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fastai/fastai | old/fastai/core.py | split_by_idxs | def split_by_idxs(seq, idxs):
'''A generator that returns sequence pieces, seperated by indexes specified in idxs. '''
last = 0
for idx in idxs:
if not (-len(seq) <= idx < len(seq)):
raise KeyError(f'Idx {idx} is out-of-bounds')
yield seq[last:idx]
last = idx
yield seq[... | python | def split_by_idxs(seq, idxs):
'''A generator that returns sequence pieces, seperated by indexes specified in idxs. '''
last = 0
for idx in idxs:
if not (-len(seq) <= idx < len(seq)):
raise KeyError(f'Idx {idx} is out-of-bounds')
yield seq[last:idx]
last = idx
yield seq[... | [
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fastai/fastai | old/fastai/core.py | partition | def partition(a, sz):
"""splits iterables a in equal parts of size sz"""
return [a[i:i+sz] for i in range(0, len(a), sz)] | python | def partition(a, sz):
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fastai/fastai | old/fastai/core.py | chunk_iter | def chunk_iter(iterable, chunk_size):
'''A generator that yields chunks of iterable, chunk_size at a time. '''
while True:
chunk = []
try:
for _ in range(chunk_size): chunk.append(next(iterable))
yield chunk
except StopIteration:
if chunk: yield chunk
... | python | def chunk_iter(iterable, chunk_size):
'''A generator that yields chunks of iterable, chunk_size at a time. '''
while True:
chunk = []
try:
for _ in range(chunk_size): chunk.append(next(iterable))
yield chunk
except StopIteration:
if chunk: yield chunk
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fastai/fastai | fastai/vision/transform.py | _brightness | def _brightness(x, change:uniform):
"Apply `change` in brightness of image `x`."
return x.add_(scipy.special.logit(change)) | python | def _brightness(x, change:uniform):
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return x.add_(scipy.special.logit(change)) | [
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fastai/fastai | fastai/vision/transform.py | _rotate | def _rotate(degrees:uniform):
"Rotate image by `degrees`."
angle = degrees * math.pi / 180
return [[cos(angle), -sin(angle), 0.],
[sin(angle), cos(angle), 0.],
[0. , 0. , 1.]] | python | def _rotate(degrees:uniform):
"Rotate image by `degrees`."
angle = degrees * math.pi / 180
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fastai/fastai | fastai/vision/transform.py | _get_zoom_mat | def _get_zoom_mat(sw:float, sh:float, c:float, r:float)->AffineMatrix:
"`sw`,`sh` scale width,height - `c`,`r` focus col,row."
return [[sw, 0, c],
[0, sh, r],
[0, 0, 1.]] | python | def _get_zoom_mat(sw:float, sh:float, c:float, r:float)->AffineMatrix:
"`sw`,`sh` scale width,height - `c`,`r` focus col,row."
return [[sw, 0, c],
[0, sh, r],
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fastai/fastai | fastai/vision/transform.py | _zoom | def _zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
s = 1-1/scale
col_c = s * (2*col_pct - 1)
row_c = s * (2*row_pct - 1)
return _get_zoom_mat(1/scale, 1/scale, col_c, row_c) | python | def _zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
s = 1-1/scale
col_c = s * (2*col_pct - 1)
row_c = s * (2*row_pct - 1)
return _get_zoom_mat(1/scale, 1/scale, col_c, row_c) | [
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fastai/fastai | fastai/vision/transform.py | _squish | def _squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
if scale <= 1:
col_c = (1-scale) * (2*col_pct - 1)
return _get_zoom_mat(scale, 1, col_c, 0.)
else:
row_c = (1-1/scale) * (2*row_pct - 1... | python | def _squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
if scale <= 1:
col_c = (1-scale) * (2*col_pct - 1)
return _get_zoom_mat(scale, 1, col_c, 0.)
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fastai/fastai | fastai/vision/transform.py | _jitter | def _jitter(c, magnitude:uniform):
"Replace pixels by random neighbors at `magnitude`."
c.flow.add_((torch.rand_like(c.flow)-0.5)*magnitude*2)
return c | python | def _jitter(c, magnitude:uniform):
"Replace pixels by random neighbors at `magnitude`."
c.flow.add_((torch.rand_like(c.flow)-0.5)*magnitude*2)
return c | [
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fastai/fastai | fastai/vision/transform.py | _flip_lr | def _flip_lr(x):
"Flip `x` horizontally."
