192 lines
6.1 KiB
Python
192 lines
6.1 KiB
Python
import random
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from abc import ABC
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from collections import OrderedDict
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import torch
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from pytorch_lightning import EvalResult
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class TestStepVariations(ABC):
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"""
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Houses all variations of test steps
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"""
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def test_step(self, batch, batch_idx, *args, **kwargs):
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"""
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Default, baseline test_step
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:param batch:
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:return:
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"""
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self.test_step_called = True
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_test = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({'test_loss': loss_test, 'test_acc': test_acc})
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return output
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if batch_idx % 2 == 0:
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return test_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({'test_loss': loss_test,
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'test_acc': test_acc,
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'test_dic': {'test_loss_a': loss_test}})
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return output
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def test_step_result_obj(self, batch, batch_idx, *args, **kwargs):
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"""
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Default, baseline test_step
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:param batch:
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:return:
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"""
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_test = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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result = EvalResult()
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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result.log_dict({'test_loss': loss_test, 'test_acc': test_acc})
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return result
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if batch_idx % 2 == 0:
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return test_acc
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if batch_idx % 3 == 0:
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result.log_dict({'test_loss': loss_test, 'test_acc': test_acc})
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result.test_dic = {'test_loss_a': loss_test}
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return result
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def test_step__multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
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"""
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Default, baseline test_step
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:param batch:
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:return:
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"""
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_test = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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# alternate possible outputs to test
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if batch_idx % 1 == 0:
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output = OrderedDict({'test_loss': loss_test, 'test_acc': test_acc})
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return output
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if batch_idx % 2 == 0:
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return test_acc
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if batch_idx % 3 == 0:
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output = OrderedDict({
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'test_loss': loss_test,
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'test_acc': test_acc,
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'test_dic': {'test_loss_a': loss_test}
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})
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return output
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if batch_idx % 5 == 0:
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output = OrderedDict({f'test_loss_{dataloader_idx}': loss_test, f'test_acc_{dataloader_idx}': test_acc})
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return output
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def test_step__empty(self, batch, batch_idx, *args, **kwargs):
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return {}
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def test_step_result_preds(self, batch, batch_idx, optimizer_idx=None):
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_test = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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test_acc = torch.tensor(test_acc)
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test_acc = test_acc.type_as(x)
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# Do regular EvalResult Logging
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result = EvalResult(checkpoint_on=loss_test)
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result.log('test_loss', loss_test)
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result.log('test_acc', test_acc)
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batch_size = x.size(0)
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lst_of_str = [random.choice(['dog', 'cat']) for i in range(batch_size)]
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lst_of_int = [random.randint(500, 1000) for i in range(batch_size)]
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lst_of_lst = [[x] for x in lst_of_int]
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lst_of_dict = [{k: v} for k, v in zip(lst_of_str, lst_of_int)]
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# This is passed in from pytest via parameterization
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option = getattr(self, 'test_option', 0)
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prediction_file = getattr(self, 'prediction_file', 'predictions.pt')
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lazy_ids = torch.arange(batch_idx * self.batch_size, batch_idx * self.batch_size + x.size(0))
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# Base
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if option == 0:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('preds', labels_hat, prediction_file)
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# Check mismatching tensor len
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elif option == 1:
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self.write_prediction('idxs', torch.cat((lazy_ids, lazy_ids)), prediction_file)
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self.write_prediction('preds', labels_hat, prediction_file)
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# write multi-dimension
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elif option == 2:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('preds', labels_hat, prediction_file)
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self.write_prediction('x', x, prediction_file)
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# write str list
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elif option == 3:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('vals', lst_of_str, prediction_file)
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# write int list
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elif option == 4:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('vals', lst_of_int, prediction_file)
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# write nested list
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elif option == 5:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('vals', lst_of_lst, prediction_file)
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# write dict list
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elif option == 6:
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self.write_prediction('idxs', lazy_ids, prediction_file)
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self.write_prediction('vals', lst_of_dict, prediction_file)
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elif option == 7:
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self.write_prediction_dict({'idxs': lazy_ids, 'preds': labels_hat}, prediction_file)
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return result
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