Merge branch 'master' of https://github.com/PaddlePaddle/hapi into cv-models

callbacks.py

@@ -242,6 +242,12 @@ 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 steps is not None, last step will update in on_epoch_end
+            if self.eval_steps and 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:

distributed.py

@@ -25,7 +25,6 @@ from paddle.fluid.layers import collective
 from paddle.fluid.dygraph.parallel import ParallelEnv, ParallelStrategy
 from paddle.fluid.io import BatchSampler
 
-
 _parallel_context_initialized = False
 
 
@@ -67,7 +66,8 @@ class DistributedBatchSampler(BatchSampler):
         self.nranks = ParallelEnv().nranks
         self.local_rank = ParallelEnv().local_rank
         self.epoch = 0
-        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.nranks))
+        self.num_samples = int(
+            math.ceil(len(self.dataset) * 1.0 / self.nranks))
         self.total_size = self.num_samples * self.nranks
 
     def __iter__(self):
@@ -78,9 +78,28 @@ class DistributedBatchSampler(BatchSampler):
         if self.shuffle:
             np.random.RandomState(self.epoch).shuffle(indices)
             self.epoch += 1
+
         # subsample
-        indices = indices[self.local_rank * self.num_samples: 
-                            (self.local_rank + 1) * self.num_samples]
+        def _get_indices_by_batch_size(indices):
+            subsampled_indices = []
+            last_batch_size = self.total_size % (self.batch_size * self.nranks)
+            assert last_batch_size % self.nranks == 0
+            last_local_batch_size = last_batch_size // self.nranks
+
+            for i in range(self.local_rank * self.batch_size,
+                           len(indices) - last_batch_size,
+                           self.batch_size * self.nranks):
+                subsampled_indices.extend(indices[i:i + self.batch_size])
+
+            indices = indices[len(indices) - last_batch_size:]
+            subsampled_indices.extend(indices[
+                self.local_rank * last_local_batch_size:(
+                    self.local_rank + 1) * last_local_batch_size])
+            return subsampled_indices
+
+        if self.nranks > 1:
+            indices = _get_indices_by_batch_size(indices)
+
         assert len(indices) == self.num_samples
         _sample_iter = iter(indices)
 
@@ -103,7 +122,8 @@ class DistributedBatchSampler(BatchSampler):
 
 
 def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
-    return collective._c_allgather(x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
+    return collective._c_allgather(
+        x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
 
 
 def wait_server_ready(endpoints):
@@ -114,8 +134,7 @@ def wait_server_ready(endpoints):
         for ep in endpoints:
             ip_port = ep.split(":")
             with contextlib.closing(
-                    socket.socket(socket.AF_INET,
-                                  socket.SOCK_STREAM)) as sock:
+                    socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
                 sock.settimeout(2)
                 result = sock.connect_ex((ip_port[0], int(ip_port[1])))
                 if result != 0:
@@ -127,8 +146,8 @@ def wait_server_ready(endpoints):
             break
 
 
-def init_communicator(program, rank, nranks, wait_port,
-                     current_endpoint, endpoints):
+def init_communicator(program, rank, nranks, wait_port, current_endpoint,
+                      endpoints):
     if nranks < 2:
         return
     other_endpoints = endpoints[:]
@@ -166,7 +185,7 @@ def prepare_distributed_context(place=None):
     if place is None:
         place = fluid.CUDAPlace(ParallelEnv().dev_id) if ParallelEnv().nranks > 1 \
             else fluid.CUDAPlace(0)
-    
+
     strategy = ParallelStrategy()
     strategy.nranks = ParallelEnv().nranks
     strategy.local_rank = ParallelEnv().local_rank
@@ -178,11 +197,14 @@ def prepare_distributed_context(place=None):
 
     global _parallel_context_initialized
 
-    if not _parallel_context_initialized and isinstance(place, fluid.CUDAPlace):
+    if not _parallel_context_initialized and isinstance(place,
+                                                        fluid.CUDAPlace):
+
         def _init_context():
             communicator_prog = fluid.Program()
-            init_communicator(communicator_prog, strategy.local_rank, strategy.nranks, 
-                            True, strategy.current_endpoint, strategy.trainer_endpoints)
+            init_communicator(communicator_prog, strategy.local_rank,
+                              strategy.nranks, True, strategy.current_endpoint,
+                              strategy.trainer_endpoints)
             exe = fluid.Executor(place)
             exe.run(communicator_prog)
 
