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from lightning.pytorch.callbacks import BaseFinetuning
import torch
import dgl
from src.layers.inference_oc import DPC_custom_CLD
from src.layers.inference_oc import match_showers
from src.layers.inference_oc import remove_bad_tracks_from_cluster
class FreezeClustering(BaseFinetuning):
def __init__(
self,
):
super().__init__()
def freeze_before_training(self, pl_module):
self.freeze(pl_module.ScaledGooeyBatchNorm2_1)
self.freeze(pl_module.gatr)
self.freeze(pl_module.clustering)
self.freeze(pl_module.beta)
print("CLUSTERING HAS BEEN FROOOZEN")
def finetune_function(self, pl_module, current_epoch, optimizer):
print("Not finetunning")
def obtain_batch_numbers(x, g):
dev = x.device
graphs_eval = dgl.unbatch(g)
number_graphs = len(graphs_eval)
batch_numbers = []
for index in range(0, number_graphs):
gj = graphs_eval[index]
num_nodes = gj.number_of_nodes()
batch_numbers.append(index * torch.ones(num_nodes).to(dev))
# num_nodes = gj.number_of_nodes()
batch = torch.cat(batch_numbers, dim=0)
return batch
def obtain_clustering_for_matched_showers(
batch_g, model_output, y_all, local_rank, use_gt_clusters=False, add_fakes=True
):
graphs_showers_matched = []
graphs_showers_fakes = []
true_energy_showers = []
reco_energy_showers = []
reco_energy_showers_fakes = []
energy_true_daughters = []
y_pids_matched = []
y_coords_matched = []
if not use_gt_clusters:
batch_g.ndata["coords"] = model_output[:, 0:3]
batch_g.ndata["beta"] = model_output[:, 3]
graphs = dgl.unbatch(batch_g)
batch_id = y_all.batch_number
for i in range(0, len(graphs)):
mask = batch_id == i
dic = {}
dic["graph"] = graphs[i]
y = y_all.copy()
y.mask(mask.flatten())
dic["part_true"] = y
if not use_gt_clusters:
betas = torch.sigmoid(dic["graph"].ndata["beta"])
X = dic["graph"].ndata["coords"]
if use_gt_clusters:
labels = dic["graph"].ndata["particle_number"].type(torch.int64)
else:
labels =DPC_custom_CLD(X, dic["graph"], model_output.device)
labels, _ = remove_bad_tracks_from_cluster(dic["graph"], labels)
particle_ids = torch.unique(dic["graph"].ndata["particle_number"])
shower_p_unique = torch.unique(labels)
shower_p_unique, row_ind, col_ind, i_m_w, _ = match_showers(
labels, dic, particle_ids, model_output, local_rank, i, None
)
row_ind = torch.Tensor(row_ind).to(model_output.device).long()
col_ind = torch.Tensor(col_ind).to(model_output.device).long()
if torch.sum(particle_ids == 0) > 0:
row_ind_ = row_ind - 1
else:
# if there is no zero then index 0 corresponds to particle 1.
row_ind_ = row_ind
index_matches = col_ind + 1
index_matches = index_matches.to(model_output.device).long()
for j, unique_showers_label in enumerate(index_matches):
if torch.sum(unique_showers_label == index_matches) == 1:
index_in_matched = torch.argmax(
(unique_showers_label == index_matches) * 1
)
mask = labels == unique_showers_label
sls_graph = graphs[i].ndata["pos_hits_xyz"][mask][:, 0:3]
g = dgl.graph(([], []))
g.add_nodes(sls_graph.shape[0])
g = g.to(sls_graph.device)
g.ndata["h"] = graphs[i].ndata["h"][mask]
if "pos_pxpypz" in graphs[i].ndata:
g.ndata["pos_pxpypz"] = graphs[i].ndata["pos_pxpypz"][mask]
if "pos_pxpypz_at_vertex" in graphs[i].ndata:
g.ndata["pos_pxpypz_at_vertex"] = graphs[i].ndata[
"pos_pxpypz_at_vertex"
][mask]
g.ndata["chi_squared_tracks"] = graphs[i].ndata["chi_squared_tracks"][mask]
energy_t = dic["part_true"].E.to(model_output.device)
energy_t_corr_daughters = dic["part_true"].m.to(
model_output.device
)
true_energy_shower = energy_t[row_ind_[j]]
y_pids_matched.append(y.pid[row_ind_[j]].item())
y_coords_matched.append(y.coord[row_ind_[j]].detach().cpu().numpy())
energy_true_daughters.append(energy_t_corr_daughters[row_ind_[j]])
reco_energy_shower = torch.sum(graphs[i].ndata["e_hits"][mask])
graphs_showers_matched.append(g)
true_energy_showers.append(true_energy_shower.view(-1))
reco_energy_showers.append(reco_energy_shower.view(-1))
pred_showers = shower_p_unique
pred_showers[index_matches] = -1
pred_showers[
0
] = (
-1
)
mask_fakes = pred_showers != -1
fakes_idx = torch.where(mask_fakes)[0]
if add_fakes:
for j in fakes_idx:
mask = labels == j
sls_graph = graphs[i].ndata["pos_hits_xyz"][mask][:, 0:3]
g = dgl.graph(([], []))
g.add_nodes(sls_graph.shape[0])
g = g.to(sls_graph.device)
g.ndata["h"] = graphs[i].ndata["h"][mask]
if "pos_pxpypz" in graphs[i].ndata:
g.ndata["pos_pxpypz"] = graphs[i].ndata["pos_pxpypz"][mask]
if "pos_pxpypz_at_vertex" in graphs[i].ndata:
g.ndata["pos_pxpypz_at_vertex"] = graphs[i].ndata[
"pos_pxpypz_at_vertex"
][mask]
g.ndata["chi_squared_tracks"] = graphs[i].ndata["chi_squared_tracks"][mask]
graphs_showers_fakes.append(g)
reco_energy_shower = torch.sum(graphs[i].ndata["e_hits"][mask])
reco_energy_showers_fakes.append(reco_energy_shower.view(-1))
graphs_showers_matched = dgl.batch(graphs_showers_matched + graphs_showers_fakes)
true_energy_showers = torch.cat(true_energy_showers, dim=0)
reco_energy_showers = torch.cat(reco_energy_showers + reco_energy_showers_fakes, dim=0)
e_true_corr_daughters = torch.cat(energy_true_daughters, dim=0)
number_of_fakes = len(reco_energy_showers_fakes)
return (
graphs_showers_matched,
true_energy_showers,
reco_energy_showers,
y_pids_matched,
e_true_corr_daughters,
y_coords_matched,
number_of_fakes,
fakes_idx
)
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