INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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Apply warp of magnitude to c. | 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) |
Apply symmetric warp of magnitude to c. | 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) |
Tilt c field with random direction and magnitude. | 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... |
Utility func to easily create a list of flip rotate zoom warp lighting transforms. | def get_transforms(do_flip:bool=True, flip_vert:bool=False, max_rotate:float=10., max_zoom:float=1.1,
max_lighting:float=0.2, max_warp:float=0.2, p_affine:float=0.75,
p_lighting:float=0.75, xtra_tfms:Optional[Collection[Transform]]=None)->Collection[Transform]:
"Utility func to... |
Utility routine to compute zoom/ squish matrix. | 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(... |
Randomly resize and crop the image to a ratio in ratios after a zoom of max_scale. | 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(... |
Sets the learning rate to the initial LR decayed by 10 every 30 epochs | 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"
for (gamma, step) in zip(gammas, schedule):
if (epoch >= ste... |
Method does some preparation before finally delegating to the fit method for fitting the model. Namely if cycle_len is defined it adds a Cosine Annealing scheduler for varying the learning rate across iterations. | 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... |
Method returns an instance of the LayerOptimizer class which allows for setting differential learning rates for different parts of the model. | 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... |
Method gets an instance of LayerOptimizer and delegates to self. fit_gen (.. ) | def fit(self, lrs, n_cycle, wds=None, **kwargs):
"""Method gets an instance of LayerOptimizer and delegates to self.fit_gen(..)
Note that one can specify a list of learning rates which, when appropriately
defined, will be applied to different segments of an architecture. This seems
mos... |
Helps you find an optimal learning rate for a model. | 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... |
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 it s slower than lr_find (). | 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
an epoch but a fixed num of iterations (which may be more or less than an epoch
depending on your data).
... |
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 | 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... |
Predict with Test Time Augmentation ( TTA ) | def TTA(self, n_aug=4, is_test=False):
""" Predict with Test Time Augmentation (TTA)
Additional to the original test/validation images, apply image augmentation to them
(just like for training images) and calculate the mean of predictions. The intent
is to increase the accuracy of predi... |
Wraps us the content of phases to send them to model. fit (.. ) | def fit_opt_sched(self, phases, cycle_save_name=None, best_save_name=None, stop_div=False, data_list=None, callbacks=None,
cut = None, use_swa=False, swa_start=1, swa_eval_freq=5, **kwargs):
"""Wraps us the content of phases to send them to model.fit(..)
This will split the train... |
Save the extra outputs for later and only returns the true output. | def on_loss_begin(self, last_output:Tuple[Tensor,Tensor,Tensor], **kwargs):
"Save the extra outputs for later and only returns the true output."
self.raw_out,self.out = last_output[1],last_output[2]
return {'last_output': last_output[0]} |
Apply AR and TAR to last_loss. | def on_backward_begin(self, last_loss:Rank0Tensor, last_input:Tensor, **kwargs):
"Apply AR and TAR to `last_loss`."
#AR and TAR
if self.alpha != 0.: last_loss += self.alpha * self.out[-1].float().pow(2).mean()
if self.beta != 0.:
h = self.raw_out[-1]
if len(h)>1:... |
Convert the model wgts to go with a new vocabulary. | def convert_weights(wgts:Weights, stoi_wgts:Dict[str,int], itos_new:Collection[str]) -> Weights:
"Convert the model `wgts` to go with a new vocabulary."
dec_bias, enc_wgts = wgts.get('1.decoder.bias', None), wgts['0.encoder.weight']
wgts_m = enc_wgts.mean(0)
if dec_bias is not None: bias_m = dec_bias.me... |
Create a language model from arch and its config maybe pretrained. | def get_language_model(arch:Callable, vocab_sz:int, config:dict=None, drop_mult:float=1.):
"Create a language model from `arch` and its `config`, maybe `pretrained`."