@@ -197,4 +219,4 @@ def prepare_distributed_context(place=None):
         assert ("Only support CUDAPlace for now.")
 
     _parallel_context_initialized = True
-    return strategy
\ No newline at end of file
+    return strategy

model.py

@@ -20,6 +20,7 @@ import pickle
 import numpy as np
 import six
 import warnings
+import tqdm
 
 from collections import Iterable
 from paddle import fluid
@@ -587,10 +588,8 @@ class DynamicGraphAdapter(object):
                 samples = outputs[0].shape[0]
                 current_count = self._merge_count.get(self.mode + '_total', 0)
                 if current_count + samples >= total_size:
-                    outputs = [
-                        o[:total_size - metric.count[0]] for o in outputs
-                    ]
-                    labels = [l[:total_size - metric.count[0]] for l in labels]
+                    outputs = [o[:total_size - current_count] for o in outputs]
+                    labels = [l[:total_size - current_count] for l in labels]
                     self._merge_count[self.mode + '_total'] = 0
                     self._merge_count[self.mode +
                                       '_batch'] = total_size - current_count
@@ -612,8 +611,9 @@ class DynamicGraphAdapter(object):
         self.mode = 'test'
         inputs = [to_variable(x) for x in to_list(inputs)]
         outputs = self.model.forward(*inputs)
-        if self._nranks > 2:
+        if self._nranks > 1 and isinstance(self.model._place, fluid.CUDAPlace):
             outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
+
         return [to_numpy(o) for o in to_list(outputs)]
 
     def parameters(self, *args, **kwargs):
@@ -696,7 +696,6 @@ class Model(fluid.dygraph.Layer):
         self._loss_weights = None
         self._optimizer = None
         self._device = None
-        self._device_ids = None
         self._optimizer = None
         self._test_dataloader = None
 
@@ -794,8 +793,7 @@ class Model(fluid.dygraph.Layer):
                 metrics=None,
                 inputs=None,
                 labels=None,
-                device=None,
-                device_ids=None):
+                device=None):
         """
         FIXME: add comments
         Args:
@@ -818,17 +816,6 @@ class Model(fluid.dygraph.Layer):
             device (str|None): specify device type, 'CPU' or 'GPU'.
                 If None, automatically select device according to
                 installation package version.
-            device_ids (list[int]|None): specify device index. If None,
-                the available device will be obtained from the environment
-                variable when the model is executed: If the GPU is used, the
-                currently available device ID is obtained from the environment
-                variable FLAGS_selected_gpus or CUDA_VISIBLE_DEVICES when the
-                model is executed; CPU, when the model is executed,
-                the currently available CPU number is obtained from the
-                environment variable CPU_NUM. For example, export CPU_NUM=4,
-                if the environment variable is not set, the executor will add
-                the variable to the environment variable and set its value to 1.
-                The default is None.
         """
 
         if isinstance(device, fluid.CUDAPlace) or \
@@ -918,7 +905,7 @@ class Model(fluid.dygraph.Layer):
             eval_freq (int): The frequency, in number of epochs, an evalutation
                 is performed.
             log_freq (int): The frequency, in number of steps, the training logs
-                is printed.
+                are printed.
             save_dir(str|None): The directory to save checkpoint during training.
                 If None, will not save checkpoint.
             save_freq (int): The frequency, in number of epochs, to save checkpoint.
@@ -991,71 +978,243 @@ class Model(fluid.dygraph.Layer):
             verbose=verbose,
             metrics=self._metrics_name(), )
 
