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import copy |
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import numpy as np |
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from torch_geometric.loader import DataLoader |
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from tqdm import tqdm |
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from confidence.dataset import ListDataset |
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from utils import so3, torus |
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from utils.sampling import randomize_position, sampling |
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import torch |
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from utils.diffusion_utils import get_t_schedule |
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def loss_function(tr_pred, rot_pred, tor_pred, data, t_to_sigma, device, tr_weight=1, rot_weight=1, |
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tor_weight=1, apply_mean=True, no_torsion=False): |
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tr_sigma, rot_sigma, tor_sigma = t_to_sigma( |
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*[torch.cat([d.complex_t[noise_type] for d in data]) if device.type == 'cuda' else data.complex_t[noise_type] |
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for noise_type in ['tr', 'rot', 'tor']]) |
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mean_dims = (0, 1) if apply_mean else 1 |
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tr_score = torch.cat([d.tr_score for d in data], dim=0) if device.type == 'cuda' else data.tr_score |
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tr_sigma = tr_sigma.unsqueeze(-1) |
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tr_loss = ((tr_pred.cpu() - tr_score) ** 2 * tr_sigma ** 2).mean(dim=mean_dims) |
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tr_base_loss = (tr_score ** 2 * tr_sigma ** 2).mean(dim=mean_dims).detach() |
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rot_score = torch.cat([d.rot_score for d in data], dim=0) if device.type == 'cuda' else data.rot_score |
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rot_score_norm = so3.score_norm(rot_sigma.cpu()).unsqueeze(-1) |
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rot_loss = (((rot_pred.cpu() - rot_score) / rot_score_norm) ** 2).mean(dim=mean_dims) |
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rot_base_loss = ((rot_score / rot_score_norm) ** 2).mean(dim=mean_dims).detach() |
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if not no_torsion: |
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edge_tor_sigma = torch.from_numpy( |
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np.concatenate([d.tor_sigma_edge for d in data] if device.type == 'cuda' else data.tor_sigma_edge)) |
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tor_score = torch.cat([d.tor_score for d in data], dim=0) if device.type == 'cuda' else data.tor_score |
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tor_score_norm2 = torch.tensor(torus.score_norm(edge_tor_sigma.cpu().numpy())).float() |
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tor_loss = ((tor_pred.cpu() - tor_score) ** 2 / tor_score_norm2) |
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tor_base_loss = ((tor_score ** 2 / tor_score_norm2)).detach() |
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if apply_mean: |
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tor_loss, tor_base_loss = tor_loss.mean() * torch.ones(1, dtype=torch.float), tor_base_loss.mean() * torch.ones(1, dtype=torch.float) |
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else: |
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index = torch.cat([torch.ones(d['ligand'].edge_mask.sum()) * i for i, d in |
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enumerate(data)]).long() if device.type == 'cuda' else data['ligand'].batch[ |
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data['ligand', 'ligand'].edge_index[0][data['ligand'].edge_mask]] |
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num_graphs = len(data) if device.type == 'cuda' else data.num_graphs |
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t_l, t_b_l, c = torch.zeros(num_graphs), torch.zeros(num_graphs), torch.zeros(num_graphs) |
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c.index_add_(0, index, torch.ones(tor_loss.shape)) |
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c = c + 0.0001 |
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t_l.index_add_(0, index, tor_loss) |
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t_b_l.index_add_(0, index, tor_base_loss) |
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tor_loss, tor_base_loss = t_l / c, t_b_l / c |
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else: |
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if apply_mean: |
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tor_loss, tor_base_loss = torch.zeros(1, dtype=torch.float), torch.zeros(1, dtype=torch.float) |
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else: |
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tor_loss, tor_base_loss = torch.zeros(len(rot_loss), dtype=torch.float), torch.zeros(len(rot_loss), dtype=torch.float) |
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loss = tr_loss * tr_weight + rot_loss * rot_weight + tor_loss * tor_weight |
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return loss, tr_loss.detach(), rot_loss.detach(), tor_loss.detach(), tr_base_loss, rot_base_loss, tor_base_loss |
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class AverageMeter(): |
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def __init__(self, types, unpooled_metrics=False, intervals=1): |
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self.types = types |
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self.intervals = intervals |
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self.count = 0 if intervals == 1 else torch.zeros(len(types), intervals) |
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self.acc = {t: torch.zeros(intervals) for t in types} |
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self.unpooled_metrics = unpooled_metrics |
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def add(self, vals, interval_idx=None): |
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if self.intervals == 1: |
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self.count += 1 if vals[0].dim() == 0 else len(vals[0]) |
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for type_idx, v in enumerate(vals): |
