Full Code of alexis-jacq/Pytorch-Sketch-RNN for AI

Repository: alexis-jacq/Pytorch-Sketch-RNN
Branch: master
Commit: 5c3e21375dfe
Files: 4
Total size: 14.2 MB

Directory structure:
gitextract_9h_nr0dt/

├── LICENSE
├── README.md
├── cat.npz
└── sketch_rnn.py

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FILE CONTENTS
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FILE: LICENSE
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MIT License

Copyright (c) 2017 Alexis David Jacq

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


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FILE: README.md
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# Pytorch-Sketch-RNN
A pytorch implementation of https://arxiv.org/abs/1704.03477

In order to draw other things than cats, you will find more drawing data here: https://github.com/googlecreativelab/quickdraw-dataset

epoch 1900:

![epoch_1900](images/1900_output_.jpg)

epoch 2400:

![epoch_2600](images/2600_output_.jpg)

epoch 3400

![epoch_3400](images/3400_output_.jpg)

Default hyperparameters for training has been found here: https://github.com/tensorflow/magenta/blob/master/magenta/models/sketch_rnn/README.md


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FILE: cat.npz
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[File too large to display: 14.2 MB]

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FILE: sketch_rnn.py
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import numpy as np
import matplotlib.pyplot as plt
import PIL

import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F

use_cuda = torch.cuda.is_available()

###################################### hyperparameters
class HParams():
    def __init__(self):
        self.data_location = 'cat.npz'
        self.enc_hidden_size = 256
        self.dec_hidden_size = 512
        self.Nz = 128
        self.M = 20
        self.dropout = 0.9
        self.batch_size = 100
        self.eta_min = 0.01
        self.R = 0.99995
        self.KL_min = 0.2
        self.wKL = 0.5
        self.lr = 0.001
        self.lr_decay = 0.9999
        self.min_lr = 0.00001
        self.grad_clip = 1.
        self.temperature = 0.4
        self.max_seq_length = 200

hp = HParams()

################################# load and prepare data
def max_size(data):
    """larger sequence length in the data set"""
    sizes = [len(seq) for seq in data]
    return max(sizes)

def purify(strokes):
    """removes to small or too long sequences + removes large gaps"""
    data = []
    for seq in strokes:
        if seq.shape[0] <= hp.max_seq_length and seq.shape[0] > 10:
            seq = np.minimum(seq, 1000)
            seq = np.maximum(seq, -1000)
            seq = np.array(seq, dtype=np.float32)
            data.append(seq)
    return data

def calculate_normalizing_scale_factor(strokes):
    """Calculate the normalizing factor explained in appendix of sketch-rnn."""
    data = []
    for i in range(len(strokes)):
        for j in range(len(strokes[i])):
            data.append(strokes[i][j, 0])
            data.append(strokes[i][j, 1])
    data = np.array(data)
    return np.std(data)

def normalize(strokes):
    """Normalize entire dataset (delta_x, delta_y) by the scaling factor."""
    data = []
    scale_factor = calculate_normalizing_scale_factor(strokes)
    for seq in strokes:
        seq[:, 0:2] /= scale_factor
        data.append(seq)
    return data

dataset = np.load(hp.data_location, encoding='latin1')
data = dataset['train']
data = purify(data)
data = normalize(data)
Nmax = max_size(data)

############################## function to generate a batch:
def make_batch(batch_size):
    batch_idx = np.random.choice(len(data),batch_size)
    batch_sequences = [data[idx] for idx in batch_idx]
    strokes = []
    lengths = []
    indice = 0
    for seq in batch_sequences:
        len_seq = len(seq[:,0])
        new_seq = np.zeros((Nmax,5))
        new_seq[:len_seq,:2] = seq[:,:2]
        new_seq[:len_seq-1,2] = 1-seq[:-1,2]
        new_seq[:len_seq,3] = seq[:,2]
        new_seq[(len_seq-1):,4] = 1
        new_seq[len_seq-1,2:4] = 0
        lengths.append(len(seq[:,0]))
        strokes.append(new_seq)
        indice += 1

    if use_cuda:
        batch = Variable(torch.from_numpy(np.stack(strokes,1)).cuda().float())
    else:
        batch = Variable(torch.from_numpy(np.stack(strokes,1)).float())
    return batch, lengths

