[MaskRCNN] split train.py & predict.py (#1163)

examples/FasterRCNN/README.md

@@ -63,12 +63,12 @@ Some reasonable configurations are listed in the table below.
 
 To predict on an image (needs DISPLAY to show the outputs):
 ```
-./train.py --predict input1.jpg input2.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING
+./predict.py --predict input1.jpg input2.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING
 ```
 
 To evaluate the performance of a model on COCO:
 ```
-./train.py --evaluate output.json --load /path/to/Trained-Model-Checkpoint \
+./predict.py --evaluate output.json --load /path/to/Trained-Model-Checkpoint \
     --config SAME-AS-TRAINING
 ```
 
@@ -99,7 +99,7 @@ be approximately reproduced.
  | R101-FPN                    | 40.4;36.6 [:arrow_down:][R101FPN2x]                            | 40.9;36.4                                          | 37h               | <details><summary>standard</summary>`MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` </details>                                                                                                                                                                                                                                                                                                    |
  | R101-FPN                    | 46.5;40.1 [:arrow_down:][R101FPN3xCasAug] <sup>[3](#ft3)</sup> |                                                    | 73h               | <details><summary>3x+Cascade+TrainAug</summary>`MODE_FPN=True FPN.CASCADE=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`<br/>`TRAIN.LR_SCHEDULE=[420000,500000,540000]` </details>                                                                                                                                       |
  | R101-FPN<br/>(From Scratch) | 47.5;41.2 [:arrow_down:][R101FPN9xGNCasAugScratch]             | 47.4;40.5<sup>[4](#ft4)</sup>                      | 45h (on 48 V100s) | <details><summary>9x+GN+Cascade+TrainAug</summary>`MODE_FPN=True FPN.CASCADE=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`<br/>`TRAIN.LR_SCHEDULE=[1500000,1580000,1620000]`<br/>`BACKBONE.FREEZE_AT=0`</details> |
- 
+
  [R50C42x]: http://models.tensorpack.com/FasterRCNN/COCO-R50C4-MaskRCNN-Standard.npz
  [R50FPN2x]: http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-Standard.npz
  [R50FPN2xGN]: http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-StandardGN.npz

examples/FasterRCNN/backbone.py

@@ -2,8 +2,8 @@
 # File: backbone.py
 
 import numpy as np
-from contextlib import ExitStack, contextmanager
 import tensorflow as tf
+from contextlib import ExitStack, contextmanager
 
 from tensorpack.models import BatchNorm, Conv2D, MaxPooling, layer_register
 from tensorpack.tfutils import argscope

examples/FasterRCNN/coco.py

 # -*- coding: utf-8 -*-
 
+import json
 import numpy as np
 import os
 import tqdm
-import json
 
 from tensorpack.utils import logger
 from tensorpack.utils.timer import timed_operation
 
 from config import config as cfg
-from dataset import DatasetSplit, DatasetRegistry
+from dataset import DatasetRegistry, DatasetSplit
 
 __all__ = ['register_coco']
 
@@ -42,7 +42,7 @@ class COCODetection(DatasetSplit):
         self.name = name
         self._imgdir = os.path.realpath(os.path.join(
             basedir, self._INSTANCE_TO_BASEDIR.get(name, name)))
-        assert os.path.isdir(self._imgdir), self._imgdir
+        assert os.path.isdir(self._imgdir), "{} is not a directory!".format(self._imgdir)
         annotation_file = os.path.join(
             basedir, 'annotations/instances_{}.json'.format(name))
         assert os.path.isfile(annotation_file), annotation_file

examples/FasterRCNN/config.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 import os
-import six
 import pprint
+import six
 
 from tensorpack.utils import logger
 from tensorpack.utils.gpu import get_num_gpu

examples/FasterRCNN/data.py

@@ -2,24 +2,26 @@
 # File: data.py
 
 import copy
+import itertools
 import numpy as np
 import cv2
-import itertools
 from tabulate import tabulate
 from termcolor import colored
 
 from tensorpack.dataflow import (
-    DataFromList, MapDataComponent, MapData, MultiProcessMapDataZMQ, MultiThreadMapData, TestDataSpeed, imgaug)
+    DataFromList, MapData, MapDataComponent, MultiProcessMapDataZMQ, MultiThreadMapData,
+    TestDataSpeed, imgaug)
 from tensorpack.utils import logger
 from tensorpack.utils.argtools import log_once, memoized
 
