def build_corpus(df, txts: list = None, corpus: list = None):
"""Generates corpus (list of str) and vocab with occurrence count (dict of
set of unique tokens).
:param df:
:param txts:
:param corpus:
:return:
"""
if corpus is None:
corpus = []
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(df.text)
logger.info(X.shape)
logger.info(vectorizer.get_feature_names())
for txt in txts:
# corpus = corpus + txt
for token in txt:
corpus.append(token)
vocab_freq = Counter(corpus)
return corpus, vocab_freq
def get_supervised_result(model,
train_iterator,
val_iterator,
test_iterator,
EPOCHS=5,
cls_thresh=None,
n_classes=cfg['data']['num_classes']):
""" Train and Predict on full supervised mode.
Returns:
"""
model_best, val_preds_trues_best, val_preds_trues_all, losses = trainer(
model, train_iterator, val_iterator, N_EPOCHS=EPOCHS)
# logger.debug(losses)
# evaluate the model
test_loss, test_preds_trues = predict_with_label(model_best, test_iterator)
if cls_thresh is None:
cls_thresh = [0.5] * n_classes
predicted_labels = logit2label(DataFrame(
test_preds_trues['preds'].numpy()),
cls_thresh,
drop_irrelevant=False)
result = calculate_performance_pl(test_preds_trues['trues'],
predicted_labels)
logger.info("Supervised result: {}".format(dumps(result, indent=4)))
return result, model_best
def read_labelled_json(data_dir=dataset_dir, filename=cfg['data']['test'],
data_keys=['text', 'classes'], data_set='train',
# rename_cols={"parsed_tweet": "text"},
):
""" Reads json data and converts to DataFrame.
Default reads labelled target data.
Args:
data_dir:
filename:
data_keys:
data_set:
rename_cols:
Returns:
"""
data_df = json_keys2df(data_keys, json_filename=filename,
dataset_dir=data_dir)
logger.info(data_df.head())
# data_df = data_df.rename(columns=rename_cols)
if data_set == 'train':
y_hot = mlb.fit_transform(data_df.classes.to_list())
else:
y_hot = mlb.transform(data_df.classes.to_list())
for i in range(y_hot.shape[1]):
data_df[i] = y_hot[:, i:i + 1]
data_df = data_df.drop(columns=['classes'], axis=0)
return data_df
def train_node_classifier(g: DGLGraph,
features: torch.Tensor,
labels: torch.Tensor,
labelled_mask: torch.Tensor,
model: GAT_Node_Classifier,
loss_func,
optimizer,
epochs: int = 5) -> None:
"""
:param g:
:param features:
:param labels:
:param labelled_mask:
:param model:
:param loss_func:
:param optimizer:
:param epochs:
"""
model.train()
dur = []
for epoch in range(epochs):
t0 = time.time()
logits = model(g, features)
logp = F.log_softmax(logits, 1)
loss = loss_func(logp[labelled_mask], labels[labelled_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
dur.append(time.time() - t0)
logger.info("Epoch {:05d} | Loss {:.4f} | Time(s) {:.4f}".format(
epoch, loss.item(), np.mean(dur)))
def graph_multiclass_classification(in_feats: int = 1,
hid_feats: int = 4,
num_heads: int = 2) -> None:
from dgl.data import MiniGCDataset
# Create training and test sets.
trainset = MiniGCDataset(320, 10, 20)
testset = MiniGCDataset(80, 10, 20)