################################ adaptive lr
def lr_decay(optimizer):
    """Decay learning rate by a factor of lr_decay"""
    for param_group in optimizer.param_groups:
        if param_group['lr']>hp.min_lr:
            param_group['lr'] *= hp.lr_decay
    return optimizer

################################# encoder and decoder modules
class EncoderRNN(nn.Module):
    def __init__(self):
        super(EncoderRNN, self).__init__()
        # bidirectional lstm:
        self.lstm = nn.LSTM(5, hp.enc_hidden_size, \
            dropout=hp.dropout, bidirectional=True)
        # create mu and sigma from lstm's last output:
        self.fc_mu = nn.Linear(2*hp.enc_hidden_size, hp.Nz)
        self.fc_sigma = nn.Linear(2*hp.enc_hidden_size, hp.Nz)
        # active dropout:
        self.train()

    def forward(self, inputs, batch_size, hidden_cell=None):
        if hidden_cell is None:
            # then must init with zeros
            if use_cuda:
                hidden = torch.zeros(2, batch_size, hp.enc_hidden_size).cuda()
                cell = torch.zeros(2, batch_size, hp.enc_hidden_size.cuda()
            else:
                hidden = torch.zeros(2, batch_size, hp.enc_hidden_size)
                cell = torch.zeros(2, batch_size, hp.enc_hidden_size)
            hidden_cell = (hidden, cell)
        _, (hidden,cell) = self.lstm(inputs.float(), hidden_cell)
        # hidden is (2, batch_size, hidden_size), we want (batch_size, 2*hidden_size):
        hidden_forward, hidden_backward = torch.split(hidden,1,0)
        hidden_cat = torch.cat([hidden_forward.squeeze(0), hidden_backward.squeeze(0)],1)
        # mu and sigma:
        mu = self.fc_mu(hidden_cat)
        sigma_hat = self.fc_sigma(hidden_cat)
        sigma = torch.exp(sigma_hat/2.)
        # N ~ N(0,1)
        z_size = mu.size()
        if use_cuda:
            N = torch.normal(torch.zeros(z_size),torch.ones(z_size)).cuda()
        else:
            N = torch.normal(torch.zeros(z_size),torch.ones(z_size))
        z = mu + sigma*N
        # mu and sigma_hat are needed for LKL loss
        return z, mu, sigma_hat

class DecoderRNN(nn.Module):
    def __init__(self):
        super(DecoderRNN, self).__init__()
        # to init hidden and cell from z:
        self.fc_hc = nn.Linear(hp.Nz, 2*hp.dec_hidden_size)
        # unidirectional lstm:
        self.lstm = nn.LSTM(hp.Nz+5, hp.dec_hidden_size, dropout=hp.dropout)
        # create proba distribution parameters from hiddens:
        self.fc_params = nn.Linear(hp.dec_hidden_size,6*hp.M+3)

    def forward(self, inputs, z, hidden_cell=None):
        if hidden_cell is None:
            # then we must init from z
            hidden,cell = torch.split(F.tanh(self.fc_hc(z)),hp.dec_hidden_size,1)
            hidden_cell = (hidden.unsqueeze(0).contiguous(), cell.unsqueeze(0).contiguous())
        outputs,(hidden,cell) = self.lstm(inputs, hidden_cell)
        # in training we feed the lstm with the whole input in one shot
        # and use all outputs contained in 'outputs', while in generate
        # mode we just feed with the last generated sample:
        if self.training:
            y = self.fc_params(outputs.view(-1, hp.dec_hidden_size))
        else:
            y = self.fc_params(hidden.view(-1, hp.dec_hidden_size))
        # separate pen and mixture params:
        params = torch.split(y,6,1)
        params_mixture = torch.stack(params[:-1]) # trajectory
        params_pen = params[-1] # pen up/down
        # identify mixture params:
        pi,mu_x,mu_y,sigma_x,sigma_y,rho_xy = torch.split(params_mixture,1,2)
        # preprocess params::
        if self.training:
            len_out = Nmax+1
        else:
            len_out = 1
                                   