meta = _model_meta[arch]
config = ifnone(config, meta['config_lm'].copy())
for k in config.keys():
if k.endswith('_p'): config[k... |
Create a Learner with a language model from data and arch. | def language_model_learner(data:DataBunch, arch, config:dict=None, drop_mult:float=1., pretrained:bool=True,
pretrained_fnames:OptStrTuple=None, **learn_kwargs) -> 'LanguageLearner':
"Create a `Learner` with a language model from `data` and `arch`."
model = get_language_model(arch, le... |
Create a text classifier from arch and its config maybe pretrained. | def get_text_classifier(arch:Callable, vocab_sz:int, n_class:int, bptt:int=70, max_len:int=20*70, config:dict=None,
drop_mult:float=1., lin_ftrs:Collection[int]=None, ps:Collection[float]=None,
pad_idx:int=1) -> nn.Module:
"Create a text classifier from `arch` and it... |
Create a Learner with a text classifier from data and arch. | def text_classifier_learner(data:DataBunch, arch:Callable, bptt:int=70, max_len:int=70*20, config:dict=None,
pretrained:bool=True, drop_mult:float=1., lin_ftrs:Collection[int]=None,
ps:Collection[float]=None, **learn_kwargs) -> 'TextClassifierLearner':
"Crea... |
Save the encoder to name inside the model directory. | def save_encoder(self, name:str):
"Save the encoder to `name` inside the model directory."
encoder = get_model(self.model)[0]
if hasattr(encoder, 'module'): encoder = encoder.module
torch.save(encoder.state_dict(), self.path/self.model_dir/f'{name}.pth') |
Load the encoder name from the model directory. | def load_encoder(self, name:str, device:torch.device=None):
"Load the encoder `name` from the model directory."
encoder = get_model(self.model)[0]
if device is None: device = self.data.device
if hasattr(encoder, 'module'): encoder = encoder.module
encoder.load_state_dict(torch.lo... |
Load a pretrained model and adapts it to the data vocabulary. | def load_pretrained(self, wgts_fname:str, itos_fname:str, strict:bool=True):
"Load a pretrained model and adapts it to the data vocabulary."
old_itos = pickle.load(open(itos_fname, 'rb'))
old_stoi = {v:k for k,v in enumerate(old_itos)}
wgts = torch.load(wgts_fname, map_location=lambda st... |
Return predictions and targets on the valid train or test set depending on ds_type. | def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
ordered:bool=False) -> List[Tensor]:
"Return predictions and targets on the valid, train, or test set, depending on `ds_type`."
self.model.reset()
... |
Return the n_words that come after text. | def predict(self, text:str, n_words:int=1, no_unk:bool=True, temperature:float=1., min_p:float=None, sep:str=' ',
decoder=decode_spec_tokens):
"Return the `n_words` that come after `text`."
ds = self.data.single_dl.dataset
self.model.reset()
xb,yb = self.data.one_item(tex... |
Return the n_words that come after text using beam search. | 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()
... |
Show rows result of predictions on ds_type dataset. | 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 = ... |
Concatenate the arrs along the batch dimension. | 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])] |
A batchnorm2d layer with nf features initialized depending on norm_type. | 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 |
Sequence of batchnorm ( if bn ) dropout ( with p ) and linear ( n_in n_out ) layers followed by actn. | def bn_drop_lin(n_in:int, n_out:int, bn:bool=True, p:float=0., actn:Optional[nn.Module]=None):
"Sequence of batchnorm (if `bn`), dropout (with `p`) and linear (`n_in`,`n_out`) layers followed by `actn`."
layers = [nn.BatchNorm1d(n_in)] if bn else []
if p != 0: layers.append(nn.Dropout(p))
layers.append(... |
Create and initialize a nn. Conv1d layer with spectral normalization. | 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)
if bias: conv.bias.data.zero_()
re... |
Create and initialize nn. Conv2d layer. padding defaults to ks// 2. | 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:
"Create and initialize `nn.Conv2d` layer. `padding` defaults to `ks//2`."
if padding is None: padding = ks//2
return init_default(nn.Conv2d(ni, nf, kernel_size=ks, stride=st... |
Create nn. ConvTranspose2d layer. | 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) |
Return a relu activation maybe leaky and inplace. | def relu(inplace:bool=False, leaky:float=None):
"Return a relu activation, maybe `leaky` and `inplace`."