# # Use PyTorch's DataLoader and the collate function defined before.
data_loader = DataLoader(trainset,
batch_size=8,
shuffle=True,
collate_fn=batch_graphs)
# Create model
model = GAT_Graph_Classifier(in_feats,
hid_feats,
num_heads=num_heads,
out_dim=trainset.num_classes)
logger.info(model)
loss_func = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
epoch_losses, epoch_predictions_dict = train_graph_classifier(
model, data_loader, loss_func=loss_func, optimizer=optimizer, epochs=5)
def graph_multilabel_classification(gdh,
in_feats: int = 100,
hid_feats: int = 50,
num_heads: int = 2,
epochs=cfg['training']['num_epoch']):
model = GAT_Graph_Classifier(in_feats,
hid_feats,
num_heads=num_heads,
out_dim=gdh.num_classes)
logger.info(model)
loss_func = torch.nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(),
lr=cfg["model"]["optimizer"]["lr"])
epoch_losses, train_epochs_output_dict = train_graph_classifier(
model,
gdh.train_dataloader(),
loss_func=loss_func,
optimizer=optimizer,
epochs=epochs,
eval_data_loader=gdh.test_dataloader())
losses, test_output = test_graph_classifier(
model, loss_func=loss_func, data_loader=gdh.test_dataloader())
logger.info(dumps(test_output['result'], indent=4))
return train_epochs_output_dict, test_output
def load_graph(self, graph_path):
if graph_path is None:
graph_path = self.graph_path
# load processed data from directory graph_path
logger.info(f'Loading graph from [{graph_path}]')
self.G = read_gpickle(graph_path)
return self.G
def eval_graph_classifier(model: GAT_GCN_Classifier,
G,
X,
loss_func,
data_loader: utils.data.dataloader.DataLoader,
n_classes=cfg['data']['num_classes'],
save_gcn_embs=False):
model.eval()
preds = []
trues = []
losses = []
for iter, (graph_batch, local_ids, label, global_ids,
node_counts) in enumerate(data_loader):
## Store emb in a separate file as self_loop removes emb info:
emb = graph_batch.ndata['emb']
# graph_batch = dgl.add_self_loop(graph_batch)
if cfg['model']['use_cuda'][plat][user] and cuda.is_available():
graph_batch = graph_batch.to(device)
emb = emb.to(device)
# local_ids = local_ids.to(device)
# node_counts = node_counts.to(device)
# global_ids = global_ids.to(device)
G = G.to(device)
X = X.to(device)
if save_gcn_embs:
save(X, 'X_glove.pt')
start_time = timeit.default_timer()
prediction = model(graph_batch, emb, local_ids, node_counts,
global_ids, G, X, save_gcn_embs)
test_time = timeit.default_timer() - start_time
test_count = label.shape[0]
logger.info(f"Test time per example: [{test_time / test_count} sec]")
if prediction.dim() == 1:
prediction = prediction.unsqueeze(1)
if cfg['model']['use_cuda'][plat][user] and cuda.is_available():
prediction = prediction.to(device)
loss = loss_func(prediction, label)
preds.append(prediction.detach())
trues.append(label.detach())
losses.append(loss.detach())
losses = mean(stack(losses))
preds = cat(preds)
## Converting raw scores to probabilities using Sigmoid:
preds = sigmoid(preds)
## Converting probabilities to class labels:
preds = logit2label(preds.detach(), cls_thresh=0.5)
trues = cat(trues)
if n_classes == 1:
result_dict = calculate_performance_bin_sk(trues, preds)
else:
result_dict = calculate_performance(trues, preds)
test_output = {'preds': preds, 'trues': trues, 'result': result_dict}
# logger.info(dumps(result_dict, indent=4))
return losses, test_output
def process(self):
## Load or create graphs, labels, local and global ids:
logger.info("Load or create graphs, labels, local and global ids.")
