fix #35: add nrtr readme; update nrtr config (#36)

configs/textdet/psenet/README.md

@@ -26,4 +26,4 @@
 |                                 Method                                 | Backbone |                                                                Extra Data                                                                 | Training set | Test set  | #epochs | Test size | Recall | Precision | Hmean |                                                                                            Download                                                                                             |
 | :--------------------------------------------------------------------: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------: | :-------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
 | [PSENet-4s](/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 |                                                                     -                                                                     |  IC15 Train  | IC15 Test |   600   |   2240    | 0.784  |   0.831   | 0.807 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015-c6131f0d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210331_214145.log.json) |
-| [PSENet-4s](/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | pretrain on IC17 MLT [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2017_as_pretrain-0af6d62c.pth) |  IC15 Train  | IC15 Test |   600   |   2240    | 0.834  |   0.861   | 0.847 |                                  [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-ac477383.pth) \| [log]()                                   |
+| [PSENet-4s](/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | pretrain on IC17 MLT [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2017_as_pretrain-3bd6056c.pth) |  IC15 Train  | IC15 Test |   600   |   2240    | 0.834  |   0.861   | 0.847 |                                  [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth) \| [log]()                                   |

configs/textrecog/nrtr/README.md

@@ -0,0 +1,47 @@
+# NRTR
+
+## Introduction
+
+[ALGORITHM]
+
+```bibtex
+@inproceedings{sheng2019nrtr,
+  title={NRTR: A no-recurrence sequence-to-sequence model for scene text recognition},
+  author={Sheng, Fenfen and Chen, Zhineng and Xu, Bo},
+  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
+  pages={781--786},
+  year={2019},
+  organization={IEEE}
+}
+```
+
+## Dataset
+
+### Train Dataset
+
+|  trainset  | instance_num | repeat_num |          source          |
+| :--------: | :----------: | :--------: | :----------------------: |
+| SynthText  |   7266686    |     1      |          synth           |
+|   Syn90k   |   8919273    |     1      |          synth           |
+
+### Test Dataset
+
+| testset | instance_num |            type             |
+| :-----: | :----------: | :-------------------------: |
+| IIIT5K  |     3000     |           regular           |
+|   SVT   |     647      |           regular           |
+|  IC13   |     1015     |           regular           |
+|  IC15   |     2077     |          irregular          |
+|  SVTP   |     645      |          irregular          |
+|  CT80   |     288      |          irregular          |
+
+## Results and Models
+
+| Methods | Backbone || Regular Text |||| Irregular Text ||download|
+| :-------: | :---------: | :----: | :----: | :--: | :-: | :--: | :------: | :--: | :-----: |
+| | | IIIT5K |     SVT      | IC13 |     | IC15 |      SVTP      | CT80 |
+| [NRTR](/configs/textrecog/nrtr/nrtr_r31_academic.py)  | R31-1/16-1/8  |  93.9  |  90.0| 93.5 |     | 74.5 |      78.5      | 86.5 |  [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_academic_20210406-954db95e.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20210406_010150.log.json)  |
+
+**Notes:**
+
+- `R31-1/16-1/8` means the height of feature from backbone is 1/16 of input image, where 1/8 for width.

configs/textrecog/nrtr/nrtr_modality_toy.py

@@ -17,7 +17,7 @@ train_pipeline = [
     dict(
         type='ResizeOCR',
         height=32,
-        min_width=100,
+        min_width=32,
         max_width=100,
         keep_aspect_ratio=False),
     dict(type='ToTensorOCR'),
@@ -40,7 +40,7 @@ test_pipeline = [
                 height=32,
                 min_width=32,
                 max_width=100,
-                keep_aspect_ratio=True),
+                keep_aspect_ratio=False),
             dict(type='ToTensorOCR'),
             dict(type='NormalizeOCR', **img_norm_cfg),
             dict(

