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saliency_maps.py
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saliency_maps.py
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import torch
from torch_geometric.data import DataLoader
import torch.optim as optim
from model.model import DeeperGCN
from tqdm import tqdm
from utils.args import ArgsInit
from data.dataset_saliency import load_dataset
import copy
import numpy as np
import os
import torch.nn.functional as F
import pandas as pd
import rdkit.Chem.Draw as Draw
import matplotlib.pyplot as plt
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import rdDepictor
from rdkit.Chem.Draw import rdMolDraw2D
from IPython.display import SVG
from model.model_concatenation import PLANet
def moltosvg(mol, highlightMap, molSize=(300, 300), kekulize=True):
mc = Chem.Mol(mol.ToBinary())
if kekulize:
try:
Chem.Kekulize(mc)
except:
mc = Chem.Mol(mol.ToBinary())
if not mc.GetNumConformers():
rdDepictor.Compute2DCoords(mc)
drawer = rdMolDraw2D.MolDraw2DSVG(molSize[0], molSize[1])
drawer.DrawMoleculeWithHighlights(mc, highlight_atom_map=highlightMap)
drawer.FinishDrawing()
svg = drawer.GetDrawingText()
return svg.replace("svg:", "")
def compute_saliency_map(model, device, loader, num_classes, args, target, num=0):
cls_criterion = torch.nn.BCELoss()
print("------Copying model 1---------")
prop_predictor1 = copy.deepcopy(model)
print("------Copying model 2---------")
prop_predictor2 = copy.deepcopy(model)
print("------Copying model 3---------")
prop_predictor3 = copy.deepcopy(model)
print("------Copying model 4---------")
prop_predictor4 = copy.deepcopy(model)
# test_model_path = '/data/lrueda/Molecules-Graphs/deep_gcns_torch-master/examples/ogb/dude_dataset/log/BINARY___'+ target
# test_model_path = '/data/pruiz/PLA-Net/LM/BINARY_'+ target
test_model_path = "/workspace/pretrained-models/BINARY_" + target
# test_model_path1 = test_model_path+'/Fold1/model_ckpt/BS_2560-NF_full_valid_best.pth'
# test_model_path2 = test_model_path+'/Fold2/model_ckpt/BS_2560-NF_full_valid_best.pth'
# test_model_path3 = test_model_path+'/Fold3/model_ckpt/BS_2560-NF_full_valid_best.pth'
# test_model_path4 = test_model_path+'/Fold4/model_ckpt/BS_2560-NF_full_valid_best.pth'
test_model_path1 = (
test_model_path + "/Fold1/Best_Model.pth"
) # /Checkpoint__Best.pth'
test_model_path2 = (
test_model_path + "/Fold2/Best_Model.pth"
) # /Checkpoint__Best.pth'
test_model_path3 = (
test_model_path + "/Fold3/Best_Model.pth"
) # /Checkpoint__Best.pth'
test_model_path4 = (
test_model_path + "/Fold4/Best_Model.pth"
) # /Checkpoint__Best.pth'
# import pdb; pdb.set_trace()
# LOAD MODELS
print("------- Loading weights----------")
ckpt1 = torch.load(test_model_path1, map_location=lambda storage, loc: storage)
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
].t()
ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
] = ckpt1["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
].t()
# for i in range(args.num_layers):
# ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'] = ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'].t()
# ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'] = ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'].t()
# ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'] = ckpt1['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'].t()
prop_predictor1.load_state_dict(ckpt1["model_state_dict"])
prop_predictor1.to(device)
ckpt2 = torch.load(test_model_path2, map_location=lambda storage, loc: storage)
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
].t()
ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
] = ckpt2["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
