def __get_metrics_map(self):
from fastai.metrics import rmse, mse, mae, accuracy, FBeta, RocAucBinary, Precision, Recall, R2Score
from .fastai_helpers import medae
from .quantile_helpers import HuberPinballLoss
metrics_map = {
# Regression
'root_mean_squared_error': rmse,
'mean_squared_error': mse,
'mean_absolute_error': mae,
'r2': R2Score(),
'median_absolute_error': medae,
# Classification
'accuracy': accuracy,
'f1': FBeta(beta=1),
'f1_macro': FBeta(beta=1, average='macro'),
'f1_micro': FBeta(beta=1, average='micro'),
'f1_weighted':
FBeta(beta=1, average='weighted'), # this one has some issues
'roc_auc': RocAucBinary(),
'precision': Precision(),
'precision_macro': Precision(average='macro'),
'precision_micro': Precision(average='micro'),
'precision_weighted': Precision(average='weighted'),
'recall': Recall(),
'recall_macro': Recall(average='macro'),
'recall_micro': Recall(average='micro'),
'recall_weighted': Recall(average='weighted'),
'log_loss': None,
'pinball_loss':
HuberPinballLoss(quantile_levels=self.quantile_levels)
# Not supported: pac_score
}
return metrics_map
def __get_objective_func_name(self):
from fastai.metrics import root_mean_squared_error, mean_squared_error, mean_absolute_error, accuracy, FBeta, AUROC, Precision, Recall, r2_score
metrics_map = {
# Regression
'root_mean_squared_error': root_mean_squared_error,
'mean_squared_error': mean_squared_error,
'mean_absolute_error': mean_absolute_error,
'r2': r2_score,
# Not supported: median_absolute_error
# Classification
'accuracy': accuracy,
'f1': FBeta(beta=1),
'f1_macro': FBeta(beta=1, average='macro'),
'f1_micro': FBeta(beta=1, average='micro'),
'f1_weighted': FBeta(beta=1, average='weighted'), # this one has some issues
'roc_auc': AUROC(),
'precision': Precision(),
'precision_macro': Precision(average='macro'),
'precision_micro': Precision(average='micro'),
'precision_weighted': Precision(average='weighted'),
'recall': Recall(),
'recall_macro': Recall(average='macro'),
'recall_micro': Recall(average='micro'),
'recall_weighted': Recall(average='weighted'),
'log_loss': None,
# Not supported: pac_score
}
# Unsupported metrics will be replaced by defaults for a given problem type
objective_func_name = self.stopping_metric.name
if objective_func_name not in metrics_map.keys():
if self.problem_type == REGRESSION:
objective_func_name = 'mean_squared_error'
else:
objective_func_name = 'log_loss'
logger.warning(f'Metric {self.stopping_metric.name} is not supported by this model - using {objective_func_name} instead')
if objective_func_name in metrics_map.keys():
nn_metric = metrics_map[objective_func_name]
else:
nn_metric = None
return nn_metric, objective_func_name
def createmodel(self, quantize=True):
"""Creates the model and attaches with the dataloader.
By default it sets up the model for quantization aware training.
Parameters
----------
quantize : bool, optional
To quantize or not, by default True
"""
print("Creating model..")
vision.learner.create_body = self.create_custom_body
self.learn = cnn_learner(
self.data,
models.mobilenet_v2,
pretrained=True,
metrics=[error_rate, FBeta(beta=1), Precision(), Recall(), AUROC()],
split_on=custom_split,
model_dir=self.model_dir,
)
if quantize:
self.learn.model[0].qconfig = torch.quantization.default_qat_qconfig
self.learn.model = torch.quantization.prepare_qat(
self.learn.model, inplace=True
)
def train(data):
learn = cnn_learner(data, models.resnet18, metrics=[Precision(), Recall()])
learn.callback_fns.append(
partial(LearnerTensorboardWriter,
base_dir=Path("data/tensorboard/camlytics_fastai"),
name="deterministic-data"))
learn.fit_one_cycle(20, 1e-2)
learn.save("finetune-epoch-20")
import pdb
pdb.set_trace()
cat_names = ['eras']
emb_szs = {}
for name in cat_names:
emb_szs[name] = 2
data = data.drop([], axis=1)
sample = data.sample(50000)
data = TabularDataBunch.from_df(os.getcwd(),
data,
dep_var,
valid_idx=valid_idx,
procs=procs,
cat_names=cat_names)
print(data.train_ds.cont_names)
precision = Precision()
recall = Recall()
mattcor = MatthewsCorreff()
learn = tabular_learner(data,
layers=[100, 300, 300, 300, 100],
emb_szs=emb_szs,
metrics=[accuracy, precision, recall, mattcor],
ps=[0.5, 0.5, 0.5, 0.5, 0.5])
learn.lr_find()
learn.recorder.plot()
learn.fit_one_cycle(10, 1e-2)
learn.recorder.plot_metrics()
preds, y, losses = learn.get_preds(with_loss=True)
interp = ClassificationInterpretation(learn, preds, y, losses)