#return x.flip(2)
if isinstance(x, ImagePoints):
x.flow.flow[...,0] *= -1
return x
return tensor(np.ascontiguousarray(np.array(x)[...,::-1])) | python | def _flip_lr(x):
"Flip `x` horizontally."
#return x.flip(2)
if isinstance(x, ImagePoints):
x.flow.flow[...,0] *= -1
return x
return tensor(np.ascontiguousarray(np.array(x)[...,::-1])) | [
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fastai/fastai | fastai/vision/transform.py | _dihedral | def _dihedral(x, k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
flips=[]
if k&1: flips.append(1)
if k&2: flips.append(2)
if flips: x = torch.flip(x,flips)
if k&4: x = x.transpose(1,2)
return x.contiguous() | python | def _dihedral(x, k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
flips=[]
if k&1: flips.append(1)
if k&2: flips.append(2)
if flips: x = torch.flip(x,flips)
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fastai/fastai | fastai/vision/transform.py | _dihedral_affine | def _dihedral_affine(k:partial(uniform_int,0,7)):
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y = -1 if k&2 else 1
if k&4: return [[0, x, 0.],
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"Randomly flip `x` image based on `k`."
x = -1 if k&1 else 1
y = -1 if k&2 else 1
if k&4: return [[0, x, 0.],
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fastai/fastai | fastai/vision/transform.py | _pad_default | def _pad_default(x, padding:int, mode='reflection'):
"Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border')."
mode = _pad_mode_convert[mode]
return F.pad(x[None], (padding,)*4, mode=mode)[0] | python | def _pad_default(x, padding:int, mode='reflection'):
"Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border')."
mode = _pad_mode_convert[mode]
return F.pad(x[None], (padding,)*4, mode=mode)[0] | [
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fastai/fastai | fastai/vision/transform.py | _cutout | def _cutout(x, n_holes:uniform_int=1, length:uniform_int=40):
"Cut out `n_holes` number of square holes of size `length` in image at random locations."
h,w = x.shape[1:]
for n in range(n_holes):
h_y = np.random.randint(0, h)
h_x = np.random.randint(0, w)
y1 = int(np.clip(h_y - length... | python | def _cutout(x, n_holes:uniform_int=1, length:uniform_int=40):
"Cut out `n_holes` number of square holes of size `length` in image at random locations."
h,w = x.shape[1:]
for n in range(n_holes):
h_y = np.random.randint(0, h)
h_x = np.random.randint(0, w)
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fastai/fastai | fastai/vision/transform.py | _rgb_randomize | def _rgb_randomize(x, channel:int=None, thresh:float=0.3):
"Randomize one of the channels of the input image"
if channel is None: channel = np.random.randint(0, x.shape[0] - 1)
x[channel] = torch.rand(x.shape[1:]) * np.random.uniform(0, thresh)
return x | python | def _rgb_randomize(x, channel:int=None, thresh:float=0.3):
"Randomize one of the channels of the input image"
if channel is None: channel = np.random.randint(0, x.shape[0] - 1)
x[channel] = torch.rand(x.shape[1:]) * np.random.uniform(0, thresh)
return x | [
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fastai/fastai | fastai/vision/transform.py | _crop_default | def _crop_default(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
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rows,cols = tis2hw(size)
row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
row = int((x.size(1)-rows+1) * row_pct)
col = int((x.size(2)-cols+1) * col_pct... | python | def _crop_default(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop."
rows,cols = tis2hw(size)
row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
row = int((x.size(1)-rows+1) * row_pct)
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if x.shape[1:] == torch.Size(size): return x
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fastai/fastai | fastai/vision/transform.py | _find_coeffs | def _find_coeffs(orig_pts:Points, targ_pts:Points)->Tensor:
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matrix = []
#The equations we'll need to solve.
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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)
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"Transform `coords` with `coeffs`."
size = coords.flow.size()
#compress all the dims expect the last one ang adds ones, coords become N * 3
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fastai/fastai | fastai/vision/transform.py | _do_perspective_warp | def _do_perspective_warp(c:FlowField, targ_pts:Points, invert=False):
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if invert: return _apply_perspective(c, _find_coeffs(targ_pts, _orig_pts))
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magnitude = magnitude.view(4,2)
targ_pts = [[x+m for x,m in zip(xs, ms)] for xs, ms in zip(_orig_pts, magnitude)]
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"Apply warp of `magnitude` to `c`."