if exists(self.graph_path):
self.graphs, self.instance_graph_local_node_ids, self.labels, \
self.instance_graph_global_node_ids = self.load_instance_dgl(self.graph_path)
else:
self.graphs, self.instance_graph_local_node_ids, self.labels, self.instance_graph_global_node_ids\
= self.create_instance_dgls(dataset=self.dataset, vocab=self.vocab, class_names=self.class_names)
self.save_instance_dgl()
def freeze_weights(self, except_layers=tuple('classifier')):
"""
:param except_layers:
"""
for name, param in self.model.named_parameters():
# for layer in except_layers:
# if layer not in name:
if name not in except_layers: ## Except specified layers
param.requires_grad = False
logger.info(f'Froze layer: {name}')
def train_graph_classifier(
model: GAT_Graph_Classifier,
data_loader: torch.utils.data.dataloader.DataLoader,
loss_func: torch.nn.modules.loss.BCEWithLogitsLoss,
optimizer,
epochs: int = 5,
eval_data_loader: torch.utils.data.dataloader.DataLoader = None):
train_epoch_losses = []
train_epoch_dict = OrderedDict()
for epoch in range(epochs):
model.train()
epoch_loss = 0
preds = []
trues = []
for iter, (graph_batch, label) in enumerate(data_loader):
## Store emb in a separate file as self_loop removes emb info:
emb = graph_batch.ndata['emb']
# graph_batch = dgl.add_self_loop(graph_batch)
prediction = model(graph_batch, emb)
loss = loss_func(prediction, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.detach().item()
preds.append(prediction.detach())
trues.append(label.detach())
epoch_loss /= (iter + 1)
losses, test_output = test_graph_classifier(
model, loss_func=loss_func, data_loader=eval_data_loader)
logger.info(
f"Epoch {epoch}, Train loss {epoch_loss}, Eval loss {losses},"
f" Macro F1 {test_output['result']['f1']['macro'].item()}")
# logger.info(dumps(test_output['result'], indent=4))
train_epoch_losses.append(epoch_loss)
preds = torch.cat(preds)
## Converting raw scores to probabilities using Sigmoid:
preds = torch.sigmoid(preds)
## Converting probabilities to class labels:
preds = logit2label(preds.detach(), cls_thresh=0.5)
trues = torch.cat(trues)
result_dict = calculate_performance(trues, preds)
# logger.info(dumps(result_dict, indent=4))
train_epoch_dict[epoch] = {
'preds': preds,
'trues': trues,
'result': result_dict
}
# logger.info(f'Epoch {epoch} result: \n{result_dict}')
return train_epoch_losses, train_epoch_dict
def load_dgl(graph_path, info_path=None):
""" Loads saved dgl graphs, labels and other info.
:param graph_path:
:param info_path:
:return:
"""
# load processed data from directory graph_path
logger.info(f'Loading graph data from: {graph_path}')
graphs, label_dict = load_graphs(graph_path)
labels = label_dict['labels']
if info_path is not None:
info = load_info(info_path)['info']
return graphs, labels, info
return graphs, labels
def main():
"""
Main module to start code
:param args:
Type: tuple
Required
Read Only
:return:
"""
import networkx as nx
G_k = nx.path_graph(5)
for i in range(5):
G_k.nodes[i]['x'] = np.random.rand(7, )
output = GCN_forward(G_k_data)
logger.info(output)
def calculate_performance_sk(true: (np.ndarray, torch.tensor),
pred: (np.ndarray, torch.tensor),
print_result=False) -> dict:
"""
:param pred: Multi-hot
:param true: Multi-hot
:param print_result:
"""
scores = {"accuracy": {}}
scores["accuracy"]["unnormalize"] = accuracy_score(true, pred)
scores["accuracy"]["normalize"] = accuracy_score(true,
pred,
normalize=True)
scores["precision"] = {}
scores["precision"]["classes"] = precision_score(true, pred,
average=None).tolist()
scores["precision"]["weighted"] = precision_score(true,
pred,
average='weighted')
scores["precision"]["micro"] = precision_score(true, pred, average='micro')
scores["precision"]["macro"] = precision_score(true, pred, average='macro')
scores["precision"]["samples"] = precision_score(true,
pred,
average='samples')
scores["recall"] = {}
scores["recall"]["classes"] = recall_score(true, pred,
average=None).tolist()
scores["recall"]["weighted"] = recall_score(true, pred, average='weighted')
scores["recall"]["micro"] = recall_score(true, pred, average='micro')
scores["recall"]["macro"] = recall_score(true, pred, average='macro')
scores["recall"]["samples"] = recall_score(true, pred, average='samples')
scores["f1"] = {}
scores["f1"]["classes"] = f1_score(true, pred, average=None).tolist()
scores["f1"]["weighted"] = f1_score(true, pred, average='weighted')
scores["f1"]["micro"] = f1_score(true, pred, average='micro')
scores["f1"]["macro"] = f1_score(true, pred, average='macro')
scores["f1"]["samples"] = f1_score(true, pred, average='samples')
if print_result:
logger.info(dumps(scores, indent=4))
return scores
def lpa_accuracy(preds_set: np.ndarray, labels: np.ndarray) -> dict:
preds_set = preds_set.numpy()
labels = labels.numpy()
# pred_argmax = []
result = {}
for cls in range(preds_set.shape[1]):
# pred_argmax.append(np.argmax(pred, axis=1))
logger.info(f'Calculating accuracy for class: [{cls}]')
test1 = np.ma.masked_where(labels[:, cls] > 0, labels[:, cls])
correct = (
labels[:, cls][test1.mask] == preds_set[:, cls][test1.mask]).sum()
total = labels[test1.mask].shape[0]
result[cls] = (correct, total, correct / total)
logger.info(f'Accuracy class: [{correct / total, correct, total}]')
return result
def save_dgl(graphs, labels, graph_path, info=None, info_path=None):
""" Saves dgl graphs, labels and other info.
