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| # coding:utf-8 | |
| import numpy as np | |
| def direct_sigmoid(x): | |
| """Apply the sigmoid operation. | |
| """ | |
| y = 1./(1.+1./np.exp(x)) | |
| dy = y*(1-y) | |
| return y | |
| def inverse_sigmoid(x): | |
| """Apply the inverse sigmoid operation. | |
| y = -ln(1-x/x) | |
| """ | |
| y = -1 * np.log((1-x)/x) | |
| return y | |
| def transform(X, components_, explained_variance_, mean_=None, whiten=False): | |
| """Apply dimensionality reduction to X. | |
| X is projected on the first principal components previously extracted | |
| from a training set. | |
| Parameters | |
| ---------- | |
| X: array-like, shape (n_samples, n_features) | |
| New data, where n_samples is the number of samples | |
| and n_features is the number of features. | |
| components_: array-like, shape (n_components, n_features) | |
| mean_: array-like, shape (n_features,) | |
| explained_variance_: array-like, shape (n_components,) | |
| Variance explained by each of the selected components. | |
| whiten : bool, optional | |
| When True (False by default) the ``components_`` vectors are divided | |
| by ``n_samples`` times ``components_`` to ensure uncorrelated outputs | |
| with unit component-wise variances. | |
| Whitening will remove some information from the transformed signal | |
| (the relative variance scales of the components) but can sometimes | |
| improve the predictive accuracy of the downstream estimators by | |
| making data respect some hard-wired assumptions. | |
| Returns | |
| ------- | |
| X_new : array-like, shape (n_samples, n_components) | |
| """ | |
| if mean_ is not None: | |
| X = X - mean_ | |
| X_transformed = np.dot(X, components_.T) | |
| if whiten: | |
| X_transformed /= np.sqrt(explained_variance_) | |
| return X_transformed | |
| def inverse_transform(X, components_, explained_variance_, mean_=None, whiten=False): | |
| """Transform data back to its original space. | |
| In other words, return an input X_original whose transform would be X. | |
| Parameters | |
| ---------- | |
| X : array-like, shape (n_samples, n_components) | |
| New data, where n_samples is the number of samples | |
| and n_components is the number of components. | |
| components_: array-like, shape (n_components, n_features) | |
| mean_: array-like, shape (n_features,) | |
| explained_variance_: array-like, shape (n_components,) | |
| Variance explained by each of the selected components. | |
| whiten : bool, optional | |
| When True (False by default) the ``components_`` vectors are divided | |
| by ``n_samples`` times ``components_`` to ensure uncorrelated outputs | |
| with unit component-wise variances. | |
| Whitening will remove some information from the transformed signal | |
| (the relative variance scales of the components) but can sometimes | |
| improve the predictive accuracy of the downstream estimators by | |
| making data respect some hard-wired assumptions. | |
| Returns | |
| ------- | |
| X_original array-like, shape (n_samples, n_features) | |
| """ | |
| if whiten: | |
| X_transformed = np.dot(X, np.sqrt(explained_variance_[:, np.newaxis]) * components_) | |
| else: | |
| X_transformed = np.dot(X, components_) | |
| if mean_ is not None: | |
| X_transformed = X_transformed + mean_ | |
| return X_transformed | |
| class IOUMetric(object): | |
| """ | |
| Class to calculate mean-iou using fast_hist method | |
| """ | |
| def __init__(self, num_classes): | |
| self.num_classes = num_classes | |
| self.hist = np.zeros((num_classes, num_classes)) | |
| def _fast_hist(self, label_pred, label_true): | |
| mask = (label_true >= 0) & (label_true < self.num_classes) | |
| hist = np.bincount( | |
| self.num_classes * label_true[mask].astype(int) + | |
| label_pred[mask], minlength=self.num_classes ** 2).reshape(self.num_classes, self.num_classes) | |
| return hist | |
| def add_batch(self, predictions, gts): | |
| for lp, lt in zip(predictions, gts): | |
| self.hist += self._fast_hist(lp.flatten(), lt.flatten()) | |
| def evaluate(self): | |
| acc = np.diag(self.hist).sum() / self.hist.sum() | |
| acc_cls = np.diag(self.hist) / self.hist.sum(axis=1) | |
| acc_cls = np.nanmean(acc_cls) | |
| iu = np.diag(self.hist) / (self.hist.sum(axis=1) + self.hist.sum(axis=0) - np.diag(self.hist)) | |
| mean_iu = np.nanmean(iu) | |
| freq = self.hist.sum(axis=1) / self.hist.sum() | |
| fwavacc = (freq[freq > 0] * iu[freq > 0]).sum() | |
| return acc, acc_cls, iu, mean_iu, fwavacc |