def run_test(model, dloader, test_data, result):
model.eval()
with torch.no_grad():
for item in dloader.batch_fv(test_data, len(test_data)):
genes, nimgs, labels, timesteps = item
inputs = torch.from_numpy(nimgs).type(torch.cuda.FloatTensor)
pd = model(inputs)
test_pd = torch_util.threshold_tensor_batch(pd)
np_pd = test_pd.data.cpu().numpy()
npmetrics.write_metrics(labels, np_pd, result)
def run_test(rnn, test_data, result):
rnn.eval()
with torch.no_grad():
# for item in fvloader.batch_fv(test_data, len(test_data)):
for item in matloader.batch_fv(test_data, len(test_data)):
genes, nimgs, labels, timesteps = item
idx = np.argsort(np.array(-timesteps))
s_nimgs = torch.from_numpy(np.stack(nimgs[idx])).type(
torch.cuda.FloatTensor)
s_timesteps = timesteps[idx]
out_pack, hidden = rnn(s_nimgs, s_timesteps)
test_pd = torch_util.threshold_tensor_batch(out_pack)
npmetrics.write_metrics(labels[idx], np.array(test_pd), result)
def evaluate_ilocator():
test_genes = datautil.get_test_gene_list(size=0)
pd = []
gt = []
d = datautil.load_enhanced_label()
for g in test_genes:
pics = datautil.get_gene_pics(g)
g_scores = []
for pic in [x.replace(".", "_") for x in pics]:
spath = os.path.abspath(
os.path.join("util/testimgs_ilocator_result/%s.txt" % pic))
with open(spath, 'r') as f:
score = [float(x) for x in f.readline().strip().split()]
if any([math.isnan(x) for x in score]):
pass
else:
g_scores.append(score)
g_scores = np.stack(g_scores)
g_scores = np.mean(g_scores, axis=0)
pd.append(g_scores)
gene_label = np.zeros(6)
for l in d[g]:
gene_label[l] = 1
gt.append(gene_label)
gt = np.stack(gt)
pd = np.stack(pd)
# rearrange label order
idx = np.array([5, 0, 6, 4, 2, 1])
pd = pd[:, idx]
thr = pd.max(axis=1)
zeros = np.zeros(thr.shape[0])
thr = np.min(np.stack([zeros, thr], axis=1), axis=1)
pd = np.greater_equal(pd, thr[:, np.newaxis]).astype(int)
npmetrics.write_metrics(gt, pd, "util/ilocator.txt")
def run_test(model, test_dataset, result, dcfg):
model.eval()
with torch.no_grad():
test_loader = DataLoader(test_dataset,
batch_size=dcfg['bsize'],
shuffle=False,
num_workers=dcfg['nworker'],
collate_fn=dcfg['collate'])
np_label = []
np_pd = []
for i_batch, sample_batched in enumerate(test_loader):
(img, label) = sample_batched
inputs = img.type(torch.cuda.FloatTensor)
gt = label.type(torch.cuda.FloatTensor)
pd = model(inputs)
test_pd = torch_util.threshold_tensor_batch(pd)
np_pd.append(test_pd.data.cpu().numpy())
np_label.append(gt.data.cpu().numpy())
np_label = np.concatenate(np_label)
np_pd = np.concatenate(np_pd)
npmetrics.write_metrics(np_label, np_pd, result)
def run_test(model, dloader, test_data, result):
model.eval()
with torch.no_grad():
gt = np.array([[0 for _ in range(10)]])
pd = np.array([[0 for _ in range(10)]])
for item in dloader.batch_fv(test_data, batch=1):
genes, nimgs, labels, timesteps = item
inputs = torch.from_numpy(nimgs).type(torch.cuda.FloatTensor)
# print(gt)
apd = model(inputs)
test_pd = torch_util.threshold_tensor_batch(apd)
np_pd = test_pd.data.cpu().numpy()
gt = np.concatenate((gt, labels))
pd = np.concatenate((pd, np_pd))
gt = gt[1:, :]
pd = pd[1:, :]
npmetrics.write_metrics(gt, pd, result)