:param instance_graph_global_node_ids:
:param info:
:param graphs:
:param labels:
:param graph_path:
:param num_classes:
:param info_path:
"""
# save graphs and labels
logger.info(f'Saving graph data: {graph_path}')
save_graphs(graph_path, graphs, {'labels': labels})
# save other information in python dict
if info_path is not None:
save_info(info_path, {'info': info})
def propagate_multilabels(features: Tensor, labels: np.ndarray) -> np.ndarray:
"""
:param features:
:param labels:
:return:
"""
all_preds = []
for i, labels_cls in enumerate(labels):
logger.info(f'Propagating labels for class [{i}].')
preds = []
for under_set in labels_cls:
pred = propagate_labels(features, np.stack(under_set))
preds.append(pred)
voted_preds = majority_voting(preds)
all_preds.append(voted_preds)
return np.stack(all_preds).T
def labels_mapper(df, class_maps: dict = None):
""" Maps FIRE16 dataset labels to SMERP17 labels.
-1 denotes the column to be deleted.
Other columns are merged using logical or.
:param class_maps:
FIRE16 -> SMERP17
0 0
1 1
2 0
3 1
4 -1
5 3
6 2
:param df:
"""
if class_maps is None:
class_maps = {
2: 0,
3: 1,
# 4: -1,
5: 3,
6: 2
}
logger.info(f'Mapping classes: [{class_maps}]')
new_cols = sorted(list(class_maps.values()))
df2 = pd.DataFrame(columns=new_cols)
# df2 = df[df.columns.difference(new_cols)]
# df2['text'] = df['text']
df2.insert(loc=0, column='text', value=df['text'])
# df2 = df[not new_cols]
# for col in df.columns:
for cls, mapped_cls in class_maps.items():
df2[mapped_cls] = np.logical_or(df[cls], df[mapped_cls]) * 1
# if mapped_cls == -1: ## Delete column
# del df[cls]
# else: ## Merge columns using OR:
# df[mapped_cls] = np.logical_or(df[cls], df[mapped_cls]) * 1
# del df[cls]
# df2.index = df.index
return df2
def label_propagation(adj: sparse,
Y: list,
labelled_masks: tuple,
lpa_epoch: int = 1) -> Tensor:
logger.info("Applying Label Propagation")
lpa = Adj_Propagator()
Y = tensor(Y)
for i in range(lpa_epoch):
Y_hat = lpa(adj, Y)
mse = lpa_mse(Y_hat[labelled_masks[0]], Y[labelled_masks[0]])
# lpa_accuracy(Y_hat[labelled_masks[1]], Y[labelled_masks[1]])
logger.info(f'Label Propagation epoch {i} MSE: {mse}')
labelled_mask = [
i or j for i, j in zip(labelled_masks[0], labelled_masks[1])
]
Y_hat[labelled_mask] = Y[labelled_mask]
return Y_hat
def read_json(file_path: str = join(dataset_dir, 'acronym'),
convert_ordereddict=True) -> OrderedDict:
""" Reads json file as OrderedDict.
:param convert_ordereddict:
:param file_path:
:return:
"""
file_path = Path(file_path + ".json")
# file_path = Path(file_path)
logger.info(f"Reading json file [{file_path}].")
if file_path.exists():
with open(file_path, "r", encoding="utf-8") as file:
data = load(file)
if convert_ordereddict:
data = OrderedDict(data)
return data
else:
raise FileNotFoundError("File [{}] not found.".format(file_path))
def majority_voting(preds_set):
logger.info("Taking majority voting.")
if isinstance(preds_set, list):
majority_count = (len(preds_set) // 2) + 1
elif isinstance(preds_set, np.ndarray):
majority_count = (preds_set.shape[0] // 2) + 1
else:
NotImplementedError(f"datatype {type(preds_set)} not supported.")
pred_major = []
for pred in preds_set:
pred_discreet = np.argmax(pred, axis=1)
pred_major.append(pred_discreet)
pred_major = np.sum(pred_major, axis=0)
pred_major[(pred_major < majority_count)] = 0.
pred_major[(pred_major >= majority_count)] = 1.
return pred_major
def load_instance_dgl(self, graph_path, infopath=None):
""" Loads instance graphs.