-        def _run_one_epoch(data_loader, callbacks, mode):
-            size = len(data_loader) if hasattr(data_loader,
-                                               '__len__') else None
-            logs = {
-                'steps': size,
-                'metrics_name': metrics_name,
-            }
-            for step, data in enumerate(data_loader):
-                if not fluid.in_dygraph_mode():
-                    data = data[0]
-                    batch_size = data[0].shape()[0]
-                else:
-                    batch_size = data[0].shape[0]
-
-                cbks.on_batch_begin(mode, step, logs)
-                if mode == 'train':
-                    outs = self.train(*data)
-                else:
-                    outs = self.eval(*data)
-
-                # losses
-                loss = outs[0] if self._metrics else outs
-                metrics = [[l[0] for l in loss]]
-
-                # metrics
-                for metric in self._metrics:
-                    res = metric.accumulate()
-                    metrics.extend(to_list(res))
-
-                assert len(metrics_name) == len(metrics)
-                for k, v in zip(metrics_name, metrics):
-                    logs[k] = v
-
-                logs['step'] = step
-                if mode == 'train' or self._adapter._merge_count.get(
-                        mode + '_batch', 0) <= 0:
-                    logs['batch_size'] = batch_size * ParallelEnv().nranks
-                else:
-                    logs['batch_size'] = self._adapter._merge_count[mode +
-                                                                    '_batch']
-
-                cbks.on_batch_end(mode, step, logs)
-            self._reset_metrics()
-            return logs
-
         cbks.on_begin('train')
         for epoch in range(epochs):
-            cbks.on_epoch_begin(epoch)
+
             # FIXME: adapt to DataLoader
             loader = train_loader
             if not isinstance(train_loader, Iterable):
                 loader = train_loader()
-            logs = _run_one_epoch(loader, cbks, 'train')
-            cbks.on_epoch_end(epoch, logs)
+            logs = self._run_one_epoch(
+                loader, cbks, 'train', metrics_name, epoch=epoch)
 
             if do_eval and epoch % eval_freq == 0:
-                cbks.on_begin('eval', logs)
                 # FIXME: adapt to DataLoader
                 loader = eval_loader
                 if not isinstance(eval_loader, Iterable):
                     loader = eval_loader()
-                logs = _run_one_epoch(loader, cbks, 'eval')
+
+                eval_steps = len(loader) if hasattr(loader,
+                                                    '__len__') else None
+                cbks.on_begin('eval', {
+                    'steps': eval_steps,
+                    'metrics_name': metrics_name
+                })
+
+                logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
+
                 cbks.on_end('eval', logs)
 
