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sttn.py
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sttn.py
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''' Spatial-Temporal Transformer Networks
'''
import numpy as np
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from core.spectral_norm import spectral_norm as _spectral_norm
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
def print_network(self):
if isinstance(self, list):
self = self[0]
num_params = 0
for param in self.parameters():
num_params += param.numel()
print('Network [%s] was created. Total number of parameters: %.1f million. '
'To see the architecture, do print(network).' % (type(self).__name__, num_params / 1000000))
def init_weights(self, init_type='normal', gain=0.02):
'''
initialize network's weights
init_type: normal | xavier | kaiming | orthogonal
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39
'''
def init_func(m):
classname = m.__class__.__name__
if classname.find('InstanceNorm2d') != -1:
if hasattr(m, 'weight') and m.weight is not None:
nn.init.constant_(m.weight.data, 1.0)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
nn.init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'xavier_uniform':
nn.init.xavier_uniform_(m.weight.data, gain=1.0)
elif init_type == 'kaiming':
nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
nn.init.orthogonal_(m.weight.data, gain=gain)
elif init_type == 'none': # uses pytorch's default init method
m.reset_parameters()
else:
raise NotImplementedError(
'initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
self.apply(init_func)
# propagate to children
for m in self.children():
if hasattr(m, 'init_weights'):
m.init_weights(init_type, gain)
class InpaintGenerator(BaseNetwork):
def __init__(self, init_weights=True):
super(InpaintGenerator, self).__init__()
channel = 256
stack_num = 8
patchsize = [(108, 60), (36, 20), (18, 10), (9, 5)]
blocks = []
for _ in range(stack_num):
blocks.append(TransformerBlock(patchsize, hidden=channel))
self.transformer = nn.Sequential(*blocks)
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, channel, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
)
# decoder: decode frames from features
self.decoder = nn.Sequential(
deconv(channel, 128, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
deconv(64, 64, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1)
)
if init_weights:
self.init_weights()
def forward(self, masked_frames, masks):
# extracting features
b, t, c, h, w = masked_frames.size()
masks = masks.view(b*t, 1, h, w)
enc_feat = self.encoder(masked_frames.view(b*t, c, h, w))
_, c, h, w = enc_feat.size()
masks = F.interpolate(masks, scale_factor=1.0/4)
enc_feat = self.transformer(
{'x': enc_feat, 'm': masks, 'b': b, 'c': c})['x']
output = self.decoder(enc_feat)
output = torch.tanh(output)
return output
def infer(self, feat, masks):
t, c, h, w = masks.size()
masks = masks.view(t, c, h, w)
masks = F.interpolate(masks, scale_factor=1.0/4)
t, c, _, _ = feat.size()
enc_feat = self.transformer(
{'x': feat, 'm': masks, 'b': 1, 'c': c})['x']
return enc_feat
class deconv(nn.Module):
def __init__(self, input_channel, output_channel, kernel_size=3, padding=0):
super().__init__()
self.conv = nn.Conv2d(input_channel, output_channel,
kernel_size=kernel_size, stride=1, padding=padding)
def forward(self, x):
x = F.interpolate(x, scale_factor=2, mode='bilinear',
align_corners=True)
return self.conv(x)
# #############################################################################
# ############################# Transformer ##################################
# #############################################################################
class Attention(nn.Module):
"""
Compute 'Scaled Dot Product Attention
"""
def forward(self, query, key, value, m):
scores = torch.matmul(query, key.transpose(-2, -1)
) / math.sqrt(query.size(-1))
scores.masked_fill(m, -1e9)
p_attn = F.softmax(scores, dim=-1)
p_val = torch.matmul(p_attn, value)
return p_val, p_attn
class MultiHeadedAttention(nn.Module):
"""
Take in model size and number of heads.