 from common import (
-    CustomResize, DataFromListOfDict, box_to_point8,
-    filter_boxes_inside_shape, point8_to_box, segmentation_to_mask, np_iou)
+    CustomResize, DataFromListOfDict, box_to_point8, filter_boxes_inside_shape, np_iou,
+    point8_to_box, segmentation_to_mask)
 from config import config as cfg
 from dataset import DatasetRegistry
 from utils.generate_anchors import generate_anchors
-from utils.np_box_ops import area as np_area, ioa as np_ioa
+from utils.np_box_ops import area as np_area
+from utils.np_box_ops import ioa as np_ioa
 
 # import tensorpack.utils.viz as tpviz
 

examples/FasterRCNN/eval.py

@@ -2,17 +2,17 @@
 # File: eval.py
 
 import itertools
-import sys
-import os
 import json
 import numpy as np
+import os
+import sys
+import tensorflow as tf
 from collections import namedtuple
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import ExitStack
 import cv2
 import pycocotools.mask as cocomask
 import tqdm
-import tensorflow as tf
 
 from tensorpack.callbacks import Callback
 from tensorpack.tfutils.common import get_tf_version_tuple
@@ -20,9 +20,9 @@ from tensorpack.utils import logger
 from tensorpack.utils.utils import get_tqdm
 
 from common import CustomResize, clip_boxes
+from config import config as cfg
 from data import get_eval_dataflow
 from dataset import DatasetRegistry
-from config import config as cfg
 
 try:
     import horovod.tensorflow as hvd

examples/FasterRCNN/generalized_rcnn.py

@@ -4,22 +4,23 @@
 import tensorflow as tf
 
 from tensorpack import ModelDesc
-from tensorpack.models import regularize_cost, l2_regularizer, GlobalAvgPooling
-from tensorpack.tfutils.tower import get_current_tower_context
-from tensorpack.tfutils.summary import add_moving_summary
+from tensorpack.models import GlobalAvgPooling, l2_regularizer, regularize_cost
 from tensorpack.tfutils import optimizer
+from tensorpack.tfutils.summary import add_moving_summary
+from tensorpack.tfutils.tower import get_current_tower_context
 
 import model_frcnn
 import model_mrcnn
 from backbone import image_preprocess, resnet_c4_backbone, resnet_conv5, resnet_fpn_backbone
+from config import config as cfg
+from data import get_all_anchors, get_all_anchors_fpn
 from model_box import RPNAnchors, clip_boxes, crop_and_resize, roi_align
 from model_cascade import CascadeRCNNHead
 from model_fpn import fpn_model, generate_fpn_proposals, multilevel_roi_align, multilevel_rpn_losses
-from model_frcnn import BoxProposals, FastRCNNHead, fastrcnn_outputs, fastrcnn_predictions, sample_fast_rcnn_targets
+from model_frcnn import (
+    BoxProposals, FastRCNNHead, fastrcnn_outputs, fastrcnn_predictions, sample_fast_rcnn_targets)
 from model_mrcnn import maskrcnn_loss, maskrcnn_upXconv_head
 from model_rpn import generate_rpn_proposals, rpn_head, rpn_losses
-from data import get_all_anchors, get_all_anchors_fpn
-from config import config as cfg
 
 
 class GeneralizedRCNN(ModelDesc):

examples/FasterRCNN/model_box.py

@@ -2,8 +2,8 @@
 # File: model_box.py
 
 import numpy as np
-from collections import namedtuple
 import tensorflow as tf
+from collections import namedtuple
 
 from tensorpack.tfutils.scope_utils import under_name_scope
 

examples/FasterRCNN/viz.py

@@ -8,8 +8,8 @@ from tensorpack.utils import viz
 from tensorpack.utils.palette import PALETTE_RGB
 
 from config import config as cfg
-from utils.np_box_ops import iou as np_iou
 from utils.np_box_ops import area as np_area
+from utils.np_box_ops import iou as np_iou
 
 
 def draw_annotation(img, boxes, klass, is_crowd=None):