def validate(model, dataset, batch_size=1):
model.eval()
with torch.no_grad():
keys, text, truth = dataset.get_val_data(batch_size=batch_size)
pred = model(text)
for i, key in enumerate(keys):
print_text, _ = dataset.val_dict[key]
print_text_class = pred[:, i][: len(print_text)].cpu().numpy()
color_print(print_text, print_text_class)
def validate(model, dataset, batch_size=1, print_size=10):
model.eval()
with torch.no_grad():
keys, text, truth = dataset.get_val_data(batch_size=batch_size)
oupt = model(text)
prob = torch.nn.functional.softmax(oupt, dim=2)
prob, pred = torch.max(prob, dim=2)
prob = prob.cpu().numpy()
pred = pred.cpu().numpy()
class_acc = 0.0
char_acc = 0.0
for i, key in enumerate(keys):
real_text, real_label = dataset.val_dict[key]
result = pred_to_dict(real_text, pred[:, i], prob[:, i])
ground_truth = truth_to_dict(real_text, real_label)
class_acc_unit = calc_accuracy(result, ground_truth)
char_acc_unit = compare_truth(result, ground_truth)
class_acc += class_acc_unit
char_acc += char_acc_unit
if i < print_size:
print("====== Val. number %d ======" % i)
for k, v in result.items():
print(f"{k:>8}: {v}")
print()
for k, v in ground_truth.items():
print(f"{k:>8}: {v}")
print("-ACCURACY(Class): %.2f" % class_acc_unit)
print("-ACCURACY(Char) : %.2f" % char_acc_unit)
print()
color_print(real_text, pred[:, i])
print("============================")
print()
print("=ACCURACY(Class): %.2f" % (class_acc * 100 / batch_size))
print("=ACCURACY(Char) : %.2f" % (char_acc * 100 / batch_size))
def validate(model, dataset, batch_size=1):
model.eval()
with torch.no_grad():
keys, text, truth = dataset.get_val_data(batch_size=batch_size)
oupt = model(text)
prob = torch.nn.functional.softmax(oupt, dim=2)
prob, pred = torch.max(prob, dim=2)
prob = prob.cpu().numpy()
pred = pred.cpu().numpy()
for i, key in enumerate(keys):
real_text, _ = dataset.val_dict[key]
result = pred_to_dict(real_text, pred[:, i], prob[:, i])
for k, v in result.items():
print(f"{k:>8}: {v}")
color_print(real_text, pred[:, i])
def inference(text):
text[0] = preprocess(text[0])
device = torch.device("cpu")
hidden_size = 256
model = MyModel0(len(VOCAB), 16, hidden_size).to(device)
model.load_state_dict(
torch.load("model.pth", map_location=torch.device('cpu')))
#text = ["shubham bisht, something happens"]
text_tensor = torch.zeros(len(text[0]), 1, dtype=torch.long)
text_tensor[:,
0] = torch.LongTensor([VOCAB.find(c) for c in text[0].upper()])
#print(text_tensor)
inp = text_tensor.to(device)
oupt = model(inp)
prob = torch.nn.functional.softmax(oupt, dim=2)
prob, pred = torch.max(prob, dim=2)
color_print(text[0], pred)
json = pred_to_dict(text[0], pred, prob)
print("\n###########################\n")
print(json)
return json
