def test_mode_1():
input_shape = (1, 2, 3, 4, 5)
data = np.random.rand(*input_shape).astype(np.float32)
x = paddle.fluid.data(name="x", shape=input_shape)
y = F.pad(x, pad=[1, 1, 1, 1, 1, 1], mode='reflect')
place = paddle.NPUPlace()
exe = Executor(place)
outputs = exe.run(feed={'x': data}, fetch_list=[y.name])
def test_dygraph_3(self):
paddle.disable_static()
input_shape = (3, 4, 5)
pad = [3, 4]
pad_3 = [3, 4, 5, 6, 7, 8]
mode = "constant"
value = 100
input_data = np.random.rand(*input_shape).astype(np.float32)
np_out1 = self._get_numpy_out(input_data,
pad,
mode,
value,
data_format="NCL")
np_out2 = self._get_numpy_out(input_data,
pad,
mode,
value,
data_format="NLC")
np_out3 = self._get_numpy_out(input_data,
pad_3,
mode,
value,
data_format="NCL")
tensor_data = paddle.to_tensor(input_data)
tensor_pad = paddle.to_tensor(pad, dtype="int32")
y1 = F.pad(tensor_data,
pad=tensor_pad,
mode=mode,
value=value,
data_format="NCL")
y2 = F.pad(tensor_data,
pad=tensor_pad,
mode=mode,
value=value,
data_format="NLC")
y3 = F.pad(tensor_data,
pad=pad_3,
mode=mode,
value=value,
data_format="NCL")
self.assertTrue(np.allclose(y1.numpy(), np_out1))
self.assertTrue(np.allclose(y2.numpy(), np_out2))
self.assertTrue(np.allclose(y3.numpy(), np_out3))
def forward(self, x):
B, C, H, W = x.shape
# assert [H, W] == self.img_size[:2], "Input image size ({H}*{W}) doesn't match model ({}*{}).".format(H, W, self.img_size[0], self.img_size[1])
if W % self.patch_size[1] != 0:
x = F.pad(x,
[0, self.patch_size[1] - W % self.patch_size[1], 0, 0])
if H % self.patch_size[0] != 0:
x = F.pad(x,
[0, 0, 0, self.patch_size[0] - H % self.patch_size[0]])
x = self.proj(x)
if self.norm is not None:
_, _, Wh, Ww = x.shape
x = x.flatten(2).transpose([0, 2, 1])
x = self.norm(x)
x = x.transpose([0, 2, 1]).reshape([-1, self.embed_dim, Wh, Ww])
return x
def test_reflect_3():
input_shape = (1, 2, 3, 4, 5)
data = np.random.rand(*input_shape).astype(np.float32)
x = paddle.to_tensor(data)
y = F.pad(x,
pad=[1, 1, 1, 1, 2, 3],
value=1,
mode='reflect',
data_format="NCDHW")
def forward(self, input):
if self.scale == 1.0:
return input
out = F.pad(input, [self.ka, self.kb, self.ka, self.kb])
out = F.conv2d(out, weight=self.weight, groups=self.groups)
out = F.interpolate(out, scale_factor=[self.scale, self.scale])
return out
def forward(self, src, pha, err, hid, tri):
'''
Args：
src: (B, 3, H, W) full resolution source image.
pha: (B, 1, Hc, Wc) coarse alpha prediction.
err: (B, 1, Hc, Hc) coarse error prediction.
hid: (B, 32, Hc, Hc) coarse hidden encoding.
tri: (B, 1, Hc, Hc) trimap prediction.
