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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import BatchNorm2d as BatchNorm
import math
from .prior_arch import PixelNorm, EqualLinear
import torchvision
from torchvision.utils import save_image
def GroupNorm(in_channels):
return torch.nn.GroupNorm(num_groups=in_channels//16, num_channels=in_channels, eps=1e-6, affine=False)
Norm = GroupNorm
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.norm1 = Norm(planes)
self.relu1 = nn.LeakyReLU(0.2)
self.conv2 = conv3x3(planes, planes, stride)
self.relu2 = nn.LeakyReLU(0.2)
self.norm2 = Norm(planes)
self.downsample = downsample
self.stride = stride
self.relu3 = nn.LeakyReLU(0.2)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.norm1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.norm2(out)
out = self.relu2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu3(out)
return out
class PSPEncoder(nn.Module):
def __init__(self, block=BasicBlock, layers=[3, 4, 6, 6, 3], strides=[(2,2),(1,2),(2,2),(1,2),(2,2)]):
self.inplanes = 32
super(PSPEncoder, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1,
bias=False)
self.relu = nn.LeakyReLU(0.2)
feature_out_dim = 256
self.layer1 = self._make_layer(block, 32, layers[0], stride=strides[0])
self.layer2 = self._make_layer(block, 64, layers[1], stride=strides[1])
self.layer3 = self._make_layer(block, 128, layers[2], stride=strides[2])
self.layer4 = self._make_layer(block, 256, layers[3], stride=strides[3])
self.layer5 = self._make_layer(block, 512, layers[4], stride=strides[4])
self.layer512_to_outdim = nn.Sequential(
nn.Conv2d(512, feature_out_dim, kernel_size=1, stride=1, bias=False),
nn.LeakyReLU(0.2)
)
self.layer256_to_512 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=1, stride=1, bias=False),
nn.LeakyReLU(0.2)
)
self.down_h = 1
for stride in strides:
self.down_h *= stride[0]
self.size_h = 32 // self.down_h * 2
self.feature2w = nn.Sequential(
PixelNorm(),
EqualLinear(self.size_h*self.size_h*feature_out_dim, 512, bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'),
EqualLinear(512, 512, bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'),
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes,
kernel_size=1, stride=stride, bias=False),
nn.LeakyReLU(0.2)
)
# GroupNorm(planes),
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _check_outliers(self, crop_feature, target_width):
B, C, H, W = crop_feature.size()
if W != target_width:
return F.interpolate(crop_feature, size=(H, target_width), mode='bilinear', align_corners=True)
else:
return crop_feature
def _check_outliers_pad(self, crop_feature, start, end, max_lr_width, center_loc, extend_W):
_, _, H, W = crop_feature.size()
fill_value = crop_feature.mean().item()
if start == 0 and end == max_lr_width:
crop_feature = torchvision.transforms.Pad([extend_W//2-center_loc, 0, extend_W-W-(extend_W//2-center_loc), 0], fill=fill_value, padding_mode='constant')(crop_feature)
else:
if start == 0:
crop_feature = torchvision.transforms.Pad([extend_W-W, 0, 0, 0], fill=fill_value, padding_mode='constant')(crop_feature)
if end == max_lr_width:
crop_feature = torchvision.transforms.Pad([0, 0, extend_W-W, 0], fill=fill_value, padding_mode='constant')(crop_feature)
# if crop_feature.size(3) != extend_W:
# print([222, crop_feature.size(), extend_W])
# crop_feature = torchvision.transforms.Pad([(extend_W-W)//2, 0, extend_W-W-(extend_W-W)//2, 0], fill=0, padding_mode='constant')(crop_feature)
return crop_feature
def forward(self, x, locs):
w_b = []
extend_W = 32*4
max_lr_width = x.size(3)
for b in range(locs.size(0)): #locs: 0~2048
x_for_w = []
for c in range(locs.size(1)):
center_loc = (locs[b][c]/4).int()
start_x = max(0, center_loc - extend_W//2)
end_x = min(center_loc + extend_W//2, max_lr_width)
crop_x = x[b:b+1, :, :, start_x:end_x].detach()
crop_x = self._check_outliers_pad(crop_x, start_x, end_x, max_lr_width, center_loc, extend_W) #
x_for_w.append(crop_x)
# crop_x[...,62:66] = 1
# save_image((crop_x+1)/2, 'trs_{}.png'.format(c))
x_for_w = torch.cat(x_for_w, dim=0)
x_c1 = self.conv1(x_for_w) #1
x_c1 = self.relu(x_c1)
x_l1 = self.layer1(x_c1) #2
x_l2 = self.layer2(x_l1) #1 [2, 64, 16, 256])
x_l3 = self.layer3(x_l2) #2 torch.Size([2, 128, 8, 128]