].t()
# for i in range(args.num_layers):
# ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'] = ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'].t()
# ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'] = ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'].t()
# ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'] = ckpt2['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'].t()
prop_predictor2.load_state_dict(ckpt2["model_state_dict"])
prop_predictor2.to(device)
ckpt3 = torch.load(test_model_path3, map_location=lambda storage, loc: storage)
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
].t()
ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
] = ckpt3["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
].t()
# for i in range(args.num_layers):
# ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'] = ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'].t()
# ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'] = ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'].t()
# ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'] = ckpt3['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'].t()
prop_predictor3.load_state_dict(ckpt3["model_state_dict"])
prop_predictor3.to(device)
ckpt4 = torch.load(test_model_path4, map_location=lambda storage, loc: storage)
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.0.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.1.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.2.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.3.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.4.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.5.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.6.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.7.weight"
].t()
ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
] = ckpt4["model_state_dict"][
"molecule_gcn.atom_encoder.atom_embedding_list.8.weight"
].t()
# for i in range(args.num_layers):
# ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'] = ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.0.weight'].t()
# ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'] = ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.1.weight'].t()
# ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'] = ckpt4['model_state_dict']['molecule_gcn.gcns.'+ str(i)+'.edge_encoder.bond_embedding_list.2.weight'].t()
prop_predictor4.load_state_dict(ckpt4["model_state_dict"])
prop_predictor4.to(device)
break_loop = False
count = 0
for step, batch in enumerate(tqdm(loader, desc="Iteration")):
if args.use_prot:
batch_mol = batch[0].to(device)
batch_prot = batch[1].to(device)
else:
batch_mol = batch.to(device)
batch_mol.x.requires_grad = True
# batch.edge_attr.requires_grad=True
if args.use_prot:
pred1 = prop_predictor1(batch_mol, batch_prot)
pred2 = prop_predictor2(batch_mol, batch_prot)
pred3 = prop_predictor3(batch_mol, batch_prot)
pred4 = prop_predictor4(batch_mol, batch_prot)
else:
pred1 = prop_predictor1(batch_mol)
pred2 = prop_predictor2(batch_mol)
pred3 = prop_predictor3(batch_mol)
pred4 = prop_predictor4(batch_mol)
pred = (pred1 + pred2 + pred3 + pred4) / 4
is_labeled = batch_mol.y == batch_mol.y
labels = torch.unsqueeze(batch_mol.y, 1)
with torch.enable_grad():
loss = 0
class_loss = cls_criterion(
F.sigmoid(pred[:, 1]).to(torch.float32), batch_mol.y.to(torch.float32)
)
loss += class_loss
# for i in range(0,args.nclasses):
# class_mask = batch.y.clone()
# class_mask[batch.y == i] = 1
# class_mask[batch.y != i] = 0
# class_loss = cls_criterion(F.sigmoid(pred[:,i]).to(torch.float32)[is_labeled], class_mask.to(torch.float32)[is_labeled])
# loss += class_loss
loss.backward()
