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"""A simple training interface using ttools."""
import argparse
import os
import logging
import random
import numpy as np
import torch
from torchvision.datasets import MNIST
import torchvision.transforms as xforms
from torch.utils.data import DataLoader
import ttools
import ttools.interfaces
import pydiffvg
LOG = ttools.get_logger(__name__)
pydiffvg.render_pytorch.print_timing = False
torch.manual_seed(123)
np.random.seed(123)
torch.backends.cudnn.deterministic = True
latent_dim = 100
img_size = 32
num_paths = 8
num_segments = 8
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
class VisdomImageCallback(ttools.callbacks.ImageDisplayCallback):
def visualized_image(self, batch, fwd_result):
return torch.cat([batch[0], fwd_result.cpu()], dim = 2)
# From https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py
class Generator(torch.nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.fc = torch.nn.Sequential(
torch.nn.Linear(latent_dim, 128),
torch.nn.LeakyReLU(0.2, inplace=True),
torch.nn.Linear(128, 256),
torch.nn.LeakyReLU(0.2, inplace=True),
torch.nn.Linear(256, 512),
torch.nn.LeakyReLU(0.2, inplace=True),
torch.nn.Linear(512, 1024),
torch.nn.LeakyReLU(0.2, inplace=True),
torch.nn.Linear(1024, 2 * num_paths * (num_segments + 1) + num_paths + num_paths),
torch.nn.Sigmoid()
)
def forward(self, z):
out = self.fc(z)
# construct paths
imgs = []
for b in range(out.shape[0]):
index = 0
shapes = []
shape_groups = []
for i in range(num_paths):
points = img_size * out[b, index: index + 2 * (num_segments + 1)].view(-1, 2).cpu()
index += 2 * (num_segments + 1)
stroke_width = img_size * out[b, index].view(1).cpu()
index += 1
num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
stroke_width = stroke_width,
is_closed = False)
shapes.append(path)
stroke_color = out[b, index].view(1).cpu()
index += 1
stroke_color = torch.cat([stroke_color, torch.tensor([0.0, 0.0, 1.0])])
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]),
fill_color = None,
stroke_color = stroke_color)
shape_groups.append(path_group)
scene_args = pydiffvg.RenderFunction.serialize_scene(img_size, img_size, shapes, shape_groups)
render = pydiffvg.RenderFunction.apply
img = render(img_size, # width
img_size, # height
2, # num_samples_x
2, # num_samples_y
random.randint(0, 1048576), # seed
None,
*scene_args)
img = img[:, :, :1]
# HWC -> NCHW
img = img.unsqueeze(0)
img = img.permute(0, 3, 1, 2) # NHWC -> NCHW
imgs.append(img)
img = torch.cat(imgs, dim = 0)
return img
class Discriminator(torch.nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
def discriminator_block(in_filters, out_filters, bn=True):
block = [torch.nn.Conv2d(in_filters, out_filters, 3, 2, 1),
torch.nn.LeakyReLU(0.2, inplace=True),
torch.nn.Dropout2d(0.25)]
if bn:
block.append(torch.nn.BatchNorm2d(out_filters, 0.8))
return block
self.model = torch.nn.Sequential(
*discriminator_block(1, 16, bn=False),
*discriminator_block(16, 32),
*discriminator_block(32, 64),
*discriminator_block(64, 128),
)
# The height and width of downsampled image
ds_size = img_size // 2 ** 4
self.adv_layer = torch.nn.Sequential(
torch.nn.Linear(128 * ds_size ** 2, 1),
torch.nn.Sigmoid())
def forward(self, img):
out = self.model(img)
out = out.view(out.shape[0], -1)
validity = self.adv_layer(out)
return validity
class MNISTInterface(ttools.interfaces.SGANInterface):
"""An adapter to run or train a model."""
def __init__(self, gen, discrim, lr=2e-4):
super(MNISTInterface, self).__init__(gen, discrim, lr, opt = 'adam')
def forward(self, batch):
return self.gen(torch.zeros([batch[0].shape[0], latent_dim], device = self.device).normal_())
def _discriminator_input(self, batch, fwd_data, fake=False):
if fake:
return fwd_data
else:
return batch[0].to(self.device)
def train(args):
"""Train a MNIST classifier."""
# Setup train and val data
_xform = xforms.Compose([xforms.Resize([32, 32]), xforms.ToTensor()])
data = MNIST("data/mnist", train=True, download=True, transform=_xform)
# Initialize asynchronous dataloaders
loader = DataLoader(data, batch_size=args.bs, num_workers=2)
# Instantiate the models
gen = Generator()
discrim = Discriminator()
gen.apply(weights_init_normal)
discrim.apply(weights_init_normal)
# Checkpointer to save/recall model parameters
checkpointer_gen = ttools.Checkpointer(os.path.join(args.out, "checkpoints"), model=gen, prefix="gen_")
checkpointer_discrim = ttools.Checkpointer(os.path.join(args.out, "checkpoints"), model=discrim, prefix="discrim_")
# resume from a previous checkpoint, if any
checkpointer_gen.load_latest()
checkpointer_discrim.load_latest()
# Setup a training interface for the model
interface = MNISTInterface(gen, discrim, lr=args.lr)
# Create a training looper with the interface we defined
trainer = ttools.Trainer(interface)
# Adds several callbacks, that will be called by the trainer --------------
# A periodic checkpointing operation
trainer.add_callback(ttools.callbacks.CheckpointingCallback(checkpointer_gen))
trainer.add_callback(ttools.callbacks.CheckpointingCallback(checkpointer_discrim))
# A simple progress bar
trainer.add_callback(ttools.callbacks.ProgressBarCallback(
keys=["loss_g", "loss_d", "loss"]))
# A volatile logging using visdom
trainer.add_callback(ttools.callbacks.VisdomLoggingCallback(
keys=["loss_g", "loss_d", "loss"],
port=8080, env="mnist_demo"))
# Image
trainer.add_callback(VisdomImageCallback(port=8080, env="mnist_demo"))
# -------------------------------------------------------------------------
# Start the training
LOG.info("Training started, press Ctrl-C to interrupt.")
trainer.train(loader, num_epochs=args.epochs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# TODO: subparsers
parser.add_argument("data", help="directory where we download and store the MNIST dataset.")
parser.add_argument("out", help="directory where we write the checkpoints and visualizations.")
parser.add_argument("--lr", type=float, default=1e-4, help="learning rate for the optimizer.")
parser.add_argument("--epochs", type=int, default=500, help="number of epochs to train for.")
parser.add_argument("--bs", type=int, default=64, help="number of elements per batch.")
args = parser.parse_args()
ttools.set_logger(True) # activate debug prints
train(args)
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