_flip:bool=True, flip_vert:bool=False, max_rotate:float=10., max_zoom:float=1.1,
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train | _compute_zs_mat | Utility routine to compute zoom/squish matrix. | fastai/vision/transform.py | def _compute_zs_mat(sz:TensorImageSize, scale:float, squish:float,
invert:bool, row_pct:float, col_pct:float)->AffineMatrix:
"Utility routine to compute zoom/squish matrix."
orig_ratio = math.sqrt(sz[1]/sz[0])
for s,r,i in zip(scale,squish, invert):
s,r = 1/math.sqrt(s),math.sqrt(... | def _compute_zs_mat(sz:TensorImageSize, scale:float, squish:float,
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"Utility routine to compute zoom/squish matrix."
orig_ratio = math.sqrt(sz[1]/sz[0])
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train | rand_resize_crop | Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`. | fastai/vision/transform.py | def rand_resize_crop(size:int, max_scale:float=2., ratios:Tuple[float,float]=(0.75,1.33)):
"Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`."
return [zoom_squish(scale=(1.,max_scale,8), squish=(*ratios,8), invert=(0.5,8), row_pct=(0.,1.), col_pct=(0.,1.)),
crop(... | def rand_resize_crop(size:int, max_scale:float=2., ratios:Tuple[float,float]=(0.75,1.33)):
"Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`."
return [zoom_squish(scale=(1.,max_scale,8), squish=(*ratios,8), invert=(0.5,8), row_pct=(0.,1.), col_pct=(0.,1.)),
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train | adjust_learning_rate | Sets the learning rate to the initial LR decayed by 10 every 30 epochs | old/fastai/models/cifar10/main_dxy.py | def adjust_learning_rate(optimizer, epoch, gammas, schedule):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.learning_rate
assert len(gammas) == len(schedule), "length of gammas and schedule should be equal"
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"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
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train | Learner.fit_gen | Method does some preparation before finally delegating to the 'fit' method for
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scheduler for varying the learning rate across iterations.
Method also computes the total number of epochs to fit based on provided 'cy... | old/fastai/learner.py | def fit_gen(self, model, data, layer_opt, n_cycle, cycle_len=None, cycle_mult=1, cycle_save_name=None, best_save_name=None,
use_clr=None, use_clr_beta=None, metrics=None, callbacks=None, use_wd_sched=False, norm_wds=False,
wds_sched_mult=None, use_swa=False, swa_start=1, swa... | def fit_gen(self, model, data, layer_opt, n_cycle, cycle_len=None, cycle_mult=1, cycle_save_name=None, best_save_name=None,
use_clr=None, use_clr_beta=None, metrics=None, callbacks=None, use_wd_sched=False, norm_wds=False,
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train | Learner.get_layer_opt | Method returns an instance of the LayerOptimizer class, which
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parts of the model.
An example of how a model maybe differentiated into different parts
for application of differential learning rates and weight decays is
... | old/fastai/learner.py | def get_layer_opt(self, lrs, wds):
"""Method returns an instance of the LayerOptimizer class, which
allows for setting differential learning rates for different
parts of the model.
An example of how a model maybe differentiated into different parts
for application of differenti... | def get_layer_opt(self, lrs, wds):
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train | Learner.lr_find | Helps you find an optimal learning rate for a model.
It uses the technique developed in the 2015 paper
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we simply keep increasing the learning rate from a very small value,
until the loss starts decreasing.
Args:
... | old/fastai/learner.py | def lr_find(self, start_lr=1e-5, end_lr=10, wds=None, linear=False, **kwargs):
"""Helps you find an optimal learning rate for a model.
It uses the technique developed in the 2015 paper
`Cyclical Learning Rates for Training Neural Networks`, where
we simply keep increasing the learnin... | def lr_find(self, start_lr=1e-5, end_lr=10, wds=None, linear=False, **kwargs):
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train | Learner.lr_find2 | A variant of lr_find() that helps find the best learning rate. It doesn't do
an epoch but a fixed num of iterations (which may be more or less than an epoch
depending on your data).