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self.acc[self.types[type_idx]] += v.sum() if self.unpooled_metrics else v |
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else: |
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for type_idx, v in enumerate(vals): |
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self.count[type_idx].index_add_(0, interval_idx[type_idx], torch.ones(len(v))) |
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if not torch.allclose(v, torch.tensor(0.0)): |
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self.acc[self.types[type_idx]].index_add_(0, interval_idx[type_idx], v) |
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def summary(self): |
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if self.intervals == 1: |
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out = {k: v.item() / self.count for k, v in self.acc.items()} |
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return out |
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else: |
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out = {} |
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for i in range(self.intervals): |
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for type_idx, k in enumerate(self.types): |
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out['int' + str(i) + '_' + k] = ( |
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list(self.acc.values())[type_idx][i] / self.count[type_idx][i]).item() |
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return out |
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def train_epoch(model, loader, optimizer, device, t_to_sigma, loss_fn, ema_weigths): |
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model.train() |
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meter = AverageMeter(['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss']) |
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for data in tqdm(loader, total=len(loader)): |
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if device.type == 'cuda' and len(data) == 1 or device.type == 'cpu' and data.num_graphs == 1: |
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print("Skipping batch of size 1 since otherwise batchnorm would not work.") |
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optimizer.zero_grad() |
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try: |
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tr_pred, rot_pred, tor_pred = model(data) |
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loss, tr_loss, rot_loss, tor_loss, tr_base_loss, rot_base_loss, tor_base_loss = \ |
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loss_fn(tr_pred, rot_pred, tor_pred, data=data, t_to_sigma=t_to_sigma, device=device) |
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loss.backward() |
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optimizer.step() |
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ema_weigths.update(model.parameters()) |
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meter.add([loss.cpu().detach(), tr_loss, rot_loss, tor_loss, tr_base_loss, rot_base_loss, tor_base_loss]) |
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except RuntimeError as e: |
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if 'out of memory' in str(e): |
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print('| WARNING: ran out of memory, skipping batch') |
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for p in model.parameters(): |
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if p.grad is not None: |
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del p.grad |
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torch.cuda.empty_cache() |
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continue |
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elif 'Input mismatch' in str(e): |
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print('| WARNING: weird torch_cluster error, skipping batch') |
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for p in model.parameters(): |
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if p.grad is not None: |
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del p.grad |
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torch.cuda.empty_cache() |
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continue |
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else: |
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raise e |
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return meter.summary() |
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def test_epoch(model, loader, device, t_to_sigma, loss_fn, test_sigma_intervals=False): |
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model.eval() |
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meter = AverageMeter(['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss'], |
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unpooled_metrics=True) |
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if test_sigma_intervals: |
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meter_all = AverageMeter( |
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['loss', 'tr_loss', 'rot_loss', 'tor_loss', 'tr_base_loss', 'rot_base_loss', 'tor_base_loss'], |
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unpooled_metrics=True, intervals=10) |
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for data in tqdm(loader, total=len(loader)): |
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try: |
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with torch.no_grad(): |
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tr_pred, rot_pred, tor_pred = model(data) |
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loss, tr_loss, rot_loss, tor_loss, tr_base_loss, rot_base_loss, tor_base_loss = \ |
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loss_fn(tr_pred, rot_pred, tor_pred, data=data, t_to_sigma=t_to_sigma, apply_mean=False, device=device) |
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meter.add([loss.cpu().detach(), tr_loss, rot_loss, tor_loss, tr_base_loss, rot_base_loss, tor_base_loss]) |
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if test_sigma_intervals > 0: |
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complex_t_tr, complex_t_rot, complex_t_tor = [torch.cat([d.complex_t[noise_type] for d in data]) for |
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noise_type in ['tr', 'rot', 'tor']] |
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sigma_index_tr = torch.round(complex_t_tr.cpu() * (10 - 1)).long() |
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sigma_index_rot = torch.round(complex_t_rot.cpu() * (10 - 1)).long() |