'''
h_full, w_full = paddle.shape(src)[2:]
h_half, w_half = h_full // 2, w_full // 2
h_quat, w_quat = h_full // 4, w_full // 4
x = paddle.concat([hid, pha, tri], axis=1)
x = F.interpolate(x,
paddle.concat((h_half, w_half)),
mode='bilinear',
align_corners=False)
y = F.interpolate(src,
paddle.concat((h_half, w_half)),
mode='bilinear',
align_corners=False)
if self.kernel_size == 3:
x = F.pad(x, [3, 3, 3, 3])
y = F.pad(y, [3, 3, 3, 3])
x = self.conv1(paddle.concat([x, y], axis=1))
x = self.conv2(x)
if self.kernel_size == 3:
x = F.interpolate(x, paddle.concat((h_full + 4, w_full + 4)))
y = F.pad(src, [2, 2, 2, 2])
else:
x = F.interpolate(x,
paddle.concat((h_full, w_full)),
mode='nearest')
y = src
x = self.conv3(paddle.concat([x, y], axis=1))
x = self.conv4(x)
pha = x
return pha
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
pad_y0, pad_y1):
_, channel, in_h, in_w = input.shape
input = input.reshape((-1, in_h, in_w, 1))
_, in_h, in_w, minor = input.shape
kernel_h, kernel_w = kernel.shape
out = input.reshape((-1, in_h, 1, in_w, 1, minor))
out = out.transpose((0, 1, 3, 5, 2, 4))
out = out.reshape((-1, 1, 1, 1))
out = F.pad(out, [0, up_x - 1, 0, up_y - 1])
out = out.reshape((-1, in_h, in_w, minor, up_y, up_x))
out = out.transpose((0, 3, 1, 4, 2, 5))
out = out.reshape((-1, minor, in_h * up_y, in_w * up_x))
out = F.pad(
out, [max(pad_x0, 0),
max(pad_x1, 0),
max(pad_y0, 0),
max(pad_y1, 0)])
out = out[:, :,
max(-pad_y0, 0):out.shape[2] - max(-pad_y1, 0),
max(-pad_x0, 0):out.shape[3] - max(-pad_x1, 0), ]
out = out.reshape(
([-1, 1, in_h * up_y + pad_y0 + pad_y1,
in_w * up_x + pad_x0 + pad_x1]))
w = paddle.flip(kernel, [0, 1]).reshape((1, 1, kernel_h, kernel_w))
out = F.conv2d(out, w)
out = out.reshape((
-1,
minor,
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
))
out = out.transpose((0, 2, 3, 1))
out = out[:, ::down_y, ::down_x, :]
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
return out.reshape((-1, channel, out_h, out_w))
def forward(self, x):
"""Forward function."""
# padding
_, _, H, W = x.shape
if W % self.patch_size[1] != 0:
x = F.pad(x,
[0, self.patch_size[1] - W % self.patch_size[1], 0, 0])
if H % self.patch_size[0] != 0:
x = F.pad(x,
[0, 0, 0, self.patch_size[0] - H % self.patch_size[0]])
x = self.proj(x) # B C Wh Ww
if self.norm is not None:
_, _, Wh, Ww = x.shape
x = x.flatten(2).transpose([0, 2, 1])
x = self.norm(x)
x = x.transpose([0, 2, 1]).reshape([-1, self.embed_dim, Wh, Ww])
return x
def inflate_tensor(tensor, scope):
"""Inflate tensor"""
max_len = max([le for _, le in scope])
batch_vecs = []
for st, le in scope:
cur_vecs = tensor[st:st + le]
cur_vecs = F.pad(cur_vecs, (0, 0, 0, max_len - le))
batch_vecs.append(cur_vecs)
return paddle.stack(batch_vecs, axis=0)
def forward(self, input):
if self.scale == 1.0:
return input
out = F.pad(input, [self.ka, self.kb, self.ka, self.kb],
mode='constant')
out = self.conv(out)
out = F.interpolate(out,
scale_factor=self.scale,
mode='NEAREST',
align_corners=False)
return out
def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1,
gsrm_slf_attn_bias2):
# ===== GSRM Visual-to-semantic embedding block =====
b, t, c = inputs.shape
pvam_features = paddle.reshape(inputs, [-1, c])
word_out = self.fc0(pvam_features)
word_ids = paddle.argmax(F.softmax(word_out), axis=1)
word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1])
#===== GSRM Semantic reasoning block =====
"""
This module is achieved through bi-transformers,
ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
"""
pad_idx = self.char_num
word1 = paddle.cast(word_ids, "float32")
word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC")
word1 = paddle.cast(word1, "int64")
word1 = word1[:, :-1, :]
word2 = word_ids
enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
gsrm_feature1 = self.wrap_encoder0(enc_inputs_1)
gsrm_feature2 = self.wrap_encoder1(enc_inputs_2)
gsrm_feature2 = F.pad(gsrm_feature2, [0, 1],