At each step, it computes the validation loss and the metrics on the next
batch of the validation data, so... | old/fastai/learner.py | def lr_find2(self, start_lr=1e-5, end_lr=10, num_it = 100, wds=None, linear=False, stop_dv=True, **kwargs):
"""A variant of lr_find() that helps find the best learning rate. It doesn't do
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... | def lr_find2(self, start_lr=1e-5, end_lr=10, num_it = 100, wds=None, linear=False, stop_dv=True, **kwargs):
"""A variant of lr_find() that helps find the best learning rate. It doesn't do
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train | Learner.predict_array | Args:
arr: a numpy array to be used as input to the model for prediction purposes
Returns:
a numpy array containing the predictions from the model | old/fastai/learner.py | def predict_array(self, arr):
"""
Args:
arr: a numpy array to be used as input to the model for prediction purposes
Returns:
a numpy array containing the predictions from the model
"""
if not isinstance(arr, np.ndarray): raise OSError(f'Not valid numpy arr... | def predict_array(self, arr):
"""
Args:
arr: a numpy array to be used as input to the model for prediction purposes
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a numpy array containing the predictions from the model
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train | Learner.TTA | Predict with Test Time Augmentation (TTA)
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""" Predict with Test Time Augmentation (TTA)
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is to increase the accuracy of predi... | def TTA(self, n_aug=4, is_test=False):
""" Predict with Test Time Augmentation (TTA)
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train | convert_weights | Convert the model `wgts` to go with a new vocabulary. | fastai/text/learner.py | def convert_weights(wgts:Weights, stoi_wgts:Dict[str,int], itos_new:Collection[str]) -> Weights:
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train | get_language_model | Create a language model from `arch` and its `config`, maybe `pretrained`. | fastai/text/learner.py | def get_language_model(arch:Callable, vocab_sz:int, config:dict=None, drop_mult:float=1.):
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train | language_model_learner | Create a `Learner` with a language model from `data` and `arch`. | fastai/text/learner.py | def language_model_learner(data:DataBunch, arch, config:dict=None, drop_mult:float=1., pretrained:bool=True,
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train | get_text_classifier | Create a text classifier from `arch` and its `config`, maybe `pretrained`. | fastai/text/learner.py | def get_text_classifier(arch:Callable, vocab_sz:int, n_class:int, bptt:int=70, max_len:int=20*70, config:dict=None,
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train | text_classifier_learner | Create a `Learner` with a text classifier from `data` and `arch`. | fastai/text/learner.py | def text_classifier_learner(data:DataBunch, arch:Callable, bptt:int=70, max_len:int=70*20, config:dict=None,
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train | RNNLearner.save_encoder | Save the encoder to `name` inside the model directory. | fastai/text/learner.py | def save_encoder(self, name:str):
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encoder = get_model(self.model)[0]
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train | RNNLearner.load_encoder | Load the encoder `name` from the model directory. | fastai/text/learner.py | def load_encoder(self, name:str, device:torch.device=None):
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train | RNNLearner.load_pretrained | Load a pretrained model and adapts it to the data vocabulary. | fastai/text/learner.py | def load_pretrained(self, wgts_fname:str, itos_fname:str, strict:bool=True):
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old_itos = pickle.load(open(itos_fname, 'rb'))
old_stoi = {v:k for k,v in enumerate(old_itos)}
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train | RNNLearner.get_preds | Return predictions and targets on the valid, train, or test set, depending on `ds_type`. | fastai/text/learner.py | def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
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self.model.reset()
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train | LanguageLearner.predict | Return the `n_words` that come after `text`. | fastai/text/learner.py | def predict(self, text:str, n_words:int=1, no_unk:bool=True, temperature:float=1., min_p:float=None, sep:str=' ',
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train | LanguageLearner.beam_search | Return the `n_words` that come after `text` using beam search. | fastai/text/learner.py | def beam_search(self, text:str, n_words:int, no_unk:bool=True, top_k:int=10, beam_sz:int=1000, temperature:float=1.,
sep:str=' ', decoder=decode_spec_tokens):
"Return the `n_words` that come after `text` using beam search."
ds = self.data.single_dl.dataset
self.model.reset()
... | def beam_search(self, text:str, n_words:int, no_unk:bool=True, top_k:int=10, beam_sz:int=1000, temperature:float=1.,
sep:str=' ', decoder=decode_spec_tokens):
"Return the `n_words` that come after `text` using beam search."