"""
def __init__(self, patchsize, d_model):
super().__init__()
self.patchsize = patchsize
self.query_embedding = nn.Conv2d(
d_model, d_model, kernel_size=1, padding=0)
self.value_embedding = nn.Conv2d(
d_model, d_model, kernel_size=1, padding=0)
self.key_embedding = nn.Conv2d(
d_model, d_model, kernel_size=1, padding=0)
self.output_linear = nn.Sequential(
nn.Conv2d(d_model, d_model, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True))
self.attention = Attention()
def forward(self, x, m, b, c):
bt, _, h, w = x.size()
t = bt // b
d_k = c // len(self.patchsize)
output = []
_query = self.query_embedding(x)
_key = self.key_embedding(x)
_value = self.value_embedding(x)
for (width, height), query, key, value in zip(self.patchsize,
torch.chunk(_query, len(self.patchsize), dim=1), torch.chunk(
_key, len(self.patchsize), dim=1),
torch.chunk(_value, len(self.patchsize), dim=1)):
out_w, out_h = w // width, h // height
mm = m.view(b, t, 1, out_h, height, out_w, width)
mm = mm.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view(
b, t*out_h*out_w, height*width)
mm = (mm.mean(-1) > 0.5).unsqueeze(1).repeat(1, t*out_h*out_w, 1)
# 1) embedding and reshape
query = query.view(b, t, d_k, out_h, height, out_w, width)
query = query.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view(
b, t*out_h*out_w, d_k*height*width)
key = key.view(b, t, d_k, out_h, height, out_w, width)
key = key.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view(
b, t*out_h*out_w, d_k*height*width)
value = value.view(b, t, d_k, out_h, height, out_w, width)
value = value.permute(0, 1, 3, 5, 2, 4, 6).contiguous().view(
b, t*out_h*out_w, d_k*height*width)
'''
# 2) Apply attention on all the projected vectors in batch.
tmp1 = []
for q,k,v in zip(torch.chunk(query, b, dim=0), torch.chunk(key, b, dim=0), torch.chunk(value, b, dim=0)):
y, _ = self.attention(q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0))
tmp1.append(y)
y = torch.cat(tmp1,1)
'''
y, _ = self.attention(query, key, value, mm)
# 3) "Concat" using a view and apply a final linear.
y = y.view(b, t, out_h, out_w, d_k, height, width)
y = y.permute(0, 1, 4, 2, 5, 3, 6).contiguous().view(bt, d_k, h, w)
output.append(y)
output = torch.cat(output, 1)
x = self.output_linear(output)
return x
# Standard 2 layerd FFN of transformer
class FeedForward(nn.Module):
def __init__(self, d_model):
super(FeedForward, self).__init__()
# We set d_ff as a default to 2048
self.conv = nn.Sequential(
nn.Conv2d(d_model, d_model, kernel_size=3, padding=2, dilation=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(d_model, d_model, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True))
def forward(self, x):
x = self.conv(x)
return x
class TransformerBlock(nn.Module):
"""
Transformer = MultiHead_Attention + Feed_Forward with sublayer connection
"""
def __init__(self, patchsize, hidden=128):
super().__init__()
self.attention = MultiHeadedAttention(patchsize, d_model=hidden)
self.feed_forward = FeedForward(hidden)
def forward(self, x):
x, m, b, c = x['x'], x['m'], x['b'], x['c']
x = x + self.attention(x, m, b, c)
x = x + self.feed_forward(x)
return {'x': x, 'm': m, 'b': b, 'c': c}
# ######################################################################
# ######################################################################
class Discriminator(BaseNetwork):
def __init__(self, in_channels=3, use_sigmoid=False, use_spectral_norm=True, init_weights=True):
super(Discriminator, self).__init__()
self.use_sigmoid = use_sigmoid
nf = 64
self.conv = nn.Sequential(
spectral_norm(nn.Conv3d(in_channels=in_channels, out_channels=nf*1, kernel_size=(3, 5, 5), stride=(1, 2, 2),
padding=1, bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(64, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv3d(nf*1, nf*2, kernel_size=(3, 5, 5), stride=(1, 2, 2),
padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(128, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv3d(nf * 2, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5),
stride=(1, 2, 2), padding=(1, 2, 2))
)
if init_weights:
self.init_weights()
def forward(self, xs):
# T, C, H, W = xs.shape
xs_t = torch.transpose(xs, 0, 1)
xs_t = xs_t.unsqueeze(0) # B, C, T, H, W
feat = self.conv(xs_t)
if self.use_sigmoid:
feat = torch.sigmoid(feat)
out = torch.transpose(feat, 1, 2) # B, T, C, H, W
return out
def spectral_norm(module, mode=True):
if mode:
return _spectral_norm(module)
return module