return nn.LeakyReLU(inplace=inplace, negative_slope=leaky) if leaky is not None else nn.ReLU(inplace=inplace) |
Create a sequence of convolutional ( ni to nf ) ReLU ( if use_activ ) and batchnorm ( if bn ) layers. | def conv_layer(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias:bool=None, is_1d:bool=False,
norm_type:Optional[NormType]=NormType.Batch, use_activ:bool=True, leaky:float=None,
transpose:bool=False, init:Callable=nn.init.kaiming_normal_, self_attention:bool=False):
"Crea... |
Resnet block of nf features. conv_kwargs are passed to conv_layer. | def res_block(nf, dense:bool=False, norm_type:Optional[NormType]=NormType.Batch, bottle:bool=False, **conv_kwargs):
"Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`."
norm2 = norm_type
if not dense and (norm_type==NormType.Batch): norm2 = NormType.BatchZero
nf_inner = nf//2 if bo... |
Sigmoid function with range ( low high ) | def sigmoid_range(x, low, high):
"Sigmoid function with range `(low, high)`"
return torch.sigmoid(x) * (high - low) + low |
ICNR init of x with scale and init function. | 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... |
Same as nn. CrossEntropyLoss but flattens input and target. | def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
return FlattenedLoss(nn.CrossEntropyLoss, *args, axis=axis, **kwargs) |
Same as nn. BCEWithLogitsLoss but flattens input and target. | 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) |
Same as nn. BCELoss but flattens input and target. | def BCEFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.BCELoss`, but flattens input and target."
return FlattenedLoss(nn.BCELoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) |
Same as nn. MSELoss but flattens input and target. | def MSELossFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.MSELoss`, but flattens input and target."
return FlattenedLoss(nn.MSELoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) |
CNN with conv_layer defined by actns kernel_szs and strides plus batchnorm if bn. | def simple_cnn(actns:Collection[int], kernel_szs:Collection[int]=None,
strides:Collection[int]=None, bn=False) -> nn.Sequential:
"CNN with `conv_layer` defined by `actns`, `kernel_szs` and `strides`, plus batchnorm if `bn`."
nl = len(actns)-1
kernel_szs = ifnone(kernel_szs, [3]*nl)
stride... |
Truncated normal initialization. | 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
return x.normal_().fmod_(2).mul_(std).add_(mean) |
Create an embedding layer. | def embedding(ni:int,nf:int) -> nn.Module:
"Create an embedding layer."
emb = nn.Embedding(ni, nf)
# See https://arxiv.org/abs/1711.09160
with torch.no_grad(): trunc_normal_(emb.weight, std=0.01)
return emb |
Prepare MLflow experiment and log params | def on_train_begin(self, **kwargs: Any) -> None:
"Prepare MLflow experiment and log params"
self.client = mlflow.tracking.MlflowClient(self.uri)
exp = self.client.get_experiment_by_name(self.exp_name)
self.exp_id = self.client.create_experiment(self.exp_name) if exp is None else exp.expe... |
Send loss and metrics values to MLFlow after each epoch | def on_epoch_end(self, epoch, **kwargs:Any)->None:
"Send loss and metrics values to MLFlow after each epoch"
if kwargs['smooth_loss'] is None or kwargs["last_metrics"] is None: return
metrics = [kwargs['smooth_loss']] + kwargs["last_metrics"]
for name, val in zip(self.metrics_names, metr... |
Store the notebook and stop run | def on_train_end(self, **kwargs: Any) -> None:
"Store the notebook and stop run"
self.client.log_artifact(run_id=self.run, local_path=self.nb_path)
self.client.set_terminated(run_id=self.run) |
Convert PIL style image array to torch style image tensor. | 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... |
Convert from torch style image to numpy/ matplotlib style. | def image2np(image:Tensor)->np.ndarray:
"Convert from torch style `image` to numpy/matplotlib style."