value=0.,
data_format="NLC")
gsrm_feature2 = gsrm_feature2[:, 1:, ]
gsrm_features = gsrm_feature1 + gsrm_feature2
gsrm_out = self.mul(gsrm_features)
b, t, c = gsrm_out.shape
gsrm_out = paddle.reshape(gsrm_out, [-1, c])
return gsrm_features, word_out, gsrm_out
def test_static(self):
paddle.enable_static()
self.place = fluid.NPUPlace(
0) if fluid.core.is_compiled_with_npu() else fluid.CPUPlace()
with program_guard(Program(), Program()):
input_shape = (1, 2, 3, 4, 5)
pad = [1, 2, 1, 1, 3, 4]
mode = "constant"
value = 0
input_data = np.random.rand(*input_shape).astype(np.float32)
x = paddle.fluid.data(name="x", shape=input_shape)
result1 = F.pad(x=x,
pad=pad,
value=value,
mode=mode,
data_format="NCDHW")
result2 = F.pad(x=x,
pad=pad,
value=value,
mode=mode,
data_format="NDHWC")
exe = Executor(self.place)
fetches = exe.run(default_main_program(),
feed={"x": input_data},
fetch_list=[result1, result2])
np_out1 = self._get_numpy_out(input_data,
pad,
mode,
value,
data_format="NCDHW")
np_out2 = self._get_numpy_out(input_data,
pad,
mode,
value,
data_format="NDHWC")
self.assertTrue(np.allclose(fetches[0], np_out1))
self.assertTrue(np.allclose(fetches[1], np_out2))
def forward(self, input):
if self.scale == 1.0:
return input
out = F.pad(input, [self.ka, self.kb, self.ka, self.kb])
out = F.conv2d(out, weight=self.weight, groups=self.groups)
out.stop_gradient = False
inv_scale = 1 / self.scale
int_inv_scale = int(inv_scale)
assert (inv_scale == int_inv_scale)
# out = out[:, :, ::int_inv_scale, ::int_inv_scale]
# patch end
out = paddle.fluid.layers.resize_nearest(out, scale=self.scale)
return out
def forward(self, x):
generated_filter = self.filter_gen_conv(self.avg_pool(x))
x = self.input_redu_conv(x)
b, c, h, w = x.shape
x = x.reshape([1, b * c, h, w])
generated_filter = generated_filter.reshape(
[b * c, 1, self.filter_size, self.filter_size])
x = F.pad(x, self.pad, mode='constant', value=0)
output = F.conv2d(x, weight=generated_filter, groups=b * c)
output = output.reshape([b, self.channels, h, w])
output = self.norm(output)
output = self.act(output)
if self.fusion:
output = self.fusion_conv(output)
return output
def forward(self, x):
ih, iw = x.shape[-2:]
kh, kw = self.weight.shape[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
pad_h = max(
(oh - 1) * self.stride[0] + (kh - 1) * self._dilation[0] + 1 - ih,
0)
pad_w = max(
(ow - 1) * self.stride[1] + (kw - 1) * self._dilation[1] + 1 - iw,
0)
if pad_h > 0 or pad_w > 0:
x = F.pad(x, [
pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
])
return F.conv2d(x, self.weight, self.bias, self.stride, self._padding,
self._dilation, self._groups)
def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
y = self.conv0(inputs)
if self.dilation > 1:
padding = self.dilation
y = F.pad(y, [padding, padding, padding, padding])
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
def forward(self, feat_list):
C3, C4 = feat_list
x = self.in_conv(C4)
x_shape = paddle.shape(x)
P_h, P_w = self.down_factor
Q_h, Q_w = paddle.ceil(x_shape[2] / P_h).astype('int32'), paddle.ceil(
x_shape[3] / P_w).astype('int32')
pad_h, pad_w = (Q_h * P_h - x_shape[2]).astype('int32'), (
Q_w * P_w - x_shape[3]).astype('int32')
if pad_h > 0 or pad_w > 0:
padding = paddle.concat([
pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
],
axis=0)
feat = F.pad(x, padding)
else:
feat = x
feat = feat.reshape([0, x_shape[1], Q_h, P_h, Q_w, P_w])
feat = feat.transpose([0, 3, 5, 1, 2,
4]).reshape([-1, self.inter_channels, Q_h, Q_w])
feat = self.global_relation(feat)
feat = feat.reshape([x_shape[0], P_h, P_w, x_shape[1], Q_h, Q_w])
feat = feat.transpose([0, 4, 5, 3, 1,
2]).reshape([-1, self.inter_channels, P_h, P_w])
feat = self.local_relation(feat)
feat = feat.reshape([x_shape[0], Q_h, Q_w, x_shape[1], P_h, P_w])
feat = feat.transpose([0, 3, 1, 4, 2, 5]).reshape(
[0, self.inter_channels, P_h * Q_h, P_w * Q_w])
if pad_h > 0 or pad_w > 0:
