Merge pull request #2 from heavengate/add_metric

add metric for mnist.

.gitignore

+*.pyc
+*.json
+output*
+*checkpoint*

metrics.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import six
+import abc
+import numpy as np
+import paddle.fluid as fluid
+
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
+__all__ = ['Metric', 'Accuracy']
+
+
+@six.add_metaclass(abc.ABCMeta)
+class Metric(object):
+    """
+    Base class for metric, encapsulates metric logic and APIs
+
+    Usage:
+    m = SomeMetric()
+    for prediction, label in ...:
+        m.update(prediction, label)
+    m.accumulate()
+    """
+
+    @abc.abstractmethod
+    def reset(self):
+        """
+        Reset states and result
+        """
+        raise NotImplementedError("function 'reset' not implemented in {}.".format(self.__class__.__name__))
+
+    @abc.abstractmethod
+    def update(self, *args, **kwargs):
+        """
+        Update states for metric
+        """
+        raise NotImplementedError("function 'update' not implemented in {}.".format(self.__class__.__name__))
+
+    @abc.abstractmethod
+    def accumulate(self):
+        """
+        Accumulates statistics, computes and returns the metric value
+        """
+        raise NotImplementedError("function 'accumulate' not implemented in {}.".format(self.__class__.__name__))
+
+    def add_metric_op(self, pred, label):
+        """
+        Add process op for metric in program
+        """
+        return pred, label
+
+
+class Accuracy(Metric):
+    """
+    Encapsulates accuracy metric logic
+    """
+
+    def __init__(self, topk=(1, ), *args, **kwargs):
+       super(Accuracy, self).__init__(*args, **kwargs) 
+       self.topk = topk
+       self.maxk = max(topk)
+       self.reset()
+
+    def add_metric_op(self, pred, label, *args, **kwargs):
+        pred = fluid.layers.argsort(pred[0], descending=True)[1][:, :self.maxk]
+        correct = pred == label[0]
+        return correct
+
+    def update(self, correct, *args, **kwargs):
+        accs = []
+        for i, k in enumerate(self.topk):
+            num_corrects = correct[:, :k].sum()
+            num_samples = len(correct)
+            accs.append(float(num_corrects) / num_samples)
+            self.total[i] += num_corrects
+            self.count[i] += num_samples
+        return accs
+
+    def reset(self):
+        self.total = [0.] * len(self.topk)
+        self.count = [0] * len(self.topk)
+
+    def accumulate(self):
+        res = []
+        for t, c in zip(self.total, self.count):
+            res.append(float(t) / c)
+        return res
+

mnist.py

@@ -27,6 +27,7 @@ from paddle.fluid.optimizer import Momentum
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
 
 from model import Model, CrossEntropy, Input
+from metrics import Accuracy
 
 
 class SimpleImgConvPool(fluid.dygraph.Layer):
@@ -145,36 +146,38 @@ def main():
             parameter_list=model.parameters())
         inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
         labels = [Input([None, 1], 'int64', name='label')]
-        model.prepare(optim, CrossEntropy(), inputs, labels)
+        model.prepare(optim, CrossEntropy(), Accuracy(topk=(1, 2)), inputs, labels)
         if FLAGS.resume is not None:
             model.load(FLAGS.resume)
 
         for e in range(FLAGS.epoch):
             train_loss = 0.0
-            train_acc = 0.0
             val_loss = 0.0
-            val_acc = 0.0
             print("======== train epoch {} ========".format(e))
             for idx, batch in enumerate(train_loader()):
-                outputs, losses = model.train(batch[0], batch[1])
+                losses, metrics = model.train(batch[0], batch[1])
 
-                acc = accuracy(outputs[0], batch[1])[0]
                 train_loss += np.sum(losses)
-                train_acc += acc
                 if idx % 10 == 0:
-                    print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format(
-                        idx, train_loss / (idx + 1), train_acc / (idx + 1)))
+                    print("{:04d}: loss {:0.3f} top1: {:0.3f}% top2: {:0.3f}%".format(
+                        idx, train_loss / (idx + 1), metrics[0][0], metrics[0][1]))
+            for metric in model._metrics:
+                res = metric.accumulate()
+                print("train epoch {:03d}: top1: {:0.3f}%, top2: {:0.3f}".format(e, res[0], res[1]))
+                metric.reset()
 
             print("======== eval epoch {} ========".format(e))
             for idx, batch in enumerate(val_loader()):
-                outputs, losses = model.eval(batch[0], batch[1])
+                losses, metrics = model.eval(batch[0], batch[1])
 
