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model.py
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model.py
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import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from ops import *
class sample_generator(nn.Module):
def __init__(self, pitch_range):
super(sample_generator, self).__init__()
self.gf_dim = 64
self.y_dim = 13
self.n_channel = 256
self.h1 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h2 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h3 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h4 = nn.ConvTranspose2d(in_channels=157, out_channels=1, kernel_size=(1,pitch_range), stride=(1,2))
self.h0_prev = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(1,pitch_range), stride=(1,2))
self.h1_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.h2_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.h3_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.linear1 = nn.Linear(113,1024)
self.linear2 = nn.Linear(1037,self.gf_dim*2*2*1)
def forward(self, z, prev_x, y ,batch_size,pitch_range):
# h3_prev = F.leaky_relu(self.batch_nor_256(self.h0_prev(prev_x)),0.2)
h0_prev = lrelu(batch_norm_2d_cpu(self.h0_prev(prev_x)),0.2) #[72, 16, 16, 1]
h1_prev = lrelu(batch_norm_2d_cpu(self.h1_prev(h0_prev)),0.2) #[72, 16, 8, 1]
h2_prev = lrelu(batch_norm_2d_cpu(self.h2_prev(h1_prev)),0.2) #[72, 16, 4, 1]
h3_prev = lrelu(batch_norm_2d_cpu(self.h3_prev(h2_prev)),0.2) #[72, 16, 2, 1])
yb = y.view(batch_size, self.y_dim, 1, 1) #(72,13,1,1)
z = torch.cat((z,y),1) #(72,113)
h0 = F.relu(batch_norm_1d_cpu(self.linear1(z))) #(72,1024)
h0 = torch.cat((h0,y),1) #(72,1037)
h1 = F.relu(batch_norm_1d_cpu(self.linear2(h0))) #(72, 256)
h1 = h1.view(batch_size, self.gf_dim * 2, 2, 1) #(72,128,2,1)
h1 = conv_cond_concat(h1,yb) #(b,141,2,1)
h1 = conv_prev_concat(h1,h3_prev) #(72, 157, 2, 1)
h2 = F.relu(batch_norm_2d_cpu(self.h1(h1))) #(72, 128, 4, 1)
h2 = conv_cond_concat(h2,yb) #([72, 141, 4, 1])
h2 = conv_prev_concat(h2,h2_prev) #([72, 157, 4, 1])
h3 = F.relu(batch_norm_2d_cpu(self.h2(h2))) #([72, 128, 8, 1])
h3 = conv_cond_concat(h3,yb) #([72, 141, 8, 1])
h3 = conv_prev_concat(h3,h1_prev) #([72, 157, 8, 1])
h4 = F.relu(batch_norm_2d_cpu(self.h3(h3))) #([72, 128, 16, 1])
h4 = conv_cond_concat(h4,yb) #([72, 141, 16, 1])
h4 = conv_prev_concat(h4,h0_prev) #([72, 157, 16, 1])
g_x = torch.sigmoid(self.h4(h4)) #([72, 1, 16, 128])
return g_x
class generator(nn.Module):
def __init__(self,pitch_range):
super(generator, self).__init__()
self.gf_dim = 64
self.y_dim = 13
self.n_channel = 256
self.h1 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h2 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h3 = nn.ConvTranspose2d(in_channels=157, out_channels=pitch_range, kernel_size=(2,1), stride=(2,2))
self.h4 = nn.ConvTranspose2d(in_channels=157, out_channels=1, kernel_size=(1,pitch_range), stride=(1,2))
self.h0_prev = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(1,pitch_range), stride=(1,2))
self.h1_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.h2_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.h3_prev = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2,1), stride=(2,2))
self.linear1 = nn.Linear(113,1024)
self.linear2 = nn.Linear(1037,self.gf_dim*2*2*1)
def forward(self, z, prev_x, y ,batch_size,pitch_range):
# h3_prev = F.leaky_relu(self.batch_nor_256(self.h0_prev(prev_x)),0.2)
h0_prev = lrelu(batch_norm_2d(self.h0_prev(prev_x)),0.2) #[72, 16, 16, 1]
h1_prev = lrelu(batch_norm_2d(self.h1_prev(h0_prev)),0.2) #[72, 16, 8, 1]
h2_prev = lrelu(batch_norm_2d(self.h2_prev(h1_prev)),0.2) #[72, 16, 4, 1]
h3_prev = lrelu(batch_norm_2d(self.h3_prev(h2_prev)),0.2) #[72, 16, 2, 1])
yb = y.view(batch_size, self.y_dim, 1, 1) #(72,13,1,1)
z = torch.cat((z,y),1) #(72,113)
h0 = F.relu(batch_norm_1d(self.linear1(z))) #(72,1024)
h0 = torch.cat((h0,y),1) #(72,1037)
h1 = F.relu(batch_norm_1d(self.linear2(h0))) #(72, 256)
h1 = h1.view(batch_size, self.gf_dim * 2, 2, 1) #(72,128,2,1)
h1 = conv_cond_concat(h1,yb) #(b,141,2,1)
h1 = conv_prev_concat(h1,h3_prev) #(72, 157, 2, 1)
h2 = F.relu(batch_norm_2d(self.h1(h1))) #(72, 128, 4, 1)
h2 = conv_cond_concat(h2,yb) #([72, 141, 4, 1])
h2 = conv_prev_concat(h2,h2_prev) #([72, 157, 4, 1])
h3 = F.relu(batch_norm_2d(self.h2(h2))) #([72, 128, 8, 1])
h3 = conv_cond_concat(h3,yb) #([72, 141, 8, 1])
h3 = conv_prev_concat(h3,h1_prev) #([72, 157, 8, 1])
h4 = F.relu(batch_norm_2d(self.h3(h3))) #([72, 128, 16, 1])
h4 = conv_cond_concat(h4,yb) #([72, 141, 16, 1])
h4 = conv_prev_concat(h4,h0_prev) #([72, 157, 16, 1])
g_x = torch.sigmoid(self.h4(h4)) #([72, 1, 16, 128])
return g_x
class discriminator(nn.Module):
def __init__(self,pitch_range):
super(discriminator, self).__init__()
self.df_dim = 64
self.dfc_dim = 1024
self.y_dim = 13
self.h0_prev = nn.Conv2d(in_channels=14, out_channels=14, kernel_size=(2,pitch_range), stride=(2,2))
#out channels = y_dim +1
self.h1_prev = nn.Conv2d(in_channels=27, out_channels=77, kernel_size=(4,1), stride=(2,2))
# out channels = df_dim + y_dim
self.linear1 = nn.Linear(244,self.dfc_dim)
self.linear2 = nn.Linear(1037,1)
def forward(self,x,y,batch_size,pitch_range):
yb = y.view(batch_size,self.y_dim, 1, 1)
x = conv_cond_concat(x, yb) #x.shape torch.Size([72, 14, 16, 128])
h0 = lrelu(self.h0_prev(x),0.2)
fm = h0
h0 = conv_cond_concat(h0, yb) #torch.Size([72, 27, 8, 1])
h1 = lrelu(batch_norm_2d(self.h1_prev(h0)),0.2) #torch.Size([72, 77, 3, 1])
h1 = h1.view(batch_size, -1) #torch.Size([72, 231])
h1 = torch.cat((h1,y),1) #torch.Size([72, 244])
h2 = lrelu(batch_norm_1d(self.linear1(h1)))
h2 = torch.cat((h2,y),1) #torch.Size([72, 1037])
h3 = self.linear2(h2)
h3_sigmoid = torch.sigmoid(h3)
return h3_sigmoid, h3, fm