add _legacy_train, compatible with 1.x train api

dygraph/paddlex/det.py

@@ -13,6 +13,10 @@
 # limitations under the License.
 
 import copy
+import os.path as osp
+import numpy as np
+import paddle
+from paddleslim import L1NormFilterPruner
 from . import cv
 from .cv.models.utils.visualize import visualize_detection, draw_pr_curve
 from paddlex.cv.transforms import det_transforms
@@ -20,6 +24,8 @@ from paddlex.cv.transforms.operators import _NormalizeBox, _PadBox, _BboxXYXY2XY
 from paddlex.cv.transforms.batch_operators import BatchCompose, BatchRandomResize, BatchRandomResizeByShort, \
     _BatchPadding, _Gt2YoloTarget
 import paddlex.utils.logging as logging
+from paddlex.utils.checkpoint import det_pretrain_weights_dict
+from paddlex.cv.models.utils.ema import ExponentialMovingAverage
 
 transforms = det_transforms
 
@@ -98,6 +104,50 @@ class FasterRCNN(cv.models.FasterRCNN):
             test_pre_nms_top_n=test_pre_nms_top_n,
             test_post_nms_top_n=test_post_nms_top_n)
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='IMAGENET',
+              optimizer=None,
+              learning_rate=0.0025,
+              warmup_steps=500,
+              warmup_start_lr=1.0 / 1200,
+              lr_decay_epochs=[8, 11],
+              lr_decay_gamma=0.1,
+              metric=None,
+              use_vdl=False,
+              early_stop=False,
+              early_stop_patience=5,
+              sensitivities_file=None,
+              pruned_flops=.2):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            metric=metric,
+            use_vdl=use_vdl,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops)
+
 
 class YOLOv3(cv.models.YOLOv3):
     def __init__(self,
@@ -137,6 +187,50 @@ class YOLOv3(cv.models.YOLOv3):
             label_smooth=label_smooth)
         self.train_random_shapes = train_random_shapes
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=8,
+              eval_dataset=None,
+              save_interval_epochs=20,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='IMAGENET',
+              optimizer=None,
+              learning_rate=1.0 / 8000,
+              warmup_steps=1000,
+              warmup_start_lr=0.0,
+              lr_decay_epochs=[213, 240],
+              lr_decay_gamma=0.1,
+              metric=None,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            metric=metric,
+            use_vdl=use_vdl,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops)
+
     def _compose_batch_transform(self, transforms, mode='train'):
         if mode == 'train':
             default_batch_transforms = [
@@ -240,6 +334,55 @@ class PPYOLO(cv.models.PPYOLO):
             nms_iou_threshold=nms_iou_threshold)
         self.train_random_shapes = train_random_shapes
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=8,
+              eval_dataset=None,
+              save_interval_epochs=20,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='IMAGENET',
+              optimizer=None,
+              learning_rate=1.0 / 8000,
+              warmup_steps=1000,
+              warmup_start_lr=0.0,
+              lr_decay_epochs=[213, 240],
+              lr_decay_gamma=0.1,
+              metric=None,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5,
+              resume_checkpoint=None,
+              use_ema=True,
+              ema_decay=0.9998):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            metric=metric,
+            use_vdl=use_vdl,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops,
+            use_ema=use_ema,
+            ema_decay=ema_decay)
+
     def _compose_batch_transform(self, transforms, mode='train'):
         if mode == 'train':
             default_batch_transforms = [
@@ -281,3 +424,125 @@ class PPYOLO(cv.models.PPYOLO):
             collate_batch=collate_batch)
 