x_l4 = self.layer4(x_l3) #1 torch.Size([2, 256, 8, 128])
x_l5 = self.layer5(x_l4) #2, torch.Size([2, 512, 4, 64])
pyramid_x1 = _upsample_add(x_l5, self.layer256_to_512(x_l4))
pyramid_x = self.layer512_to_outdim(pyramid_x1)
w_each_b = self.feature2w(pyramid_x.view(pyramid_x.size(0), -1)) #
w_c = w_each_b
w_b.append(w_c)
w_b = torch.stack(w_b, dim=0)
return w_b
# w_b = []
# for b in range(locs.size(0)): #locs: 0~2048
# w_c = []
# for c in range(locs.size(1)):
# if locs[b][c] < 2048:
# center_loc = (locs[b][c]/4).int() # 32*512
# start_x = center_loc - 16
# end_x = center_loc + 16
# crop_x0 = x[b:b+1, :, :, start_x:end_x].clone()
# crop_x = self._check_outliers_pad(crop_x0, start_x, end_x) # 1, 512, 4, 4 or 1, 512, 8, 8
# # save_image(crop_x[0], 'ss_{}.png'.format(c))
# x_c1 = self.conv1(crop_x) #1
# x_c1 = self.relu(x_c1)
# x_l1 = self.layer1(x_c1) #2
# x_l2 = self.layer2(x_l1) #1 [2, 64, 16, 256])
# x_l3 = self.layer3(x_l2) #2 torch.Size([2, 128, 8, 128]
# x_l4 = self.layer4(x_l3) #1 torch.Size([2, 256, 8, 128])
# x_l5 = self.layer5(x_l4) #2, torch.Size([2, 512, 4, 64])
# pyramid_x1 = _upsample_add(x_l5, self.layer256_to_512(x_l4))
# pyramid_x = self.layer512_to_outdim(pyramid_x1)
# w = self.feature2w(pyramid_x.view(1, -1)) # 1*512
# w_c.append(w.squeeze(0))
# else:
# w_c.append(w.squeeze(0).detach()*0)
# w_c = torch.stack(w_c, dim=0)
# w_b.append(w_c)
# w_b = torch.stack(w_b, dim=0)
# print(w_b.size())
# return w_b #, lr
# # lr = x.clone()
# x_c1 = self.conv1(x) #1
# x_c1 = self.relu(x_c1)
# x_l1 = self.layer1(x_c1) #2
# x_l2 = self.layer2(x_l1) #1 [2, 64, 16, 256])
# x_l3 = self.layer3(x_l2) #2 torch.Size([2, 128, 8, 128]
# x_l4 = self.layer4(x_l3) #1 torch.Size([2, 256, 8, 128])
# x_l5 = self.layer5(x_l4) #2, torch.Size([2, 512, 4, 64]) B, 512, 4, 64, 17M parameters
# pyramid_x1 = _upsample_add(x_l5, self.layer256_to_512(x_l4))
# pyramid_x = self.layer512_to_outdim(pyramid_x1)
# # pyramid_x2 = _upsample_add(self.layer128_to_outdim(x_l3), pyramid_x1)
# B, C, H, W = pyramid_x.size()
# w_b = []
# for b in range(locs.size(0)): #locs: 0~2048
# w_c = []
# for c in range(locs.size(1)):
# if locs[b][c] < 2048:
# center_loc = (locs[b][c]/4/self.down_h).int() # from 32*512 to 4*64
# start_x = max(0, center_loc-self.size_h//2)
# end_x = min(center_loc+self.size_h//2, 512//self.down_h)
# # crop_feature = pyramid_x2[b:b+1, :, :, start_x:end_x].clone()
# # if end_x - start_x != self.size_h:
# # bgfill = torch.zeros((B, C, H, self.size_h), dtype=pyramid_x2.dtype, layout=pyramid_x2.layout, device=pyramid_x2.device)
# # bgfill[:, :, :, self.size_h//2 - (center_loc - start_x):self.size_h//2 - (center_loc - start_x) + end_x - start_x] += pyramid_x2[b:b+1, :, :, start_x:end_x].clone()
# # crop_feature = bgfill.clone()
# # else:
# # crop_feature = pyramid_x2[b:b+1, :, :, start_x:end_x].clone()
# crop_feature = pyramid_x[b:b+1, :, :, start_x:end_x].clone()
# crop_feature = self._check_outliers(crop_feature, self.size_h) # 1, 512, 4, 4 or 1, 512, 8, 8
# # crop_feature = self._check_outliers(crop_feature, self.size_h, start_x, end_x) # 1, 512, 4, 4 or 1, 512, 8, 8
# print(crop_feature.size())
# w = self.feature2w(crop_feature.view(1, -1)) # 1*512
# w_c.append(w.squeeze(0))
# else:
# w_c.append(w.squeeze(0).detach()*0)
# # lr[b:b+1, :, :, center_loc-1:center_loc+1] = 255
# w_c = torch.stack(w_c, dim=0)
# w_b.append(w_c)
# w_b = torch.stack(w_b, dim=0)
# return w_b #, x #, lr
def GroupNorm(in_channels):
return torch.nn.GroupNorm(num_groups=in_channels//32, num_channels=in_channels, eps=1e-6, affine=False)
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def _upsample_add(x, y):
'''Upsample and add two feature maps.
Args:
x: (Variable) top feature map to be upsampled.
y: (Variable) lateral feature map.
Returns:
(Variable) added feature map.
Note in PyTorch, when input size is odd, the upsampled feature map
with `F.upsample(..., scale_factor=2, mode='nearest')`
maybe not equal to the lateral feature map size.
e.g.
original input size: [N,_,15,15] ->
conv2d feature map size: [N,_,8,8] ->
upsampled feature map size: [N,_,16,16]
So we choose bilinear upsample which supports arbitrary output sizes.
'''
_, _, H, W = y.size()
return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
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