Z-demo
commented
3 years ago D:\ProgramData\Anaconda3\envs\pt_new\lib\site-packages\torch\nn\modules\container.py:434: UserWarning: Setting attributes on ParameterList is not supported.
warnings.warn("Setting attributes on ParameterList is not supported.")
Traceback (most recent call last):
File "E:/Projects/kivy_projects/kive_test1/main.py", line 36, in
C = torch.matmul(A, B)
# print(C.shape)
feature = torch.matmul(feature, self.fc_bin[0])
~~~~~~~~~~~~~~ <--- HERE
feature = feature.permute(1, 0, 2).contiguous()
# feature.shape (batch_size, scale, d)源码：
@torch.jit.script_method
def forward(self, silho, batch_frame: int = 50):
# n: batch_size, s: frame_num, k: keypoints_num, c: channel
if batch_frame is not None:
# 待解决问题1 RuntimeError:
# Tried to access nonexistent attribute or method 'numpy' of type 'Tensor'.:
# batch_frame = batch_frame[0].data.cpu().numpy().tolist()
#batch_frame = batch_frame.numpy().tolist()
# batch_frame = list()
# _ = len(batch_frame)
# for i in range(len(batch_frame)):
# if batch_frame[-(i + 1)] != 0:
# break
# else:
# _ -= 1
# batch_frame = batch_frame[:_]
# frame_sum = np.sum(batch_frame)
frame_sum = batch_frame
# if frame_sum < silho.size(1):
# silho = silho[:, :frame_sum, :, :]
# self.batch_frame = [0] + np.cumsum(batch_frame).tolist()
self.batch_frame.append(frame_sum)
n = silho.size(0)
x = silho.unsqueeze(2)
del silho
x = self.set_layer1(x)
x = self.set_layer2(x)
gl = self.gl_layer1(self.frame_max(x)[0])
gl = self.gl_layer2(gl)
gl = self.gl_pooling(gl)
x = self.set_layer3(x)
x = self.set_layer4(x)
gl = self.gl_layer3(gl + self.frame_max(x)[0])
gl = self.gl_layer4(gl)
x = self.set_layer5(x)
x = self.set_layer6(x)
x = self.frame_max(x)[0]
gl = gl + x
# 问题3 feature = list()
feature = [torch.tensor(1)]
# del (feature[0])
n, c, h, w = gl.size()
for num_bin in self.bin_num:
z = x.view(n, c, num_bin, -1)
z = z.mean(3) + z.max(3)[0]
feature.append(z)
z = gl.view(n, c, num_bin, -1)
z = z.mean(3) + z.max(3)[0]
feature.append(z)
del(feature[0])
feature = torch.cat(feature, 2).permute(2, 0, 1).contiguous()
# feature = feature.matmul(self.fc_bin[0])
# error code!!!!!!!!!
feature = torch.matmul(feature, self.fc_bin[0])
feature = feature.permute(1, 0, 2).contiguous()
# feature.shape (batch_size, scale, d)
# h0 = torch.randn(self.lstm_num_layers, feature.shape[0], self.lstm_hidden_size)
# c0 = torch.randn(self.lstm_num_layers, feature.shape[0], self.lstm_hidden_size)
# feature, (hn, cn) = self.lstm_layer(feature, (h0, c0))
return feature, None
或者哪位大佬知道有哪些相对可行的可以部署在移动端的步态识别网络么？