atom_grad = batch_mol.x.grad.detach()
# edge_grad = batch.edge_attr.grad.detach()
mol_atom_dict = {}
# mol_edge_dict = {}
curr_idb = 0
curr_ida = 0
jet = plt.get_cmap("jet")
for idx, mol in enumerate(batch_mol.mol):
atom_ids = []
atom_val = []
for ida, atom in enumerate(mol.GetAtoms()):
atom_ids.append(atom.GetIdx())
atom_val.append(sum(atom_grad[curr_ida + ida, :]).item())
atom_val = np.array(atom_val)
atom_val = (atom_val - np.min(atom_val)) / (
np.max(atom_val) - np.min(atom_val)
)
atom_val = 1 - atom_val
atom_val_norm = [jet(i) for i in atom_val]
mol_atom_dict[batch_mol.smiles[idx]] = dict(zip(atom_ids, atom_val_norm))
curr_ida += ida + 1
img = Draw.MolToImage(
mol, highlightMap=mol_atom_dict[batch_mol.smiles[idx]]
)
img.save(
"saliency_maps/"
+ target
+ "_PLANET/molecule"
+ str(idx + count)
+ ".png"
)
count += 1
if idx >= len(batch_mol.y[batch_mol.y == 1]):
break_loop = True
break
if break_loop:
break
def main(target, num=0):
args = ArgsInit().args
if args.use_gpu:
device = (
torch.device("cuda:" + str(args.device))
if torch.cuda.is_available()
else torch.device("cpu")
)
else:
device = torch.device("cpu")
if args.advs:
args.edge_dict = {}
if args.binary:
args.nclasses = 2
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if device.type == "cuda":
torch.cuda.manual_seed(args.seed)
print(args)
if not os.path.exists("saliency_maps/" + target + "_PLANET"):
os.makedirs("saliency_maps/" + target + "_PLANET")
(
train_dataset,
valid_dataset,
test_dataset,
data_train,
data_val,
data_test,
) = load_dataset(
cross_val=args.cross_val,
binary_task=args.binary,
target=target,
args=args,
advs=args.advs,
use_prot=args.use_prot,
test=True,
)
test_loader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
)
if args.use_prot:
model = PLANet(args, saliency=True).to(device)
else:
model = DeeperGCN(args, saliency=True).to(device)
compute_saliency_map(model, device, test_loader, args.nclasses, args, target, num)
if __name__ == "__main__":
"""
targets = ['aa2ar', 'abl1', 'ace', 'aces', 'ada', 'ada17', 'adrb1', 'adrb2',
'akt1', 'akt2', 'aldr', 'ampc', 'andr', 'aofb',
'braf', 'cah2', 'casp3', 'cdk2', 'comt', 'cp2c9', 'cp3a4', 'csf1r',
'cxcr4', 'def', 'dhi1', 'dpp4', 'drd3', 'dyr', 'egfr', 'esr1',
'esr2', 'fa10', 'fa7', 'fabp4', 'fak1', 'fgfr1', 'fkb1a', 'fnta',
'fpps', 'gcr', 'glcm', 'gria2', 'grik1', 'hdac2', 'hivint', 'hivpr', 'hivrt', 'hmdh', 'hs90a', 'hxk4', 'igf1r',
'inha', 'ital', 'jak2', 'kif11', 'kit', 'kith', 'kpcb', 'lck',
'lkha4', 'mapk2', 'mcr', 'met', 'mk01', 'mk10', 'mk14', 'mmp13',
'mp2k1', 'nos1', 'nram', 'pa2ga', 'parp1', 'pde5a', 'pgh1', 'pgh2',
'plk1', 'pnph', 'ppara', 'ppard', 'pparg', 'prgr', 'ptn1', 'pur2',
'pygm', 'pyrd', 'reni', 'rock1', 'rxra', 'sahh', 'src', 'tgfr1',
'thb', 'thrb', 'try1', 'tryb1', 'tysy', 'urok', 'vgfr2', 'wee1',
'xiap', 'bace1', 'hdac8']
targets_faltan = ['bace1','hdac8']
targets_faltan = []
"""
# nums = [48,28,46,24,21,29,10,15,4,26,11]
# targets = ['aa2ar', 'ace', 'aces', 'adrb1', 'adrb2','akt1', 'akt2', 'aldr',
# nums=[]
# targets=['cah2', 'casp3', 'cdk2', 'comt', 'cp2c9', 'cp3a4', 'csf1r','dhi1', 'dpp4', 'drd3', 'dyr',
# 'bace1', 'esr1', 'esr2', 'fa10', 'fa7', 'fak1', 'fgfr1', 'fkb1a', 'gria2', 'hdac2', 'hdac8', 'hivint', 'hivrt','hmdh', 'hs90a', 'hxk4',
# 'ital', 'jak2', 'kif11', 'kit', 'lck', 'lkha4', 'mapk2', 'met', 'mk01', 'mk14', 'mmp13', 'mp2k1', 'nram', 'parp1', 'pgh1', 'pgh2',
# 'plk1', 'ppara', 'ppard', 'pparg', 'prgr', 'ptn1', 'pygm', 'pyrd', 'thb', 'thrb', 'vgfr2', 'wee1', 'kpcb', 'tgfr1','rock1','nos1',
# 'pde5a','mk10', 'reni' ,'rxra', 'urok']
# targets=['igf1r', 'fnta','inha','src',
targets = ["ada"]
for target in targets:
main(target)