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Fixed `mode` `padding` and random crop of `size` | def rand_pad(padding:int, size:int, mode:str='reflection'):
"Fixed `mode` `padding` and random crop of `size`"
return [pad(padding=padding,mode=mode),
crop(size=size, **rand_pos)] |
Randomized version of `zoom`. | def rand_zoom(scale:uniform=1.0, p:float=1.):
"Randomized version of `zoom`."
return zoom(scale=scale, **rand_pos, p=p) |
Randomized version of `crop_pad`. | def rand_crop(*args, padding_mode='reflection', p:float=1.):
"Randomized version of `crop_pad`."
return crop_pad(*args, **rand_pos, padding_mode=padding_mode, p=p) |
Randomly zoom and/or crop. | def zoom_crop(scale:float, do_rand:bool=False, p:float=1.0):
"Randomly zoom and/or crop."
zoom_fn = rand_zoom if do_rand else zoom
crop_fn = rand_crop if do_rand else crop_pad
return [zoom_fn(scale=scale, p=p), crop_fn()] |
Find 8 coeff mentioned [here](https://web.archive.org/web/20150222120106/xenia.media.mit.edu/~cwren/interpolator/). | def _find_coeffs(orig_pts:Points, targ_pts:Points)->Tensor:
"Find 8 coeff mentioned [here](https://web.archive.org/web/20150222120106/xenia.media.mit.edu/~cwren/interpolator/)."
matrix = []
#The equations we'll need to solve.
for p1, p2 in zip(targ_pts, orig_pts):
matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])
matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])
A = FloatTensor(matrix)
B = FloatTensor(orig_pts).view(8, 1)
#The 8 scalars we seek are solution of AX = B
return _solve_func(B,A)[0][:,0] |
Transform `coords` with `coeffs`. | def _apply_perspective(coords:FlowField, coeffs:Points)->FlowField:
"Transform `coords` with `coeffs`."
size = coords.flow.size()
#compress all the dims expect the last one ang adds ones, coords become N * 3
coords.flow = coords.flow.view(-1,2)
#Transform the coeffs in a 3*3 matrix with a 1 at the bottom left
coeffs = torch.cat([coeffs, FloatTensor([1])]).view(3,3)
coords.flow = torch.addmm(coeffs[:,2], coords.flow, coeffs[:,:2].t())
coords.flow.mul_(1/coords.flow[:,2].unsqueeze(1))
coords.flow = coords.flow[:,:2].view(size)
return coords |
Apply warp to `targ_pts` from `_orig_pts` to `c` `FlowField`. | def _do_perspective_warp(c:FlowField, targ_pts:Points, invert=False):
"Apply warp to `targ_pts` from `_orig_pts` to `c` `FlowField`."
if invert: return _apply_perspective(c, _find_coeffs(targ_pts, _orig_pts))
return _apply_perspective(c, _find_coeffs(_orig_pts, targ_pts)) |
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-magnitude], [-1,1+magnitude], [1,-1], [1,1]]
elif direction == 2: targ_pts = [[-1,-1], [-1-magnitude,1], [1,-1], [1+magnitude,1]]
elif direction == 3: targ_pts = [[-1-magnitude,-1], [-1,1], [1+magnitude,-1], [1,1]]
coeffs = _find_coeffs(targ_pts, _orig_pts) if invert else _find_coeffs(_orig_pts, targ_pts)
return _apply_perspective(c, coeffs) |
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 easily create a list of flip, rotate, `zoom`, warp, lighting transforms."
res = [rand_crop()]
if do_flip: res.append(dihedral_affine() if flip_vert else flip_lr(p=0.5))
if max_warp: res.append(symmetric_warp(magnitude=(-max_warp,max_warp), p=p_affine))
if max_rotate: res.append(rotate(degrees=(-max_rotate,max_rotate), p=p_affine))
if max_zoom>1: res.append(rand_zoom(scale=(1.,max_zoom), p=p_affine))
if max_lighting:
res.append(brightness(change=(0.5*(1-max_lighting), 0.5*(1+max_lighting)), p=p_lighting))
res.append(contrast(scale=(1-max_lighting, 1/(1-max_lighting)), p=p_lighting))
# train , valid
return (res + listify(xtra_tfms), [crop_pad()]) |
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(r)
if s * r <= 1 and s / r <= 1: #Test if we are completely inside the picture
w,h = (s/r, s*r) if i else (s*r,s/r)
col_c = (1-w) * (2*col_pct - 1)
row_c = (1-h) * (2*row_pct - 1)
return _get_zoom_mat(w, h, col_c, row_c)
#Fallback, hack to emulate a center crop without cropping anything yet.