:param graph_path:
:param infopath:
:return:
"""
if graph_path is None:
graph_path = self.graph_path
logger.info(
f'Loading graphs from {join(self.data_dir, self.dataset_name)}')
self.instance_graph_global_node_ids = load_pickle(
self.dataset_name + '_instance_graph_global_node_ids',
filepath=self.data_dir)
self.instance_graph_local_node_ids = load_pickle(
self.dataset_name + 'instance_graph_local_node_ids',
filepath=self.data_dir)
self.graphs, self.labels = load_dgl(graph_path, infopath)
return self.graphs, self.instance_graph_local_node_ids, \
self.labels, self.instance_graph_global_node_ids
def main():
""" Main module to start code
:param args:
Type: tuple
Required
Read Only
:return:
"""
true = torch.tensor([[0, 1], [1, 0]])
pred = torch.tensor([[0, 1], [0, 0]])
pl_dict = calculate_performance_pl(true, pred)
logger.info(pl_dict)
pl_sk_dict = calculate_performance_sk(true, pred)
logger.info(pl_sk_dict)
sk_dict = calculate_performance_sk(true.numpy(), pred.numpy())
logger.info(sk_dict)
true = np.array([[0, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]])
pred = np.array([[0, 1, 0, 1], [0, 1, 0, 0], [0, 0, 1, 0]])
true = torch.from_numpy(true)
pred = torch.from_numpy(pred)
precision_k_hot(true, pred)
def read_csv(data_dir=dataset_dir,
data_file='fire16_labeled_train',
index_col=0,
header=0):
""" Reads csv file as DF.
:param header:
:param index_col:
:param data_dir:
:param data_file:
:return:
"""
data_dir = Path(data_dir)
if not data_dir.exists():
raise FileNotFoundError("Directory [{}] not found.".format(data_dir))
data_file = data_dir / (data_file + '.csv')
logger.info(f"Reading csv file from [{data_file}]")
if not data_file.exists():
raise FileNotFoundError("File [{}] not found.".format(data_file))
df = pd.read_csv(data_file,
index_col=index_col,
header=header,
encoding='utf-8',
engine='python')
df = df.sample(frac=1.)
logger.info("Dataset size: [{}]".format(df.shape))
logger.info("Few dataset samples: \n[{}]".format(df.head()))
return df
def plot_occurance(losses: list,
title="Losses",
ylabel="Loss",
xlabel="Epoch",
clear=True,
log_scale=False,
plot_name=None,
plot_dir="",
show_plot=False):
""" Plots the validation loss against epochs.
:param plot_name:
:param plot_dir:
:param xlabel:
:param ylabel:
:param title:
:param losses:
:param clear:
:param log_scale:
"""
## Turn interactive plotting off
plt.ioff()
fig = plt.figure()
plt.plot(losses)
plt.xlabel(xlabel)
if log_scale:
plt.yscale('log')
plt.ylabel(ylabel)
plt.title(title)
if plot_name is None:
plot_name = title + "_" + ylabel + "_" + xlabel + ".jpg"
plt.savefig(join(plot_dir, plot_name))
logger.info(f"Saved plot with title [{title}] and ylabel [{ylabel}] and "
f"xlabel [{xlabel}] at [{join(plot_dir, plot_name)}].")
if clear:
plt.cla()
if show_plot: plt.show()
plt.close(fig) # Closing the figure so it won't get displayed in console.
