mirror of
https://github.com/AntoineHX/smart_augmentation.git
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Transfert TF loader vers transformations.py
This commit is contained in:
Harle, Antoine (Contracteur)
parent
d53b385c43
commit
f2cf244801
3 changed files with 123 additions and 123 deletions
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@ -6,9 +6,10 @@ from LeNet import *
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from dataug import *
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#from utils import *
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from train_utils import *
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#from transformations import TF_loader
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postfix=''
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TF_loader=TF_loader()
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TF_loader=TF.TF_loader()
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device = torch.device('cuda') #Select device to use
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@ -17,6 +17,7 @@
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import torch
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import kornia
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import random
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import json
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#TF that don't have use for magnitude parameter.
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TF_no_mag={'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend', 'identity', 'flip'}
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@ -88,6 +89,126 @@ TF_dict={ #Dataugv5+
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#'Equalize': (lambda mag: None),
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}
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'''
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class TF_loader(object):
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""" Transformations builder.
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See 'config' folder for pre-defined config files.
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Attributes:
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_filename (str): Path to config file (JSON) used.
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_TF_dict (dict): Transformations dictionnary built from config file.
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_TF_ignore_mag (set): Ensemble of transformations names for which magnitude should be ignored.
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_TF_names (list): List of transformations names/keys.
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"""
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def __init__(self):
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""" Initialize TF_loader.
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"""
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self._filename=''
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self._TF_dict={}
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self._TF_ignore_mag=set()
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self._TF_names=[]
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def load_TF_dict(self, filename):
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""" Build a TF dictionnary.
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Args:
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filename (str): Path to config file (JSON) defining the transformations.
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Returns:
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(dict, set) : TF dicttionnary built and ensemble of TF names for which mag should be ignored.
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"""
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self._filename=filename
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self._TF_names=[]
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self._TF_dict={}
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self._TF_ignore_mag=set()
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with open(filename) as json_file:
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TF_params = json.load(json_file)
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for tf in TF_params:
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self._TF_names.append(tf['name'])
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if tf['function'] in TF_ignore_mag:
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self._TF_ignore_mag.add(tf['name'])
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if tf['function'] == 'identity':
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self._TF_dict[tf['name']]=(lambda x, mag: x)
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elif tf['function'] == 'flip':
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#Inverser axes ?
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if tf['param']['axis'] == 'X':
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self._TF_dict[tf['name']]=(lambda x, mag: flipLR(x))
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elif tf['param']['axis'] == 'Y':
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self._TF_dict[tf['name']]=(lambda x, mag: flipUD(x))
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else:
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raise Exception("Unknown TF axis : %s in %s"%(tf['function'], self._filename))
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elif tf['function'] in {'translate', 'shear'}:
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rand_fct= 'invScale_rand_floats' if tf['param']['invScale'] else 'rand_floats'
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self._TF_dict[tf['name']]=self.build_lambda(tf['function'], rand_fct, tf['param']['min'], tf['param']['max'], tf['param']['absolute'], tf['param']['axis'])
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else:
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rand_fct= 'invScale_rand_floats' if tf['param']['invScale'] else 'rand_floats'
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self._TF_dict[tf['name']]=self.build_lambda(tf['function'], rand_fct, tf['param']['min'], tf['param']['max'])
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return self._TF_dict, self._TF_ignore_mag
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def build_lambda(self, fct_name, rand_fct_name, minval, maxval, absolute=True, axis=None):
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""" Build a lambda function performing transformations.
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Args:
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fct_name (str): Name of the transformations to use (see transformations.py).
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rand_fct_name (str): Name of the random mapping function to use (see transformations.py).
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minval (float): minimum magnitude value of the TF.
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maxval (float): maximum magnitude value of the TF.
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absolute (bool): Wether the maxval should be relative (absolute=False) to the image size. (default: True)
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axis (str): Axis ('X' / 'Y') of the TF, if relevant. Should be used for (flip)/translate/shear functions. (default: None)
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Returns:
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(function) transformations function : Tensor=f(Tensor, magnitude)
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"""
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if absolute:
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max_val_fct=(lambda x: maxval)
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else: #Relative to img size
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max_val_fct=(lambda x: x*maxval)
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if axis is None:
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return (lambda x, mag:
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globals()[fct_name]( #getattr(TF, fct_name)
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x,
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globals()[rand_fct_name](
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size=x.shape[0],
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mag=mag,
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minval=minval,
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maxval=maxval)))
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elif axis =='X':
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return (lambda x, mag:
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globals()[fct_name](
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x,
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zero_stack(
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globals()[rand_fct_name](
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size=(x.shape[0],),
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mag=mag,
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minval=minval,
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maxval=max_val_fct(x.shape[2])),
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zero_pos=0)))
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elif axis == 'Y':
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return (lambda x, mag:
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globals()[fct_name](
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x,
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zero_stack(
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globals()[rand_fct_name](
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size=(x.shape[0],),
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mag=mag,
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minval=minval,
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maxval=max_val_fct(x.shape[3])),
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zero_pos=1)))
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else:
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raise Exception("Unknown TF axis : %s in %s"%(fct_name, self._filename))
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def get_TF_names(self):
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return self._TF_names
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def get_TF_dict(self):
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return self._TF_dict
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## Image type cast ##
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def int_image(float_image):
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"""Convert a float Tensor/Image to an int Tensor/Image.