-                acc = accuracy(outputs[0], batch[1])[0]
                 val_loss += np.sum(losses)
-                val_acc += acc
                 if idx % 10 == 0:
-                    print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format(
-                        idx, val_loss / (idx + 1), val_acc / (idx + 1)))
+                    print("{:04d}: loss {:0.3f} top1: {:0.3f}% top2: {:0.3f}%".format(
+                        idx, val_loss / (idx + 1), metrics[0][0], metrics[0][1]))
+            for metric in model._metrics:
+                res = metric.accumulate()
+                print("eval epoch {:03d}: top1: {:0.3f}%, top2: {:0.3f}".format(e, res[0], res[1]))
+                metric.reset()
             model.save('mnist_checkpoints/{:02d}'.format(e))
 
 

model.py

@@ -26,6 +26,7 @@ from paddle.fluid.framework import in_dygraph_mode, Variable
 from paddle.fluid.executor import global_scope
 from paddle.fluid.io import is_belong_to_optimizer
 from paddle.fluid.dygraph.base import to_variable
+from metrics import Metric
 
 __all__ = ['Model', 'Loss', 'CrossEntropy', 'Input']
 
@@ -46,6 +47,26 @@ def to_numpy(var):
     return np.array(t)
 
 
+def flatten_list(l):
+    assert isinstance(l, list), "not a list"
+    outl = []
+    splits = []
+    for sl in l:
+        assert isinstance(sl, list), "sub content not a list"
+        splits.append(len(sl))
+        outl += sl
+    return outl, splits
+
+
+def restore_flatten_list(l, splits):
+    outl = []
+    for split in splits:
+        assert len(l) >= split, "list length invalid"
+        sl, l = l[:split], l[split:]
+        outl.append(sl)
+    return outl
+
+
 def extract_args(func):
     if hasattr(inspect, 'getfullargspec'):
         return inspect.getfullargspec(func)[0]
@@ -309,15 +330,26 @@ class StaticGraphAdapter(object):
                 feed[v.name] = labels[idx]
 
         endpoints = self._endpoints[self.mode]
-        fetch_list = endpoints['output'] + endpoints['loss']
-        num_output = len(endpoints['output'])
-        out = self._executor.run(compiled_prog,
-                                 feed=feed,
-                                 fetch_list=fetch_list)
         if self.mode == 'test':
-            return out[:num_output]
+            fetch_list = endpoints['output']
         else:
-            return out[:num_output], out[num_output:]
+            metric_list, metric_splits = flatten_list(endpoints['metric'])
+            fetch_list = endpoints['loss'] + metric_list
+            num_loss = len(endpoints['loss'])
+        rets = self._executor.run(
+            compiled_prog, feed=feed,
+            fetch_list=fetch_list,
+            return_numpy=False)
+        # LoDTensor cannot be fetch as numpy directly
+        rets = [np.array(v) for v in rets]
+        if self.mode == 'test':
+            return rets[:]
+        losses = rets[:num_loss]
+        metric_states = restore_flatten_list(rets[num_loss:], metric_splits)
+        metrics = []
+        for metric, state in zip(self.model._metrics, metric_states):
+            metrics.append(metric.update(*state))
+        return (losses, metrics) if len(metrics) > 0 else losses
 