ds = self.data.single_dl.dataset
self.model.reset()
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train | LanguageLearner.show_results | Show `rows` result of predictions on `ds_type` dataset. | fastai/text/learner.py | def show_results(self, ds_type=DatasetType.Valid, rows:int=5, max_len:int=20):
from IPython.display import display, HTML
"Show `rows` result of predictions on `ds_type` dataset."
ds = self.dl(ds_type).dataset
x,y = self.data.one_batch(ds_type, detach=False, denorm=False)
preds = ... | def show_results(self, ds_type=DatasetType.Valid, rows:int=5, max_len:int=20):
from IPython.display import display, HTML
"Show `rows` result of predictions on `ds_type` dataset."
ds = self.dl(ds_type).dataset
x,y = self.data.one_batch(ds_type, detach=False, denorm=False)
preds = ... | [
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train | MultiBatchEncoder.concat | Concatenate the `arrs` along the batch dimension. | fastai/text/learner.py | def concat(self, arrs:Collection[Tensor])->Tensor:
"Concatenate the `arrs` along the batch dimension."
return [torch.cat([l[si] for l in arrs], dim=1) for si in range_of(arrs[0])] | def concat(self, arrs:Collection[Tensor])->Tensor:
"Concatenate the `arrs` along the batch dimension."
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train | batchnorm_2d | A batchnorm2d layer with `nf` features initialized depending on `norm_type`. | fastai/layers.py | def batchnorm_2d(nf:int, norm_type:NormType=NormType.Batch):
"A batchnorm2d layer with `nf` features initialized depending on `norm_type`."
bn = nn.BatchNorm2d(nf)
with torch.no_grad():
bn.bias.fill_(1e-3)
bn.weight.fill_(0. if norm_type==NormType.BatchZero else 1.)
return bn | def batchnorm_2d(nf:int, norm_type:NormType=NormType.Batch):
"A batchnorm2d layer with `nf` features initialized depending on `norm_type`."
bn = nn.BatchNorm2d(nf)
with torch.no_grad():
bn.bias.fill_(1e-3)
bn.weight.fill_(0. if norm_type==NormType.BatchZero else 1.)
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train | bn_drop_lin | Sequence of batchnorm (if `bn`), dropout (with `p`) and linear (`n_in`,`n_out`) layers followed by `actn`. | fastai/layers.py | def bn_drop_lin(n_in:int, n_out:int, bn:bool=True, p:float=0., actn:Optional[nn.Module]=None):
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"Sequence of batchnorm (if `bn`), dropout (with `p`) and linear (`n_in`,`n_out`) layers followed by `actn`."
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train | conv1d | Create and initialize a `nn.Conv1d` layer with spectral normalization. | fastai/layers.py | def conv1d(ni:int, no:int, ks:int=1, stride:int=1, padding:int=0, bias:bool=False):
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conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias=bias)
nn.init.kaiming_normal_(conv.weight)
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re... | def conv1d(ni:int, no:int, ks:int=1, stride:int=1, padding:int=0, bias:bool=False):
"Create and initialize a `nn.Conv1d` layer with spectral normalization."
conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias=bias)
nn.init.kaiming_normal_(conv.weight)
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train | conv2d | Create and initialize `nn.Conv2d` layer. `padding` defaults to `ks//2`. | fastai/layers.py | def conv2d(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias=False, init:LayerFunc=nn.init.kaiming_normal_) -> nn.Conv2d:
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"Create and initialize `nn.Conv2d` layer. `padding` defaults to `ks//2`."
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train | conv2d_trans | Create `nn.ConvTranspose2d` layer. | fastai/layers.py | def conv2d_trans(ni:int, nf:int, ks:int=2, stride:int=2, padding:int=0, bias=False) -> nn.ConvTranspose2d:
"Create `nn.ConvTranspose2d` layer."
return nn.ConvTranspose2d(ni, nf, kernel_size=ks, stride=stride, padding=padding, bias=bias) | def conv2d_trans(ni:int, nf:int, ks:int=2, stride:int=2, padding:int=0, bias=False) -> nn.ConvTranspose2d:
"Create `nn.ConvTranspose2d` layer."
