def forward_gpu(self, x):
if (cuda.cudnn_enabled and self.use_cudnn and
pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(AveragePooling2D, self).forward_gpu(x)
n, c, h, w = x[0].shape
y_h = conv.get_conv_outsize(h, self.kh, self.sy, self.ph)
y_w = conv.get_conv_outsize(w, self.kw, self.sx, self.pw)
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
coeff = 1. / (self.kh * self.kw)
kern = cuda.elementwise(
'raw T in, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw, T coeff',
'T out', '''
int c0 = i / (out_h * out_w);
int out_y = i / out_w % out_h;
int out_x = i % out_w;
int in_y_0 = max(0, out_y * sy - ph);
int in_y_1 = min(h, out_y * sy + kh - ph);
int in_x_0 = max(0, out_x * sx - pw);
int in_x_1 = min(w, out_x * sx + kw - pw);
T val = 0;
for (int y = in_y_0; y < in_y_1; ++y) {
int offset_y = w * (y + h * c0);
for (int x = in_x_0; x < in_x_1; ++x) {
val = val + in[x + offset_y];
}
}
out = val * coeff;
''', 'avg_pool_fwd')
kern(x[0].reduced_view(), h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw, coeff, y)
return y,
def backward_gpu(self, x, gy):
if (cuda.cudnn_enabled and self.use_cudnn
and pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(AveragePooling2D, self).backward_gpu(x, gy)
n, c, h, w = x[0].shape
y_h, y_w = gy[0].shape[2:]
gx = cuda.cupy.empty_like(x[0])
coeff = 1. / (self.kh * self.kw)
cuda.elementwise(
'raw T gy, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw, T coeff', 'T gx', '''
int c0 = i / (h * w);
int y = i / w % h + ph;
int x = i % w + pw;
int out_y_0 = max(0, (y - kh + sy) / sy);
int out_y_1 = min(out_h, (y + sy) / sy);
int out_x_0 = max(0, (x - kw + sx) / sx);
int out_x_1 = min(out_w, (x + sx) / sx);
int hc0 = out_h * c0;
T val = 0;
for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
val = val + gy[out_x + out_w * (out_y + hc0)];
}
}
gx = val * coeff;
''', 'avg_pool_bwd')(gy[0].reduced_view(), h, w, y_h, y_w, self.kh,
self.kw, self.sy, self.sx, self.ph, self.pw,
coeff, gx)
return gx,
def forward_gpu(self, x):
if (chainer.should_use_cudnn('>=auto') and
pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(MaxPooling2D, self).forward_gpu(x)
self.retain_inputs(())
self._in_shape = x[0].shape
self._in_dtype = x[0].dtype
n, c, h, w = x[0].shape
y_h = conv.get_conv_outsize(
h, self.kh, self.sy, self.ph, self.cover_all)
assert y_h > 0, 'Height in the output should be positive.'
y_w = conv.get_conv_outsize(
w, self.kw, self.sx, self.pw, self.cover_all)
assert y_w > 0, 'Width in the output should be positive.'
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
self.indexes = cuda.cupy.empty((n, c, y_h, y_w), dtype=numpy.int32)
cuda.elementwise(
'raw T in, int32 h, int32 w, int32 out_h, int32 out_w,'
'int32 kh, int32 kw, int32 sy, int32 sx, int32 ph, int32 pw',
'T out, S indexes',
'''
int c0 = i / (out_h * out_w);
int out_y = i / out_w % out_h;
int out_x = i % out_w;
int in_y_0 = max(0, out_y * sy - ph);
int in_y_1 = min(h, out_y * sy + kh - ph);
int in_x_0 = max(0, out_x * sx - pw);
int in_x_1 = min(w, out_x * sx + kw - pw);
T maxval = in[in_x_0 + w * (in_y_0 + h * c0)];
int argmax_y = in_y_0;
int argmax_x = in_x_0;
for (int y = in_y_0; y < in_y_1; ++y) {
int offset_y = w * (y + h * c0);
for (int x = in_x_0; x < in_x_1; ++x) {
float v = in[x + offset_y];
if (maxval < v) {
maxval = v;
argmax_y = y;
argmax_x = x;
}
}
}
out = maxval;
int argmax_ky = argmax_y + ph - out_y * sy;
int argmax_kx = argmax_x + pw - out_x * sx;
indexes = argmax_kx + kw * argmax_ky;
''', 'max_pool_fwd')(x[0].reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw,
y, self.indexes)
return y,
def forward_gpu(self, x):
if (chainer.should_use_cudnn('>=auto')
and pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(MaxPooling2D, self).forward_gpu(x)
self.retain_inputs(())
self._in_shape = x[0].shape
self._in_dtype = x[0].dtype
n, c, h, w = x[0].shape
y_h = conv.get_conv_outsize(h, self.kh, self.sy, self.ph,
self.cover_all)
assert y_h > 0, 'Height in the output should be positive.'
y_w = conv.get_conv_outsize(w, self.kw, self.sx, self.pw,
self.cover_all)
assert y_w > 0, 'Width in the output should be positive.'