     def prepare(self):
         modes = ['train', 'eval', 'test']
@@ -345,6 +377,7 @@ class StaticGraphAdapter(object):
             lr_var = self.model._optimizer._learning_rate_map[self._orig_prog]
             self.model._optimizer._learning_rate_map[prog] = lr_var
         losses = []
+        metrics = []
         with fluid.program_guard(prog, self._startup_prog):
             if isinstance(self.model._inputs, dict):
                 ins = [self.model._inputs[n] \
@@ -358,6 +391,8 @@ class StaticGraphAdapter(object):
             if mode != 'test':
                 if self.model._loss_function:
                     losses = self.model._loss_function(outputs, labels)
+                    for metric in self.model._metrics:
+                        metrics.append(to_list(metric.add_metric_op(outputs, labels)))
                 if mode == 'train' and self.model._optimizer:
                     self._loss_endpoint = fluid.layers.sum(losses)
                     self.model._optimizer.minimize(self._loss_endpoint)
@@ -367,7 +402,7 @@ class StaticGraphAdapter(object):
         self._input_vars[mode] = inputs
         self._label_vars[mode] = labels
         self._progs[mode] = prog
-        self._endpoints[mode] = {"output": outputs, "loss": losses}
+        self._endpoints[mode] = {"output": outputs, "loss": losses, "metric": metrics}
 
     def _compile_and_initialize(self, prog, mode):
         compiled_prog = self._compiled_progs.get(mode, None)
@@ -429,33 +464,44 @@ class DynamicGraphAdapter(object):
         self.mode = 'train'
         inputs = to_list(inputs)
         if labels is not None:
-            labels = to_list(labels)
-        outputs = self.model.forward(* [to_variable(x) for x in inputs])
+            labels = [to_variable(l) for l in to_list(labels)]
+        outputs = to_list(self.model.forward(*[to_variable(x) for x in inputs]))
         losses = self.model._loss_function(outputs, labels)
         final_loss = fluid.layers.sum(losses)
         final_loss.backward()
         self.model._optimizer.minimize(final_loss)
         self.model.clear_gradients()
-        return [to_numpy(o) for o in to_list(outputs)], \
-            [to_numpy(l) for l in losses]
+        metrics = []
+        for metric in self.model._metrics:
+            metric_outs = metric.add_metric_op(outputs, to_list(labels))
+            m = metric.update(*[to_numpy(m) for m in to_list(metric_outs)])
+            metrics.append(m)
+        return ([to_numpy(l) for l in losses], metrics) \
+                if len(metrics) > 0 else [to_numpy(l) for l in losses]
 
     def eval(self, inputs, labels=None):
         super(Model, self.model).eval()
         self.mode = 'eval'
         inputs = to_list(inputs)
         if labels is not None:
-            labels = to_list(labels)
-        outputs = self.model.forward(* [to_variable(x) for x in inputs])
+            labels = [to_variable(l) for l in to_list(labels)]
+        outputs = to_list(self.model.forward(*[to_variable(x) for x in inputs]))
 
         if self.model._loss_function:
             losses = self.model._loss_function(outputs, labels)
         else:
             losses = []
 
+        metrics = []
+        for metric in self.model._metrics:
+            metric_outs = metric.add_metric_op(outputs, labels)
+            m = metric.update(*[to_numpy(m) for m in to_list(metric_outs)])
+            metrics.append(m)
+
         # To be consistent with static graph
         # return empty loss if loss_function is None
-        return [to_numpy(o) for o in to_list(outputs)], \
-            [to_numpy(l) for l in losses]
+        return ([to_numpy(l) for l in losses], metrics) \
+                if len(metrics) > 0 else [to_numpy(l) for l in losses]
 
     def test(self, inputs):
         super(Model, self.model).eval()
@@ -567,6 +613,7 @@ class Model(fluid.dygraph.Layer):
     def prepare(self,
                 optimizer=None,
                 loss_function=None,
+                metrics=None,
                 inputs=None,
                 labels=None,
                 device=None,
@@ -580,6 +627,8 @@ class Model(fluid.dygraph.Layer):
             loss_function (Loss|None): loss function must be set in training
                 and should be a Loss instance. It can be None when there is
                 no loss.
+            metrics (Metric|list of Metric|None): if metrics is set, all
+                metric will be calculate and output in train/eval mode.
             inputs (Input|list|dict|None): inputs, entry points of network,
                 could be a Input layer, or lits of Input layers,
                 or dict (name: Input), or None. For static graph,
@@ -615,6 +664,13 @@ class Model(fluid.dygraph.Layer):
                     "'inputs' must be list or dict in static graph mode")
             if loss_function and not isinstance(labels, (list, Input)):
                 raise TypeError("'labels' must be list in static graph mode")
+
+        metrics = metrics or []
+        for metric in to_list(metrics):
+            assert isinstance(metric, Metric), \
+                "{} is not sub class of Metric".format(metric.__class__.__name__)
+        self._metrics = to_list(metrics)
+
         self._inputs = inputs
         self._labels = labels
         self._device = device