configs/textrecog/nrtr/nrtr_r31_academic.py

@@ -0,0 +1,163 @@
+_base_ = [
+    '../../_base_/default_runtime.py', '../../_base_/recog_models/nrtr.py'
+]
+
+label_convertor = dict(
+    type='AttnConvertor', dict_type='DICT90', with_unknown=True)
+
+model = dict(
+    type='NRTR',
+    backbone=dict(
+        type='ResNet31OCR',
+        layers=[1, 2, 5, 3],
+        channels=[32, 64, 128, 256, 512, 512],
+        stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)),
+        last_stage_pool=True),
+    encoder=dict(type='TFEncoder'),
+    decoder=dict(type='TFDecoder'),
+    loss=dict(type='TFLoss'),
+    label_convertor=label_convertor,
+    max_seq_len=40)
+
+# optimizer
+optimizer = dict(type='Adam', lr=1e-3)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='step', step=[3, 4])
+total_epochs = 6
+
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='ResizeOCR',
+        height=32,
+        min_width=32,
+        max_width=160,
+        keep_aspect_ratio=True,
+        width_downsample_ratio=0.25),
+    dict(type='ToTensorOCR'),
+    dict(type='NormalizeOCR', **img_norm_cfg),
+    dict(
+        type='Collect',
+        keys=['img'],
+        meta_keys=[
+            'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio'
+        ]),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiRotateAugOCR',
+        rotate_degrees=[0, 90, 270],
+        transforms=[
+            dict(
+                type='ResizeOCR',
+                height=32,
+                min_width=32,
+                max_width=160,
+                keep_aspect_ratio=True,
+                width_downsample_ratio=0.25),
+            dict(type='ToTensorOCR'),
+            dict(type='NormalizeOCR', **img_norm_cfg),
+            dict(
+                type='Collect',
+                keys=['img'],
+                meta_keys=[
+                    'filename', 'ori_shape', 'img_shape', 'valid_ratio'
+                ]),
+        ])
+]
+
+dataset_type = 'OCRDataset'
+
+train_prefix = 'data/mixture/'
+
+train_img_prefix1 = train_prefix + \
+    'SynthText/synthtext/SynthText_patch_horizontal'
+train_img_prefix2 = train_prefix + 'Syn90k/mnt/ramdisk/max/90kDICT32px'
+
+train_ann_file1 = train_prefix + 'SynthText/label.lmdb',
+train_ann_file2 = train_prefix + 'Syn90k/label.lmdb'
+
+train1 = dict(
+    type=dataset_type,
+    img_prefix=train_img_prefix1,
+    ann_file=train_ann_file1,
+    loader=dict(
+        type='LmdbLoader',
+        repeat=1,
+        parser=dict(
+            type='LineStrParser',
+            keys=['filename', 'text'],
+            keys_idx=[0, 1],
+            separator=' ')),
+    pipeline=train_pipeline,
+    test_mode=False)
+
+train2 = {key: value for key, value in train1.items()}
+train2['img_prefix'] = train_img_prefix2
+train2['ann_file'] = train_ann_file2
+
+test_prefix = 'data/mixture/'
+test_img_prefix1 = test_prefix + 'IIIT5K/'
+test_img_prefix2 = test_prefix + 'svt/'
+test_img_prefix3 = test_prefix + 'icdar_2013/'
+test_img_prefix4 = test_prefix + 'icdar_2015/'
+test_img_prefix5 = test_prefix + 'svtp/'
+test_img_prefix6 = test_prefix + 'ct80/'
+
+test_ann_file1 = test_prefix + 'IIIT5K/test_label.txt'
+test_ann_file2 = test_prefix + 'svt/test_label.txt'
+test_ann_file3 = test_prefix + 'icdar_2013/test_label_1015.txt'
+test_ann_file4 = test_prefix + 'icdar_2015/test_label.txt'
+test_ann_file5 = test_prefix + 'svtp/test_label.txt'
+test_ann_file6 = test_prefix + 'ct80/test_label.txt'
+
+test1 = dict(
+    type=dataset_type,
+    img_prefix=test_img_prefix1,
+    ann_file=test_ann_file1,
+    loader=dict(
+        type='HardDiskLoader',
+        repeat=1,
+        parser=dict(
+            type='LineStrParser',
+            keys=['filename', 'text'],
+            keys_idx=[0, 1],
+            separator=' ')),
+    pipeline=test_pipeline,
+    test_mode=True)
+
+test2 = {key: value for key, value in test1.items()}
+test2['img_prefix'] = test_img_prefix2
+test2['ann_file'] = test_ann_file2
+
+test3 = {key: value for key, value in test1.items()}
+test3['img_prefix'] = test_img_prefix3
+test3['ann_file'] = test_ann_file3
+
+test4 = {key: value for key, value in test1.items()}
+test4['img_prefix'] = test_img_prefix4
+test4['ann_file'] = test_ann_file4
+
+test5 = {key: value for key, value in test1.items()}
+test5['img_prefix'] = test_img_prefix5
+test5['ann_file'] = test_ann_file5
+
+test6 = {key: value for key, value in test1.items()}
+test6['img_prefix'] = test_img_prefix6
+test6['ann_file'] = test_ann_file6
+
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=4,
+    train=dict(type='ConcatDataset', datasets=[train1, train2]),
+    val=dict(
+        type='ConcatDataset',
+        datasets=[test1, test2, test3, test4, test5, test6]),
+    test=dict(
+        type='ConcatDataset',
+        datasets=[test1, test2, test3, test4, test5, test6]))
+
+evaluation = dict(interval=1, metric='acc')

docs/install.md

@@ -15,7 +15,7 @@
 
 - Linux (Windows is not officially supported)
 - Python 3.7
-- PyTorch 1.5
+- PyTorch 1.5 or higher
 - torchvision 0.6.0
 - CUDA 10.1
 - NCCL 2