# Encode and transform the data
# Since the target classification is categorical, we need to have a way to interpret the neural network's output.
# In this case, we are assigning each possible output with an integer 0..n-1, another option would be to use one-hot
# encoding here. This may be explored further.
encoder = LabelEncoder()
encoder.fit(df_y)
data_y = encoder.transform(df_y)
# Normalize the x data and rename for consistency
data_x = (df - df.mean()) / (df.max() - df.min())
data_x = data_x.values
# Set up the metrics we want to collect. I wanted TP,TN,FP,FN but that wasn't available. Recall and precision are still
# extremely helpful for evaluating the model
metrics = [accuracy, Recall(), Precision()]
# Keep track of which fold we are on
fold_num = 1
total_folds = 10
# Get the indices for the fold and train on that fold
# Our goal here is to implement statified 10-fold cross validation
for train_idx, test_idx in StratifiedKFold(n_splits=total_folds, shuffle=True, random_state=1).split(data_x, data_y):
# This will create the datafold the way we need to hand it to the tabular learner class
data_fold = (TabularList.from_df(data, path=path, cont_names=cont_names, procs=procs)
.split_by_idxs(train_idx, test_idx)
.label_from_df(cols=dep_var)
.databunch())
print('Fold {}/{}'.format(fold_num, total_folds))
x = F.relu(self.fc1(x))
x = F.log_softmax(self.fc2(x), dim=1)
return x
# %%
net = ConvNet()
print(summary(net, torch.zeros((1, 1, 28, 28)), show_input=True))
print(summary(net, torch.zeros((1, 1, 28, 28)), show_input=False))
# %%
learner = Learner(
mnist_dls,
ConvNet(),
loss_func=F.nll_loss,
metrics=[accuracy, Precision(average="macro"), Recall(average="macro")],
)
# %%[markdown]
#
# These are too many epochs, but we want to see the behavior of the net when it
# is trained for some time.
# %%
learner.fit(n_epoch=20)
# %%
pprint(list(learner.metrics))
# %%
model = learner.model
def train(learn):
import fastprogress
# import cv2 as cv
import numpy as np
import pandas as pd
import fastai
from sklearn.model_selection import KFold
from wandb.fastai import WandbCallback
import wandb
from torch import nn
import torch.nn.functional as F
ALPHA = 2.0
BETA = 10000.5
GAMMA = 10
import pretrainedmodels
def resnext50_32x4d(pretrained=False):
pretrained = 'imagenet' if pretrained else None
model = pretrainedmodels.se_resnext50_32x4d(pretrained=pretrained)
return nn.Sequential(*list(model.children()))
class FocalLoss(nn.Module):
def __init__(self, alpha=1., gamma=2.):
super().__init__()
self.alpha = alpha
self.gamma = gamma
def forward(self, inputs, targets, **kwargs):
CE_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)
pt = torch.exp(-CE_loss)
F_loss = self.alpha * ((1-pt)**self.gamma) * CE_loss
return F_loss.mean()
print(1)
path = '/cs/home/khfy6uat/data/classification_1024/classifier_data'
fastprogress.fastprogress.NO_BAR = True
master_bar, progress_bar = fastprogress.fastprogress.force_console_behavior()
fastai.basic_train.master_bar, fastai.basic_train.progress_bar = master_bar, progress_bar
fastai.basic_data.master_bar, fastai.basic_data.progress_bar = master_bar, progress_bar
dataclass.master_bar, dataclass.progress_bar = master_bar, progress_bar
fastai.core.master_bar, fastai.core.progress_bar = master_bar, progress_bar