        pi = F.softmax(pi.transpose(0,1).squeeze()).view(len_out,-1,hp.M)
        sigma_x = torch.exp(sigma_x.transpose(0,1).squeeze()).view(len_out,-1,hp.M)
        sigma_y = torch.exp(sigma_y.transpose(0,1).squeeze()).view(len_out,-1,hp.M)
        rho_xy = torch.tanh(rho_xy.transpose(0,1).squeeze()).view(len_out,-1,hp.M)
        mu_x = mu_x.transpose(0,1).squeeze().contiguous().view(len_out,-1,hp.M)
        mu_y = mu_y.transpose(0,1).squeeze().contiguous().view(len_out,-1,hp.M)
        q = F.softmax(params_pen).view(len_out,-1,3)
        return pi,mu_x,mu_y,sigma_x,sigma_y,rho_xy,q,hidden,cell

class Model():
    def __init__(self):
        if use_cuda:
            self.encoder = EncoderRNN().cuda()
            self.decoder = DecoderRNN().cuda()
        else:
            self.encoder = EncoderRNN()
            self.decoder = DecoderRNN()
        self.encoder_optimizer = optim.Adam(self.encoder.parameters(), hp.lr)
        self.decoder_optimizer = optim.Adam(self.decoder.parameters(), hp.lr)
        self.eta_step = hp.eta_min

    def make_target(self, batch, lengths):
        if use_cuda:
            eos = torch.stack([torch.Tensor([0,0,0,0,1])]*batch.size()[1]).cuda().unsqueeze(0)
        else:
            eos = torch.stack([torch.Tensor([0,0,0,0,1])]*batch.size()[1]).unsqueeze(0)
        batch = torch.cat([batch, eos], 0)
        mask = torch.zeros(Nmax+1, batch.size()[1])
        for indice,length in enumerate(lengths):
            mask[:length,indice] = 1
        if use_cuda:
            mask = mask.cuda()
        dx = torch.stack([batch.data[:,:,0]]*hp.M,2)
        dy = torch.stack([batch.data[:,:,1]]*hp.M,2)
        p1 = batch.data[:,:,2]
        p2 = batch.data[:,:,3]
        p3 = batch.data[:,:,4]
        p = torch.stack([p1,p2,p3],2)
        return mask,dx,dy,p