return nn.ConvTranspose2d(ni, nf, kernel_size=ks, stride=stride, padding=padding, bias=bias) | [
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train | relu | Return a relu activation, maybe `leaky` and `inplace`. | fastai/layers.py | def relu(inplace:bool=False, leaky:float=None):
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return nn.LeakyReLU(inplace=inplace, negative_slope=leaky) if leaky is not None else nn.ReLU(inplace=inplace) | def relu(inplace:bool=False, leaky:float=None):
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train | conv_layer | Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers. | fastai/layers.py | def conv_layer(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias:bool=None, is_1d:bool=False,
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train | res_block | Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`. | fastai/layers.py | def res_block(nf, dense:bool=False, norm_type:Optional[NormType]=NormType.Batch, bottle:bool=False, **conv_kwargs):
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train | sigmoid_range | Sigmoid function with range `(low, high)` | fastai/layers.py | def sigmoid_range(x, low, high):
"Sigmoid function with range `(low, high)`"
return torch.sigmoid(x) * (high - low) + low | def sigmoid_range(x, low, high):
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train | icnr | ICNR init of `x`, with `scale` and `init` function. | fastai/layers.py | def icnr(x, scale=2, init=nn.init.kaiming_normal_):
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ni,nf,h,w = x.shape
ni2 = int(ni/(scale**2))
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k = k.contiguous().view(ni2, nf, -1)
k = k.repeat(1, 1, scale**2)
k = k.contiguous().view... | def icnr(x, scale=2, init=nn.init.kaiming_normal_):
"ICNR init of `x`, with `scale` and `init` function."
ni,nf,h,w = x.shape
ni2 = int(ni/(scale**2))
k = init(torch.zeros([ni2,nf,h,w])).transpose(0, 1)
k = k.contiguous().view(ni2, nf, -1)
k = k.repeat(1, 1, scale**2)
k = k.contiguous().view... | [
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train | CrossEntropyFlat | Same as `nn.CrossEntropyLoss`, but flattens input and target. | fastai/layers.py | def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
return FlattenedLoss(nn.CrossEntropyLoss, *args, axis=axis, **kwargs) | def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
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train | BCEWithLogitsFlat | Same as `nn.BCEWithLogitsLoss`, but flattens input and target. | fastai/layers.py | def BCEWithLogitsFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.BCEWithLogitsLoss`, but flattens input and target."
return FlattenedLoss(nn.BCEWithLogitsLoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) | def BCEWithLogitsFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.BCEWithLogitsLoss`, but flattens input and target."
return FlattenedLoss(nn.BCEWithLogitsLoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) | [
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train | BCEFlat | Same as `nn.BCELoss`, but flattens input and target. | fastai/layers.py | def BCEFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
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train | MSELossFlat | Same as `nn.MSELoss`, but flattens input and target. | fastai/layers.py | def MSELossFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.MSELoss`, but flattens input and target."
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train | simple_cnn | CNN with `conv_layer` defined by `actns`, `kernel_szs` and `strides`, plus batchnorm if `bn`. | fastai/layers.py | def simple_cnn(actns:Collection[int], kernel_szs:Collection[int]=None,
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kernel_szs = ifnone(kernel_szs, [3]*nl)
stride... | def simple_cnn(actns:Collection[int], kernel_szs:Collection[int]=None,
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train | trunc_normal_ | Truncated normal initialization. | fastai/layers.py | def trunc_normal_(x:Tensor, mean:float=0., std:float=1.) -> Tensor:
"Truncated normal initialization."
# From https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/12
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train | embedding | Create an embedding layer. | fastai/layers.py | def embedding(ni:int,nf:int) -> nn.Module:
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# See https://arxiv.org/abs/1711.09160
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train | MLFlowTracker.on_train_begin | Prepare MLflow experiment and log params | fastai/callbacks/mlflow.py | def on_train_begin(self, **kwargs: Any) -> None:
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train | MLFlowTracker.on_train_end | Store the notebook and stop run | fastai/callbacks/mlflow.py | def on_train_end(self, **kwargs: Any) -> None:
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train | pil2tensor | Convert PIL style `image` array to torch style image tensor. | fastai/vision/image.py | def pil2tensor(image:Union[NPImage,NPArray],dtype:np.dtype)->TensorImage:
"Convert PIL style `image` array to torch style image tensor."
a = np.asarray(image)
if a.ndim==2 : a = np.expand_dims(a,2)
a = np.transpose(a, (1, 0, 2))
a = np.transpose(a, (2, 1, 0))
return torch.from_numpy(a.astype(dty... | def pil2tensor(image:Union[NPImage,NPArray],dtype:np.dtype)->TensorImage:
"Convert PIL style `image` array to torch style image tensor."
