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eval running - offline logger implemented -> Test it!

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Si11ium
2020-05-30 18:12:42 +02:00
parent ba7c0280ae
commit 8d0577b756
9 changed files with 212 additions and 102 deletions
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156
utils/module_mixins.py
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@ -1,10 +1,17 @@
from collections import defaultdict
from itertools import cycle
from abc import ABC
from argparse import Namespace
import torch
import numpy as np
from numpy import interp
from sklearn.metrics import roc_curve, auc, confusion_matrix, ConfusionMatrixDisplay, f1_score, roc_auc_score
import matplotlib.pyplot as plt
from torch import nn
from torch.optim import Adam
from torch.utils.data import DataLoader
@ -12,7 +19,9 @@ from torchcontrib.optim import SWA
from torchvision.transforms import Compose
from ml_lib.modules.util import LightningBaseModule
from ml_lib.utils.tools import to_one_hot
from ml_lib.utils.transforms import ToTensor
from ml_lib.point_toolset.point_io import BatchToData
from .project_config import GlobalVar
@ -43,16 +52,21 @@ class BaseOptimizerMixin:
class BaseTrainMixin:
# Absolute Error
absolute_loss = nn.L1Loss()
# negative Log Likelyhood
nll_loss = nn.NLLLoss()
# Binary Cross Entropy
bce_loss = nn.BCELoss()
# Batch To Data
batch_to_data = BatchToData()
def training_step(self, batch_xy, batch_nb, *_, **__):
def training_step(self, batch_pos_x_y, batch_nb, *_, **__):
assert isinstance(self, LightningBaseModule)
batch_x, batch_y = batch_xy
y = self(batch_x).main_out
bce_loss = self.bce_loss(y, batch_y)
return dict(loss=bce_loss, log=dict(batch_nb=batch_nb))
data = self.batch_to_data(*batch_pos_x_y)
y = self(data).main_out
nll_loss = self.nll_loss(y, data.y)
return dict(loss=nll_loss, log=dict(batch_nb=batch_nb))
def training_epoch_end(self, outputs):
assert isinstance(self, LightningBaseModule)
@ -66,17 +80,20 @@ class BaseTrainMixin:
class BaseValMixin:
# Absolute Error
absolute_loss = nn.L1Loss()
# negative Log Likelyhood
nll_loss = nn.NLLLoss()
# Binary Cross Entropy
bce_loss = nn.BCELoss()
def validation_step(self, batch_xy, batch_idx, _, *__, **___):
def validation_step(self, batch_pos_x_y, batch_idx, *_, **__):
assert isinstance(self, LightningBaseModule)
batch_x, batch_y = batch_xy
y = self(batch_x).main_out
val_bce_loss = self.bce_loss(y, batch_y)
return dict(val_bce_loss=val_bce_loss,
batch_idx=batch_idx, y=y, batch_y=batch_y)
data = self.batch_to_data(*batch_pos_x_y)
y = self(data).main_out
nll_loss = self.nll_loss(y, data.y)
return dict(val_nll_loss=nll_loss,
batch_idx=batch_idx, y=y, batch_y=data.y)
def validation_epoch_end(self, outputs, *_, **__):
assert isinstance(self, LightningBaseModule)
@ -84,25 +101,107 @@ class BaseValMixin:
# In case of Multiple given dataloader this will outputs will be: list[list[dict[]]]
# for output_idx, output in enumerate(outputs):
# else:list[dict[]]
keys = list(outputs.keys())
keys = list(outputs[0].keys())
# Add Every Value das has a "loss" in it, by calc. mean over all occurences.
summary_dict['log'].update({f'mean_{key}': torch.mean(torch.stack([output[key]
for output in outputs]))
for key in keys if 'loss' in key}
)
"""
# Additional Score like the unweighted Average Recall:
# UnweightedAverageRecall
#######################################################################################
# Additional Score - UAR - ROC - Conf. Matrix - F1
#######################################################################################
#
# INIT
y_true = torch.cat([output['batch_y'] for output in outputs]) .cpu().numpy()
y_true_one_hot = to_one_hot(y_true)
y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().numpy()
y_pred_max = np.argmax(y_pred, axis=1)
y_pred = (y_pred >= 0.5).astype(np.float32)
class_names = {val: key for key, val in GlobalVar.classes.__dict__().items()}
######################################################################################
#
# F1 SCORE
micro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='micro', sample_weight=None,
zero_division=True)
macro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='macro', sample_weight=None,
zero_division=True)
summary_dict['log'].update(dict(micro_f1_score=micro_f1_score, macro_f1_score=macro_f1_score))
uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
sample_weight=None, zero_division='warn')
#######################################################################################
#
# ROC Curve
summary_dict['log'].update({f'uar_score': uar_score})
"""
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(len(GlobalVar.classes)):
fpr[i], tpr[i], _ = roc_curve(y_true_one_hot[:, i], y_pred[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_true_one_hot.ravel(), y_pred.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(len(GlobalVar.classes))]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(len(GlobalVar.classes)):
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= len(GlobalVar.classes)
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
label=f'micro ROC ({round(roc_auc["micro"], 2)})',
color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr["macro"], tpr["macro"],
label=f'macro ROC({round(roc_auc["macro"], 2)})]',
color='navy', linestyle=':', linewidth=4)
colors = cycle(['firebrick', 'orangered', 'gold', 'olive', 'limegreen', 'aqua',
'dodgerblue', 'slategrey', 'royalblue', 'indigo', 'fuchsia'], )
for i, color in zip(range(len(GlobalVar.classes)), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=2, label=f'{class_names[i]} ({round(roc_auc[i],2 )})')
plt.plot([0, 1], [0, 1], 'k--', lw=2)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(loc="lower right")
self.logger.log_image('ROC', image=plt.gcf(), step=self.current_epoch)
plt.clf()
#######################################################################################
#
# ROC SCORE
macro_roc_auc_ovr = roc_auc_score(y_true_one_hot, y_pred, multi_class="ovr",
average="macro")
summary_dict['log'].update(macro_roc_auc_ovr=macro_roc_auc_ovr)
#######################################################################################
#
# Confusion matrix
cm = confusion_matrix(y_true, y_pred_max, labels=[class_name for class_name in class_names], normalize='all')
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot(include_values=True)
self.logger.log_image('Confusion Matrix', image=plt.gcf(), step=self.current_epoch)
return summary_dict
@ -122,18 +221,17 @@ class DatasetMixin:
dataset = Namespace(
**dict(
# TRAIN DATASET
train_dataset=dataset_class(self.params.root, setting=GlobalVar.train,
transforms=transforms
),
# VALIDATION DATASET
val_dataset=dataset_class(self.params.root, setting=GlobalVar.vali,
train_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.train,
transforms=transforms
),
# TEST DATASET
test_dataset=dataset_class(self.params.root, setting=GlobalVar.test,
),
# VALIDATION DATASET
val_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.devel,
),
# TEST DATASET
test_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.test,
),
)
)
return dataset