    def train(self, epoch):
        self.encoder.train()
        self.decoder.train()
        batch, lengths = make_batch(hp.batch_size)
        # encode:
        z, self.mu, self.sigma = self.encoder(batch, hp.batch_size)
        # create start of sequence:
        if use_cuda:
            sos = torch.stack([torch.Tensor([0,0,1,0,0])]*hp.batch_size).cuda().unsqueeze(0)
        else:
            sos = torch.stack([torch.Tensor([0,0,1,0,0])]*hp.batch_size).unsqueeze(0)
        # had sos at the begining of the batch:
        batch_init = torch.cat([sos, batch],0)
        # expend z to be ready to concatenate with inputs:
        z_stack = torch.stack([z]*(Nmax+1))
        # inputs is concatenation of z and batch_inputs
        inputs = torch.cat([batch_init, z_stack],2)
        # decode:
        self.pi, self.mu_x, self.mu_y, self.sigma_x, self.sigma_y, \
            self.rho_xy, self.q, _, _ = self.decoder(inputs, z)
        # prepare targets:
        mask,dx,dy,p = self.make_target(batch, lengths)
        # prepare optimizers:
        self.encoder_optimizer.zero_grad()
        self.decoder_optimizer.zero_grad()
        # update eta for LKL:
        self.eta_step = 1-(1-hp.eta_min)*hp.R
        # compute losses:
        LKL = self.kullback_leibler_loss()
        LR = self.reconstruction_loss(mask,dx,dy,p,epoch)
        loss = LR + LKL
        # gradient step
        loss.backward()
        # gradient cliping
        nn.utils.clip_grad_norm(self.encoder.parameters(), hp.grad_clip)
        nn.utils.clip_grad_norm(self.decoder.parameters(), hp.grad_clip)
        # optim step
        self.encoder_optimizer.step()
        self.decoder_optimizer.step()
        # some print and save:
        if epoch%1==0:
            print('epoch',epoch,'loss',loss.data[0],'LR',LR.data[0],'LKL',LKL.data[0])
            self.encoder_optimizer = lr_decay(self.encoder_optimizer)
            self.decoder_optimizer = lr_decay(self.decoder_optimizer)
        if epoch%100==0:
            #self.save(epoch)
            self.conditional_generation(epoch)

    def bivariate_normal_pdf(self, dx, dy):
        z_x = ((dx-self.mu_x)/self.sigma_x)**2
        z_y = ((dy-self.mu_y)/self.sigma_y)**2
        z_xy = (dx-self.mu_x)*(dy-self.mu_y)/(self.sigma_x*self.sigma_y)
        z = z_x + z_y -2*self.rho_xy*z_xy
        exp = torch.exp(-z/(2*(1-self.rho_xy**2)))
        norm = 2*np.pi*self.sigma_x*self.sigma_y*torch.sqrt(1-self.rho_xy**2)
        return exp/norm

    def reconstruction_loss(self, mask, dx, dy, p, epoch):
        pdf = self.bivariate_normal_pdf(dx, dy)
        LS = -torch.sum(mask*torch.log(1e-5+torch.sum(self.pi * pdf, 2)))\
            /float(Nmax*hp.batch_size)
        LP = -torch.sum(p*torch.log(self.q))/float(Nmax*hp.batch_size)
        return LS+LP

    def kullback_leibler_loss(self):
        LKL = -0.5*torch.sum(1+self.sigma-self.mu**2-torch.exp(self.sigma))\
            /float(hp.Nz*hp.batch_size)
        if use_cuda:
            KL_min = Variable(torch.Tensor([hp.KL_min]).cuda()).detach()
        else:
            KL_min = Variable(torch.Tensor([hp.KL_min])).detach()
        return hp.wKL*self.eta_step * torch.max(LKL,KL_min)

    def save(self, epoch):
        sel = np.random.rand()
        torch.save(self.encoder.state_dict(), \
            'encoderRNN_sel_%3f_epoch_%d.pth' % (sel,epoch))
        torch.save(self.decoder.state_dict(), \
            'decoderRNN_sel_%3f_epoch_%d.pth' % (sel,epoch))

    def load(self, encoder_name, decoder_name):
        saved_encoder = torch.load(encoder_name)
        saved_decoder = torch.load(decoder_name)
        self.encoder.load_state_dict(saved_encoder)
        self.decoder.load_state_dict(saved_decoder)