a = np.asarray(image)
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train | image2np | Convert from torch style `image` to numpy/matplotlib style. | fastai/vision/image.py | def image2np(image:Tensor)->np.ndarray:
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train | bb2hw | Convert bounding box points from (width,height,center) to (height,width,top,left). | fastai/vision/image.py | def bb2hw(a:Collection[int])->np.ndarray:
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train | tis2hw | Convert `int` or `TensorImageSize` to (height,width) of an image. | fastai/vision/image.py | def tis2hw(size:Union[int,TensorImageSize]) -> Tuple[int,int]:
"Convert `int` or `TensorImageSize` to (height,width) of an image."
if type(size) is str: raise RuntimeError("Expected size to be an int or a tuple, got a string.")
return listify(size, 2) if isinstance(size, int) else listify(size[-2:],2) | def tis2hw(size:Union[int,TensorImageSize]) -> Tuple[int,int]:
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if type(size) is str: raise RuntimeError("Expected size to be an int or a tuple, got a string.")
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train | _draw_outline | Outline bounding box onto image `Patch`. | fastai/vision/image.py | def _draw_outline(o:Patch, lw:int):
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train | _draw_rect | Draw bounding box on `ax`. | fastai/vision/image.py | def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
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patch = ax.add_patch(patches.Rectangle(b[:2], *b[-2:], fill=False, edgecolor=color, lw=2))
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train | open_image | Return `Image` object created from image in file `fn`. | fastai/vision/image.py | def open_image(fn:PathOrStr, div:bool=True, convert_mode:str='RGB', cls:type=Image,
after_open:Callable=None)->Image:
"Return `Image` object created from image in file `fn`."
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
x = P... | def open_image(fn:PathOrStr, div:bool=True, convert_mode:str='RGB', cls:type=Image,
after_open:Callable=None)->Image:
"Return `Image` object created from image in file `fn`."
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
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train | open_mask | Return `ImageSegment` object create from mask in file `fn`. If `div`, divides pixel values by 255. | fastai/vision/image.py | def open_mask(fn:PathOrStr, div=False, convert_mode='L', after_open:Callable=None)->ImageSegment:
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train | open_mask_rle | Return `ImageSegment` object create from run-length encoded string in `mask_lre` with size in `shape`. | fastai/vision/image.py | def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
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x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
x = x.view(shape[1], shape[0], -1)
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x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
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train | rle_encode | Return run-length encoding string from `img`. | fastai/vision/image.py | def rle_encode(img:NPArrayMask)->str:
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runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
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train | rle_decode | Return an image array from run-length encoded string `mask_rle` with `shape`. | fastai/vision/image.py | def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
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train | show_image | Display `Image` in notebook. | fastai/vision/image.py | def show_image(img:Image, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, cmap:str='binary',
alpha:float=None, **kwargs)->plt.Axes:
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train | scale_flow | Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`. | fastai/vision/image.py | def scale_flow(flow, to_unit=True):
"Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`."
s = tensor([flow.size[0]/2,flow.size[1]/2])[None]
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else: flow.flow = (flow.flow+1)*s
return flow | def scale_flow(flow, to_unit=True):
"Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`."
s = tensor([flow.size[0]/2,flow.size[1]/2])[None]
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train | _grid_sample | Resample pixels in `coords` from `x` by `mode`, with `padding_mode` in ('reflection','border','zeros'). | fastai/vision/image.py | def _grid_sample(x:TensorImage, coords:FlowField, mode:str='bilinear', padding_mode:str='reflection', remove_out:bool=True)->TensorImage:
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train | _affine_mult | Multiply `c` by `m` - can adjust for rectangular shaped `c`. | fastai/vision/image.py | def _affine_mult(c:FlowField,m:AffineMatrix)->FlowField:
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if m is None: return c
size = c.flow.size()
h,w = c.size
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train | _affine_inv_mult | Applies the inverse affine transform described in `m` to `c`. | fastai/vision/image.py | def _affine_inv_mult(c, m):
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size = c.flow.size()
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m[0,1] *= h/w
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train | _round_multiple | Calc `x` to nearest multiple of `mult`. | fastai/vision/image.py | def _round_multiple(x:int, mult:int=None)->int:
"Calc `x` to nearest multiple of `mult`."