feat = paddle.slice(
feat,
axes=[2, 3],
starts=[pad_h // 2, pad_w // 2],
ends=[pad_h // 2 + x_shape[2], pad_w // 2 + x_shape[3]])
feat = self.out_conv(paddle.concat([feat, x], axis=1))
output = self.cls(feat)
if self.enable_auxiliary_loss:
auxout = self.aux(C3)
return [output, auxout]
else:
return [output]
def check_static_result_1(self, place):
paddle.enable_static()
with program_guard(Program(), Program()):
input_shape = (1, 2, 3, 4, 5)
pad = [1, 2, 1, 1, 3, 4]
mode = "constant"
value = 100
input_data = np.random.rand(*input_shape).astype(np.float32)
x = paddle.fluid.data(name="x", shape=input_shape)
result = F.pad(x=x,
pad=pad,
value=value,
mode=mode,
data_format="NCDHW")
exe = Executor(place)
fetches = exe.run(default_main_program(),
feed={"x": input_data},
fetch_list=[result])
np_out = self._get_numpy_out(input_data, pad, mode, value)
self.assertTrue(np.allclose(fetches[0], np_out))
def forward(self, inputs):
y = self.conv0(inputs)
####################################################################
# If given dilation rate > 1, using corresponding padding.
# The performance drops down without the follow padding.
if self.dilation > 1:
padding = self.dilation
y = F.pad(y, [padding, padding, padding, padding])
#####################################################################
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
def local_pairwise_distances2(x, y, max_distance=9):
"""Computes pairwise squared l2 distances using a local search window.
Naive implementation using map_fn.
Used as a slow fallback for when correlation_cost is not available.
Args:
x: Float32 tensor of shape [height, width, feature_dim].
y: Float32 tensor of shape [height, width, feature_dim].
max_distance: Integer, the maximum distance in pixel coordinates
per dimension which is considered to be in the search window.
Returns:
Float32 distances tensor of shape
[height, width, (2 * max_distance + 1) ** 2].
"""
ori_h, ori_w, _ = x.shape
x = paddle.transpose(x, [2, 0, 1]).unsqueeze(0)
x = F.avg_pool2d(x, (2, 2), (2, 2))
y = paddle.transpose(y, [2, 0, 1]).unsqueeze(0)
y = F.avg_pool2d(y, (2, 2), (2, 2))
_, channels, height, width = x.shape
padding_val = 1e20
padded_y = F.pad(y,
(max_distance, max_distance, max_distance, max_distance),
mode='constant',
value=padding_val)
offset_y = F.unfold(padded_y, kernel_sizes=[height, width]).reshape(
[1, channels, height, width, -1])
x = x.reshape([1, channels, height, width, 1])
minus = x - offset_y
dists = paddle.sum(paddle.multiply(minus, minus),
axis=1).reshape([1, height, width,
-1]).transpose([0, 3, 1, 2])
dists = (paddle.nn.functional.sigmoid(dists) - 0.5) * 2
dists = F.interpolate(dists,
size=[ori_h, ori_w],
mode='bilinear',
align_corners=True)
dists = dists.squeeze(0).transpose([1, 2, 0])
return dists
def fluid_layer(self, place):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
input_shape = (-1, -1, -1,self.num_channels) \
if self.channel_last else (-1, self.num_channels, -1, -1)
x_var = fluid.data("input", input_shape, dtype=self.dtype)
weight_attr = I.NumpyArrayInitializer(self.weight)
if self.bias is None:
bias_attr = False
else:
bias_attr = I.NumpyArrayInitializer(self.bias)
if self.padding_mode != 'zeros':
x_var = F.pad(x_var,
self._reversed_padding_repeated_twice,
mode=self.padding_mode,
data_format=self.data_format)
padding = 0
else:
padding = self.padding
y_var = fluid.layers.conv2d(
x_var,
self.num_filters,
self.filter_size,
padding=padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
param_attr=weight_attr,
bias_attr=bias_attr,
data_format=self.data_format)
feed_dict = {"input": self.input}
exe = fluid.Executor(place)
exe.run(start)
y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
return y_np
def forward(self, input):
if self.scale == 1.0:
return input
out = F.pad(input, [self.ka, self.kb, self.ka, self.kb])
out = F.conv2d(out, weight=self.weight, groups=self.groups)
