Merge pull request #64 from qingqing01/ut_and_doc

Add unit testing and doc.

hapi/callbacks.py

@@ -185,6 +185,9 @@ class ProgBarLogger(Callback):
         self.verbose = verbose
         self.log_freq = log_freq
 
+    def _is_print(self):
+        return self.verbose and ParallelEnv().local_rank == 0
+
     def on_train_begin(self, logs=None):
         self.epochs = self.params['epochs']
         assert self.epochs
@@ -195,7 +198,7 @@ class ProgBarLogger(Callback):
         self.steps = self.params['steps']
         self.epoch = epoch
         self.train_step = 0
-        if self.verbose and self.epochs and ParallelEnv().local_rank == 0:
+        if self.epochs and self._is_print():
             print('Epoch %d/%d' % (epoch + 1, self.epochs))
         self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose)
 
@@ -213,15 +216,13 @@ class ProgBarLogger(Callback):
         logs = logs or {}
         self.train_step += 1
 
-        if self.train_step % self.log_freq == 0 and self.verbose and ParallelEnv(
-        ).local_rank == 0:
+        if self._is_print() and self.train_step % self.log_freq == 0:
             if self.steps is None or self.train_step < self.steps:
                 self._updates(logs, 'train')
 
     def on_epoch_end(self, epoch, logs=None):
         logs = logs or {}
-        if self.train_step % self.log_freq != 0 and self.verbose and ParallelEnv(
-        ).local_rank == 0:
+        if self._is_print() and (self.steps is not None):
             self._updates(logs, 'train')
 
     def on_eval_begin(self, logs=None):
@@ -231,7 +232,7 @@ class ProgBarLogger(Callback):
         self.evaled_samples = 0
         self.eval_progbar = ProgressBar(
             num=self.eval_steps, verbose=self.verbose)
-        if ParallelEnv().local_rank == 0:
+        if self._is_print():
             print('Eval begin...')
 
     def on_eval_batch_end(self, step, logs=None):
@@ -240,15 +241,13 @@ class ProgBarLogger(Callback):
         samples = logs.get('batch_size', 1)
         self.evaled_samples += samples
 
-        if self.eval_step % self.log_freq == 0 and self.verbose and ParallelEnv(
-        ).local_rank == 0:
+        if self._is_print() and self.eval_step % self.log_freq == 0:
             if self.eval_steps is None or self.eval_step < self.eval_steps:
                 self._updates(logs, 'eval')
 
     def on_eval_end(self, logs=None):
         logs = logs or {}
-        if self.verbose and ParallelEnv().local_rank == 0:
-            if self.eval_step % self.log_freq != 0:
+        if self._is_print() and (self.steps is not None):
             self._updates(logs, 'eval')
             print('Eval samples: %d' % (self.evaled_samples))
 

hapi/datasets/mnist.py

@@ -45,6 +45,8 @@ class MNIST(Dataset):
             :attr:`download` is True. Default None
         label_path(str): path to label file, can be set None if
             :attr:`download` is True. Default None
+        chw_format(bool): If set True, the output shape is [1, 28, 28],
+            otherwise, output shape is [1, 784]. Default True.
         mode(str): 'train' or 'test' mode. Default 'train'.
         download(bool): whether auto download mnist dataset if
             :attr:`image_path`/:attr:`label_path` unset. Default
@@ -70,13 +72,14 @@ class MNIST(Dataset):
     def __init__(self,
                  image_path=None,
                  label_path=None,
+                 chw_format=True,
                  mode='train',
                  transform=None,
                  download=True):
         assert mode.lower() in ['train', 'test'], \
                 "mode should be 'train' or 'test', but got {}".format(mode)
         self.mode = mode.lower()
-
+        self.chw_format = chw_format
         self.image_path = image_path
         if self.image_path is None:
             assert download, "image_path not set and auto download disabled"
@@ -144,10 +147,13 @@ class MNIST(Dataset):
 
                     for i in range(buffer_size):
                         self.images.append(images[i, :])
-                        self.labels.append(np.array([labels[i]]).astype('int64'))
+                        self.labels.append(
+                            np.array([labels[i]]).astype('int64'))
 
     def __getitem__(self, idx):
         image, label = self.images[idx], self.labels[idx]
+        if self.chw_format:
+            image = np.reshape(image, [1, 28, 28])
         if self.transform is not None:
             image = self.transform(image)
         return image, label

hapi/loss.py

@@ -66,7 +66,7 @@ class CrossEntropy(Loss):
     """
 