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sigma_index_tor = torch.round(complex_t_tor.cpu() * (10 - 1)).long() |
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meter_all.add( |
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[loss.cpu().detach(), tr_loss, rot_loss, tor_loss, tr_base_loss, rot_base_loss, tor_base_loss], |
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[sigma_index_tr, sigma_index_tr, sigma_index_rot, sigma_index_tor, sigma_index_tr, sigma_index_rot, |
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sigma_index_tor, sigma_index_tr]) |
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except RuntimeError as e: |
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if 'out of memory' in str(e): |
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print('| WARNING: ran out of memory, skipping batch') |
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for p in model.parameters(): |
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if p.grad is not None: |
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del p.grad |
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torch.cuda.empty_cache() |
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continue |
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elif 'Input mismatch' in str(e): |
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print('| WARNING: weird torch_cluster error, skipping batch') |
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for p in model.parameters(): |
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if p.grad is not None: |
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del p.grad |
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torch.cuda.empty_cache() |
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continue |
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else: |
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raise e |
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out = meter.summary() |
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if test_sigma_intervals > 0: out.update(meter_all.summary()) |
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return out |
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def inference_epoch(model, complex_graphs, device, t_to_sigma, args): |
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t_schedule = get_t_schedule(inference_steps=args.inference_steps) |
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tr_schedule, rot_schedule, tor_schedule = t_schedule, t_schedule, t_schedule |
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dataset = ListDataset(complex_graphs) |
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loader = DataLoader(dataset=dataset, batch_size=1, shuffle=False) |
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rmsds = [] |
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for orig_complex_graph in tqdm(loader): |
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data_list = [copy.deepcopy(orig_complex_graph)] |
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randomize_position(data_list, args.no_torsion, False, args.tr_sigma_max) |
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predictions_list = None |
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failed_convergence_counter = 0 |
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while predictions_list == None: |
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try: |
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predictions_list, confidences = sampling(data_list=data_list, model=model.module if device.type=='cuda' else model, |
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inference_steps=args.inference_steps, |
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tr_schedule=tr_schedule, rot_schedule=rot_schedule, |
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tor_schedule=tor_schedule, |
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device=device, t_to_sigma=t_to_sigma, model_args=args) |
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except Exception as e: |
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if 'failed to converge' in str(e): |
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failed_convergence_counter += 1 |
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if failed_convergence_counter > 5: |
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print('| WARNING: SVD failed to converge 5 times - skipping the complex') |
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break |
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print('| WARNING: SVD failed to converge - trying again with a new sample') |
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else: |
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raise e |
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if failed_convergence_counter > 5: continue |
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if args.no_torsion: |
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orig_complex_graph['ligand'].orig_pos = (orig_complex_graph['ligand'].pos.cpu().numpy() + |
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orig_complex_graph.original_center.cpu().numpy()) |
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filterHs = torch.not_equal(predictions_list[0]['ligand'].x[:, 0], 0).cpu().numpy() |
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if isinstance(orig_complex_graph['ligand'].orig_pos, list): |
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orig_complex_graph['ligand'].orig_pos = orig_complex_graph['ligand'].orig_pos[0] |
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ligand_pos = np.asarray( |
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[complex_graph['ligand'].pos.cpu().numpy()[filterHs] for complex_graph in predictions_list]) |
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orig_ligand_pos = np.expand_dims( |
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orig_complex_graph['ligand'].orig_pos[filterHs] - orig_complex_graph.original_center.cpu().numpy(), axis=0) |
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rmsd = np.sqrt(((ligand_pos - orig_ligand_pos) ** 2).sum(axis=2).mean(axis=1)) |
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rmsds.append(rmsd) |
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rmsds = np.array(rmsds) |
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losses = {'rmsds_lt2': (100 * (rmsds < 2).sum() / len(rmsds)), |
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'rmsds_lt5': (100 * (rmsds < 5).sum() / len(rmsds))} |
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return losses |
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