         return batch_transforms
+
+
+def _legacy_train(model,
+                  num_epochs,
+                  train_dataset,
+                  train_batch_size,
+                  eval_dataset,
+                  save_interval_epochs,
+                  log_interval_steps,
+                  save_dir,
+                  pretrain_weights,
+                  optimizer,
+                  learning_rate,
+                  warmup_steps,
+                  warmup_start_lr,
+                  lr_decay_epochs,
+                  lr_decay_gamma,
+                  metric,
+                  use_vdl,
+                  early_stop,
+                  early_stop_patience,
+                  sensitivities_file,
+                  pruned_flops,
+                  use_ema=False,
+                  ema_decay=0.9998):
+    if train_dataset.__class__.__name__ == 'VOCDetection':
+        train_dataset.data_fields = {
+            'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'difficult'
+        }
+    elif train_dataset.__class__.__name__ == 'CocoDetection':
+        if model.__class__.__name__ == 'MaskRCNN':
+            train_dataset.data_fields = {
+                'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
+                'gt_poly', 'is_crowd'
+            }
+        else:
+            train_dataset.data_fields = {
+                'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
+                'is_crowd'
+            }
+
+    if metric is None:
+        if eval_dataset.__class__.__name__ == 'VOCDetection':
+            model.metric = 'voc'
+        elif eval_dataset.__class__.__name__ == 'CocoDetection':
+            model.metric = 'coco'
+    else:
+        assert metric.lower() in ['coco', 'voc'], \
+            "Evaluation metric {} is not supported, please choose form 'COCO' and 'VOC'"
+        model.metric = metric.lower()
+
+    model.labels = train_dataset.labels
+    model.num_max_boxes = train_dataset.num_max_boxes
+    train_dataset.batch_transforms = model._compose_batch_transform(
+        train_dataset.transforms, mode='train')
+
+    if sensitivities_file is not None:
+        dataset = eval_dataset or train_dataset
+        im_shape = dataset[0]['image'].shape[:2]
+        if getattr(model, 'with_fpn', False):
+            im_shape[0] = int(np.ceil(im_shape[0] / 32) * 32)
+            im_shape[1] = int(np.ceil(im_shape[1] / 32) * 32)
+        inputs = [{
+            "image": paddle.ones(
+                shape=[1, 3] + list(im_shape), dtype='float32'),
+            "im_shape": paddle.full(
+                [1, 2], 640, dtype='float32'),
+            "scale_factor": paddle.ones(
+                shape=[1, 2], dtype='float32')
+        }]
+        model.pruner = L1NormFilterPruner(
+            model.net, inputs=inputs, sen_file=sensitivities_file)
+        model.pruner.sensitive_prune(pruned_flops=pruned_flops)
+
+    # build optimizer if not defined
+    if optimizer is None:
+        num_steps_each_epoch = len(train_dataset) // train_batch_size
+        model.optimizer = model.default_optimizer(
+            parameters=model.net.parameters(),
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            num_steps_each_epoch=num_steps_each_epoch)
+    else:
+        model.optimizer = optimizer
+
+    # initiate weights
+    if pretrain_weights is not None and not osp.exists(pretrain_weights):
+        if pretrain_weights not in det_pretrain_weights_dict['_'.join(
+            [model.model_name, model.backbone_name])]:
+            logging.warning("Path of pretrain_weights('{}') does not exist!".
+                            format(pretrain_weights))
+            pretrain_weights = det_pretrain_weights_dict['_'.join(
+                [model.model_name, model.backbone_name])][0]
+            logging.warning("Pretrain_weights is forcibly set to '{}'. "
+                            "If you don't want to use pretrain weights, "
+                            "set pretrain_weights to be None.".format(
+                                pretrain_weights))
+    pretrained_dir = osp.join(save_dir, 'pretrain')
+    model.net_initialize(
+        pretrain_weights=pretrain_weights, save_dir=pretrained_dir)
+
+    if use_ema:
+        ema = ExponentialMovingAverage(
+            decay=ema_decay, model=model.net, use_thres_step=True)
+    else:
+        ema = None
+    # start train loop
+    model.train_loop(
+        num_epochs=num_epochs,
+        train_dataset=train_dataset,
+        train_batch_size=train_batch_size,
+        eval_dataset=eval_dataset,
+        save_interval_epochs=save_interval_epochs,
+        log_interval_steps=log_interval_steps,
+        save_dir=save_dir,
+        ema=ema,
+        early_stop=early_stop,
+        early_stop_patience=early_stop_patience,
+        use_vdl=use_vdl)

dygraph/paddlex/seg.py

@@ -12,10 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os.path as osp
+from paddleslim import L1NormFilterPruner
 from . import cv
 from .cv.models.utils.visualize import visualize_segmentation
 from paddlex.cv.transforms import seg_transforms
 import paddlex.utils.logging as logging
+from paddlex.utils.checkpoint import seg_pretrain_weights_dict
 