     def __init__(self, average=True):
-        super(CrossEntropy, self).__init__()
+        super(CrossEntropy, self).__init__(average)
 
     def forward(self, outputs, labels):
         return [
@@ -88,7 +88,7 @@ class SoftmaxWithCrossEntropy(Loss):
     """
 
     def __init__(self, average=True):
-        super(SoftmaxWithCrossEntropy, self).__init__()
+        super(SoftmaxWithCrossEntropy, self).__init__(average)
 
     def forward(self, outputs, labels):
         return [

hapi/tests/test_model.py

@@ -18,27 +18,25 @@ from __future__ import print_function
 import unittest
 
 import os
-import cv2
 import numpy as np
-import tempfile
 import shutil
+import tempfile
 
 import paddle
 from paddle import fluid
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
 from paddle.fluid.dygraph.container import Sequential
-from paddle.io import BatchSampler, DataLoader
+from paddle.io import DataLoader
+from paddle.fluid.dygraph.base import to_variable
 
 from hapi.model import Model, Input, set_device
-from hapi.loss import Loss
+from hapi.loss import CrossEntropy
 from hapi.metrics import Accuracy
 from hapi.datasets import MNIST
 from hapi.vision.models import LeNet
-from hapi.download import get_weights_path_from_url
 
 
 class LeNetDygraph(fluid.dygraph.Layer):
-
     def __init__(self, num_classes=10, classifier_activation='softmax'):
         super(LeNetDygraph, self).__init__()
         self.num_classes = num_classes
@@ -67,12 +65,16 @@ class LeNetDygraph(fluid.dygraph.Layer):
 
 
 class MnistDataset(MNIST):
-    def __init__(self, mode, return_label=True):
+    def __init__(self, mode, return_label=True, sample_num=None):
         super(MnistDataset, self).__init__(mode=mode)
         self.return_label = return_label
+        if sample_num:
+            self.images = self.images[:sample_num]
+            self.labels = self.labels[:sample_num]
 
     def __getitem__(self, idx):
-        img = np.reshape(self.images[idx], [1, 28, 28])
+        img, label = self.images[idx], self.labels[idx]
+        img = np.reshape(img, [1, 28, 28])
         if self.return_label:
             return img, np.array(self.labels[idx]).astype('int64')
         return img,
@@ -81,15 +83,14 @@ class MnistDataset(MNIST):
         return len(self.images)
 
 
-def get_predict_accuracy(pred, gt):
+def compute_acc(pred, label):
     pred = np.argmax(pred, -1)
-    gt = np.array(gt)
-
-    correct = pred[:, np.newaxis] == gt
+    label = np.array(label)
+    correct = pred[:, np.newaxis] == label
     return np.sum(correct) / correct.shape[0]
 
 
-def low_level_lenet_dygraph_train(model, dataloader):
+def dynamic_train(model, dataloader):
     optim = fluid.optimizer.Adam(
         learning_rate=0.001, parameter_list=model.parameters())
     model.train()
@@ -102,7 +103,7 @@ def low_level_lenet_dygraph_train(model, dataloader):
         model.clear_gradients()
 
 
-def low_level_dynamic_evaluate(model, dataloader):
+def dynamic_evaluate(model, dataloader):
     with fluid.dygraph.no_grad():
         model.eval()
         cnt = 0
@@ -115,56 +116,65 @@ def low_level_dynamic_evaluate(model, dataloader):
     return cnt / len(dataloader.dataset)
 
 
-class TestEvaluatePredict(unittest.TestCase):
-    def setUp(self):
-        self.device = set_device('gpu')
-        self.train_dataset = MnistDataset(mode='train')
-        self.val_dataset = MnistDataset(mode='test')
-        self.test_dataset = MnistDataset(mode='test', return_label=False)
+class TestModel(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.device = set_device('gpu')
+        fluid.enable_dygraph(cls.device)
 
-        fluid.enable_dygraph(self.device)
-        train_dataloader = fluid.io.DataLoader(
-            self.train_dataset, places=self.device, batch_size=64)
-        val_dataloader = fluid.io.DataLoader(
-            self.val_dataset, places=self.device, batch_size=64)
-        self.lenet_dygraph = LeNetDygraph()
-        low_level_lenet_dygraph_train(self.lenet_dygraph, train_dataloader)
-        self.acc1 = low_level_dynamic_evaluate(self.lenet_dygraph,
-                                               val_dataloader)
-        self.save_dir = tempfile.mkdtemp()
+        sp_num = 1280
+        cls.train_dataset = MnistDataset(mode='train', sample_num=sp_num)
+        cls.val_dataset = MnistDataset(mode='test', sample_num=sp_num)
+        cls.test_dataset = MnistDataset(
+            mode='test', return_label=False, sample_num=sp_num)
 
