def test_assertion_error_with_too_large_kernel(self):
n = 1
c_i = 1
h_i = 3
w_i = 3
x = np.arange(n * c_i * h_i * w_i).reshape(n, c_i, h_i,
w_i).astype(np.float32)
c_o = 1
h_k = 4 # > h_i
w_k = 4 # > w_i
W = np.full(c_o * c_i * h_k * w_k, 2).reshape(c_o, c_i, h_k,
w_k).astype(np.float32)
with pytest.raises(AssertionError):
convolution_with_im2col(x.tolist(), W.tolist())
def test_assertion_error_with_too_large_stride(self):
n = 1
c_i = 1
h_i = 3
w_i = 3
x = np.arange(n * c_i * h_i * w_i).reshape(n, c_i, h_i,
w_i).astype(np.float32)
c_o = 1
h_k = 2
w_k = 2
W = np.full(c_o * c_i * h_k * w_k, 2).reshape(c_o, c_i, h_k,
w_k).astype(np.float32)
pad = 1
stride = 5 # > h_i - h_k + 2 * pad + 1
with pytest.raises(AssertionError):
convolution_with_im2col(x.tolist(),
W.tolist(),
stride=stride,
pad=pad)
def test_basis(self):
n = 1
c_i = 1
h_i = 3
w_i = 3
x = np.arange(n * c_i * h_i * w_i).reshape(n, c_i, h_i,
w_i).astype(np.float32)
c_o = 1
h_k = 2
w_k = 2
W = np.full(c_o * c_i * h_k * w_k, 2).reshape(c_o, c_i, h_k,
w_k).astype(np.float32)
stride = 1
pad = 0
expected = convolution_2d(x, W, stride=stride, pad=pad).data.tolist()
actual = convolution_with_im2col(x.tolist(),
W.tolist(),
stride=stride,
pad=pad)
self.__assert_eq_arrays(actual, expected, n, c_o, h_i, w_i, h_k, w_k,
pad, stride)
def test_with_c_o_64_c_i_3_n_1_stride_1_and_padding_1(self):
n = 1
c_i = 3
h_i = 224
w_i = 224
x = np.arange(n * c_i * h_i * w_i).reshape(n, c_i, h_i,
w_i).astype(np.float32)
c_o = 64
h_k = 3
w_k = 3
W = np.full(c_o * c_i * h_k * w_k, 2).reshape(c_o, c_i, h_k,
w_k).astype(np.float32)
stride = 1
pad = 1
expected = convolution_2d(x, W, stride=stride, pad=pad).data.tolist()
actual = convolution_with_im2col(x.tolist(),
W.tolist(),
stride=stride,
pad=pad)
self.__assert_eq_arrays(actual, expected, n, c_o, h_i, w_i, h_k, w_k,
pad, stride)
def test_with_c_o_3_c_i_3_n_3_stride_3_and_padding_5(self):
n = 3
c_i = 3
h_i = 10
w_i = 10
x = np.arange(n * c_i * h_i * w_i).reshape(n, c_i, h_i,
w_i).astype(np.float32)
c_o = 3
h_k = 2
w_k = 2
W = np.full(c_o * c_i * h_k * w_k, 2).reshape(c_o, c_i, h_k,
w_k).astype(np.float32)
stride = 3
pad = 5
expected = convolution_2d(x, W, stride=stride, pad=pad).data.tolist()
actual = convolution_with_im2col(x.tolist(),
W.tolist(),
stride=stride,
pad=pad)
self.__assert_eq_arrays(actual, expected, n, c_o, h_i, w_i, h_k, w_k,
pad, stride)