 transforms = seg_transforms
 
@@ -69,6 +72,42 @@ class UNet(cv.models.UNet):
             use_mixed_loss=use_mixed_loss,
             use_deconv=use_deconv)
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='COCO',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            lr_decay_power=lr_decay_power,
+            use_vdl=use_vdl,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience)
+
 
 class DeepLabv3p(cv.models.DeepLabV3P):
     def __init__(self,
@@ -140,6 +179,42 @@ class DeepLabv3p(cv.models.DeepLabV3P):
             use_mixed_loss=use_mixed_loss,
             output_stride=output_stride)
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='IMAGENET',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            lr_decay_power=lr_decay_power,
+            use_vdl=use_vdl,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience)
+
 
 class HRNet(cv.models.HRNet):
     def __init__(self,
@@ -184,6 +259,42 @@ class HRNet(cv.models.HRNet):
             width=width,
             use_mixed_loss=use_mixed_loss)
 
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='IMAGENET',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            lr_decay_power=lr_decay_power,
+            use_vdl=use_vdl,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience)
+
 
 class FastSCNN(cv.models.FastSCNN):
     def __init__(self,
@@ -229,3 +340,92 @@ class FastSCNN(cv.models.FastSCNN):
 
         super(FastSCNN, self).__init__(
             num_classes=num_classes, use_mixed_loss=use_mixed_loss)
+
+    def train(self,
+              num_epochs,
+              train_dataset,
+              train_batch_size=2,
+              eval_dataset=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='CITYSCAPES',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              pruned_flops=.2,
+              early_stop=False,
+              early_stop_patience=5):
+        _legacy_train(
+            self,
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=pretrain_weights,
+            optimizer=optimizer,
+            learning_rate=learning_rate,
+            lr_decay_power=lr_decay_power,
+            use_vdl=use_vdl,
+            sensitivities_file=sensitivities_file,
+            pruned_flops=pruned_flops,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience)
+
+
+def _legacy_train(model, num_epochs, train_dataset, train_batch_size,
+                  eval_dataset, save_interval_epochs, log_interval_steps,
+                  save_dir, pretrain_weights, optimizer, learning_rate,
+                  lr_decay_power, use_vdl, sensitivities_file, pruned_flops,
+                  early_stop, early_stop_patience):
+    model.labels = train_dataset.labels
+    if model.losses is None:
+        model.losses = model.default_loss()
+
+    if sensitivities_file is not None:
+        dataset = eval_dataset or train_dataset
+        inputs = [1] + list(dataset[0]['image'].shape)
+        model.pruner = L1NormFilterPruner(
+            model.net, inputs=inputs, sen_file=sensitivities_file)
+        model.pruner.sensitive_prune(pruned_flops=pruned_flops)
+
+    # build optimizer if not defined
+    if optimizer is None:
+        num_steps_each_epoch = train_dataset.num_samples // train_batch_size
+        model.optimizer = model.default_optimizer(
+            model.net.parameters(), learning_rate, num_epochs,
+            num_steps_each_epoch, lr_decay_power)
+    else:
+        model.optimizer = optimizer
+
+    # initiate weights
+    if pretrain_weights is not None and not osp.exists(pretrain_weights):
+        if pretrain_weights not in seg_pretrain_weights_dict[model.model_name]:
+            logging.warning("Path of pretrain_weights('{}') does not exist!".
+                            format(pretrain_weights))
+            logging.warning("Pretrain_weights is forcibly set to '{}'. "
+                            "If don't want to use pretrain weights, "
+                            "set pretrain_weights to be None.".format(
+                                seg_pretrain_weights_dict[model.model_name][
+                                    0]))
+            pretrain_weights = seg_pretrain_weights_dict[model.model_name][0]
+    pretrained_dir = osp.join(save_dir, 'pretrain')
+    model.net_initialize(
+        pretrain_weights=pretrain_weights, save_dir=pretrained_dir)
+
+    model.train_loop(
+        num_epochs=num_epochs,
+        train_dataset=train_dataset,
+        train_batch_size=train_batch_size,
+        eval_dataset=eval_dataset,
+        save_interval_epochs=save_interval_epochs,
+        log_interval_steps=log_interval_steps,
+        save_dir=save_dir,
+        early_stop=early_stop,
+        early_stop_patience=early_stop_patience,
+        use_vdl=use_vdl)