out.stop_gradient = False
# The high version of pytorch has a bug that affects the convergence of this model
# original code
# out = F.interpolate(out, scale_factor=[self.scale, self.scale])
# original code end
# a patch 'might be' work for this bug.
# see https://github.com/AliaksandrSiarohin/first-order-model/issues/146#issue-624354694
inv_scale = 1 / self.scale
int_inv_scale = int(inv_scale)
assert (inv_scale == int_inv_scale)
out = out[:, :, ::int_inv_scale, ::int_inv_scale]
# patch end
return out
def relax_onehot(self, label, num_classes):
# pad label, and let ignore_index as num_classes
if len(label.shape) == 3:
label = label.unsqueeze(1)
h, w = label.shape[-2], label.shape[-1]
label = F.pad(label, [self.border] * 4, value=num_classes)
label = label.squeeze(1)
ignore_mask = (label == self.ignore_index).astype('int64')
label = label * (1 - ignore_mask) + num_classes * ignore_mask
onehot = 0
for i in range(-self.border, self.border + 1):
for j in range(-self.border, self.border + 1):
h_start, h_end = 1 + i, h + 1 + i
w_start, w_end = 1 + j, w + 1 + j
label_ = label[:, h_start:h_end, w_start:w_end]
onehot_ = F.one_hot(label_, num_classes + 1)
onehot += onehot_
onehot = (onehot > 0).astype('int64')
onehot = paddle.transpose(onehot, (0, 3, 1, 2))
return onehot
def functional(self, place):
main = fluid.Program()
start = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, start):
input_shape = (-1, -1, -1,self.num_channels) \
if self.channel_last else (-1, self.num_channels, -1, -1)
x_var = fluid.data("input", input_shape, dtype=self.dtype)
w_var = fluid.data("weight",
self.weight_shape,
dtype=self.dtype)
b_var = fluid.data("bias", (self.num_filters, ),
dtype=self.dtype)
if self.padding_mode != 'zeros':
x_var = F.pad(x_var,
self._reversed_padding_repeated_twice,
mode=self.padding_mode,
data_format=self.data_format)
padding = 0
else:
padding = self.padding
y_var = F.conv2d(x_var,
w_var,
b_var if not self.no_bias else None,
padding=padding,
stride=self.stride,
dilation=self.dilation,
groups=self.groups,
data_format=self.data_format)
feed_dict = {"input": self.input, "weight": self.weight}
if self.bias is not None:
feed_dict["bias"] = self.bias
exe = fluid.Executor(place)
exe.run(start)
y_np, = exe.run(main, feed=feed_dict, fetch_list=[y_var])
return y_np
def forward(self, feat_list):
C3, C4 = feat_list
x = self.in_conv(C4)
n, c, h, w = x.shape
P_h, P_w = self.down_factor
Q_h, Q_w = math.ceil(h / P_h), math.ceil(w / P_w)
pad_h, pad_w = Q_h * P_h - h, Q_w * P_w - w
if pad_h > 0 or pad_w > 0:
padding = [
pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
]
feat = F.pad(x, padding)
else:
feat = x
feat = feat.reshape([n, c, Q_h, P_h, Q_w, P_w])
feat = feat.transpose([0, 3, 5, 1, 2, 4]).reshape([-1, c, Q_h, Q_w])
feat = self.global_relation(feat)
feat = feat.reshape([n, P_h, P_w, c, Q_h, Q_w])
feat = feat.transpose([0, 4, 5, 3, 1, 2]).reshape([-1, c, P_h, P_w])
feat = self.local_relation(feat)
feat = feat.reshape([n, Q_h, Q_w, c, P_h, P_w])
feat = feat.transpose([0, 3, 1, 4, 2,
5]).reshape([n, c, P_h * Q_h, P_w * Q_w])
if pad_h > 0 or pad_w > 0:
feat = feat[:, :, pad_h // 2:pad_h // 2 + h,
pad_w // 2:pad_w // 2 + w]
feat = self.out_conv(paddle.concat([feat, x], axis=1))
output = self.cls(feat)
if self.enable_auxiliary_loss:
auxout = self.aux(C3)
return [output, auxout]
else:
return [output]
def forward(self, x):
return F.pad(x, self.size, mode="replicate")
def forward(self, x, mask_matrix):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
mask_matrix: Attention mask for cyclic shift.