magnitude = magnitude.view(4,2)
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fastai/fastai | fastai/vision/transform.py | _symmetric_warp | def _symmetric_warp(c, magnitude:partial(uniform,size=4)=0, invert=False):
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m = listify(magnitude, 4)
targ_pts = [[-1-m[3],-1-m[1]], [-1-m[2],1+m[1]], [1+m[3],-1-m[0]], [1+m[2],1+m[0]]]
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"Apply symmetric warp of `magnitude` to `c`."
m = listify(magnitude, 4)
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fastai/fastai | fastai/vision/transform.py | _tilt | def _tilt(c, direction:uniform_int, magnitude:uniform=0, invert=False):
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orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
if direction == 0: targ_pts = [[-1,-1], [-1,1], [1,-1-magnitude], [1,1+magnitude]]
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"Tilt `c` field with random `direction` and `magnitude`."
orig_pts = [[-1,-1], [-1,1], [1,-1], [1,1]]
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fastai/fastai | fastai/vision/transform.py | _compute_zs_mat | def _compute_zs_mat(sz:TensorImageSize, scale:float, squish:float,
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"Utility routine to compute zoom/squish matrix."
orig_ratio = math.sqrt(sz[1]/sz[0])
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"Utility routine to compute zoom/squish matrix."
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fastai/fastai | fastai/vision/transform.py | rand_resize_crop | def rand_resize_crop(size:int, max_scale:float=2., ratios:Tuple[float,float]=(0.75,1.33)):
"Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`."
return [zoom_squish(scale=(1.,max_scale,8), squish=(*ratios,8), invert=(0.5,8), row_pct=(0.,1.), col_pct=(0.,1.)),
crop(... | python | def rand_resize_crop(size:int, max_scale:float=2., ratios:Tuple[float,float]=(0.75,1.33)):
"Randomly resize and crop the image to a ratio in `ratios` after a zoom of `max_scale`."
return [zoom_squish(scale=(1.,max_scale,8), squish=(*ratios,8), invert=(0.5,8), row_pct=(0.,1.), col_pct=(0.,1.)),
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fastai/fastai | old/fastai/models/cifar10/main_dxy.py | adjust_learning_rate | def adjust_learning_rate(optimizer, epoch, gammas, schedule):
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"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
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fastai/fastai | old/fastai/learner.py | Learner.fit_gen | def fit_gen(self, model, data, layer_opt, n_cycle, cycle_len=None, cycle_mult=1, cycle_save_name=None, best_save_name=None,
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fastai/fastai | old/fastai/learner.py | Learner.fit | def fit(self, lrs, n_cycle, wds=None, **kwargs):
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fastai/fastai | old/fastai/learner.py | Learner.lr_find | 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.
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`Cyclical Learning Rates for Training Neural Networks`, where
we simply keep increasing the learnin... | python | def lr_find(self, start_lr=1e-5, end_lr=10, wds=None, linear=False, **kwargs):
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fastai/fastai | old/fastai/learner.py | Learner.lr_find2 | def lr_find2(self, start_lr=1e-5, end_lr=10, num_it = 100, wds=None, linear=False, stop_dv=True, **kwargs):
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... | python | def lr_find2(self, start_lr=1e-5, end_lr=10, num_it = 100, wds=None, linear=False, stop_dv=True, **kwargs):
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fastai/fastai | old/fastai/learner.py | Learner.predict_array | 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... | python | def predict_array(self, arr):
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Args:
arr: a numpy array to be used as input to the model for prediction purposes
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fastai/fastai | old/fastai/learner.py | Learner.TTA | def TTA(self, n_aug=4, is_test=False):
""" Predict with Test Time Augmentation (TTA)
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(just like for training images) and calculate the mean of predictions. The intent
is to increase the accuracy of predi... | python | def TTA(self, n_aug=4, is_test=False):
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fastai/fastai | old/fastai/learner.py | Learner.fit_opt_sched | 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... | python | 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):
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fastai/fastai | fastai/callbacks/rnn.py | RNNTrainer.on_loss_begin | 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]} | python | def on_loss_begin(self, last_output:Tuple[Tensor,Tensor,Tensor], **kwargs):
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fastai/fastai | fastai/callbacks/rnn.py | RNNTrainer.on_backward_begin | def on_backward_begin(self, last_loss:Rank0Tensor, last_input:Tensor, **kwargs):
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h = self.raw_out[-1]
if len(h)>1:... | python | def on_backward_begin(self, last_loss:Rank0Tensor, last_input:Tensor, **kwargs):
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#AR and TAR
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fastai/fastai | fastai/text/learner.py | convert_weights | def convert_weights(wgts:Weights, stoi_wgts:Dict[str,int], itos_new:Collection[str]) -> Weights:
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dec_bias, enc_wgts = wgts.get('1.decoder.bias', None), wgts['0.encoder.weight']
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dec_bias, enc_wgts = wgts.get('1.decoder.bias', None), wgts['0.encoder.weight']
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fastai/fastai | fastai/text/learner.py | get_language_model | 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... | python | 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():
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fastai/fastai | fastai/text/learner.py | language_model_learner | 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... | python | 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`."