return (int(x/mult+0.5)*mult) if mult is not None else x | def _round_multiple(x:int, mult:int=None)->int:
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train | _get_crop_target | Calc crop shape of `target_px` to nearest multiple of `mult`. | fastai/vision/image.py | def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
"Calc crop shape of `target_px` to nearest multiple of `mult`."
target_r,target_c = tis2hw(target_px)
return _round_multiple(target_r,mult),_round_multiple(target_c,mult) | def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
"Calc crop shape of `target_px` to nearest multiple of `mult`."
target_r,target_c = tis2hw(target_px)
return _round_multiple(target_r,mult),_round_multiple(target_c,mult) | [
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train | _get_resize_target | Calc size of `img` to fit in `crop_target` - adjust based on `do_crop`. | fastai/vision/image.py | def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
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if crop_target is None: return None
ch,r,c = img.shape
target_r,target_c = crop_target
ratio = (min if do_crop else max)(r/target_r, c/target_c)
ret... | def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
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if crop_target is None: return None
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ratio = (min if do_crop else max)(r/target_r, c/target_c)
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train | plot_flat | Shortcut for `enumerate(subplots.flatten())` | fastai/vision/image.py | def plot_flat(r, c, figsize):
"Shortcut for `enumerate(subplots.flatten())`"
return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten()) | def plot_flat(r, c, figsize):
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return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten()) | [
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train | plot_multi | Call `func` for every combination of `r,c` on a subplot | fastai/vision/image.py | def plot_multi(func:Callable[[int,int,plt.Axes],None], r:int=1, c:int=1, figsize:Tuple=(12,6)):
"Call `func` for every combination of `r,c` on a subplot"
axes = plt.subplots(r, c, figsize=figsize)[1]
for i in range(r):
for j in range(c): func(i,j,axes[i,j]) | def plot_multi(func:Callable[[int,int,plt.Axes],None], r:int=1, c:int=1, figsize:Tuple=(12,6)):
"Call `func` for every combination of `r,c` on a subplot"
axes = plt.subplots(r, c, figsize=figsize)[1]
for i in range(r):
for j in range(c): func(i,j,axes[i,j]) | [
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train | show_multi | Call `func(i,j).show(ax)` for every combination of `r,c` | fastai/vision/image.py | def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)):
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plot_multi(lambda i,j,ax: func(i,j).show(ax), r, c, figsize=figsize) | def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)):
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train | show_all | Show all `imgs` using `r` rows | fastai/vision/image.py | def show_all(imgs:Collection[Image], r:int=1, c:Optional[int]=None, figsize=(12,6)):
"Show all `imgs` using `r` rows"
imgs = listify(imgs)
if c is None: c = len(imgs)//r
for i,ax in plot_flat(r,c,figsize): imgs[i].show(ax) | def show_all(imgs:Collection[Image], r:int=1, c:Optional[int]=None, figsize=(12,6)):
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imgs = listify(imgs)
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train | Image.apply_tfms | Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args. | fastai/vision/image.py | def apply_tfms(self, tfms:TfmList, do_resolve:bool=True, xtra:Optional[Dict[Callable,dict]]=None,
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train | Image.refresh | Apply any logit, flow, or affine transfers that have been sent to the `Image`. | fastai/vision/image.py | def refresh(self)->None:
"Apply any logit, flow, or affine transfers that have been sent to the `Image`."
if self._logit_px is not None:
self._px = self._logit_px.sigmoid_()
self._logit_px = None
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self.... | def refresh(self)->None:
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self._logit_px = None
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train | Image.save | Save the image to `fn`. | fastai/vision/image.py | def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn) | def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
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train | Image.flow | Access the flow-field grid after applying queued affine transforms. | fastai/vision/image.py | def flow(self)->FlowField:
"Access the flow-field grid after applying queued affine transforms."
if self._flow is None:
self._flow = _affine_grid(self.shape)
if self._affine_mat is not None:
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self._affine_m... | def flow(self)->FlowField:
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self._flow = _affine_mult(self._flow,self._affine_mat)
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train | Image.lighting | Equivalent to `image = sigmoid(func(logit(image)))`. | fastai/vision/image.py | def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any):
"Equivalent to `image = sigmoid(func(logit(image)))`."
self.logit_px = func(self.logit_px, *args, **kwargs)
return self | def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any):
"Equivalent to `image = sigmoid(func(logit(image)))`."