yolov3.py

@@ -33,9 +33,14 @@ from paddle.fluid.dygraph.nn import Conv2D
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
-from model import Model, Loss, shape_hints
+from model import Model, Loss, Input
 from resnet import ResNet, ConvBNLayer
 
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
 
 # XXX transfer learning
 class ResNetBackBone(ResNet):
@@ -102,13 +107,14 @@ class YoloDetectionBlock(fluid.dygraph.Layer):
 
 
 class YOLOv3(Model):
-    def __init__(self):
+    def __init__(self, num_classes=80):
         super(YOLOv3, self).__init__()
-        self.num_classes = 80
+        self.num_classes = num_classes
         self.anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45,
                         59, 119, 116, 90, 156, 198, 373, 326]
         self.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
         self.valid_thresh = 0.005
+        self.nms_thresh = 0.45
         self.nms_topk = 400
         self.nms_posk = 100
         self.draw_thresh = 0.5
@@ -146,8 +152,7 @@ class YOLOv3(Model):
                                 act='leaky_relu'))
                 self.route_blocks.append(route)
 
-    @shape_hints(inputs=[None, 3, None, None], im_shape=[None, 2])
-    def forward(self, inputs, im_shape):
+    def forward(self, inputs, img_info):
         outputs = []
         boxes = []
         scores = []
@@ -161,48 +166,50 @@ class YOLOv3(Model):
                 feat = fluid.layers.concat(input=[route, feat], axis=1)
             route, tip = self.yolo_blocks[idx](feat)
             block_out = self.block_outputs[idx](tip)
+            outputs.append(block_out)
 
             if idx < 2:
                 route = self.route_blocks[idx](route)
                 route = fluid.layers.resize_nearest(route, scale=2)
 
-            anchor_mask = self.anchor_masks[idx]
-            mask_anchors = []
-            for m in anchor_mask:
-                mask_anchors.append(self.anchors[2 * m])
-                mask_anchors.append(self.anchors[2 * m + 1])
-            b, s = fluid.layers.yolo_box(
-                x=block_out,
-                img_size=im_shape,
-                anchors=mask_anchors,
-                class_num=self.num_classes,
-                conf_thresh=self.valid_thresh,
-                downsample_ratio=downsample)
-
-            outputs.append(block_out)
-            boxes.append(b)
-            scores.append(fluid.layers.transpose(s, perm=[0, 2, 1]))
+            if self.mode == 'test':
+                anchor_mask = self.anchor_masks[idx]
+                mask_anchors = []
+                for m in anchor_mask:
+                    mask_anchors.append(self.anchors[2 * m])
+                    mask_anchors.append(self.anchors[2 * m + 1])
+                img_shape = fluid.layers.slice(img_info, axes=[1], starts=[1], ends=[3])
+                img_id = fluid.layers.slice(img_info, axes=[1], starts=[0], ends=[1])
+                b, s = fluid.layers.yolo_box(
+                    x=block_out,
+                    img_size=img_shape,
+                    anchors=mask_anchors,
+                    class_num=self.num_classes,
+                    conf_thresh=self.valid_thresh,
+                    downsample_ratio=downsample)
+
+                boxes.append(b)
+                scores.append(fluid.layers.transpose(s, perm=[0, 2, 1]))
 
             downsample //= 2
 
         if self.mode != 'test':
             return outputs
 
-        return fluid.layers.multiclass_nms(
+        return [img_id, fluid.layers.multiclass_nms(
             bboxes=fluid.layers.concat(boxes, axis=1),
             scores=fluid.layers.concat(scores, axis=2),
             score_threshold=self.valid_thresh,
             nms_top_k=self.nms_topk,
             keep_top_k=self.nms_posk,
             nms_threshold=self.nms_thresh,
-            background_label=-1)
+            background_label=-1)]
 