if orig_ratio > 1: return _get_zoom_mat(1/orig_ratio**2, 1, 0, 0.)
else: return _get_zoom_mat(1, orig_ratio**2, 0, 0.) |
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(size=size)] |
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 >= step):
lr = lr * gamma
else:
break
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr |
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.
Method also computes the total number of epochs to fit based on provided 'cycle_len',
'cycle_mult', and 'n_cycle' parameters.
Args:
model (Learner): Any neural architecture for solving a supported problem.
Eg. ResNet-34, RNN_Learner etc.
data (ModelData): An instance of ModelData.
layer_opt (LayerOptimizer): An instance of the LayerOptimizer class
n_cycle (int): number of cycles
cycle_len (int): number of epochs before lr is reset to the initial value.
E.g if cycle_len = 3, then the lr is varied between a maximum
and minimum value over 3 epochs.
cycle_mult (int): additional parameter for influencing how the lr resets over
the cycles. For an intuitive explanation, please see
https://github.com/fastai/fastai/blob/master/courses/dl1/lesson1.ipynb
cycle_save_name (str): use to save the weights at end of each cycle (requires
use_clr, use_clr_beta or cycle_len arg)
best_save_name (str): use to save weights of best model during training.
metrics (function): some function for evaluating a desired metric. Eg. accuracy.
callbacks (list(Callback)): callbacks to apply during the training.
use_wd_sched (bool, optional): set to True to enable weight regularization using
the technique mentioned in https://arxiv.org/abs/1711.05101. When this is True
alone (see below), the regularization is detached from gradient update and
applied directly to the weights.
norm_wds (bool, optional): when this is set to True along with use_wd_sched, the
regularization factor is normalized with each training cycle.
wds_sched_mult (function, optional): when this is provided along with use_wd_sched
as True, the value computed by this function is multiplied with the regularization
strength. This function is passed the WeightDecaySchedule object. And example
function that can be passed is:
f = lambda x: np.array(x.layer_opt.lrs) / x.init_lrs
use_swa (bool, optional): when this is set to True, it will enable the use of
Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will
include an additional model (in the swa_model attribute) for keeping track of the
average weights as described in the paper. All testing of this technique so far has
been in image classification, so use in other contexts is not guaranteed to work.
swa_start (int, optional): if use_swa is set to True, then this determines the epoch
to start keeping track of the average weights. It is 1-indexed per the paper's
conventions.
swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency
at which to evaluate the performance of the swa_model. This evaluation can be costly
for models using BatchNorm (requiring a full pass through the data), which is why the
default is not to evaluate after each epoch.
Returns:
None | 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_eval_freq=5, **kwargs):
"""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.
Method also computes the total number of epochs to fit based on provided 'cycle_len',
'cycle_mult', and 'n_cycle' parameters.
Args:
model (Learner): Any neural architecture for solving a supported problem.
Eg. ResNet-34, RNN_Learner etc.
data (ModelData): An instance of ModelData.
layer_opt (LayerOptimizer): An instance of the LayerOptimizer class
n_cycle (int): number of cycles
cycle_len (int): number of epochs before lr is reset to the initial value.
