From 3a76b2f09a537fff3b78b4ee2e67c94557eb09dc Mon Sep 17 00:00:00 2001
From: Jon Almazan <jon.almazan@gmail.com>
Date: Tue, 13 Aug 2019 10:41:55 +0100
Subject: [PATCH] clean unused functions
---
dirtorch/utils/common.py | 50 +++++-----------------------------------
1 file changed, 6 insertions(+), 44 deletions(-)
diff --git a/dirtorch/utils/common.py b/dirtorch/utils/common.py
index cf03917..a430349 100644
--- a/dirtorch/utils/common.py
+++ b/dirtorch/utils/common.py
@@ -189,12 +189,15 @@ def freeze_batch_norm(model, freeze=True, only_running=False):
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
- m.eval() # Eval mode freezes the running mean and std
+ # Eval mode freezes the running mean and std
+ m.eval()
for param in m.named_parameters():
if only_running:
- param[1].requires_grad = True # Weight and bias can be updated
+ # Weight and bias can be updated
+ param[1].requires_grad = True
else:
- param[1].requires_grad = False # Freeze the weight and bias
+ # Freeze the weight and bias
+ param[1].requires_grad = False
def variables(inputs, iscuda, not_on_gpu=[]):
@@ -213,47 +216,6 @@ def variables(inputs, iscuda, not_on_gpu=[]):
return inputs_var
-def learn_pca(X, n_components=None, whiten=False, use_sklearn=True):
- ''' Learn Principal Component Analysis
-
- input:
- X: input matrix with size samples x features
- n_components: number of components to keep
- whiten: applies feature whitening
-
- output:
- PCA: weights and means of the PCA learned
- '''
- if use_sklearn:
- pca = sklearn.decomposition.PCA(n_components=n_components, svd_solver='full', whiten=whiten)
- pca.fit(X)
- else:
- fudge = 1E-8
- means = np.mean(X, axis=0)
- X = X - means
-
- # get the covariance matrix
- Xcov = np.dot(X.T, X)
-
- # eigenvalue decomposition of the covariance matrix
- d, V = np.linalg.eigh(Xcov)
- d[d < 0] = fudge
-
- # a fudge factor can be used so that eigenvectors associated with
- # small eigenvalues do not get overamplified.
- D = np.diag(1. / np.sqrt(d+fudge))
-
- # whitening matrix
- W = np.dot(np.dot(V, D), V.T)
-
- # multiply by the whitening matrix
- X_white = np.dot(X, W)
-
- pca = {'W': W, 'means': means}
-
- return pca
-
-
def transform(pca, X, whitenp=0.5, whitenv=None, whitenm=1.0, use_sklearn=True):
if use_sklearn:
# https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/base.py#L99
--
GitLab