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fastai/fastai | fastai/text/learner.py | get_text_classifier | 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,
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drop_mult:float=1., lin_ftrs:Collection[int]=None, ps:Collection[float]=None,
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fastai/fastai | fastai/text/learner.py | text_classifier_learner | 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... | python | 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,
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fastai/fastai | fastai/text/learner.py | RNNLearner.save_encoder | 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') | python | 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') | [
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fastai/fastai | fastai/text/learner.py | RNNLearner.load_encoder | 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
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"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
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fastai/fastai | fastai/text/learner.py | RNNLearner.load_pretrained | 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... | python | 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... | [
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fastai/fastai | fastai/text/learner.py | RNNLearner.get_preds | 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()
... | python | 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`."
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fastai/fastai | fastai/text/learner.py | LanguageLearner.predict | 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... | python | 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... | [
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fastai/fastai | fastai/text/learner.py | LanguageLearner.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()
... | python | 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."
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fastai/fastai | fastai/text/learner.py | LanguageLearner.show_results | 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 = ... | python | def show_results(self, ds_type=DatasetType.Valid, rows:int=5, max_len:int=20):
from IPython.display import display, HTML
"Show `rows` result of predictions on `ds_type` dataset."
ds = self.dl(ds_type).dataset
x,y = self.data.one_batch(ds_type, detach=False, denorm=False)
preds = ... | [
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fastai/fastai | fastai/text/learner.py | MultiBatchEncoder.concat | 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])] | python | 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])] | [
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fastai/fastai | fastai/layers.py | batchnorm_2d | 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 | python | def batchnorm_2d(nf:int, norm_type:NormType=NormType.Batch):
"A batchnorm2d layer with `nf` features initialized depending on `norm_type`."
bn = nn.BatchNorm2d(nf)
with torch.no_grad():
bn.bias.fill_(1e-3)
bn.weight.fill_(0. if norm_type==NormType.BatchZero else 1.)
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fastai/fastai | fastai/layers.py | bn_drop_lin | 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))
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"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))
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fastai/fastai | fastai/layers.py | conv1d | 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... | python | 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_()
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fastai/fastai | fastai/layers.py | conv2d | 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
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"Create and initialize `nn.Conv2d` layer. `padding` defaults to `ks//2`."
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fastai/fastai | fastai/layers.py | conv2d_trans | 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) | python | def conv2d_trans(ni:int, nf:int, ks:int=2, stride:int=2, padding:int=0, bias=False) -> nn.ConvTranspose2d:
"Create `nn.ConvTranspose2d` layer."
return nn.ConvTranspose2d(ni, nf, kernel_size=ks, stride=stride, padding=padding, bias=bias) | [
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fastai/fastai | fastai/layers.py | relu | def relu(inplace:bool=False, leaky:float=None):
"Return a relu activation, maybe `leaky` and `inplace`."
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"Return a relu activation, maybe `leaky` and `inplace`."
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fastai/fastai | fastai/layers.py | conv_layer | def conv_layer(ni:int, nf:int, ks:int=3, stride:int=1, padding:int=None, bias:bool=None, is_1d:bool=False,
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norm_type:Optional[NormType]=NormType.Batch, use_activ:bool=True, leaky:float=None,
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fastai/fastai | fastai/layers.py | res_block | def res_block(nf, dense:bool=False, norm_type:Optional[NormType]=NormType.Batch, bottle:bool=False, **conv_kwargs):
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norm2 = norm_type
if not dense and (norm_type==NormType.Batch): norm2 = NormType.BatchZero
nf_inner = nf//2 if bo... | python | 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`."