E.g if cycle_len = 3, then the lr is varied between a maximum
and minimum value over 3 epochs.
cycle_mult (int): additional parameter for influencing how the lr resets over
the cycles. For an intuitive explanation, please see
https://github.com/fastai/fastai/blob/master/courses/dl1/lesson1.ipynb
cycle_save_name (str): use to save the weights at end of each cycle (requires
use_clr, use_clr_beta or cycle_len arg)
best_save_name (str): use to save weights of best model during training.
metrics (function): some function for evaluating a desired metric. Eg. accuracy.
callbacks (list(Callback)): callbacks to apply during the training.
use_wd_sched (bool, optional): set to True to enable weight regularization using
the technique mentioned in https://arxiv.org/abs/1711.05101. When this is True
alone (see below), the regularization is detached from gradient update and
applied directly to the weights.
norm_wds (bool, optional): when this is set to True along with use_wd_sched, the
regularization factor is normalized with each training cycle.
wds_sched_mult (function, optional): when this is provided along with use_wd_sched
as True, the value computed by this function is multiplied with the regularization
strength. This function is passed the WeightDecaySchedule object. And example
function that can be passed is:
f = lambda x: np.array(x.layer_opt.lrs) / x.init_lrs
use_swa (bool, optional): when this is set to True, it will enable the use of
Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will
include an additional model (in the swa_model attribute) for keeping track of the
average weights as described in the paper. All testing of this technique so far has
been in image classification, so use in other contexts is not guaranteed to work.
swa_start (int, optional): if use_swa is set to True, then this determines the epoch
to start keeping track of the average weights. It is 1-indexed per the paper's
conventions.
swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency
at which to evaluate the performance of the swa_model. This evaluation can be costly
for models using BatchNorm (requiring a full pass through the data), which is why the
default is not to evaluate after each epoch.
Returns:
None
"""
if cycle_save_name:
assert use_clr or use_clr_beta or cycle_len, "cycle_save_name argument requires either of the following arguments use_clr, use_clr_beta, cycle_len"
if callbacks is None: callbacks=[]
if metrics is None: metrics=self.metrics
if use_wd_sched:
# This needs to come before CosAnneal() because we need to read the initial learning rate from
# layer_opt.lrs - but CosAnneal() alters the layer_opt.lrs value initially (divides by 100)
if np.sum(layer_opt.wds) == 0:
print('fit() warning: use_wd_sched is set to True, but weight decay(s) passed are 0. Use wds to '
'pass weight decay values.')
batch_per_epoch = len(data.trn_dl)
cl = cycle_len if cycle_len else 1
self.wd_sched = WeightDecaySchedule(layer_opt, batch_per_epoch, cl, cycle_mult, n_cycle,
norm_wds, wds_sched_mult)
callbacks += [self.wd_sched]
if use_clr is not None:
clr_div,cut_div = use_clr[:2]
moms = use_clr[2:] if len(use_clr) > 2 else None
cycle_end = self.get_cycle_end(cycle_save_name)
assert cycle_len, "use_clr requires cycle_len arg"
self.sched = CircularLR(layer_opt, len(data.trn_dl)*cycle_len, on_cycle_end=cycle_end, div=clr_div, cut_div=cut_div,
momentums=moms)
elif use_clr_beta is not None:
div,pct = use_clr_beta[:2]
moms = use_clr_beta[2:] if len(use_clr_beta) > 3 else None
cycle_end = self.get_cycle_end(cycle_save_name)
assert cycle_len, "use_clr_beta requires cycle_len arg"
self.sched = CircularLR_beta(layer_opt, len(data.trn_dl)*cycle_len, on_cycle_end=cycle_end, div=div,
pct=pct, momentums=moms)
elif cycle_len:
cycle_end = self.get_cycle_end(cycle_save_name)
cycle_batches = len(data.trn_dl)*cycle_len
self.sched = CosAnneal(layer_opt, cycle_batches, on_cycle_end=cycle_end, cycle_mult=cycle_mult)
elif not self.sched: self.sched=LossRecorder(layer_opt)
callbacks+=[self.sched]
if best_save_name is not None:
callbacks+=[SaveBestModel(self, layer_opt, metrics, best_save_name)]
if use_swa:
# make a copy of the model to track average weights
self.swa_model = copy.deepcopy(model)
callbacks+=[SWA(model, self.swa_model, swa_start)]
n_epoch = int(sum_geom(cycle_len if cycle_len else 1, cycle_mult, n_cycle))
return fit(model, data, n_epoch, layer_opt.opt, self.crit,
metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
swa_eval_freq=swa_eval_freq, **kwargs) |
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 differential learning rates and weight decays is
seen in ../.../courses/dl1/fastai/conv_learner.py, using the dict
'model_meta'. Currently, this seems supported only for convolutional
networks such as VGG-19, ResNet-XX etc.