"""
B, L, C = x.shape
H, W = self.H, self.W
assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.reshape([B, H, W, C])
# pad feature maps to multiples of window size
pad_l = pad_t = 0
pad_r = (self.window_size - W % self.window_size) % self.window_size
pad_b = (self.window_size - H % self.window_size) % self.window_size
x = F.pad(x, [0, pad_l, 0, pad_b, 0, pad_r, 0, pad_t])
_, Hp, Wp, _ = x.shape
# cyclic shift
if self.shift_size > 0:
shifted_x = paddle.roll(
x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))
attn_mask = mask_matrix
else:
shifted_x = x
attn_mask = None
# partition windows
x_windows = window_partition(
shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.reshape(
[-1, self.window_size * self.window_size,
C]) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(
x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.reshape(
[-1, self.window_size, self.window_size, C])
shifted_x = window_reverse(attn_windows, self.window_size, Hp,
Wp) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = paddle.roll(
shifted_x,
shifts=(self.shift_size, self.shift_size),
axis=(1, 2))
else:
x = shifted_x
if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :]
x = x.reshape([B, H * W, C])
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
def _forward(self, x):
offset = x.shape[-1] + 1 - paddle.ones([x.shape[-1]]).cumsum(-1)
z = F.sigmoid(x - offset.log())
z_cumprod = (1 - z).cumprod(-1)
return F.pad(z, [0]*2*(len(x.shape)-1) + [0, 1], value=1) * \
F.pad(z_cumprod, [0]*2*(len(x.shape)-1) + [1, 0], value=1)
def test_variable():
input_shape = (1, 2, 3, 4, 5)
data = np.random.rand(*input_shape).astype(np.float32)
y = F.pad(x=data, pad=[1, 1, 1, 1, 1, 1], data_format="NCDHW")
def forward(self, x, offset, mask):
in_C = self.in_channels
out_C = self.out_channels
stride = self.stride
padding = self.padding
# dilation = self.dilation
groups = self.groups
N, _, H, W = x.shape
_, w_in, kH, kW = self.weight.shape
out_W = (W + 2 * padding - (kW - 1)) // stride
out_H = (H + 2 * padding - (kH - 1)) // stride
# ================== 1.先对图片x填充得到填充后的图片pad_x ==================
pad_x_H = H + padding * 2 + 1
pad_x_W = W + padding * 2 + 1
pad_x = F.pad(x, pad=[0, 0, 0, 0, padding, padding + 1, padding, padding + 1], value=0.0)
# ================== 2.求所有采样点的坐标 ==================
# 卷积核中心点在pad_x中的位置
y_outer, x_outer = paddle.meshgrid([paddle.arange(out_H), paddle.arange(out_W)])
y_outer = y_outer * stride + padding
x_outer = x_outer * stride + padding
start_pos_yx = paddle.stack((y_outer, x_outer), 2).cast(dtype='float32') # [out_H, out_W, 2] 仅仅是卷积核中心点在pad_x中的位置
start_pos_yx = paddle.unsqueeze(start_pos_yx, axis=[0, 3]) # [1, out_H, out_W, 1, 2] 仅仅是卷积核中心点在pad_x中的位置
start_pos_yx = paddle.tile(start_pos_yx, [N, 1, 1, kH * kW, 1]) # [N, out_H, out_W, kH*kW, 2] 仅仅是卷积核中心点在pad_x中的位置