res = image.cpu().permute(1,2,0).numpy()
return res[...,0] if res.shape[2]==1 else res |
Convert bounding box points from ( width height center ) to ( height width top left ). | def bb2hw(a:Collection[int])->np.ndarray:
"Convert bounding box points from (width,height,center) to (height,width,top,left)."
return np.array([a[1],a[0],a[3]-a[1],a[2]-a[0]]) |
Convert int or TensorImageSize to ( height width ) of an image. | 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) |
Outline bounding box onto image Patch. | def _draw_outline(o:Patch, lw:int):
"Outline bounding box onto image `Patch`."
o.set_path_effects([patheffects.Stroke(
linewidth=lw, foreground='black'), patheffects.Normal()]) |
Draw bounding box on ax. | def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
"Draw bounding box on `ax`."
patch = ax.add_patch(patches.Rectangle(b[:2], *b[-2:], fill=False, edgecolor=color, lw=2))
_draw_outline(patch, 4)
if text is not None:
patch = ax.text(*b[:2], text, verticala... |
Return Image object created from image in file fn. | 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... |
Return ImageSegment object create from mask in file fn. If div divides pixel values by 255. | def open_mask(fn:PathOrStr, div=False, convert_mode='L', after_open:Callable=None)->ImageSegment:
"Return `ImageSegment` object create from mask in file `fn`. If `div`, divides pixel values by 255."
return open_image(fn, div=div, convert_mode=convert_mode, cls=ImageSegment, after_open=after_open) |
Return ImageSegment object create from run - length encoded string in mask_lre with size in shape. | def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
"Return `ImageSegment` object create from run-length encoded string in `mask_lre` with size in `shape`."
x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
x = x.view(shape[1], shape[0], -1)
return ImageSegment(x.permu... |
Return run - length encoding string from img. | def rle_encode(img:NPArrayMask)->str:
"Return run-length encoding string from `img`."
pixels = np.concatenate([[0], img.flatten() , [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x) for x in runs) |
Return an image array from run - length encoded string mask_rle with shape. | def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
"Return an image array from run-length encoded string `mask_rle` with `shape`."
s = mask_rle.split()
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
img = np.zeros(... |
Display Image in notebook. | 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:
"Display `Image` in notebook."
if ax is None: fig,ax = plt.subplots(figsize=figsize)
ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha, **kwargs)... |
Scale the coords in flow to - 1/ 1 or the image size depending on to_unit. | 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]
if to_unit: flow.flow = flow.flow/s-1
else: flow.flow = (flow.flow+1)*s
return flow |
Resample pixels in coords from x by mode with padding_mode in ( reflection border zeros ). | def _grid_sample(x:TensorImage, coords:FlowField, mode:str='bilinear', padding_mode:str='reflection', remove_out:bool=True)->TensorImage:
"Resample pixels in `coords` from `x` by `mode`, with `padding_mode` in ('reflection','border','zeros')."
coords = coords.flow.permute(0, 3, 1, 2).contiguous().permute(0, 2, ... |
Multiply c by m - can adjust for rectangular shaped c. | def _affine_mult(c:FlowField,m:AffineMatrix)->FlowField:
"Multiply `c` by `m` - can adjust for rectangular shaped `c`."
if m is None: return c
size = c.flow.size()
h,w = c.size
m[0,1] *= h/w
m[1,0] *= w/h
c.flow = c.flow.view(-1,2)
c.flow = torch.addmm(m[:2,2], c.flow, m[:2,:2].t()).vie... |
Applies the inverse affine transform described in m to c. | def _affine_inv_mult(c, m):
"Applies the inverse affine transform described in `m` to `c`."
size = c.flow.size()
h,w = c.size
m[0,1] *= h/w
m[1,0] *= w/h
c.flow = c.flow.view(-1,2)
a = torch.inverse(m[:2,:2].t())
c.flow = torch.mm(c.flow - m[:2,2], a).view(size)
return c |
Calc x to nearest multiple of mult. | 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 |
Calc crop shape of target_px to nearest multiple of 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) |
Calc size of img to fit in crop_target - adjust based on do_crop. | def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
"Calc size of `img` to fit in `crop_target` - adjust based on `do_crop`."