Args:
lrs (float or list(float)): learning rate(s) for the model
wds (float or list(float)): weight decay parameter(s).
Returns:
An instance of a LayerOptimizer | 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 differential learning rates and weight decays is
seen in ../.../courses/dl1/fastai/conv_learner.py, using the dict
'model_meta'. Currently, this seems supported only for convolutional
networks such as VGG-19, ResNet-XX etc.
Args:
lrs (float or list(float)): learning rate(s) for the model
wds (float or list(float)): weight decay parameter(s).
Returns:
An instance of a LayerOptimizer
"""
return LayerOptimizer(self.opt_fn, self.get_layer_groups(), lrs, wds) |
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
mostly relevant to ImageNet-trained models, where we want to alter the layers
closest to the images by much smaller amounts.
Likewise, a single or list of weight decay parameters can be specified, which
if appropriate for a model, will apply variable weight decay parameters to
different segments of the model.
Args:
lrs (float or list(float)): learning rate for the model
n_cycle (int): number of cycles (or iterations) to fit the model for
wds (float or list(float)): weight decay parameter(s).
kwargs: other arguments
Returns:
None | 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
mostly relevant to ImageNet-trained models, where we want to alter the layers
closest to the images by much smaller amounts.
Likewise, a single or list of weight decay parameters can be specified, which
if appropriate for a model, will apply variable weight decay parameters to
different segments of the model.
Args:
lrs (float or list(float)): learning rate for the model
n_cycle (int): number of cycles (or iterations) to fit the model for
wds (float or list(float)): weight decay parameter(s).
kwargs: other arguments
Returns:
None
"""
self.sched = None
layer_opt = self.get_layer_opt(lrs, wds)
return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **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 learning rate from a very small value,
until the loss starts decreasing.
Args:
start_lr (float/numpy array) : Passing in a numpy array allows you
to specify learning rates for a learner's layer_groups
end_lr (float) : The maximum learning rate to try.
wds (iterable/float)
Examples:
As training moves us closer to the optimal weights for a model,
the optimal learning rate will be smaller. We can take advantage of
that knowledge and provide lr_find() with a starting learning rate
1000x smaller than the model's current learning rate as such:
>> learn.lr_find(lr/1000)
>> lrs = np.array([ 1e-4, 1e-3, 1e-2 ])
>> learn.lr_find(lrs / 1000)
Notes:
lr_find() may finish before going through each batch of examples if
the loss decreases enough.
.. _Cyclical Learning Rates for Training Neural Networks:
http://arxiv.org/abs/1506.01186 | 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 learning rate from a very small value,
until the loss starts decreasing.
Args:
start_lr (float/numpy array) : Passing in a numpy array allows you
to specify learning rates for a learner's layer_groups
end_lr (float) : The maximum learning rate to try.
wds (iterable/float)
Examples:
As training moves us closer to the optimal weights for a model,
the optimal learning rate will be smaller. We can take advantage of
that knowledge and provide lr_find() with a starting learning rate
1000x smaller than the model's current learning rate as such:
>> learn.lr_find(lr/1000)
>> lrs = np.array([ 1e-4, 1e-3, 1e-2 ])
>> learn.lr_find(lrs / 1000)
Notes:
lr_find() may finish before going through each batch of examples if
the loss decreases enough.
.. _Cyclical Learning Rates for Training Neural Networks:
http://arxiv.org/abs/1506.01186
"""
self.save('tmp')
layer_opt = self.get_layer_opt(start_lr, wds)
self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr, linear=linear)
self.fit_gen(self.model, self.data, layer_opt, 1, **kwargs)
self.load('tmp') |
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().
Args:
start_lr (float/numpy array) : Passing in a numpy array allows you
to specify learning rates for a learner's layer_groups
end_lr (float) : The maximum learning rate to try.
num_it : the number of iterations you want it to run
wds (iterable/float)
stop_dv : stops (or not) when the losses starts to explode. | 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).
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().
Args:
start_lr (float/numpy array) : Passing in a numpy array allows you
to specify learning rates for a learner's layer_groups
end_lr (float) : The maximum learning rate to try.
num_it : the number of iterations you want it to run
wds (iterable/float)
stop_dv : stops (or not) when the losses starts to explode.
