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view_results.py
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view_results.py
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"""
## Script for visualising results at each stage of the pipeline
## Example:
## >> python3 view_results.py --name perovskite
--------------------------------------------------
## Author: Callum J. Court.
## Email: cc889@cam.ac.uk
## Version: 1.0.0
--------------------------------------------------
## License: MIT
## Copyright: Copyright Callum Court & Batuhan Yildirim 2020, ICSG3D
-------------------------------------------------
"""
import argparse
import os
import re
import warnings
from itertools import product
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from keras.utils import to_categorical
from scipy.spatial.distance import cdist, euclidean
from sklearn.metrics import mean_squared_error
import pymatgen as mg
import tensorflow as tf
from unet.data import UnetDataGenerator
from unet.unet import AtomUnet, f1_m
from utils import (coordinate_grid, create_crystal, data_split, density_matrix,
get_sites, to_lattice_params, to_pymatgen_structure)
from vae.data import VAEDataGenerator
from vae.lattice_vae import LatticeDFCVAE
from viz import animate, plot_points_3d, points, viz
from watershed import watershed_clustering
warnings.filterwarnings("ignore")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # surpress tf warnings
matplotlib.use("TkAgg")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--name", metavar="name", type=str, help="Name of data folder")
parser.add_argument(
"--batch_size", metavar="batch_size", type=int, help="Batch size", default=1
)
parser.add_argument("--nrot", metavar="nrot", type=int, help="nrot", default=10)
parser.add_argument(
"--samples",
metavar="samples",
type=int,
help="Number of samples",
default=78750,
)
parser.add_argument(
"--split",
metavar="split",
type=float,
help="Train-test split fraction",
default=0.8,
)
parser.add_argument(
"--d",
metavar="d",
type=int,
help="Dimension of density matrices (number of voxels)",
default=32,
)
namespace = parser.parse_args()
batch_size = namespace.batch_size
d = namespace.d
input_shape = (d,d,d,4)
n = namespace.samples
mode = namespace.name
path = os.path.join("data", mode, "matrices")
cif_path = os.path.join("data", mode, "cifs")
csv_path = os.path.join("data", mode, mode + ".csv")
vae_weights = os.path.join(
"saved_models", "vae", mode, "vae_weights_" + mode + ".best.hdf5"
)
unet_weights = os.path.join(
"saved_models", "unet", mode, "unet_weights_" + mode + ".best.hdf5"
)
perceptual_model = os.path.join(
"saved_models", "unet", mode, "unet_weights_" + mode + ".best.h5"
)
training_ids, validation_ids = data_split(
path, n, frac=namespace.split, n_rot=namespace.nrot
)
_generator = VAEDataGenerator(
validation_ids,
data_path=path,
property_csv=csv_path,
batch_size=batch_size,
n_channels=input_shape[-1],
shuffle=True,
return_S=True,
)
# LOAD VAE
vae = LatticeDFCVAE(perceptual_model=perceptual_model)
vae._set_model(batch_size=batch_size, weights=vae_weights)
# LOAD UNET
unet = AtomUnet(weights=unet_weights)
#
for M, t in _generator:
ids = _generator.list_IDs_temp
print(ids[0])
cond = t[0]
S = np.argmax(t[1], axis=-1).reshape(batch_size, 32, 32, 32, 1)
S_b = t[2]
c = np.load(path + "/coordinate_grids/" + ids[0]).reshape(1, 32, 32, 32, 3)
Sg = np.concatenate([S, c], axis=-1)
print(np.unique(S, return_counts=True))
try:
species, mus, R = watershed_clustering(
M[:, :, :, :, 0], S, S_b, return_ws=True, max_iters=5
)
except Exception:
continue
coords = np.concatenate([species.reshape((len(species), 1)), mus], axis=-1)
M_recon = vae.model.predict([M, cond])
mse = mean_squared_error(
M.reshape(32 * 32 * 32, 4), M_recon.reshape(32 * 32 * 32, 4)
)
c_recon = M_recon[:, :, :, :, 1:].reshape(1, 32, 32, 32, 3)
S_recon, S_b_recon = unet.model.predict(M_recon) # (1,32,32,32,95)
f1 = f1_m(to_categorical(S, num_classes=95), S_recon)
S_recon = np.argmax(S_recon, axis=-1).reshape(1, 32, 32, 32, 1)
print(np.unique(S_recon, return_counts=True))
S_b_recon = np.where(S_b_recon >= 0.8, 1, 0)
try:
species_pred, mus_pred, R_pred = watershed_clustering(
M_recon[:, :, :, :, 0], S_recon, S_b_recon, max_iters=5, return_ws=True
)
except Exception:
continue
coords_pred = np.concatenate(
[species_pred.reshape((len(species_pred), 1)), mus_pred], axis=-1
)
# Plot results
fig = plt.figure()
ax = fig.add_subplot(241, projection="3d")
ax = viz(M[0, :, :, :, 0], ax=ax, show=False, resample_d=(10, 10, 10))
ax.set_title("M True")
ax = fig.add_subplot(242, projection="3d")
ax = plot_points_3d(S.squeeze(), ax=ax)
ax.set_title("S True")
ax = fig.add_subplot(243, projection="3d")
ax = plot_points_3d(R.squeeze(), ax=ax)
ax.set_title("WS True")
ax = fig.add_subplot(244, projection="3d")
ax = points(coords, ax=ax, show=False)
ax.set_title("Coords True")
ax = fig.add_subplot(245, projection="3d")
ax = viz(M_recon[0, :, :, :, 0], ax=ax, show=False, resample_d=(10, 10, 10))
ax.set_title("M Pred, MSE = %.3f" % mse)
ax = fig.add_subplot(246, projection="3d")
ax = plot_points_3d(S_recon.squeeze(), ax=ax)
ax.set_title("S pred F1 = %.3f" % f1)
ax = fig.add_subplot(247, projection="3d")
ax = plot_points_3d(R_pred.squeeze(), ax=ax)
ax.set_title("WS Pred")
ax = fig.add_subplot(248, projection="3d")
ax = points(coords_pred, ax=ax, show=False)
ax.set_title("Coords Pred")
plt.show(block=True)
inp = input("...")
# Continue until user requests a stop
if inp == "n":
break