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
self.indexes = cuda.cupy.empty((n, c, y_h, y_w), dtype=numpy.int32)
cuda.elementwise(
'raw T in, int32 h, int32 w, int32 out_h, int32 out_w,'
'int32 kh, int32 kw, int32 sy, int32 sx, int32 ph, int32 pw',
'T out, S indexes', '''
int c0 = i / (out_h * out_w);
int out_y = i / out_w % out_h;
int out_x = i % out_w;
int in_y_0 = max(0, out_y * sy - ph);
int in_y_1 = min(h, out_y * sy + kh - ph);
int in_x_0 = max(0, out_x * sx - pw);
int in_x_1 = min(w, out_x * sx + kw - pw);
T maxval = in[in_x_0 + w * (in_y_0 + h * c0)];
int argmax_y = in_y_0;
int argmax_x = in_x_0;
for (int y = in_y_0; y < in_y_1; ++y) {
int offset_y = w * (y + h * c0);
for (int x = in_x_0; x < in_x_1; ++x) {
float v = in[x + offset_y];
if (maxval < v) {
maxval = v;
argmax_y = y;
argmax_x = x;
}
}
}
out = maxval;
int argmax_ky = argmax_y + ph - out_y * sy;
int argmax_kx = argmax_x + pw - out_x * sx;
indexes = argmax_kx + kw * argmax_ky;
''', 'max_pool_fwd')(x[0].reduced_view(), h, w, y_h, y_w, self.kh,
self.kw, self.sy, self.sx, self.ph, self.pw,
y, self.indexes)
return y,
def backward_gpu(self, x, gy):
if (cuda.cudnn_enabled and self.use_cudnn and
pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(MaxPooling2D, self).backward_gpu(x, gy)
n, c, h, w = x[0].shape
y_h, y_w = gy[0].shape[2:]
gx = cuda.cupy.empty_like(x[0])
cuda.elementwise(
'raw T gy, raw S indexes, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw',
'T gx',
'''
int c0 = i / (h * w);
int y = i / w % h + ph;
int x = i % w + pw;
int out_y_0 = max(0, (y - kh + sy) / sy);
int out_y_1 = min(out_h, (y + sy) / sy);
int out_x_0 = max(0, (x - kw + sx) / sx);
int out_x_1 = min(out_w, (x + sx) / sx);
T val = 0;
for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
int ky = y - out_y * sy;
for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
int kx = x - out_x * sx;
int offset = out_x + out_w * (out_y + out_h * c0);
if (indexes[offset] == kx + kw * ky) {
val = val + gy[offset];
}
}
}
gx = val;
''',
'max_pool_bwd')(gy[0].reduced_view(), self.indexes.reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw,
gx)
return gx,
def backward_gpu(self, x, gy):
if (cuda.cudnn_enabled and self.use_cudnn and
pooling_2d._check_cudnn_acceptable_type(x[0].dtype)):
return super(AveragePooling2D, self).backward_gpu(x, gy)
n, c, h, w = x[0].shape
y_h, y_w = gy[0].shape[2:]
gx = cuda.cupy.empty_like(x[0])
coeff = 1. / (self.kh * self.kw)
cuda.elementwise(
'raw T gy, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw, T coeff',
'T gx',
'''
int c0 = i / (h * w);
int y = i / w % h + ph;
int x = i % w + pw;
int out_y_0 = max(0, (y - kh + sy) / sy);
int out_y_1 = min(out_h, (y + sy) / sy);
int out_x_0 = max(0, (x - kw + sx) / sx);
int out_x_1 = min(out_w, (x + sx) / sx);
int hc0 = out_h * c0;
T val = 0;
for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
val = val + gy[out_x + out_w * (out_y + hc0)];
}
}
gx = val * coeff;
''', 'avg_pool_bwd')(gy[0].reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw, coeff,
gx)
return gx,