"""
self.save('tmp')
layer_opt = self.get_layer_opt(start_lr, wds)
self.sched = LR_Finder2(layer_opt, num_it, end_lr, linear=linear, metrics=self.metrics, stop_dv=stop_dv)
self.fit_gen(self.model, self.data, layer_opt, num_it//len(self.data.trn_dl) + 1, all_val=True, **kwargs)
self.load('tmp') |
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 array')
self.model.eval()
return to_np(self.model(to_gpu(V(T(arr))))) |
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 predictions by examining the images using multiple
perspectives.
n_aug: a number of augmentation images to use per original image
is_test: indicate to use test images; otherwise use validation images
Returns:
(tuple): a tuple containing:
log predictions (numpy.ndarray): log predictions (i.e. `np.exp(log_preds)` will return probabilities)
targs (numpy.ndarray): target values when `is_test==False`; zeros otherwise. | 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 predictions by examining the images using multiple
perspectives.
n_aug: a number of augmentation images to use per original image
is_test: indicate to use test images; otherwise use validation images
Returns:
(tuple): a tuple containing:
log predictions (numpy.ndarray): log predictions (i.e. `np.exp(log_preds)` will return probabilities)
targs (numpy.ndarray): target values when `is_test==False`; zeros otherwise.
"""
dl1 = self.data.test_dl if is_test else self.data.val_dl
dl2 = self.data.test_aug_dl if is_test else self.data.aug_dl
preds1,targs = predict_with_targs(self.model, dl1)
preds1 = [preds1]*math.ceil(n_aug/4)
preds2 = [predict_with_targs(self.model, dl2)[0] for i in tqdm(range(n_aug), leave=False)]
return np.stack(preds1+preds2), targs |
Wraps us the content of phases to send them to model.fit(..)
This will split the training in several parts, each with their own learning rates/
wds/momentums/optimizer detailed in phases.
Additionaly we can add a list of different data objets in data_list to train
on different datasets (to change the size for instance) for each of these groups.
Args:
phases: a list of TrainingPhase objects
stop_div: when True, stops the training if the loss goes too high
data_list: a list of different Data objects.
kwargs: other arguments
use_swa (bool, optional): when this is set to True, it will enable the use of
Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will
include an additional model (in the swa_model attribute) for keeping track of the
average weights as described in the paper. All testing of this technique so far has
been in image classification, so use in other contexts is not guaranteed to work.
swa_start (int, optional): if use_swa is set to True, then this determines the epoch
to start keeping track of the average weights. It is 1-indexed per the paper's
conventions.
swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency
at which to evaluate the performance of the swa_model. This evaluation can be costly
for models using BatchNorm (requiring a full pass through the data), which is why the
default is not to evaluate after each epoch.
Returns:
None | 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 training in several parts, each with their own learning rates/
wds/momentums/optimizer detailed in phases.
Additionaly we can add a list of different data objets in data_list to train
on different datasets (to change the size for instance) for each of these groups.
Args:
phases: a list of TrainingPhase objects
stop_div: when True, stops the training if the loss goes too high
data_list: a list of different Data objects.
kwargs: other arguments
use_swa (bool, optional): when this is set to True, it will enable the use of
Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will
include an additional model (in the swa_model attribute) for keeping track of the
average weights as described in the paper. All testing of this technique so far has
been in image classification, so use in other contexts is not guaranteed to work.
swa_start (int, optional): if use_swa is set to True, then this determines the epoch
to start keeping track of the average weights. It is 1-indexed per the paper's
conventions.
swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency
at which to evaluate the performance of the swa_model. This evaluation can be costly
for models using BatchNorm (requiring a full pass through the data), which is why the
default is not to evaluate after each epoch.
