def backward_0(self, grad):
"""
Parameters
----------
grad : ndarray
"""
x, kernel_size = self.inputs
data = x
batch, channels, height, width = data.shape
output_grad = np.zeros_like(data)
output_grad = utils.make_padding(output_grad, self.padding)
output_height = int((height - kernel_size[0]) / self.stride[0] + 1)
output_width = int((width - kernel_size[1]) / self.stride[1] + 1)
area = reduce(lambda x, y: x * y, kernel_size)
for b in range(batch):
for c in range(channels):
for y in range(output_height):
y_min = y * self.stride[0]
y_max = y_min + kernel_size[0]
for x in range(output_width):
x_min = x * self.stride[1]
x_max = x_min + kernel_size[1]
output_grad[b, c, y_min:y_max,
x_min:x_max] = grad[b, c, y, x] / area
return output_grad
def forward(self, x, kernel_size):
data = x
data = utils.make_padding(data, self.padding)
batch, channels, height, width = data.shape
output_height = int((height - kernel_size[0]) / self.stride[0] + 1)
output_width = int((width - kernel_size[1]) / self.stride[1] + 1)
output = np.zeros((batch, channels, output_height, output_width))
for b in range(batch):
for c in range(channels):
for y in range(output_height):
y_min = y * self.stride[0]
y_max = y_min + kernel_size[0]
for x in range(output_width):
x_min = x * self.stride[1]
x_max = x_min + kernel_size[1]
field = data[b, c, y_min:y_max, x_min:x_max]
y_max_values = np.max(field, axis=1)
y_idxs = np.argmax(field, axis=1)
x_idx = np.argmax(y_max_values, axis=0)
max_y = y_idxs[x_idx]
max_x = x_idx
max_value = field[max_y, max_x]
output[b, c, y, x] = max_value
self.max_coords.append(
[b, c, y_min + max_y, x_min + max_x, max_value])
return output
def backward_0(self, grad):
"""
Parameters
----------
grad : ndarray
"""
x, kernel_size = self.inputs
data = x
output_grad = np.zeros_like(data)
output_grad = utils.make_padding(output_grad, self.padding)
grad = grad.reshape((-1, ))
for i, coord in enumerate(self.max_coords):
b, c, h, w, value = coord
output_grad[b, c, h, w] = grad[i]
# output_grad[b, c, h, w] = value
return utils.unwrap_padding(output_grad, self.padding)
def forward(self, x, kernel_size):
data = x
data = utils.make_padding(data, self.padding)
batch, channels, height, width = data.shape
output_height = int((height - kernel_size[0]) / self.stride[0] + 1)
output_width = int((width - kernel_size[1]) / self.stride[1] + 1)
output = np.zeros((batch, channels, output_height, output_width))
for b in range(batch):
for c in range(channels):
for y in range(output_height):
y_min = y * self.stride[0]
y_max = y_min + kernel_size[0]
for x in range(output_width):
x_min = x * self.stride[1]
x_max = x_min + kernel_size[1]
field = data[b, c, y_min:y_max, x_min:x_max]
output[b, c, y, x] = np.mean(field)
return output