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fastai/fastai | fastai/layers.py | sigmoid_range | def sigmoid_range(x, low, high):
"Sigmoid function with range `(low, high)`"
return torch.sigmoid(x) * (high - low) + low | python | def sigmoid_range(x, low, high):
"Sigmoid function with range `(low, high)`"
return torch.sigmoid(x) * (high - low) + low | [
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fastai/fastai | fastai/layers.py | icnr | def icnr(x, scale=2, init=nn.init.kaiming_normal_):
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ni,nf,h,w = x.shape
ni2 = int(ni/(scale**2))
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... | python | 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)
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fastai/fastai | fastai/layers.py | CrossEntropyFlat | def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
return FlattenedLoss(nn.CrossEntropyLoss, *args, axis=axis, **kwargs) | python | def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
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fastai/fastai | fastai/layers.py | BCEWithLogitsFlat | def BCEWithLogitsFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
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return FlattenedLoss(nn.BCEWithLogitsLoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) | python | def BCEWithLogitsFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
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fastai/fastai | fastai/layers.py | BCEFlat | def BCEFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
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"Same as `nn.BCELoss`, but flattens input and target."
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fastai/fastai | fastai/layers.py | MSELossFlat | 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) | python | def MSELossFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
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fastai/fastai | fastai/layers.py | simple_cnn | 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... | python | 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`."
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fastai/fastai | fastai/layers.py | trunc_normal_ | 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) | python | 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) | [
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fastai/fastai | fastai/layers.py | embedding | def embedding(ni:int,nf:int) -> nn.Module:
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# See https://arxiv.org/abs/1711.09160
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"Create an embedding layer."
emb = nn.Embedding(ni, nf)
# See https://arxiv.org/abs/1711.09160
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fastai/fastai | fastai/callbacks/mlflow.py | MLFlowTracker.on_train_begin | def on_train_begin(self, **kwargs: Any) -> None:
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if type(size) is str: raise RuntimeError("Expected size to be an int or a tuple, got a string.")
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fastai/fastai | fastai/vision/image.py | _draw_outline | def _draw_outline(o:Patch, lw:int):
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fastai/fastai | fastai/vision/image.py | _draw_rect | def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
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fastai/fastai | fastai/vision/image.py | open_mask_rle | def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
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"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))
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runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x) for x in runs) | python | def rle_encode(img:NPArrayMask)->str:
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fastai/fastai | fastai/vision/image.py | rle_decode | def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
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"Display `Image` in notebook."
if ax is None: fig,ax = plt.subplots(figsize=figsize)
ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha, **kwargs)... | python | 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)... | [
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"Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`."
s = tensor([flow.size[0]/2,flow.size[1]/2])[None]
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fastai/fastai | fastai/vision/image.py | _affine_mult | 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... | python | 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
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fastai/fastai | fastai/vision/image.py | _affine_inv_mult | 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 | python | 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)
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fastai/fastai | fastai/vision/image.py | _round_multiple | def _round_multiple(x:int, mult:int=None)->int:
"Calc `x` to nearest multiple of `mult`."
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"Calc `x` to nearest multiple of `mult`."
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fastai/fastai | fastai/vision/image.py | _get_crop_target | 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) | python | def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
"Calc crop shape of `target_px` to nearest multiple of `mult`."
target_r,target_c = tis2hw(target_px)
return _round_multiple(target_r,mult),_round_multiple(target_c,mult) | [
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fastai/fastai | fastai/vision/image.py | _get_resize_target | 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... | python | 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
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fastai/fastai | fastai/vision/image.py | plot_flat | def plot_flat(r, c, figsize):
"Shortcut for `enumerate(subplots.flatten())`"
return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten()) | python | def plot_flat(r, c, figsize):
"Shortcut for `enumerate(subplots.flatten())`"
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fastai/fastai | fastai/vision/image.py | plot_multi | 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]) | python | 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]
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fastai/fastai | fastai/vision/image.py | show_multi | 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) | python | 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) | [
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fastai/fastai | fastai/vision/image.py | show_all | 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) | python | 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
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fastai/fastai | fastai/vision/image.py | Image.apply_tfms | 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:
... | python | 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:
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fastai/fastai | fastai/vision/image.py | Image.refresh | 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.... | python | def refresh(self)->None:
"Apply any logit, flow, or affine transfers that have been sent to the `Image`."
if self._logit_px is not None:
self._px = self._logit_px.sigmoid_()
self._logit_px = None
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] | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L126-L135 | train |
fastai/fastai | fastai/vision/image.py | Image.save | def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn) | python | def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn) | [
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] | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L137-L140 | train |
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