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import torch
import torch.nn as nn
import torch.nn.functional as F
import geffnet
INPUT_CHANNELS_DICT = {
0: [1280, 112, 40, 24, 16],
1: [1280, 112, 40, 24, 16],
2: [1408, 120, 48, 24, 16],
3: [1536, 136, 48, 32, 24],
4: [1792, 160, 56, 32, 24],
5: [2048, 176, 64, 40, 24],
6: [2304, 200, 72, 40, 32],
7: [2560, 224, 80, 48, 32]
}
class Encoder(nn.Module):
def __init__(self, B=5, pretrained=True):
""" e.g. B=5 will return EfficientNet-B5
"""
super(Encoder, self).__init__()
basemodel = geffnet.create_model('tf_efficientnet_b%s_ap' % B, pretrained=pretrained)
# Remove last layer
basemodel.global_pool = nn.Identity()
basemodel.classifier = nn.Identity()
self.original_model = basemodel
def forward(self, x):
features = [x]
for k, v in self.original_model._modules.items():
if (k == 'blocks'):
for ki, vi in v._modules.items():
features.append(vi(features[-1]))
else:
features.append(v(features[-1]))
return features
class ConvGRU(nn.Module):
def __init__(self, hidden_dim, input_dim, ks=3):
super(ConvGRU, self).__init__()
p = (ks - 1) // 2
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p)
self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p)
self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, ks, padding=p)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx))
r = torch.sigmoid(self.convr(hx))
q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
return h
class RayReLU(nn.Module):
def __init__(self, eps=1e-2):
super(RayReLU, self).__init__()
self.eps = eps
def forward(self, pred_norm, ray):
# angle between the predicted normal and ray direction
cos = torch.cosine_similarity(pred_norm, ray, dim=1).unsqueeze(1) # (B, 1, H, W)
# component of pred_norm along view
norm_along_view = ray * cos
# cos should be bigger than eps
norm_along_view_relu = ray * (torch.relu(cos - self.eps) + self.eps)
# difference
diff = norm_along_view_relu - norm_along_view
# updated pred_norm
new_pred_norm = pred_norm + diff
new_pred_norm = F.normalize(new_pred_norm, dim=1)
return new_pred_norm
class UpSampleBN(nn.Module):
def __init__(self, skip_input, output_features, align_corners=True):
super(UpSampleBN, self).__init__()
self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(output_features),
nn.LeakyReLU(),
nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(output_features),
nn.LeakyReLU())
self.align_corners = align_corners
def forward(self, x, concat_with):
up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=self.align_corners)
f = torch.cat([up_x, concat_with], dim=1)
return self._net(f)
class Conv2d_WS(nn.Conv2d):
""" weight standardization
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d_WS, self).__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias)
def forward(self, x):
weight = self.weight
weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
keepdim=True).mean(dim=3, keepdim=True)
weight = weight - weight_mean
std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
weight = weight / std.expand_as(weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class UpSampleGN(nn.Module):
""" UpSample with GroupNorm
"""
def __init__(self, skip_input, output_features, align_corners=True):
super(UpSampleGN, self).__init__()
self._net = nn.Sequential(Conv2d_WS(skip_input, output_features, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, output_features),
nn.LeakyReLU(),
Conv2d_WS(output_features, output_features, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, output_features),
nn.LeakyReLU())
self.align_corners = align_corners
def forward(self, x, concat_with):
up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=self.align_corners)
f = torch.cat([up_x, concat_with], dim=1)
return self._net(f)
def upsample_via_bilinear(out, up_mask, downsample_ratio):
""" bilinear upsampling (up_mask is a dummy variable)
"""
return F.interpolate(out, scale_factor=downsample_ratio, mode='bilinear', align_corners=True)
def upsample_via_mask(out, up_mask, downsample_ratio):
""" convex upsampling
"""
# out: low-resolution output (B, o_dim, H, W)
# up_mask: (B, 9*k*k, H, W)
k = downsample_ratio
N, o_dim, H, W = out.shape
up_mask = up_mask.view(N, 1, 9, k, k, H, W)
up_mask = torch.softmax(up_mask, dim=2) # (B, 1, 9, k, k, H, W)
up_out = F.unfold(out, [3, 3], padding=1) # (B, 2, H, W) -> (B, 2 X 3*3, H*W)
up_out = up_out.view(N, o_dim, 9, 1, 1, H, W) # (B, 2, 3*3, 1, 1, H, W)
up_out = torch.sum(up_mask * up_out, dim=2) # (B, 2, k, k, H, W)
up_out = up_out.permute(0, 1, 4, 2, 5, 3) # (B, 2, H, k, W, k)
return up_out.reshape(N, o_dim, k*H, k*W) # (B, 2, kH, kW)
def convex_upsampling(out, up_mask, k):
# out: low-resolution output (B, C, H, W)
# up_mask: (B, 9*k*k, H, W)
B, C, H, W = out.shape
up_mask = up_mask.view(B, 1, 9, k, k, H, W)
up_mask = torch.softmax(up_mask, dim=2) # (B, 1, 9, k, k, H, W)
out = F.pad(out, pad=(1,1,1,1), mode='replicate')
up_out = F.unfold(out, [3, 3], padding=0) # (B, C, H, W) -> (B, C X 3*3, H*W)
up_out = up_out.view(B, C, 9, 1, 1, H, W) # (B, C, 9, 1, 1, H, W)
up_out = torch.sum(up_mask * up_out, dim=2) # (B, C, k, k, H, W)
up_out = up_out.permute(0, 1, 4, 2, 5, 3) # (B, C, H, k, W, k)
return up_out.reshape(B, C, k*H, k*W) # (B, C, kH, kW)
def get_unfold(pred_norm, ps, pad):
B, C, H, W = pred_norm.shape
pred_norm = F.pad(pred_norm, pad=(pad,pad,pad,pad), mode='replicate') # (B, C, h, w)
pred_norm_unfold = F.unfold(pred_norm, [ps, ps], padding=0) # (B, C X ps*ps, h*w)
pred_norm_unfold = pred_norm_unfold.view(B, C, ps*ps, H, W) # (B, C, ps*ps, h, w)
return pred_norm_unfold
def get_prediction_head(input_dim, hidden_dim, output_dim):
return nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 1),
nn.ReLU(inplace=True),
nn.Conv2d(hidden_dim, output_dim, 1),
)
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