    def conditional_generation(self, epoch):
        batch,lengths = make_batch(1)
        # should remove dropouts:
        self.encoder.train(False)
        self.decoder.train(False)
        # encode:
        z, _, _ = self.encoder(batch, 1)
        if use_cuda:
            sos = Variable(torch.Tensor([0,0,1,0,0]).view(1,1,-1).cuda())
        else:
            sos = Variable(torch.Tensor([0,0,1,0,0]).view(1,1,-1))
        s = sos
        seq_x = []
        seq_y = []
        seq_z = []
        hidden_cell = None
        for i in range(Nmax):
            input = torch.cat([s,z.unsqueeze(0)],2)
            # decode:
            self.pi, self.mu_x, self.mu_y, self.sigma_x, self.sigma_y, \
                self.rho_xy, self.q, hidden, cell = \
                    self.decoder(input, z, hidden_cell)
            hidden_cell = (hidden, cell)
            # sample from parameters:
            s, dx, dy, pen_down, eos = self.sample_next_state()
            #------
            seq_x.append(dx)
            seq_y.append(dy)
            seq_z.append(pen_down)
            if eos:
                print(i)
                break
        # visualize result:
        x_sample = np.cumsum(seq_x, 0)
        y_sample = np.cumsum(seq_y, 0)
        z_sample = np.array(seq_z)
        sequence = np.stack([x_sample,y_sample,z_sample]).T
        make_image(sequence, epoch)

    def sample_next_state(self):

        def adjust_temp(pi_pdf):
            pi_pdf = np.log(pi_pdf)/hp.temperature
            pi_pdf -= pi_pdf.max()
            pi_pdf = np.exp(pi_pdf)
            pi_pdf /= pi_pdf.sum()
            return pi_pdf

        # get mixture indice:
        pi = self.pi.data[0,0,:].cpu().numpy()
        pi = adjust_temp(pi)
        pi_idx = np.random.choice(hp.M, p=pi)
        # get pen state:
        q = self.q.data[0,0,:].cpu().numpy()
        q = adjust_temp(q)
        q_idx = np.random.choice(3, p=q)
        # get mixture params:
        mu_x = self.mu_x.data[0,0,pi_idx]
        mu_y = self.mu_y.data[0,0,pi_idx]
        sigma_x = self.sigma_x.data[0,0,pi_idx]
        sigma_y = self.sigma_y.data[0,0,pi_idx]
        rho_xy = self.rho_xy.data[0,0,pi_idx]
        x,y = sample_bivariate_normal(mu_x,mu_y,sigma_x,sigma_y,rho_xy,greedy=False)
        next_state = torch.zeros(5)
        next_state[0] = x
        next_state[1] = y
        next_state[q_idx+2] = 1
        if use_cuda:
            return Variable(next_state.cuda()).view(1,1,-1),x,y,q_idx==1,q_idx==2
        else:
            return Variable(next_state).view(1,1,-1),x,y,q_idx==1,q_idx==2

def sample_bivariate_normal(mu_x,mu_y,sigma_x,sigma_y,rho_xy, greedy=False):
    # inputs must be floats
    if greedy:
      return mu_x,mu_y
    mean = [mu_x, mu_y]
    sigma_x *= np.sqrt(hp.temperature)
    sigma_y *= np.sqrt(hp.temperature)
    cov = [[sigma_x * sigma_x, rho_xy * sigma_x * sigma_y],\
        [rho_xy * sigma_x * sigma_y, sigma_y * sigma_y]]
    x = np.random.multivariate_normal(mean, cov, 1)
    return x[0][0], x[0][1]

def make_image(sequence, epoch, name='_output_'):
    """plot drawing with separated strokes"""
    strokes = np.split(sequence, np.where(sequence[:,2]>0)[0]+1)
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    for s in strokes:
        plt.plot(s[:,0],-s[:,1])
    canvas = plt.get_current_fig_manager().canvas
    canvas.draw()
    pil_image = PIL.Image.frombytes('RGB', canvas.get_width_height(),
                 canvas.tostring_rgb())
    name = str(epoch)+name+'.jpg'
    pil_image.save(name,"JPEG")
    plt.close("all")

if __name__=="__main__":
    model = Model()
    for epoch in range(50001):
        model.train(epoch)

    '''
    model.load('encoder.pth','decoder.pth')
    model.conditional_generation(0)
    #'''