Returns:
None
"""
if data_list is None: data_list=[]
if callbacks is None: callbacks=[]
layer_opt = LayerOptimizer(phases[0].opt_fn, self.get_layer_groups(), 1e-2, phases[0].wds)
if len(data_list) == 0: nb_batches = [len(self.data.trn_dl)] * len(phases)
else: nb_batches = [len(data.trn_dl) for data in data_list]
self.sched = OptimScheduler(layer_opt, phases, nb_batches, stop_div)
callbacks.append(self.sched)
metrics = self.metrics
if best_save_name is not None:
callbacks+=[SaveBestModel(self, layer_opt, metrics, best_save_name)]
if use_swa:
# make a copy of the model to track average weights
self.swa_model = copy.deepcopy(self.model)
callbacks+=[SWA(self.model, self.swa_model, swa_start)]
n_epochs = [phase.epochs for phase in phases] if cut is None else cut
if len(data_list)==0: data_list = [self.data]
return fit(self.model, data_list, n_epochs,layer_opt, self.crit,
metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
swa_eval_freq=swa_eval_freq, **kwargs) |
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: last_loss += self.beta * (h[:,1:] - h[:,:-1]).float().pow(2).mean()
return {'last_loss': last_loss} |
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.mean(0)
new_w = enc_wgts.new_zeros((len(itos_new),enc_wgts.size(1))).zero_()
if dec_bias is not None: new_b = dec_bias.new_zeros((len(itos_new),)).zero_()
for i,w in enumerate(itos_new):
r = stoi_wgts[w] if w in stoi_wgts else -1
new_w[i] = enc_wgts[r] if r>=0 else wgts_m
if dec_bias is not None: new_b[i] = dec_bias[r] if r>=0 else bias_m
wgts['0.encoder.weight'] = new_w
if '0.encoder_dp.emb.weight' in wgts: wgts['0.encoder_dp.emb.weight'] = new_w.clone()
wgts['1.decoder.weight'] = new_w.clone()
if dec_bias is not None: wgts['1.decoder.bias'] = new_b
return wgts |
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] *= drop_mult
tie_weights,output_p,out_bias = map(config.pop, ['tie_weights', 'output_p', 'out_bias'])
init = config.pop('init') if 'init' in config else None
encoder = arch(vocab_sz, **config)
enc = encoder.encoder if tie_weights else None
decoder = LinearDecoder(vocab_sz, config[meta['hid_name']], output_p, tie_encoder=enc, bias=out_bias)
model = SequentialRNN(encoder, decoder)
return model if init is None else model.apply(init) |
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, len(data.vocab.itos), config=config, drop_mult=drop_mult)
meta = _model_meta[arch]
learn = LanguageLearner(data, model, split_func=meta['split_lm'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames)
learn.freeze()
if pretrained_fnames is not None:
fnames = [learn.path/learn.model_dir/f'{fn}.{ext}' for fn,ext in zip(pretrained_fnames, ['pth', 'pkl'])]
learn.load_pretrained(*fnames)
learn.freeze()
return learn |
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 its `config`, maybe `pretrained`."
meta = _model_meta[arch]
config = ifnone(config, meta['config_clas'].copy())
for k in config.keys():
if k.endswith('_p'): config[k] *= drop_mult
if lin_ftrs is None: lin_ftrs = [50]
if ps is None: ps = [0.1]*len(lin_ftrs)
layers = [config[meta['hid_name']] * 3] + lin_ftrs + [n_class]
ps = [config.pop('output_p')] + ps
init = config.pop('init') if 'init' in config else None
encoder = MultiBatchEncoder(bptt, max_len, arch(vocab_sz, **config), pad_idx=pad_idx)
model = SequentialRNN(encoder, PoolingLinearClassifier(layers, ps))
return model if init is None else model.apply(init) |
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':
"Create a `Learner` with a text classifier from `data` and `arch`."
model = get_text_classifier(arch, len(data.vocab.itos), data.c, bptt=bptt, max_len=max_len,
config=config, drop_mult=drop_mult, lin_ftrs=lin_ftrs, ps=ps)
meta = _model_meta[arch]
learn = RNNLearner(data, model, split_func=meta['split_clas'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames, strict=False)
learn.freeze()
return learn |
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.load(self.path/self.model_dir/f'{name}.pth'))
encoder.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device))
self.freeze() |
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 storage, loc: storage)
if 'model' in wgts: wgts = wgts['model']
wgts = convert_weights(wgts, old_stoi, self.data.train_ds.vocab.itos)
self.model.load_state_dict(wgts, strict=strict) |
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()
if ordered: np.random.seed(42)
preds = super().get_preds(ds_type=ds_type, with_loss=with_loss, n_batch=n_batch, pbar=pbar)
if ordered and hasattr(self.dl(ds_type), 'sampler'):
np.random.seed(42)
sampler = [i for i in self.dl(ds_type).sampler]
reverse_sampler = np.argsort(sampler)
preds = [p[reverse_sampler] for p in preds]
return(preds) |
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(text)
new_idx = []
for _ in range(n_words): #progress_bar(range(n_words), leave=False):
res = self.pred_batch(batch=(xb,yb))[0][-1]
#if len(new_idx) == 0: self.model[0].select_hidden([0])
if no_unk: res[self.data.vocab.stoi[UNK]] = 0.