         cbks.on_end('train', logs)
+        self._test_dataloader = None
+
+    def evaluate(
+            self,
+            eval_data,
+            batch_size=1,
+            log_freq=10,
+            verbose=2,
+            num_workers=0,
+            callbacks=None, ):
+        """
+        FIXME: add more comments and usage
+        Args:
+            eval_data (Dataset|DataLoader): An iterable data loader is used for
+                evaluation. An instance of paddle.fluid.io.Dataset or 
+                paddle.fluid.io.Dataloader is recomended.
+            batch_size (int): Integer number. The batch size of train_data and eval_data. 
+                When train_data and eval_data are both the instance of Dataloader, this 
+                parameter will be ignored.
+            log_freq (int): The frequency, in number of steps, the eval logs
+                are printed.
+            verbose (int): The verbosity mode, should be 0, 1, or 2.
+                0 = silent, 1 = progress bar, 2 = one line per epoch.
+            num_workers (int): The number of subprocess to load data, 0 for no subprocess 
+                used and loading data in main process. When train_data and eval_data are
+                both the instance of Dataloader, this parameter will be ignored.
+            callbacks (Callback|None): A list of `Callback` instances to apply
+                during training. If None, `ProgBarLogger` and `ModelCheckpoint`
+                are automatically inserted.
+        """
+
+        if fluid.in_dygraph_mode():
+            feed_list = None
+        else:
+            feed_list = [x.forward() for x in self._inputs + self._labels]
+
+        if eval_data is not None and isinstance(eval_data, Dataset):
+            eval_sampler = DistributedBatchSampler(
+                eval_data, batch_size=batch_size)
+            eval_loader = DataLoader(
+                eval_data,
+                batch_sampler=eval_sampler,
+                places=self._place,
+                feed_list=feed_list,
+                num_workers=num_workers,
+                return_list=True)
+        else:
+            eval_loader = eval_data
+
+        self._test_dataloader = eval_loader
+        metrics_name = self._metrics_name()
+
+        cbks = config_callbacks(
+            callbacks,
+            model=self,
+            log_freq=log_freq,
+            verbose=verbose,
+            metrics=self._metrics_name(), )
+
+        loader = eval_loader
+        if not isinstance(eval_loader, Iterable):
+            loader = eval_loader()
+
+        eval_steps = len(loader) if hasattr(loader, '__len__') else None
+        cbks.on_begin('eval',
+                      {'steps': eval_steps,
+                       'metrics_name': metrics_name})
+
+        logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
+
+        cbks.on_end('eval', logs)
+
+        self._test_dataloader = None
+
+        eval_result = {}
+        for k in self._metrics_name():
+            eval_result[k] = logs[k]
+
+        return eval_result
+
+    def predict(self, test_data, batch_size=1, num_workers=0):
+        """
+        FIXME: add more comments and usage
+        Args:
+            test_data (Dataset|DataLoader): An iterable data loader is used for
+                predict. An instance of paddle.fluid.io.Dataset or paddle.fluid.io.Dataloader 
+                is recomended.
+            batch_size (int): Integer number. The batch size of train_data and eval_data. 
+                When train_data and eval_data are both the instance of Dataloader, this 
+                parameter will be ignored.
+            num_workers (int): the number of subprocess to load data, 0 for no subprocess 
+                used and loading data in main process. When train_data and eval_data are
+                both the instance of Dataloader, this parameter will be ignored.
+        """
+
+        if fluid.in_dygraph_mode():
+            feed_list = None
+        else:
+            feed_list = [x.forward() for x in self._inputs + self._labels]
+
+        if test_data is not None and isinstance(test_data, Dataset):
+            test_sampler = DistributedBatchSampler(
+                test_data, batch_size=batch_size)
+            test_loader = DataLoader(
+                test_data,
+                batch_sampler=test_sampler,
+                places=self._place,
+                feed_list=feed_list,
+                num_workers=num_workers,
+                return_list=True)
+        else:
+            test_loader = test_data
+
+        self._test_dataloader = test_loader
+
+        loader = test_loader
+        if not isinstance(test_loader, Iterable):
+            loader = test_loader()
+
+        outputs = None
+        for data in tqdm.tqdm(loader):
+            if not fluid.in_dygraph_mode():
+                data = data[0]
+
+            outs = self.test(*data)
+
+            if outputs is None:
+                outputs = outs
+            else:
+                outputs = [
+                    np.vstack([x, outs[i]]) for i, x in enumerate(outputs)
+                ]
+
+        self._test_dataloader = None
+        if test_loader is not None and self._adapter._nranks > 1 \
+                    and isinstance(test_loader, DataLoader):
+            outputs = [o[:len(test_loader.dataset)] for o in outputs]
+        return outputs
+
+    def set_eval_data(self, eval_data):
+        """
+        Args:
+            eval_data (Dataset|DataLoader|None): An iterable data loader is used for 
+                eval. An instance of paddle.fluid.io.Dataset or 
+                paddle.fluid.io.Dataloader is recomended. 
+        """
+        assert isinstance(
+            eval_data,
+            DataLoader), "eval_data must be a instance of Dataloader!"
+        self._test_dataloader = eval_data
+
+    def _run_one_epoch(self,
+                       data_loader,
+                       callbacks,
+                       mode,
+                       metrics_name,
+                       epoch=None):
+        size = len(data_loader) if hasattr(data_loader, '__len__') else None
+        logs = {
+            'steps': size,
+            'metrics_name': metrics_name,
+        }
+
+        if mode == 'train':
+            assert epoch is not None, 'when mode is train, epoch must be given'
+            callbacks.on_epoch_begin(epoch)
+
+        for step, data in enumerate(data_loader):
+            if not fluid.in_dygraph_mode():
+                data = data[0]
+                batch_size = data[0].shape()[0]
+            else:
+                batch_size = data[0].shape[0]
+
+            callbacks.on_batch_begin(mode, step, logs)
+            if mode == 'train':
+                outs = self.train(*data)
+            else:
+                outs = self.eval(*data)
+
+            # losses
+            loss = outs[0] if self._metrics else outs
+            metrics = [[l[0] for l in loss]]
+
+            # metrics
+            for metric in self._metrics:
+                res = metric.accumulate()
+                metrics.extend(to_list(res))
+
+            assert len(metrics_name) == len(metrics)
+            for k, v in zip(metrics_name, metrics):
+                logs[k] = v
+
+            logs['step'] = step
+            if mode == 'train' or self._adapter._merge_count.get(
+                    mode + '_batch', 0) <= 0:
+                logs['batch_size'] = batch_size * ParallelEnv().nranks
+            else:
+                logs['batch_size'] = self._adapter._merge_count[mode +
+                                                                '_batch']
+
+            callbacks.on_batch_end(mode, step, logs)
+        self._reset_metrics()
+
+        if mode == 'train':
+            assert epoch is not None, 'when mode is train, epoch must be given'
+            callbacks.on_epoch_end(epoch)
+
+        return logs
 