-        self.weight_path = os.path.join(self.save_dir, 'lenet')
-        fluid.dygraph.save_dygraph(self.lenet_dygraph.state_dict(), self.weight_path)
+        cls.train_loader = fluid.io.DataLoader(
+            cls.train_dataset, places=cls.device, batch_size=64)
+        cls.val_loader = fluid.io.DataLoader(
+            cls.val_dataset, places=cls.device, batch_size=64)
+        cls.test_loader = fluid.io.DataLoader(
+            cls.test_dataset, places=cls.device, batch_size=64)
 
-        fluid.disable_dygraph()
+        seed = 333
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
 
-    def tearDown(self):
-        shutil.rmtree(self.save_dir)
+        dy_lenet = LeNetDygraph()
+        cls.init_param = dy_lenet.state_dict()
+        dynamic_train(dy_lenet, cls.train_loader)
 
-    def evaluate(self, dynamic):
-        fluid.enable_dygraph(self.device) if dynamic else None
+        cls.acc1 = dynamic_evaluate(dy_lenet, cls.val_loader)
 
-        inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
-        labels = [Input([None, 1], 'int64', name='label')]
+        cls.inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
+        cls.labels = [Input([None, 1], 'int64', name='label')]
 
-        val_dataloader = fluid.io.DataLoader(
-            self.val_dataset,
-            places=self.device,
-            batch_size=64,
-            return_list=True)
+        cls.save_dir = tempfile.mkdtemp()
+        cls.weight_path = os.path.join(cls.save_dir, 'lenet')
+        fluid.dygraph.save_dygraph(dy_lenet.state_dict(), cls.weight_path)
 
-        model = LeNet()
+        fluid.disable_dygraph()
 
-        model.load(self.weight_path)
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.save_dir)
 
-        model.prepare(metrics=Accuracy(), inputs=inputs, labels=labels)
+    def test_fit_dygraph(self):
+        self.fit(True)
 
-        result = model.evaluate(val_dataloader)
+    def test_fit_static(self):
+        self.fit(False)
 
-        np.testing.assert_allclose(result['acc'], self.acc1)
+    def test_evaluate_dygraph(self):
+        self.evaluate(True)
 
-        if fluid.in_dygraph_mode():
-            fluid.disable_dygraph()
+    def test_evaluate_static(self):
+        self.evaluate(False)
+
+    def test_predict_dygraph(self):
+        self.predict(True)
+
+    def test_predict_static(self):
+        self.predict(False)
 
     def predict(self, dynamic):
         fluid.enable_dygraph(self.device) if dynamic else None
@@ -186,26 +196,161 @@ class TestEvaluatePredict(unittest.TestCase):
 
         output = model.predict(test_dataloader, stack_outputs=True)
 
-        np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
+    def fit(self, dynamic):
+        fluid.enable_dygraph(self.device) if dynamic else None
+        seed = 333
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
 
-        acc = get_predict_accuracy(output[0], self.val_dataset.labels)
+        model = LeNet()
+        optim_new = fluid.optimizer.Adam(
+            learning_rate=0.001, parameter_list=model.parameters())
+        model.prepare(
+            optim_new,
+            loss_function=CrossEntropy(average=False),
+            metrics=Accuracy(),
+            inputs=self.inputs,
+            labels=self.labels)
+        model.fit(self.train_dataset, batch_size=64, shuffle=False)
+
+        result = model.evaluate(self.val_dataset, batch_size=64)
+        np.testing.assert_allclose(result['acc'], self.acc1)
+        fluid.disable_dygraph() if dynamic else None
 
-        np.testing.assert_allclose(acc, self.acc1)
+    def evaluate(self, dynamic):
+        fluid.enable_dygraph(self.device) if dynamic else None
+        model = LeNet()
+        model.prepare(
+            metrics=Accuracy(), inputs=self.inputs, labels=self.labels)
+        model.load(self.weight_path)
+        result = model.evaluate(self.val_dataset, batch_size=64)
+        np.testing.assert_allclose(result['acc'], self.acc1)
+        fluid.disable_dygraph() if dynamic else None
 
