def infer(model_in):
# === dataloader defination ===
train_batch_size = 1
valid_batch_size = 1
test_batch_size = 1
dataloaders = VSLdataset.create_dataloader_train_valid_test(
train_batch_size, valid_batch_size, test_batch_size)
#dataloaders = VSLdataset.create_dataloader_valid(valid_batch_size)
valid_dataloader = dataloaders['valid']
# =============================
# === got model ===
save_file = os.path.join('../saved_model',
'CLSTM_50_l10_h512_loss021_best.pth')
model = load_checkpoint(model_in, save_file)
# =================
print(model)
# === runing no gpu ===
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
torch.backends.cudnn.benchmark = True
# =====================
loss_function = nn.CrossEntropyLoss()
# validation
class_correct = list(0. for i in range(len(VSLdataset.class_name_to_id_)))
class_total = list(0. for i in range(len(VSLdataset.class_name_to_id_)))
class_name = list(VSLdataset.class_name_to_id_.keys())
model.eval()
print('Test:')
all_targets = np.zeros((len(valid_dataloader), 1))
all_scores = np.zeros((len(valid_dataloader), 8))
all_predicted_flatten = np.zeros((len(valid_dataloader), 1))
loss_eval = 0.0
for index_eval, (data_eval, target_eval) in enumerate(valid_dataloader):
data_eval, target_eval = data_eval.to(device), target_eval.to(device)
output_eval = model(data_eval)
loss_i = loss_function(output_eval, target_eval).item()
loss_eval += loss_i
all_targets[index_eval, :] = target_eval[0].cpu().detach().numpy()
all_scores[index_eval, :] = output_eval[0].cpu().detach().numpy()
_, predicted = torch.max(output_eval, 1)
all_predicted_flatten[
index_eval, :] = predicted[0].cpu().detach().numpy()
if (predicted != target_eval): # batch_size, timesteps, C, H, W
print('mis_classified: ', index_eval)
#visualize_mis_class(data_eval[0].permute(0, 2, 3, 1).cpu(), str(index_eval) + '.png', class_name[target_eval[0].cpu().numpy()], class_name[predicted[0].cpu().numpy()])
c = (predicted == target_eval).squeeze()
for i in range(valid_batch_size):
try:
label = target_eval[i]
class_correct[label] += c[i].item()
except:
label = target_eval
class_correct[label] += c.item()
class_total[label] += 1
for i in range(len(VSLdataset.class_name_to_id_)):
accuracy = 100 * (class_correct[i] + 1) / (class_total[i] + 1)
print('Accuracy of %5s : %2d %%' % (class_name[i], accuracy))
print('avg_loss: ', loss_eval / len(valid_dataloader))
# === draw roc and confusion mat ===
evaluate.draw_roc_bin(all_targets, all_scores)
evaluate.draw_confusion_matrix(all_targets, all_predicted_flatten)
def infer(model_in, file_name, fp16=False, int8=False):
# === dataloader defination ===
train_batch_size = 1
valid_batch_size = 1
test_batch_size = 1
dataloaders = VSLdataset.create_dataloader_train_valid_test(
train_batch_size, valid_batch_size, test_batch_size)
valid_dataloader = dataloaders['valid']
# =============================
save_file = os.path.join('../saved_model', file_name)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = model_in.eval().cuda()
data_example = torch.randn(8, 10, 3, 224, 224)
data_example = data_example.to(device)
model_trt = torch2trt(model_in, [data_example],
max_workspace_size=1 << 26,
fp16_mode=fp16,
int8_mode=int8)
model_trt.load_state_dict(torch.load(save_file))
loss_function = nn.CrossEntropyLoss()
# validation
class_correct = list(0. for i in range(len(VSLdataset.class_name_to_id_)))
class_total = list(0. for i in range(len(VSLdataset.class_name_to_id_)))
class_name = list(VSLdataset.class_name_to_id_.keys())
print('Test:')
all_targets = np.zeros((len(valid_dataloader), 1))
all_scores = np.zeros((len(valid_dataloader), 8))
all_predicted_flatten = np.zeros((len(valid_dataloader), 1))
loss_eval = 0.0
for index_eval, (data_eval, target_eval) in enumerate(valid_dataloader):
data_eval, target_eval = data_eval.to(device), target_eval.to(device)
output_eval = model_trt(data_eval)
loss_i = loss_function(output_eval, target_eval).item()
loss_eval += loss_i
all_targets[index_eval, :] = target_eval[0].cpu().detach().numpy()
all_scores[index_eval, :] = output_eval[0].cpu().detach().numpy()
_, predicted = torch.max(output_eval, 1)
all_predicted_flatten[
index_eval, :] = predicted[0].cpu().detach().numpy()
if (predicted != target_eval): # batch_size, timesteps, C, H, W
print('mis_classified: ', index_eval)
#visualize_mis_class(data_eval[0].permute(0, 2, 3, 1).cpu(), str(index_eval) + '.png', class_name[target_eval[0].cpu().numpy()], class_name[predicted[0].cpu().numpy()])
c = (predicted == target_eval).squeeze()
for i in range(valid_batch_size):
try:
label = target_eval[i]
class_correct[label] += c[i].item()
except:
label = target_eval
class_correct[label] += c.item()
class_total[label] += 1
for i in range(len(VSLdataset.class_name_to_id_)):
accuracy = 100 * (class_correct[i] + 1) / (class_total[i] + 1)
print('Accuracy of %5s : %2d %%' % (class_name[i], accuracy))
print('avg_loss: ', loss_eval / len(valid_dataloader))
# === draw roc and confusion mat ===
evaluate.draw_roc_bin(all_targets, all_scores)
evaluate.draw_confusion_matrix(all_targets, all_predicted_flatten)