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... |
Shortcut for enumerate ( subplots. flatten () ) | def plot_flat(r, c, figsize):
"Shortcut for `enumerate(subplots.flatten())`"
return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten()) |
Call func for every combination of r c on a subplot | 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]) |
Call func ( i j ). show ( ax ) for every combination of r c | def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)):
"Call `func(i,j).show(ax)` for every combination of `r,c`"
plot_multi(lambda i,j,ax: func(i,j).show(ax), r, c, figsize=figsize) |
Show all imgs using r rows | 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) |
Apply all tfms to the Image if do_resolve picks value for random args. | def apply_tfms(self, tfms:TfmList, do_resolve:bool=True, xtra:Optional[Dict[Callable,dict]]=None,
size:Optional[Union[int,TensorImageSize]]=None, resize_method:ResizeMethod=None,
mult:int=None, padding_mode:str='reflection', mode:str='bilinear', remove_out:bool=True)->TensorImage:
... |
Apply any logit flow or affine transfers that have been sent to the Image. | 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
if self._affine_mat is not None or self._flow is not None:
self.... |
Save the image to fn. | def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn) |
Access the flow - field grid after applying queued affine transforms. | 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:
self._flow = _affine_mult(self._flow,self._affine_mat)
self._affine_m... |
Equivalent to image = sigmoid ( func ( logit ( image ))). | 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 |
Equivalent to image. px = func ( image. px ). | def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':
"Equivalent to `image.px = func(image.px)`."
self.px = func(self.px, *args, **kwargs)
return self |
Equivalent to image. flow = func ( image. flow image. size ). | 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 |
Equivalent to image. affine_mat = image. affine_mat | 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)
self.affine_mat = self.affine_mat @ m
return self |
Resize the image to size size can be a single int. | 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)
... |
Get the affine matrix that will be applied by refresh. | 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 |
Get logit ( image. px ). | 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 |
Show image on ax with title using cmap if single - channel overlaid with optional y | 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... |
Show the ImageSegment on ax. | 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... |
Mimic the behavior of torch. clone for ImagePoints objects. | def clone(self):
"Mimic the behavior of torch.clone for `ImagePoints` objects."
return self.__class__(FlowField(self.size, self.flow.flow.clone()), scale=False, y_first=False) |
Access the flow - field grid after applying queued affine and coord transforms. | def flow(self)->FlowField:
"Access the flow-field grid after applying queued affine and coord transforms."
if self._affine_mat is not None:
self._flow = _affine_inv_mult(self._flow, self._affine_mat)
self._affine_mat = None
self.transformed = True
if len(self.... |
Put func with args and kwargs in self. flow_func for later. | def coord(self, func:CoordFunc, *args, **kwargs)->'ImagePoints':
"Put `func` with `args` and `kwargs` in `self.flow_func` for later."
if 'invert' in kwargs: kwargs['invert'] = True
else: warn(f"{func.__name__} isn't implemented for {self.__class__}.")
self.flow_func.append(partial(func, ... |
Equivalent to self = func_flow ( self ). | def pixel(self, func:PixelFunc, *args, **kwargs)->'ImagePoints':
"Equivalent to `self = func_flow(self)`."
self = func(self, *args, **kwargs)
self.transformed=True
return self |
Resize the image to size size can be a single int. | 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 |
Return the points associated to this object. | 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']:
flow = _remove_poi... |
Show the ImagePoints on ax. | 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... |
Mimic the behavior of torch. clone for Image objects. | 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) |
Create an ImageBBox object from bboxes. | 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`."
if isinstance(bboxes, np.ndarray) and bboxes.dtype == np.object: bboxes = np.array([bb for... |
Show the ImageBBox on ax. | 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):
"Show the `ImageBBox` on `ax`."
if ax is None: _,ax = plt.subplots(figsize=figsize)
bboxes, lbls = self._compute_boxes()
h,w = self.flo... |
Apply to image x wrapping it if necessary. | 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) |
Change url to a path. | 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) |
Return model path to filename checking locally first then in the config file. | 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 |
Return data path to filename checking locally first then in the config file. | 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... |
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