 
 class YoloLoss(Loss):
-    def __init__(self, num_classes=80, num_max_boxes=50):
+    def __init__(self, num_classes=80):
         super(YoloLoss, self).__init__()
         self.num_classes = num_classes
-        self.num_max_boxes = num_max_boxes
         self.ignore_thresh = 0.7
         self.anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45,
                         59, 119, 116, 90, 156, 198, 373, 326]
@@ -226,20 +233,11 @@ class YoloLoss(Loss):
                 class_num=self.num_classes,
                 ignore_thresh=self.ignore_thresh,
                 use_label_smooth=True)
+            loss = fluid.layers.reduce_mean(loss)
             losses.append(loss)
             downsample //= 2
         return losses
 
-    def infer_shape(self, _):
-        return [
-            [None, self.num_max_boxes, 4],
-            [None, self.num_max_boxes],
-            [None, self.num_max_boxes]
-        ]
-
-    def infer_dtype(self, _):
-        return ['float32', 'int32', 'float32']
-
 
 def make_optimizer(parameter_list=None):
     base_lr = FLAGS.lr
@@ -293,7 +291,7 @@ def random_crop(inputs):
     thresholds = [.0, .1, .3, .5, .7, .9]
     scaling = [.3, 1.]
 
-    img, gt_box, gt_label = inputs
+    img, img_ids, gt_box, gt_label = inputs
     h, w = img.shape[:2]
 
     if len(gt_box) == 0:
@@ -327,7 +325,7 @@ def random_crop(inputs):
             img = img[y1:y2, x1:x2, :]
             gt_box = np.take(cropped_box, valid_ids, axis=0)
             gt_label = np.take(gt_label, valid_ids, axis=0)
-            return img, gt_box, gt_label
+            return img, img_ids, gt_box, gt_label
 
         return inputs
 
@@ -335,9 +333,9 @@ def random_crop(inputs):
 # XXX mix up, color distort and random expand are skipped for simplicity
 def sample_transform(inputs, mode='train', num_max_boxes=50):
     if mode == 'train':
-        img, gt_box, gt_label = random_crop(inputs)
+        img, img_id, gt_box, gt_label = random_crop(inputs)
     else:
-        img, gt_box, gt_label = inputs
+        img, img_id, gt_box, gt_label = inputs
 
     h, w = img.shape[:2]
     # random flip
@@ -350,7 +348,7 @@ def sample_transform(inputs, mode='train', num_max_boxes=50):
 
     if len(gt_label) == 0:
         gt_box = np.zeros([num_max_boxes, 4], dtype=np.float32)
-        gt_label = np.zeros([num_max_boxes, 1], dtype=np.int32)
+        gt_label = np.zeros([num_max_boxes], dtype=np.int32)
         return img, gt_box, gt_label
 
     gt_box = gt_box[:num_max_boxes, :]
@@ -362,9 +360,9 @@ def sample_transform(inputs, mode='train', num_max_boxes=50):
 
     pad = num_max_boxes - gt_label.size
     gt_box = np.pad(gt_box, ((0, pad), (0, 0)), mode='constant')
-    gt_label = np.pad(gt_label, [(0, pad)], mode='constant')
+    gt_label = np.pad(gt_label, ((0, pad)), mode='constant')
 
-    return img, gt_box, gt_label
+    return img, img_id, gt_box, gt_label
 
 
 def batch_transform(batch, mode='train'):
@@ -376,7 +374,8 @@ def batch_transform(batch, mode='train'):
         d = 608
         interp = cv2.INTER_CUBIC
     # transpose batch
-    imgs, gt_boxes, gt_labels = list(zip(*batch))
+    imgs, img_ids, gt_boxes, gt_labels = list(zip(*batch))
+    img_shapes = np.array([[im.shape[0], im.shape[1]] for im in imgs]).astype('int32')
     imgs = np.array([cv2.resize(
         img, (d, d), interpolation=interp) for img in imgs])
 
@@ -389,12 +388,13 @@ def batch_transform(batch, mode='train'):
     imgs *= invstd
     imgs = imgs.transpose((0, 3, 1, 2))
 
