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submodule.py
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submodule.py
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from typing import List, Tuple, Dict, Callable, Any
import torch
from torch import nn
class StackConvNorm(nn.Module):
def __init__(self,
dim_inp: int,
filters: List[int],
kernel_sizes: List[int],
strides: List[int],
groupings: List[int],
norm_act_final: bool,
activation: Callable = nn.CELU):
super(StackConvNorm, self).__init__()
layers = []
dim_prev = dim_inp
for i, (f, k, s) in enumerate(zip(filters, kernel_sizes, strides)):
if s == 0:
layers.append(nn.Conv2d(dim_prev, f, k, 1, 0))
else:
layers.append(nn.Conv2d(dim_prev, f, k, s, (k - 1) // 2))
if i == len(filters) - 1 and norm_act_final == False:
break
layers.append(activation())
layers.append(nn.GroupNorm(groupings[i], f))
# layers.append(nn.BatchNorm2d(f))
dim_prev = f
self.conv = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
return x
class StackSubPixelNorm(nn.Module):
def __init__(self,
dim_inp: int,
filters: List[int],
kernel_sizes: List[int],
upscale: List[int],
groupings: List[int],
norm_act_final: bool,
activation: Callable = nn.CELU):
super(StackSubPixelNorm, self).__init__()
layers = []
dim_prev = dim_inp
for i, (f, k, u) in enumerate(zip(filters, kernel_sizes, upscale)):
if u == 1:
layers.append(nn.Conv2d(dim_prev, f, k, 1, (k - 1) // 2))
else:
layers.append(nn.Conv2d(dim_prev, f * u ** 2, k, 1, (k - 1) // 2))
layers.append(nn.PixelShuffle(u))
if i == len(filters) - 1 and norm_act_final == False:
break
layers.append(activation())
layers.append(nn.GroupNorm(groupings[i], f))
dim_prev = f
self.conv = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
return x
class StackMLP(nn.Module):
def __init__(self,
dim_inp: int,
filters: List[int],
norm_act_final: bool,
activation: Callable = nn.CELU,
phase_layer_norm: bool = True):
super(StackMLP, self).__init__()
layers = []
dim_prev = dim_inp
for i, f in enumerate(filters):
layers.append(nn.Linear(dim_prev, f))
if i == len(filters) - 1 and norm_act_final == False:
break
layers.append(activation())
if phase_layer_norm:
layers.append(nn.LayerNorm(f))
dim_prev = f
self.mlp = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.mlp(x)
return x
class ConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size=3, num_cell=4):
super(ConvLSTMCell, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.padding = (kernel_size - 1) // 2
self.conv = nn.Conv2d(in_channels=self.input_dim + hidden_dim,
out_channels=4 * self.hidden_dim,
kernel_size=self.kernel_size,
padding=self.padding,
bias=True)
self.register_parameter('h_0', torch.nn.Parameter(torch.zeros(1, self.hidden_dim, num_cell, num_cell),
requires_grad=True))
self.register_parameter('c_0', torch.nn.Parameter(torch.zeros(1, self.hidden_dim, num_cell, num_cell),
requires_grad=True))
def forward(self, x, h_c):
h_cur, c_cur = h_c
conv_inp = torch.cat([x, h_cur], dim=1)
i, f, o, c = self.conv(conv_inp).split(self.hidden_dim, dim=1)
i = torch.sigmoid(i)
f = torch.sigmoid(f)
c = torch.tanh(c)
o = torch.sigmoid(o)
c_next = f * c_cur + i * c
h_next = o * torch.tanh(c_next)
return h_next, c_next
def init_hidden(self, batch_size):
return self.h_0.expand(batch_size, -1, -1, -1), \
self.c_0.expand(batch_size, -1, -1, -1)