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import torch.nn as nn
from models.resnet import Resnet1D
class PrintModule(nn.Module):
def __init__(self, me=''):
super().__init__()
self.me = me
def forward(self, x):
print(self.me, x.shape)
return x
class Encoder(nn.Module):
def __init__(self,
input_emb_width = 3,
output_emb_width = 512,
down_t = 3,
stride_t = 2,
width = 512,
depth = 3,
dilation_growth_rate = 3,
activation='relu',
norm=None):
super().__init__()
blocks = []
filter_t, pad_t = stride_t * 2, stride_t // 2
blocks.append(nn.Conv1d(input_emb_width, width, 3, 1, 1))
blocks.append(nn.ReLU())
for i in range(down_t):
input_dim = width
block = nn.Sequential(
nn.Conv1d(input_dim, width, filter_t, stride_t, pad_t),
Resnet1D(width, depth, dilation_growth_rate, activation=activation, norm=norm),
)
blocks.append(block)
blocks.append(nn.Conv1d(width, output_emb_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x)
class Decoder(nn.Module):
def __init__(self,
input_emb_width = 3,
output_emb_width = 512,
down_t = 3,
stride_t = 2,
width = 512,
depth = 3,
dilation_growth_rate = 3,
activation='relu',
norm=None):
super().__init__()
blocks = []
filter_t, pad_t = stride_t * 2, stride_t // 2
blocks.append(nn.Conv1d(output_emb_width, width, 3, 1, 1))
blocks.append(nn.ReLU())
for i in range(down_t):
out_dim = width
block = nn.Sequential(
Resnet1D(width, depth, dilation_growth_rate, reverse_dilation=True, activation=activation, norm=norm),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv1d(width, out_dim, 3, 1, 1)
)
blocks.append(block)
blocks.append(nn.Conv1d(width, width, 3, 1, 1))
blocks.append(nn.ReLU())
blocks.append(nn.Conv1d(width, input_emb_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x)
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