Merge pull request #61 from cuhk-hbsun/feature/iss_60

fix #60: update readme

README.md

@@ -43,12 +43,12 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v1.0 was released on 07/04/2021.
+v0.1.0 was released on 07/04/2021.
 
 
 ## Benchmark and Model Zoo
 
-Please refer to [modelzoo.md](modelzoo.md) for more details.
+Please refer to [modelzoo.md](https://mmocr.readthedocs.io/en/latest/modelzoo.html) for more details.
 
 ## Installation
 

configs/textrecog/nrtr/README.md

@@ -54,8 +54,10 @@
 | 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)  |
+| [NRTR](/configs/textrecog/nrtr/nrtr_r31_1by16_1by8_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)  |
+| [NRTR](/configs/textrecog/nrtr/nrtr_r31_1by8_1by4_academic.py)  | R31-1/8-1/4  |  94.7  |  87.5| 93.3 |     | 75.1 |      78.9      | 87.9 |  [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_1by8_1by4_academic_20210406-ce16e7cc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20210406_160845.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.
+- `R31-1/8-1/4` means the height of feature from backbone is 1/8 of input image, where 1/4 for width.

configs/textrecog/nrtr/nrtr_modality_toy.py

@@ -1,112 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/recog_models/nrtr.py',
-]
-
-# 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='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
-    dict(
-        type='ResizeOCR',
-        height=32,
-        min_width=32,
-        max_width=100,
-        keep_aspect_ratio=False),
-    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=100,
-                keep_aspect_ratio=False),
-            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'
-img_prefix = 'tests/data/ocr_toy_dataset/imgs'
-train_anno_file1 = 'tests/data/ocr_toy_dataset/label.txt'
-train1 = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=train_anno_file1,
-    loader=dict(
-        type='HardDiskLoader',
-        repeat=100,
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=train_pipeline,
-    test_mode=False)
-
-train_anno_file2 = 'tests/data/ocr_toy_dataset/label.lmdb'
-train2 = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=train_anno_file2,
-    loader=dict(
-        type='LmdbLoader',
-        repeat=100,
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=train_pipeline,
-    test_mode=False)
-
-test_anno_file1 = 'tests/data/ocr_toy_dataset/label.lmdb'
-test = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=test_anno_file1,
-    loader=dict(
-        type='LmdbLoader',
-        repeat=1,
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=test_pipeline,
-    test_mode=True)
-
-data = dict(
-    samples_per_gpu=16,
-    workers_per_gpu=2,
-    train=dict(type='ConcatDataset', datasets=[train1, train2]),
-    val=dict(type='ConcatDataset', datasets=[test]),
-    test=dict(type='ConcatDataset', datasets=[test]))
-
-evaluation = dict(interval=1, metric='acc')

configs/textrecog/nrtr/nrtr_r31_1by8_1by4_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=False),
+    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')