-    im_shapes = np.full([len(imgs), 2], d, dtype=np.int32)
+    img_ids = np.array(img_ids)
+    img_info = np.concatenate([img_ids, img_shapes], axis=1)
     gt_boxes = np.array(gt_boxes)
     gt_labels = np.array(gt_labels)
     # XXX since mix up is not used, scores are all ones
     gt_scores = np.ones_like(gt_labels, dtype=np.float32)
-    return [imgs, im_shapes], [gt_boxes, gt_labels, gt_scores]
+    return [imgs, img_info], [gt_boxes, gt_labels, gt_scores]
 
 
 def coco2017(root_dir, mode='train'):
@@ -434,17 +434,18 @@ def coco2017(root_dir, mode='train'):
         gt_box = np.array(gt_box, dtype=np.float32)
         gt_label = np.array([class_map[cls] for cls in gt_label],
                             dtype=np.int32)[:, np.newaxis]
+        im_id = np.array([img['id']], dtype=np.int32)
 
         if gt_label.size == 0 and not mode == 'train':
             continue
-        samples.append((file_path, gt_box.copy(), gt_label.copy()))
+        samples.append((file_path, im_id.copy(), gt_box.copy(), gt_label.copy()))
 
     def iterator():
         if mode == 'train':
-            random.shuffle(samples)
-        for file_path, gt_box, gt_label in samples:
+            np.random.shuffle(samples)
+        for file_path, im_id, gt_box, gt_label in samples:
             img = cv2.imread(file_path)
-            yield img, gt_box, gt_label
+            yield img, im_id, gt_box, gt_label
 
     return iterator
 
@@ -457,14 +458,13 @@ def run(model, loader, mode='train'):
     start = time.time()
 
     for idx, batch in enumerate(loader()):
-        outputs, losses = getattr(model, mode)(
-            batch[0], batch[1], device='gpu', device_ids=device_ids)
+        losses = getattr(model, mode)(batch[0], batch[1])
 
         total_loss += np.sum(losses)
         if idx > 1:  # skip first two steps
             total_time += time.time() - start
         if idx % 10 == 0:
-            print("{:04d}: loss {:0.3f} time: {:0.3f}".format(
+            logger.info("{:04d}: loss {:0.3f} time: {:0.3f}".format(
                 idx, total_loss / (idx + 1), total_time / max(1, (idx - 1))))
         start = time.time()
 
@@ -501,26 +501,46 @@ def main():
                 coco2017(FLAGS.data, 'val'),
                 process_num=8,
                 buffer_size=4 * batch_size),
-            batch_size=batch_size),
+            batch_size=1),
         process_num=2, buffer_size=4)
 
     if not os.path.exists('yolo_checkpoints'):
         os.mkdir('yolo_checkpoints')
 
     with guard:
-        model = YOLOv3()
+        NUM_CLASSES = 7
+        NUM_MAX_BOXES = 50
+        model = YOLOv3(num_classes=NUM_CLASSES)
         # XXX transfer learning
+        if FLAGS.pretrain_weights is not None:
+            model.backbone.load(FLAGS.pretrain_weights)
         if FLAGS.weights is not None:
-            model.backbone.load(FLAGS.weights)
+            model.load(FLAGS.weights)
         optim = make_optimizer(parameter_list=model.parameters())
-        model.prepare(optim, YoloLoss())
+        anno_path = os.path.join(FLAGS.data, 'annotations', 'instances_val2017.json')
+        inputs = [Input([None, 3, None, None], 'float32', name='image'),
+                  Input([None, 3], 'int32', name='img_info')]
+        labels = [Input([None, NUM_MAX_BOXES, 4], 'float32', name='gt_bbox'),
+                  Input([None, NUM_MAX_BOXES], 'int32', name='gt_label'),
+                  Input([None, NUM_MAX_BOXES], 'float32', name='gt_score')]
+        model.prepare(optim,
+                      YoloLoss(num_classes=NUM_CLASSES),
+                      # For YOLOv3, output variable in train/eval is different,
+                      # which is not supported by metric, add by callback later?
+                      # metrics=COCOMetric(anno_path, with_background=False)
+                      inputs=inputs,
+                      labels = labels)
 