start_pos_y = start_pos_yx[:, :, :, :, :1] # [N, out_H, out_W, kH*kW, 1] 仅仅是卷积核中心点在pad_x中的位置
start_pos_x = start_pos_yx[:, :, :, :, 1:] # [N, out_H, out_W, kH*kW, 1] 仅仅是卷积核中心点在pad_x中的位置
start_pos_y.stop_gradient = True
start_pos_x.stop_gradient = True
# 卷积核内部的偏移
half_W = (kW - 1) // 2
half_H = (kH - 1) // 2
y_inner, x_inner = paddle.meshgrid([paddle.arange(kH), paddle.arange(kW)])
y_inner -= half_H
x_inner -= half_W
filter_inner_offset_yx = paddle.stack((y_inner, x_inner), 2).cast(dtype='float32') # [kH, kW, 2] 卷积核内部的偏移
filter_inner_offset_yx = paddle.reshape(filter_inner_offset_yx, (1, 1, 1, kH * kW, 2)) # [1, 1, 1, kH*kW, 2] 卷积核内部的偏移
filter_inner_offset_yx = paddle.tile(filter_inner_offset_yx, [N, out_H, out_W, 1, 1]) # [N, out_H, out_W, kH*kW, 2] 卷积核内部的偏移
filter_inner_offset_y = filter_inner_offset_yx[:, :, :, :, :1] # [N, out_H, out_W, kH*kW, 1] 卷积核内部的偏移
filter_inner_offset_x = filter_inner_offset_yx[:, :, :, :, 1:] # [N, out_H, out_W, kH*kW, 1] 卷积核内部的偏移
filter_inner_offset_y.stop_gradient = True
filter_inner_offset_x.stop_gradient = True
# 预测的偏移
offset = paddle.transpose(offset, [0, 2, 3, 1]) # [N, out_H, out_W, kH*kW*2]
offset_yx = paddle.reshape(offset, (N, out_H, out_W, kH * kW, 2)) # [N, out_H, out_W, kH*kW, 2]
offset_y = offset_yx[:, :, :, :, :1] # [N, out_H, out_W, kH*kW, 1]
offset_x = offset_yx[:, :, :, :, 1:] # [N, out_H, out_W, kH*kW, 1]
# 最终采样位置。
pos_y = start_pos_y + filter_inner_offset_y + offset_y # [N, out_H, out_W, kH*kW, 1]
pos_x = start_pos_x + filter_inner_offset_x + offset_x # [N, out_H, out_W, kH*kW, 1]
pos_y = paddle.clip(pos_y, 0.0, H + padding * 2 - 1.0) # 最终采样位置限制在pad_x内
pos_x = paddle.clip(pos_x, 0.0, W + padding * 2 - 1.0) # 最终采样位置限制在pad_x内
# ================== 3.采样。用F.grid_sample()双线性插值采样。 ==================
pos_x = pos_x / (pad_x_W - 1) * 2.0 - 1.0
pos_y = pos_y / (pad_x_H - 1) * 2.0 - 1.0
xtyt = paddle.concat([pos_x, pos_y], -1) # [N, out_H, out_W, kH*kW, 2]
xtyt = paddle.reshape(xtyt, (N, out_H, out_W * kH * kW, 2)) # [N, out_H, out_W*kH*kW, 2]
value = F.grid_sample(pad_x, xtyt, mode='bilinear', padding_mode='zeros', align_corners=True) # [N, in_C, out_H, out_W*kH*kW]
value = paddle.reshape(value, (N, in_C, out_H, out_W, kH * kW)) # [N, in_C, out_H, out_W, kH * kW]
value = value.transpose((0, 1, 4, 2, 3)) # [N, in_C, kH * kW, out_H, out_W]
# ================== 4.乘以重要程度 ==================
# 乘以重要程度
mask = paddle.unsqueeze(mask, [1]) # [N, 1, kH * kW, out_H, out_W]
value = value * mask # [N, in_C, kH * kW, out_H, out_W]
new_x = paddle.reshape(value, (N, in_C * kH * kW, out_H, out_W)) # [N, in_C * kH * kW, out_H, out_W]
# ================== 5.乘以本层的权重，加上偏置 ==================
# 1x1卷积
rw = paddle.reshape(self.weight, (out_C, w_in * kH * kW, 1, 1)) # [out_C, w_in, kH, kW] -> [out_C, w_in*kH*kW, 1, 1] 变成1x1卷积核
out = F.conv2d(new_x, rw, bias=self.bias, stride=1, groups=groups) # [N, out_C, out_H, out_W]
return out