# def resnext50_32x4d(pretrained=False):
# pretrained = 'imagenet' if pretrained else None
# model = pretrainedmodels.se_resnext50_32x4d(pretrained=pretrained)
# return nn.Sequential(*list(model.children()))
# class FocalLoss(nn.Module):
# def __init__(self, alpha=1., gamma=2.):
# super().__init__()
# self.alpha = alpha
# self.gamma = gamma
# def forward(self, inputs, targets, **kwargs):
# CE_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)
# pt = torch.exp(-CE_loss)
# F_loss = self.alpha * ((1-pt)**self.gamma) * CE_loss
# return F_loss.mean()
sz=0
sz1=0
# Default values for hyper-parameters we're going to sweep over
config_defaults = {
'epochs': 2,
'batch_size': 12,
'weight_decay': 0.0005,
'learning_rate': 1e-3,
'seed': 42,
'encoder_size':128,
'decoder_size':224
}
# Initialize a new wandb run
wandb.init(config=config_defaults)
# Config is a variable that holds and saves hyperparameters and inputs
config = wandb.config
sz=(config.encoder_size)
sz1=(config.decoder_size)
tfms = get_transforms(do_flip=True, flip_vert=False, max_lighting=0.1, max_zoom=1.05,
max_warp=0.,
xtra_tfms=[rand_crop(), rand_zoom(1, 1.5),
symmetric_warp(magnitude=(-0.2, 0.2))])
print('right before data')
data = (ImageList.from_folder(path)
.split_by_rand_pct(seed=10)
.label_from_folder()
.transform(tfms, size=sz)
.databunch(bs=12).normalize(imagenet_stats))
print('right before learner')
wandbclc=partial(WandbCallback,log="all",input_type='images',monitor='recall',mode='max')
per = Precision()
rec= Recall()
# learn = cnn_learner(data, resnext50_32x4d, pretrained=True, cut=-2,
# split_on=lambda m: (m[0][3], m[1]),
# metrics=[per,rec,error_rate],callback_fns=[wandbclc])
print(learn.data.train_dl.batch_size)
lr=config.learning_rate
print(config.epochs)
learn.fit_one_cycle(config.epochs, max_lr=slice(lr), wd=1e-5)
learn.unfreeze();
learn = learn.clip_grad();
lr = [lr/200, lr/20, lr/10]
learn.fit_one_cycle(config.epochs, max_lr=slice(lr), wd=1e-5)
if (sz1 > 0):
SZ = sz1
cutout_frac = 0.20
p_cutout = 0.75
cutout_sz = round(SZ*cutout_frac)
cutout_tfm = cutout(n_holes=(1,1), length=(cutout_sz, cutout_sz), p=p_cutout)
tfms = get_transforms(do_flip=True, max_rotate=15, flip_vert=False, max_lighting=0.1,
max_zoom=1.05, max_warp=0.,
xtra_tfms=[rand_crop(), rand_zoom(1, 1.5),
symmetric_warp(magnitude=(-0.2, 0.2)), cutout_tfm])
data = (ImageList.from_folder(path)
.split_by_rand_pct(seed=10)
.label_from_folder()
.transform(tfms, size=sz1)
.databunch(bs=12).normalize(imagenet_stats))
learn.data=data
learn.fit_one_cycle(config.epochs, max_lr=slice(lr), wd=1e-5)
learn.unfreeze();
learn = learn.clip_grad();
lr = [lr/200, lr/20, lr/10]
learn.fit_one_cycle(config.epochs, max_lr=slice(lr), wd=1e-5)
data.show_batch(rows=3, figsize=(12,9)) # displays a batch of the training set
# %%
data # displays the data details
# %%
# class names and number of classes
print(data.classes)
len(data.classes),data.c
# %%
f_score = partial(fbeta, thresh=0.2, beta = 0.5)
per = Precision()
rec= Recall()
# %%
import pretrainedmodels # model library
# %%
import pretrainedmodels
def resnext50_32x4d(pretrained=True):
pretrained = 'imagenet' if pretrained else None
model = pretrainedmodels.se_resnext50_32x4d(pretrained=pretrained)
return nn.Sequential(*list(model.children()))
# %%
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
# %%
mnist_dls = ImageDataLoaders.from_folder(
mnist_dir,
train="training",
valid="testing",
device=device,
batch_tfms=aug_transforms(mult=2, do_flip=False),
item_tfms=Resize(224),
)
# %%
resnet_learner = cnn_learner(
mnist_dls,
resnet18,
metrics=[accuracy,
Precision(average="macro"),
Recall(average="macro")],
)
# %%
with resnet_learner.no_bar():
resnet_learner.fine_tune(1)
# %%