         for e in range(epoch):
-            print("======== train epoch {} ========".format(e))
+            logger.info("======== train epoch {} ========".format(e))
             run(model, train_loader)
             model.save('yolo_checkpoints/{:02d}'.format(e))
-            print("======== eval epoch {} ========".format(e))
+            logger.info("======== eval epoch {} ========".format(e))
             run(model, val_loader, mode='eval')
+            # should be called in fit()
+            for metric in model._metrics:
+                metric.accumulate()
+                metric.reset()
 
 
 if __name__ == '__main__':
@@ -538,8 +558,11 @@ if __name__ == '__main__':
     parser.add_argument(
         "-n", "--num_devices", default=8, type=int, help="number of devices")
     parser.add_argument(
-        "-w", "--weights", default=None, type=str,
+        "-p", "--pretrain_weights", default=None, type=str,
         help="path to pretrained weights")
+    parser.add_argument(
+        "-w", "--weights", default=None, type=str,
+        help="path to model weights")
     FLAGS = parser.parse_args()
     assert FLAGS.data, "error: must provide data path"
     main()

yolov3/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+

yolov3/coco_metric.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import json
+from pycocotools.cocoeval import COCOeval
+from pycocotools.coco import COCO
+
+from metrics import Metric
+
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
+__all__ = ['COCOMetric']
+
+
+OUTFILE = './bbox.json'
+
+
+# considered to change to a callback later
+class COCOMetric(Metric):
+    """
+    Metrci for MS-COCO dataset, only support update with batch
+    size as 1.
+
+    Args:
+        anno_path(str): path to COCO annotation json file
+        with_background(bool): whether load category id with
+                               background as 0, default True
+    """
+
+    def __init__(self, anno_path, with_background=True, **kwargs):
+        super(COCOMetric, self).__init__(**kwargs)
+        self.anno_path = anno_path
+        self.with_background = with_background
+        self.bbox_results = []
+
+        self.coco_gt = COCO(anno_path)
+        cat_ids = self.coco_gt.getCatIds()
+	self.clsid2catid = dict(
+	    {i + int(with_background): catid
+	     for i, catid in enumerate(cat_ids)})
+
+    def update(self, preds, *args, **kwargs):
+        im_ids, bboxes = preds
+        assert im_ids.shape[0] == 1, \
+            "COCOMetric can only update with batch size = 1"
+        if bboxes.shape[1] != 6:
+            # no bbox detected in this batch
+            return
+
+        im_id = int(im_ids)
+        for i in range(bboxes.shape[0]):
+            dt = bboxes[i, :]
+            clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
+            catid = (self.clsid2catid[int(clsid)])
+
+            w = xmax - xmin + 1
+            h = ymax - ymin + 1
+            bbox = [xmin, ymin, w, h]
+            coco_res = {
+                'image_id': im_id,
+                'category_id': catid,
+                'bbox': bbox,
+                'score': score
+            }
+            self.bbox_results.append(coco_res)
+
+    def reset(self):
+        self.bbox_results = []
+
+    def accumulate(self):
+	if len(self.bbox_results) == 0:
+	    logger.warning("The number of valid bbox detected is zero.\n \
+		Please use reasonable model and check input data.\n \
+		stop COCOMetric accumulate!")
+	    return [0.0]
+	with open(OUTFILE, 'w') as f:
+	    json.dump(self.bbox_results, f)
+
+	map_stats = self.cocoapi_eval(OUTFILE, 'bbox', coco_gt=self.coco_gt)
+	# flush coco evaluation result
+	sys.stdout.flush()
+        self.result = map_stats[0]
+	return self.result
+
+    def cocoapi_eval(self, jsonfile, style, coco_gt=None, anno_file=None):
+	assert coco_gt != None or anno_file != None
+
+	if coco_gt == None:
+	    coco_gt = COCO(anno_file)
+	logger.info("Start evaluate...")
+	coco_dt = coco_gt.loadRes(jsonfile)
+	coco_eval = COCOeval(coco_gt, coco_dt, style)
+	coco_eval.evaluate()
+	coco_eval.accumulate()
+	coco_eval.summarize()
+	return coco_eval.stats
+