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@ -14,128 +14,6 @@ import torch.nn.functional as F
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import time
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import transformations as TF
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class TF_loader(object):
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""" Transformations builder.
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See 'config' folder for pre-defined config files.
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Attributes:
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_filename (str): Path to config file (JSON) used.
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_TF_dict (dict): Transformations dictionnary built from config file.
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_TF_ignore_mag (set): Ensemble of transformations names for which magnitude should be ignored.
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_TF_names (list): List of transformations names/keys.
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"""
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def __init__(self):
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""" Initialize TF_loader.
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"""
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self._filename=''
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self._TF_dict={}
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self._TF_ignore_mag=set()
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self._TF_names=[]
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def load_TF_dict(self, filename):
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""" Build a TF dictionnary.
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Args:
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filename (str): Path to config file (JSON) defining the transformations.
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Returns:
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(dict, set) : TF dicttionnary built and ensemble of TF names for which mag should be ignored.
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"""
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self._filename=filename
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self._TF_names=[]
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self._TF_dict={}
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self._TF_ignore_mag=set()
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with open(filename) as json_file:
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TF_params = json.load(json_file)
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for tf in TF_params:
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self._TF_names.append(tf['name'])
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if tf['function'] in TF.TF_ignore_mag:
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self._TF_ignore_mag.add(tf['name'])
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if tf['function'] == 'identity':
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self._TF_dict[tf['name']]=(lambda x, mag: x)
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elif tf['function'] == 'flip':
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#Inverser axes ?
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if tf['param']['axis'] == 'X':
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self._TF_dict[tf['name']]=(lambda x, mag: TF.flipLR(x))
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elif tf['param']['axis'] == 'Y':
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self._TF_dict[tf['name']]=(lambda x, mag: TF.flipUD(x))
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else:
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raise Exception("Unknown TF axis : %s in %s"%(tf['function'], self._filename))
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elif tf['function'] in {'translate', 'shear'}:
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rand_fct= 'invScale_rand_floats' if tf['param']['invScale'] else 'rand_floats'
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self._TF_dict[tf['name']]=self.build_lambda(tf['function'], rand_fct, tf['param']['min'], tf['param']['max'], tf['param']['absolute'], tf['param']['axis'])
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else:
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rand_fct= 'invScale_rand_floats' if tf['param']['invScale'] else 'rand_floats'
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self._TF_dict[tf['name']]=self.build_lambda(tf['function'], rand_fct, tf['param']['min'], tf['param']['max'])
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return self._TF_dict, self._TF_ignore_mag
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def build_lambda(self, fct_name, rand_fct_name, minval, maxval, absolute=True, axis=None):
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""" Build a lambda function performing transformations.
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Args:
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fct_name (str): Name of the transformations to use (see transformations.py).
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rand_fct_name (str): Name of the random mapping function to use (see transformations.py).
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minval (float): minimum magnitude value of the TF.
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maxval (float): maximum magnitude value of the TF.
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absolute (bool): Wether the maxval should be relative (absolute=False) to the image size. (default: True)
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axis (str): Axis ('X' / 'Y') of the TF, if relevant. Should be used for (flip)/translate/shear functions. (default: None)
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Returns:
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(function) transformations function : Tensor=f(Tensor, magnitude)
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"""
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if absolute:
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max_val_fct=(lambda x: maxval)
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else: #Relative to img size
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max_val_fct=(lambda x: x*maxval)
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if axis is None:
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return (lambda x, mag:
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getattr(TF, fct_name)(
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x,
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getattr(TF, rand_fct_name)(
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size=x.shape[0],
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mag=mag,
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minval=minval,
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maxval=maxval)))
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elif axis =='X':
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return (lambda x, mag:
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getattr(TF, fct_name)(
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x,
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TF.zero_stack(
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getattr(TF, rand_fct_name)(
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size=(x.shape[0],),
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mag=mag,
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minval=minval,
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maxval=max_val_fct(x.shape[2])),
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zero_pos=0)))
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elif axis == 'Y':
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return (lambda x, mag:
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getattr(TF, fct_name)(
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x,
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TF.zero_stack(
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getattr(TF, rand_fct_name)(
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size=(x.shape[0],),
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mag=mag,
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minval=minval,
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maxval=max_val_fct(x.shape[3])),
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zero_pos=1)))
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else:
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raise Exception("Unknown TF axis : %s in %s"%(fct_name, self._filename))
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def get_TF_names(self):
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return self._TF_names
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def get_TF_dict(self):
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return self._TF_dict
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class ConfusionMatrix(object):
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""" Confusion matrix.
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