def node_binary_classification(hid_feats: int = 4,
out_feats: int = 7,
num_heads: int = 2) -> None:
"""
:param hid_feats:
:param out_feats:
:param num_heads:
:return:
"""
from dgl.data import citation_graph as citegrh
def load_cora_data():
data = citegrh.load_cora()
features = torch.FloatTensor(data.features)
labels = torch.LongTensor(data.labels)
mask = torch.BoolTensor(data.train_mask)
g = DGLGraph(data.graph)
return g, features, labels, mask
g, features, labels, mask = load_cora_data()
net = GAT_Node_Classifier(in_dim=features.size(1),
hidden_dim=hid_feats,
out_dim=out_feats,
num_heads=num_heads)
logger.info(net)
loss_func = F.nll_loss
optimizer = optim.Adam(net.parameters(), lr=1e-3)
train_node_classifier(g,
features,
labels,
labelled_mask=mask,
model=net,
loss_func=loss_func,
optimizer=optimizer,
epochs=5)
def discretize_labelled(labelled_dict: dict,
thresh1=0.1,
k=2,
label_neg=0.,
label_pos=1.):
""" Discretize probabilities of labelled tokens.
:param k:
:param thresh1:
:param label_pos:
:param labelled_dict: token:np.array(vector)
:param label_neg: Value to assign when no class should be assigned [0., -1.]
"""
logger.info(f'Discretizing label vector with threshold [{thresh1}].')
labelled_vecs = np.array(list(labelled_dict.values()))
## value greater than threshold:
labelled_vecs[(labelled_vecs > thresh1)] = 1.
# labelled_vecs[(thresh2 < labelled_vecs) & (labelled_vecs <= thresh1)] = 1.
discretized_dict = {}
## Top k values of each row:
for token, vec in zip(labelled_dict.keys(), labelled_vecs):
row_sum = vec.sum()
if 0.5 < row_sum <= 1.: ## for non -1 rows only
row_idx = np.argpartition(-vec, k)
vec[row_idx[:k]] = label_pos
vec[row_idx[k:]] = label_neg
elif 0. <= row_sum <= 0.5:
row_idx = np.argmax(vec)
vec[row_idx] = label_pos
vec[(vec != 1.0)] = label_neg
elif row_sum > 1.:
vec[(vec < 1.0)] = label_neg
discretized_dict[token] = vec
return discretized_dict
def undersample_major_class(X: np.ndarray, Y: np.ndarray, k=3):
""" Undersamples the majority class k times.
:param X:
:param Y:
:param k:
:return:
"""
logger.info(f'Undersampling the majority class [{k}] times.')
under_sampler = RandomUnderSampler()
k_undersampled_list = []
for i in range(k):
X_resampled, Y_resampled = under_sampler.fit_resample(X, Y)
X_resampled, Y_resampled = unison_shuffled_copies(
X_resampled, Y_resampled)
undersampled_dict = {}
for x, y in zip(X_resampled, Y_resampled):
x = str(x[0])
undersampled_dict[x] = y
k_undersampled_list.append(undersampled_dict)
return k_undersampled_list
def split_data(self, df, train_size=0.8):
train_df = df.sample(frac=train_size, random_state=self.seed)
test_df = df.drop(train_df.index).reset_index(drop=True)
train_df = train_df.reset_index(drop=True)
logger.info("FULL Dataset: {}".format(df.shape))
logger.info("TRAIN Dataset: {}".format(train_df.shape))
logger.info("TEST Dataset: {}".format(test_df.shape))
return train_df, test_df
def train(self):
self.model.to(device)
self.model.train()
epoch_loss = 0
stores = {'preds': [], 'trues': [], 'ids': []}
for _, data in enumerate(self.train_loader, 0):
ids = data['ids'].to(device, dtype=torch.long)
mask = data['mask'].to(device, dtype=torch.long)
token_type_ids = data['token_type_ids'].to(device,
dtype=torch.long)
targets = data['targets'].to(device, dtype=torch.float)
## Set pool_output = True for classification:
outputs = self.model(ids,
mask,
token_type_ids=token_type_ids,
pool_output=True)
self.optimizer.zero_grad()
loss = self.loss_fn(outputs[0], targets)
stores['preds'].append(outputs[0])
stores['trues'].append(targets)
if _ % 20 == 0:
logger.info(f'Batch: {_}, Loss: {loss.item()}')
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
epoch_loss += loss.item()
stores['preds'] = torch.cat(stores['preds'])
stores['trues'] = torch.cat(stores['trues'])
# stores['ids'] = torch.cat(stores['ids'])
return epoch_loss / len(
self.train_loader), stores # , epoch_acc / len(iterator)