-        if fluid.in_dygraph_mode():
-            fluid.disable_dygraph()
+    def predict(self, dynamic):
+        fluid.enable_dygraph(self.device) if dynamic else None
+        model = LeNet()
+        model.prepare(inputs=self.inputs)
+        model.load(self.weight_path)
+        output = model.predict(
+            self.test_dataset, batch_size=64, stack_outputs=True)
+        np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
 
-    def test_evaluate_dygraph(self):
-        self.evaluate(True)
+        acc = compute_acc(output[0], self.val_dataset.labels)
+        np.testing.assert_allclose(acc, self.acc1)
+        fluid.disable_dygraph() if dynamic else None
+
+
+class MyModel(Model):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self._fc = Linear(20, 10, act='softmax')
+
+    def forward(self, x):
+        y = self._fc(x)
+        return y
+
+
+class TestModelFunction(unittest.TestCase):
+    def set_seed(self, seed=1024):
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
+
+    def test_train_batch(self, dynamic=True):
+        dim = 20
+        data = np.random.random(size=(4, dim)).astype(np.float32)
+        label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
+
+        def get_expect():
+            fluid.enable_dygraph(fluid.CPUPlace())
+            self.set_seed()
+            m = MyModel()
+            optim = fluid.optimizer.SGD(learning_rate=0.001,
+                                        parameter_list=m.parameters())
+            m.train()
+            output = m(to_variable(data))
+            l = to_variable(label)
+            loss = fluid.layers.cross_entropy(output, l)
+            avg_loss = fluid.layers.reduce_sum(loss)
+            avg_loss.backward()
+            optim.minimize(avg_loss)
+            m.clear_gradients()
+            fluid.disable_dygraph()
+            return avg_loss.numpy()
 
-    def test_evaluate_static(self):
-        self.evaluate(False)
+        ref = get_expect()
+        for dynamic in [True, False]:
+            device = set_device('cpu')
+            fluid.enable_dygraph(device) if dynamic else None
+            self.set_seed()
+            model = MyModel()
 
-    def test_predict_dygraph(self):
-        self.predict(True)
+            optim2 = fluid.optimizer.SGD(learning_rate=0.001,
+                                         parameter_list=model.parameters())
 
-    def test_predict_static(self):
-        self.predict(False)
+            inputs = [Input([None, dim], 'float32', name='x')]
+            labels = [Input([None, 1], 'int64', name='label')]
+            model.prepare(
+                optim2,
+                loss_function=CrossEntropy(average=False),
+                inputs=inputs,
+                labels=labels,
+                device=device)
+            loss, = model.train_batch([data], [label])
+
+            np.testing.assert_allclose(loss.flatten(), ref.flatten())
+            fluid.disable_dygraph() if dynamic else None
+
+    def test_test_batch(self, dynamic=True):
+        dim = 20
+        data = np.random.random(size=(4, dim)).astype(np.float32)
+
+        def get_expect():
+            fluid.enable_dygraph(fluid.CPUPlace())
+            self.set_seed()
+            m = MyModel()
+            m.eval()
+            output = m(to_variable(data))
+            fluid.disable_dygraph()
+            return output.numpy()
+
+        ref = get_expect()
+        for dynamic in [True, False]:
+            device = set_device('cpu')
+            fluid.enable_dygraph(device) if dynamic else None
+            self.set_seed()
+            model = MyModel()
+            inputs = [Input([None, dim], 'float32', name='x')]
+            model.prepare(inputs=inputs, device=device)
+            out, = model.test_batch([data])
+
+            np.testing.assert_allclose(out, ref)
+            fluid.disable_dygraph() if dynamic else None
+
+    def test_save_load(self):
+        path = tempfile.mkdtemp()
+        for dynamic in [True, False]:
+            device = set_device('cpu')
+            fluid.enable_dygraph(device) if dynamic else None
+            model = MyModel()
+            inputs = [Input([None, 20], 'float32', name='x')]
+            model.prepare(inputs=inputs)
+            model.save(path + '/test')
+            model.load(path + '/test')
+            shutil.rmtree(path)
+            fluid.disable_dygraph() if dynamic else None
+
+    def test_parameters(self):
+        for dynamic in [True, False]:
+            device = set_device('cpu')
+            fluid.enable_dygraph(device) if dynamic else None
+            model = MyModel()
+            inputs = [Input([None, 20], 'float32', name='x')]
+            model.prepare(inputs=inputs)
+            params = model.parameters()
+            self.assertTrue(params[0].shape[0] == 20)
+            self.assertTrue(params[0].shape[1] == 10)
+            fluid.disable_dygraph() if dynamic else None
 
 
 if __name__ == '__main__':