     def _reset_metrics(self):
         for metric in self._metrics:

tests/test_model.py

@@ -139,6 +139,26 @@ class MyCrossEntropy(Loss):
         return [loss1, loss2]
 
 
+class TestMnistDataset(MnistDataset):
+    def __init__(self):
+        super(TestMnistDataset, self).__init__(mode='test')
+
+    def __getitem__(self, idx):
+        return self.images[idx],
+
+    def __len__(self):
+        return len(self.images)
+
+
+def get_predict_accuracy(pred, gt):
+    pred = np.argmax(pred, -1)
+    gt = np.array(gt)
+
+    correct = pred[:, np.newaxis] == gt
+
+    return np.sum(correct) / correct.shape[0]
+
+
 class TestModel(unittest.TestCase):
     def fit(self, dynamic, is_mlp=False):
         device = set_device('gpu')
@@ -152,6 +172,7 @@ class TestModel(unittest.TestCase):
 
         train_dataset = MnistDataset(mode='train')
         val_dataset = MnistDataset(mode='test')
+        test_dataset = TestMnistDataset()
 
         model = MNIST() if not is_mlp else MLP()
         optim = fluid.optimizer.Momentum(
@@ -159,12 +180,23 @@ class TestModel(unittest.TestCase):
         loss = CrossEntropy() if not is_mlp else MyCrossEntropy()
         model.prepare(optim, loss, Accuracy(), inputs, labels, device=device)
         cbk = ProgBarLogger(50)
+
         model.fit(train_dataset,
                   val_dataset,
                   epochs=2,
                   batch_size=batch_size,
                   callbacks=cbk)
 
+        eval_result = model.evaluate(val_dataset, batch_size=batch_size)
+
+        output = model.predict(test_dataset, batch_size=batch_size)
+
+        np.testing.assert_equal(output[0].shape[0], len(test_dataset))
+
+        acc = get_predict_accuracy(output[0], val_dataset.labels)
+
+        np.testing.assert_allclose(acc, eval_result['acc'])
+
     def test_fit_static(self):
         self.fit(False)