if min_p is not None:
if (res >= min_p).float().sum() == 0:
warn(f"There is no item with probability >= {min_p}, try a lower value.")
else: res[res < min_p] = 0.
if temperature != 1.: res.pow_(1 / temperature)
idx = torch.multinomial(res, 1).item()
new_idx.append(idx)
xb = xb.new_tensor([idx])[None]
return text + sep + sep.join(decoder(self.data.vocab.textify(new_idx, sep=None))) |
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()
xb, yb = self.data.one_item(text)
nodes = None
xb = xb.repeat(top_k, 1)
nodes = xb.clone()
scores = xb.new_zeros(1).float()
with torch.no_grad():
for k in progress_bar(range(n_words), leave=False):
out = F.log_softmax(self.model(xb)[0][:,-1], dim=-1)
if no_unk: out[:,self.data.vocab.stoi[UNK]] = -float('Inf')
values, indices = out.topk(top_k, dim=-1)
scores = (-values + scores[:,None]).view(-1)
indices_idx = torch.arange(0,nodes.size(0))[:,None].expand(nodes.size(0), top_k).contiguous().view(-1)
sort_idx = scores.argsort()[:beam_sz]
scores = scores[sort_idx]
nodes = torch.cat([nodes[:,None].expand(nodes.size(0),top_k,nodes.size(1)),
indices[:,:,None].expand(nodes.size(0),top_k,1),], dim=2)
nodes = nodes.view(-1, nodes.size(2))[sort_idx]
self.model[0].select_hidden(indices_idx[sort_idx])
xb = nodes[:,-1][:,None]
if temperature != 1.: scores.div_(temperature)
node_idx = torch.multinomial(torch.exp(-scores), 1).item()
return text + sep + sep.join(decoder(self.data.vocab.textify([i.item() for i in nodes[node_idx][1:] ], sep=None))) |
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 = self.pred_batch(batch=(x,y))
y = y.view(*x.size())
z = preds.view(*x.size(),-1).argmax(dim=2)
xs = [ds.x.reconstruct(grab_idx(x, i)) for i in range(rows)]
ys = [ds.x.reconstruct(grab_idx(y, i)) for i in range(rows)]
zs = [ds.x.reconstruct(grab_idx(z, i)) for i in range(rows)]
items,names = [],['text', 'target', 'pred']
for i, (x,y,z) in enumerate(zip(xs,ys,zs)):
txt_x = ' '.join(x.text.split(' ')[:max_len])
txt_y = ' '.join(y.text.split(' ')[max_len-1:2*max_len-1])
txt_z = ' '.join(z.text.split(' ')[max_len-1:2*max_len-1])
items.append([txt_x, txt_y, txt_z])
items = np.array(items)
df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
with pd.option_context('display.max_colwidth', -1):
display(HTML(df.to_html(index=False))) |
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(nn.Linear(n_in, n_out))
if actn is not None: layers.append(actn)
return layers |
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_()
return spectral_norm(conv) |
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=stride, padding=padding, bias=bias), init) |
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):
"Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers."
