Spaces:
Sleeping
Sleeping
File size: 6,230 Bytes
31726e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
"""Evaluate a pretrained GAN model.
Usage:
`python eval_gan.py <path/to/model/folder>`, e.g.
`../results/quickdraw_gan_vector_bezier_fc_wgan`.
"""
import os
import argparse
import torch as th
import numpy as np
import ttools
import imageio
from subprocess import call
import pydiffvg
import models
LOG = ttools.get_logger(__name__)
def postprocess(im, invert=False):
im = th.clamp((im + 1.0) / 2.0, 0, 1)
if invert:
im = (1.0 - im)
im = ttools.tensor2image(im)
return im
def imsave(im, path):
os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.imwrite(path, im)
def save_scene(scn, path):
os.makedirs(os.path.dirname(path), exist_ok=True)
pydiffvg.save_svg(path, *scn, use_gamma=False)
def run(args):
th.manual_seed(0)
np.random.seed(0)
meta = ttools.Checkpointer.load_meta(args.model, "vect_g_")
if meta is None:
LOG.warning("Could not load metadata at %s, aborting.", args.model)
return
LOG.info("Loaded model %s with metadata:\n %s", args.model, meta)
if args.output_dir is None:
outdir = os.path.join(args.model, "eval")
else:
outdir = args.output_dir
os.makedirs(outdir, exist_ok=True)
model_params = meta["model_params"]
if args.imsize is not None:
LOG.info("Overriding output image size to: %dx%d", args.imsize,
args.imsize)
old_size = model_params["imsize"]
scale = args.imsize * 1.0 / old_size
model_params["imsize"] = args.imsize
model_params["stroke_width"] = [w*scale for w in
model_params["stroke_width"]]
LOG.info("Overriding width to: %s", model_params["stroke_width"])
# task = meta["task"]
generator = meta["generator"]
if generator == "fc":
model = models.VectorGenerator(**model_params)
elif generator == "bezier_fc":
model = models.BezierVectorGenerator(**model_params)
elif generator in ["rnn"]:
model = models.RNNVectorGenerator(**model_params)
elif generator in ["chain_rnn"]:
model = models.ChainRNNVectorGenerator(**model_params)
else:
raise NotImplementedError()
model.eval()
device = "cpu"
if th.cuda.is_available():
device = "cuda"
model.to(device)
checkpointer = ttools.Checkpointer(
args.model, model, meta=meta, prefix="vect_g_")
checkpointer.load_latest()
LOG.info("Computing latent space interpolation")
for i in range(args.nsamples):
z0 = model.sample_z(1)
z1 = model.sample_z(1)
# interpolation
alpha = th.linspace(0, 1, args.nsteps).view(args.nsteps, 1).to(device)
alpha_video = th.linspace(0, 1, args.nframes).view(args.nframes, 1)
alpha_video = alpha_video.to(device)
length = [args.nsteps, args.nframes]
for idx, a in enumerate([alpha, alpha_video]):
_z0 = z0.repeat(length[idx], 1).to(device)
_z1 = z1.repeat(length[idx], 1).to(device)
batch = _z0*(1-a) + _z1*a
out = model(batch)
if idx == 0: # image viz
n, c, h, w = out.shape
out = out.permute(1, 2, 0, 3)
out = out.contiguous().view(1, c, h, w*n)
out = postprocess(out, invert=args.invert)
imsave(out, os.path.join(outdir,
"latent_interp", "%03d.png" % i))
scenes = model.get_vector(batch)
for scn_idx, scn in enumerate(scenes):
save_scene(scn, os.path.join(outdir, "latent_interp_svg",
"%03d" % i, "%03d.svg" %
scn_idx))
else: # video viz
anim_root = os.path.join(outdir,
"latent_interp_video", "%03d" % i)
LOG.info("Rendering animation %d", i)
for frame_idx, frame in enumerate(out):
LOG.info("frame %d", frame_idx)
frame = frame.unsqueeze(0)
frame = postprocess(frame, invert=args.invert)
imsave(frame, os.path.join(anim_root,
"frame%04d.png" % frame_idx))
call(["ffmpeg", "-framerate", "30", "-i",
os.path.join(anim_root, "frame%04d.png"), "-vb", "20M",
os.path.join(outdir,
"latent_interp_video", "%03d.mp4" % i)])
LOG.info(" saved %d", i)
LOG.info("Sampling latent space")
for i in range(args.nsamples):
n = 8
bs = n*n
z = model.sample_z(bs).to(device)
out = model(z)
_, c, h, w = out.shape
out = out.view(n, n, c, h, w).permute(2, 0, 3, 1, 4)
out = out.contiguous().view(1, c, h*n, w*n)
out = postprocess(out)
imsave(out, os.path.join(outdir, "samples_%03d.png" % i))
LOG.info(" saved %d", i)
LOG.info("output images saved to %s", outdir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("model")
parser.add_argument("--output_dir", help="output directory for "
" the samples. Defaults to the model's path")
parser.add_argument("--nsamples", default=16, type=int,
help="number of output to compute")
parser.add_argument("--imsize", type=int,
help="if provided, override the raster output "
"resolution")
parser.add_argument("--nsteps", default=9, type=int, help="number of "
"interpolation steps for the interpolation")
parser.add_argument("--nframes", default=120, type=int, help="number of "
"frames for the interpolation video")
parser.add_argument("--invert", default=False, action="store_true",
help="if True, render black on white rather than the"
" opposite")
args = parser.parse_args()
pydiffvg.set_use_gpu(False)
ttools.set_logger(False)
run(args)
|