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train | Image.pixel | Equivalent to `image.px = func(image.px)`. | fastai/vision/image.py | def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':
"Equivalent to `image.px = func(image.px)`."
self.px = func(self.px, *args, **kwargs)
return self | def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':
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train | Image.coord | Equivalent to `image.flow = func(image.flow, image.size)`. | fastai/vision/image.py | def coord(self, func:CoordFunc, *args, **kwargs)->'Image':
"Equivalent to `image.flow = func(image.flow, image.size)`."
self.flow = func(self.flow, *args, **kwargs)
return self | def coord(self, func:CoordFunc, *args, **kwargs)->'Image':
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train | Image.affine | Equivalent to `image.affine_mat = image.affine_mat @ func()`. | fastai/vision/image.py | def affine(self, func:AffineFunc, *args, **kwargs)->'Image':
"Equivalent to `image.affine_mat = image.affine_mat @ func()`."
m = tensor(func(*args, **kwargs)).to(self.device)
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train | Image.resize | Resize the image to `size`, size can be a single int. | fastai/vision/image.py | def resize(self, size:Union[int,TensorImageSize])->'Image':
"Resize the image to `size`, size can be a single int."
assert self._flow is None
if isinstance(size, int): size=(self.shape[0], size, size)
if tuple(size)==tuple(self.shape): return self
self.flow = _affine_grid(size)
... | def resize(self, size:Union[int,TensorImageSize])->'Image':
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train | Image.affine_mat | Get the affine matrix that will be applied by `refresh`. | fastai/vision/image.py | def affine_mat(self)->AffineMatrix:
"Get the affine matrix that will be applied by `refresh`."
if self._affine_mat is None:
self._affine_mat = torch.eye(3).to(self.device)
return self._affine_mat | def affine_mat(self)->AffineMatrix:
"Get the affine matrix that will be applied by `refresh`."
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self._affine_mat = torch.eye(3).to(self.device)
return self._affine_mat | [
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train | Image.logit_px | Get logit(image.px). | fastai/vision/image.py | def logit_px(self)->LogitTensorImage:
"Get logit(image.px)."
if self._logit_px is None: self._logit_px = logit_(self.px)
return self._logit_px | def logit_px(self)->LogitTensorImage:
"Get logit(image.px)."
if self._logit_px is None: self._logit_px = logit_(self.px)
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train | Image.show | Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y` | fastai/vision/image.py | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str=None, y:Any=None, **kwargs):
"Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`"
cmap = ifnone(cmap, defaults.cmap)
ax = show_imag... | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str=None, y:Any=None, **kwargs):
"Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`"
cmap = ifnone(cmap, defaults.cmap)
ax = show_imag... | [
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train | ImageSegment.show | Show the `ImageSegment` on `ax`. | fastai/vision/image.py | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str='tab20', alpha:float=0.5, **kwargs):
"Show the `ImageSegment` on `ax`."
ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
interpolatio... | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str='tab20', alpha:float=0.5, **kwargs):
"Show the `ImageSegment` on `ax`."
ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
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train | ImagePoints.clone | Mimic the behavior of torch.clone for `ImagePoints` objects. | fastai/vision/image.py | def clone(self):
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return self.__class__(FlowField(self.size, self.flow.flow.clone()), scale=False, y_first=False) | def clone(self):
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train | ImagePoints.flow | Access the flow-field grid after applying queued affine and coord transforms. | fastai/vision/image.py | def flow(self)->FlowField:
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train | ImagePoints.coord | Put `func` with `args` and `kwargs` in `self.flow_func` for later. | fastai/vision/image.py | def coord(self, func:CoordFunc, *args, **kwargs)->'ImagePoints':
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train | ImagePoints.pixel | Equivalent to `self = func_flow(self)`. | fastai/vision/image.py | def pixel(self, func:PixelFunc, *args, **kwargs)->'ImagePoints':
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self = func(self, *args, **kwargs)
self.transformed=True
return self | def pixel(self, func:PixelFunc, *args, **kwargs)->'ImagePoints':
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self = func(self, *args, **kwargs)
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train | ImagePoints.resize | Resize the image to `size`, size can be a single int. | fastai/vision/image.py | def resize(self, size:Union[int,TensorImageSize]) -> 'ImagePoints':
"Resize the image to `size`, size can be a single int."
