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科技匠|怎样用 Python 控制图片人物动起来？一文就能 Get( 四 )


self.slice1 = torch.nn.Sequential
self.slice2 = torch.nn.Sequential
self.slice3 = torch.nn.Sequential
self.slice4 = torch.nn.Sequential
self.slice5 = torch.nn.Sequential
for x in range(2):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(2, 7):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(7, 12):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 21):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(21, 30):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
self.mean = torch.nn.Parameter(data=http://kandian.youth.cn/index/torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
requires_grad=False)
self.std = torch.nn.Parameter(data=http://kandian.youth.cn/index/torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
requires_grad=False)
if not requires_grad:
for param in self.parameters:
param.requires_grad = False
def forward(self, X):
X = (X - self.mean) / self.std
h_relu1 = self.slice1(X)
h_relu2 = self.slice2(h_relu1)
h_relu3 = self.slice3(h_relu2)
h_relu4 = self.slice4(h_relu3)
h_relu5 = self.slice5(h_relu4)
out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
return out
（2）创建图像金字塔计算金字塔感知损失：
class ImagePyramide(torch.nn.Module):
"""
Create image pyramide for computing pyramide perceptual loss. See Sec 3.3
"""
def __init__(self, scales, num_channels):
super(ImagePyramide, self).__init__
downs = {}
for scale in scales:
downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
self.downs = nn.ModuleDict(downs)
def forward(self, x):
out_dict = {}
for scale, down_module in self.downs.items:
out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
return out_dict
（3）等方差约束的随机tps变换
class Transform:
"""
Random tps transformation for equivariance constraints. See Sec 3.3
"""
def __init__(self, bs, **kwargs):
noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3]))
self.theta = noise + torch.eye(2, 3).view(1, 2, 3)
self.bs = bs
if ('sigma_tps' in kwargs) and ('points_tps' in kwargs):
self.tps = True
self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type)
self.control_points = self.control_points.unsqueeze(0)
self.control_params = torch.normal(mean=0,
std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2]))
else:
self.tps = False
def transform_frame(self, frame):
grid = make_coordinate_grid(frame.shape[2:], type=frame.type).unsqueeze(0)
grid = grid.view(1, frame.shape[2] * frame.shape[3], 2)
grid = self.warp_coordinates(grid).view(self.bs, frame.shape[2], frame.shape[3], 2)
return F.grid_sample(frame, grid, padding_mode="reflection")


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