  The aim of this paper is to establish non-asymptotic minimax rates of testing
for goodness-of-fit hypotheses in a heteroscedastic setting. More precisely, we
deal with sequences $(Y_j)_{j\in J}$ of independent Gaussian random variables,
having mean $(\theta_j)_{j\in J}$ and variance $(\sigma_j)_{j\in J}$. The set
$J$ will be either finite or countable. In particular, such a model covers the
inverse problem setting where few results in test theory have been obtained.
The rates of testing are obtained with respect to $l_2$ and $l_{\infty}$ norms,
without assumption on $(\sigma_j)_{j\in J}$ and on several functions spaces.
Our point of view is completely non-asymptotic.