if padding is None: padding = (ks-1)//2 if not transpose else 0
bn = norm_type in (NormType.Batch, NormType.BatchZero)
if bias is None: bias = not bn
conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d
conv = init_default(conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding), init)
if norm_type==NormType.Weight: conv = weight_norm(conv)
elif norm_type==NormType.Spectral: conv = spectral_norm(conv)
layers = [conv]
if use_activ: layers.append(relu(True, leaky=leaky))
if bn: layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf))
if self_attention: layers.append(SelfAttention(nf))
return nn.Sequential(*layers) |
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 bottle else nf
return SequentialEx(conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs),
MergeLayer(dense)) |
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([nf,ni,h,w]).transpose(0, 1)
x.data.copy_(k) |
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)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i],
norm_type=(NormType.Batch if bn and i<(len(strides)-1) else None)) for i in range_of(strides)]
layers.append(PoolFlatten())
return nn.Sequential(*layers) |
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.experiment_id
run = self.client.create_run(experiment_id=self.exp_id)
self.run = run.info.run_uuid
for k,v in self.params.items():
self.client.log_param(run_id=self.run, key=k, value=v) |
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, metrics):
self.client.log_metric(self.run, name, np.float(val)) |
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(dtype, copy=False) ) |
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, verticalalignment='top', color=color, fontsize=text_size, weight='bold')
_draw_outline(patch,1) |
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 = PIL.Image.open(fn).convert(convert_mode)
if after_open: x = after_open(x)
x = pil2tensor(x,np.float32)
if div: x.div_(255)
return cls(x) |
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.permute(2,0,1)) |
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(shape[0]*shape[1], dtype=np.uint)
for low, up in zip(starts, ends): img[low:up] = 1
return img.reshape(shape) |
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)
if hide_axis: ax.axis('off')
return ax |
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, 3, 1) # optimize layout for grid_sample
if mode=='bilinear': # hack to get smoother downwards resampling
mn,mx = coords.min(),coords.max()
# max amount we're affine zooming by (>1 means zooming in)
z = 1/(mx-mn).item()*2
# amount we're resizing by, with 100% extra margin
d = min(x.shape[1]/coords.shape[1], x.shape[2]/coords.shape[2])/2
# If we're resizing up by >200%, and we're zooming less than that, interpolate first
if d>1 and d>z: x = F.interpolate(x[None], scale_factor=1/d, mode='area')[0]
return F.grid_sample(x[None], coords, mode=mode, padding_mode=padding_mode)[0] |
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()).view(size)
return c |
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)
return ch,int(round(r/ratio)),int(round(c/ratio)) |
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 all `tfms` to the `Image`, if `do_resolve` picks value for random args."
if not (tfms or xtra or size): return self
tfms = listify(tfms)
xtra = ifnone(xtra, {})
default_rsz = ResizeMethod.SQUISH if (size is not None and is_listy(size)) else ResizeMethod.CROP
resize_method = ifnone(resize_method, default_rsz)
if resize_method <= 2 and size is not None: tfms = self._maybe_add_crop_pad(tfms)
tfms = sorted(tfms, key=lambda o: o.tfm.order)
if do_resolve: _resolve_tfms(tfms)
x = self.clone()
x.set_sample(padding_mode=padding_mode, mode=mode, remove_out=remove_out)
if size is not None:
crop_target = _get_crop_target(size, mult=mult)
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
target = _get_resize_target(x, crop_target, do_crop=(resize_method==ResizeMethod.CROP))
x.resize(target)
elif resize_method==ResizeMethod.SQUISH: x.resize((x.shape[0],) + crop_target)
else: size = x.size
size_tfms = [o for o in tfms if isinstance(o.tfm,TfmCrop)]
for tfm in tfms:
if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])
elif tfm in size_tfms:
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
else: x = tfm(x)
return x.refresh() |
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._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
self.sample_kwargs = {}
self._flow = None
return 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_mat = None
return self._flow |
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 @ func()`. | 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)
return self |
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_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize)
if y is not None: y.show(ax=ax, **kwargs)
if title is not None: ax.set_title(title) |
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,
interpolation='nearest', alpha=alpha, vmin=0)
if title: ax.set_title(title) |
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.flow_func) != 0:
for f in self.flow_func[::-1]: self._flow = f(self._flow)
self.transformed = True
self.flow_func = []
return self._flow |
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, *args, **kwargs))
return self |
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_points_out(flow)
self.transformed=False
return flow.flow.flip(1) |
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': 10, 'marker': '.', 'c': 'r', **kwargs}
ax.scatter(pnt[:, 0], pnt[:, 1], **params)
if hide_axis: ax.axis('off')
if title: ax.set_title(title) |
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