if isinstance(size, int): size=(1, size, size)
self._flow.size = size[1:]
return self | def resize(self, size:Union[int,TensorImageSize]) -> 'ImagePoints':
"Resize the image to `size`, size can be a single int."
if isinstance(size, int): size=(1, size, size)
self._flow.size = size[1:]
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train | ImagePoints.data | Return the points associated to this object. | fastai/vision/image.py | def data(self)->Tensor:
"Return the points associated to this object."
flow = self.flow #This updates flow before we test if some transforms happened
if self.transformed:
if 'remove_out' not in self.sample_kwargs or self.sample_kwargs['remove_out']:
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train | ImagePoints.show | Show the `ImagePoints` on `ax`. | fastai/vision/image.py | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, **kwargs):
"Show the `ImagePoints` on `ax`."
if ax is None: _,ax = plt.subplots(figsize=figsize)
pnt = scale_flow(FlowField(self.size, self.data), to_unit=False).flow.flip(1)
params = {'s... | def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, **kwargs):
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if ax is None: _,ax = plt.subplots(figsize=figsize)
pnt = scale_flow(FlowField(self.size, self.data), to_unit=False).flow.flip(1)
params = {'s... | [
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train | ImageBBox.clone | Mimic the behavior of torch.clone for `Image` objects. | fastai/vision/image.py | def clone(self) -> 'ImageBBox':
"Mimic the behavior of torch.clone for `Image` objects."
flow = FlowField(self.size, self.flow.flow.clone())
return self.__class__(flow, scale=False, y_first=False, labels=self.labels, pad_idx=self.pad_idx) | def clone(self) -> 'ImageBBox':
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flow = FlowField(self.size, self.flow.flow.clone())
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train | ImageBBox.create | Create an ImageBBox object from `bboxes`. | fastai/vision/image.py | def create(cls, h:int, w:int, bboxes:Collection[Collection[int]], labels:Collection=None, classes:dict=None,
pad_idx:int=0, scale:bool=True)->'ImageBBox':
"Create an ImageBBox object from `bboxes`."
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train | ImageBBox.show | Show the `ImageBBox` on `ax`. | fastai/vision/image.py | def show(self, y:Image=None, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
color:str='white', **kwargs):
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bboxes, lbls = self._compute_boxes()
h,w = self.flo... | def show(self, y:Image=None, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
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train | Transform.calc | Apply to image `x`, wrapping it if necessary. | fastai/vision/image.py | def calc(self, x:Image, *args:Any, **kwargs:Any)->Image:
"Apply to image `x`, wrapping it if necessary."
if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
else: return self.func(x, *args, **kwargs) | def calc(self, x:Image, *args:Any, **kwargs:Any)->Image:
"Apply to image `x`, wrapping it if necessary."
if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
else: return self.func(x, *args, **kwargs) | [
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train | url2path | Change `url` to a path. | fastai/datasets.py | def url2path(url, data=True, ext:str='.tgz'):
"Change `url` to a path."
name = url2name(url)
return datapath4file(name, ext=ext, archive=False) if data else modelpath4file(name, ext=ext) | def url2path(url, data=True, ext:str='.tgz'):
"Change `url` to a path."
name = url2name(url)
return datapath4file(name, ext=ext, archive=False) if data else modelpath4file(name, ext=ext) | [
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train | modelpath4file | Return model path to `filename`, checking locally first then in the config file. | fastai/datasets.py | def modelpath4file(filename, ext:str='.tgz'):
"Return model path to `filename`, checking locally first then in the config file."
local_path = URLs.LOCAL_PATH/'models'/filename
if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
else: return Config.model_path()/filename | def modelpath4file(filename, ext:str='.tgz'):
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local_path = URLs.LOCAL_PATH/'models'/filename
if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
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train | datapath4file | Return data path to `filename`, checking locally first then in the config file. | fastai/datasets.py | def datapath4file(filename, ext:str='.tgz', archive=True):
"Return data path to `filename`, checking locally first then in the config file."
local_path = URLs.LOCAL_PATH/'data'/filename
if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
elif archive: return Config.data_arc... | def datapath4file(filename, ext:str='.tgz', archive=True):
"Return data path to `filename`, checking locally first then in the config file."
local_path = URLs.LOCAL_PATH/'data'/filename
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