Add ABINet cfg

configs/_base_/recog_models/abinet.py

@@ -1,45 +1,10 @@
-# num_chars depends on the configuration of label_convertor. The actual
+_base_ = 'abinet_vision_only.py'
-# dictionary size is 36 + 1 (<BOS/EOS>).
-# TODO: Automatically update num_chars based on the configuration of
-# label_convertor
-num_chars = 37
-max_seq_len = 26
-
-label_convertor = dict(
-    type='ABIConvertor',
-    dict_type='DICT36',
-    with_unknown=False,
-    with_padding=False,
-    lower=True,
-)
 
 model = dict(
-    type='ABINet',
+    decoder=dict(
-    backbone=dict(type='ResNetABI'),
-    encoder=dict(
-        type='ABIVisionModel',
-        encoder=dict(
-            type='TransformerEncoder',
-            n_layers=3,
-            n_head=8,
         d_model=512,
-            d_inner=2048,
+        num_iters=3,
-            dropout=0.1,
+        language_decoder=dict(
-            max_len=8 * 32,
-        ),
-        decoder=dict(
-            type='ABIVisionDecoder',
-            in_channels=512,
-            num_channels=64,
-            attn_height=8,
-            attn_width=32,
-            attn_mode='nearest',
-            use_result='feature',
-            num_chars=num_chars,
-            max_seq_len=max_seq_len,
-            init_cfg=dict(type='Xavier', layer='Conv2d')),
-    ),
-    decoder=dict(
             type='ABILanguageDecoder',
             d_model=512,
             n_head=8,
@@ -48,23 +13,4 @@ model = dict(
             dropout=0.1,
             detach_tokens=True,
             use_self_attn=False,
-        pad_idx=num_chars - 1,
+        )), )
-        num_chars=num_chars,
-        max_seq_len=max_seq_len,
-        init_cfg=None),
-    fuser=dict(
-        type='ABIFuser',
-        d_model=512,
-        num_chars=num_chars,
-        init_cfg=None,
-        max_seq_len=max_seq_len,
-    ),
-    loss=dict(
-        type='ABILoss',
-        enc_weight=1.0,
-        dec_weight=1.0,
-        fusion_weight=1.0,
-        num_classes=num_chars),
-    label_convertor=label_convertor,
-    max_seq_len=max_seq_len,
-    iter_size=3)

configs/_base_/recog_models/abinet_vision_only.py

@@ -0,0 +1,40 @@
+dictionary = dict(
+    type='Dictionary',
+    dict_file='dicts/lower_english_digits.txt',
+    with_start=True,
+    with_end=True,
+    same_start_end=True,
+    with_padding=False,
+    with_unknown=False)
+
+model = dict(
+    type='ABINet',
+    backbone=dict(type='ResNetABI'),
+    encoder=dict(
+        type='ABIEncoder',
+        n_layers=3,
+        n_head=8,
+        d_model=512,
+        d_inner=2048,
+        dropout=0.1,
+        max_len=8 * 32,
+    ),
+    decoder=dict(
+        type='ABIFuser',
+        vision_decoder=dict(
+            type='ABIVisionDecoder',
+            in_channels=512,
+            num_channels=64,
+            attn_height=8,
+            attn_width=32,
+            attn_mode='nearest',
+            init_cfg=dict(type='Xavier', layer='Conv2d')),
+        loss_module=dict(type='ABILoss', letter_case='lower'),
+        postprocessor=dict(type='AttentionPostprocessor'),
+    ),
+    dictionary=dictionary,
+    max_seq_len=26,
+    data_preprocessor=dict(
+        type='TextRecogDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375]))

configs/_base_/recog_pipelines/abinet_pipeline.py

@@ -1,97 +0,0 @@
-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=128,
-        max_width=128,
-        keep_aspect_ratio=False,
-        width_downsample_ratio=0.25),
-    dict(
-        type='RandomWrapper',
-        p=0.5,
-        transforms=[
-            dict(
-                type='OneOfWrapper',
-                transforms=[
-                    dict(
-                        type='RandomRotate',
-                        rotate_ratio=1.0,
-                        max_angle=15,
-                    ),
-                    dict(
-                        type='TorchVisionWrapper',
-                        op='RandomAffine',
-                        degrees=15,
-                        translate=(0.3, 0.3),
-                        scale=(0.5, 2.),
-                        shear=(-45, 45),
-                    ),
-                    dict(
-                        type='TorchVisionWrapper',
-                        op='RandomPerspective',
-                        distortion_scale=0.5,
-                        p=1,
-                    ),
-                ])
-        ],
-    ),
-    dict(
-        type='RandomWrapper',
-        p=0.25,
-        transforms=[
-            dict(type='PyramidRescale'),
-            dict(
-                type='Albu',
-                transforms=[
-                    dict(type='GaussNoise', var_limit=(20, 20), p=0.5),
-                    dict(type='MotionBlur', blur_limit=6, p=0.5),
-                ]),
-        ]),
-    dict(
-        type='RandomWrapper',
-        p=0.25,
-        transforms=[
-            dict(
-                type='TorchVisionWrapper',
-                op='ColorJitter',
-                brightness=0.5,
-                saturation=0.5,
-                contrast=0.5,
-                hue=0.1),
-        ]),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio',
-            'resize_shape'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiRotateAugOCR',
-        rotate_degrees=[0, 90, 270],
-        transforms=[
-            dict(
-                type='ResizeOCR',
-                height=32,
-                min_width=128,
-                max_width=128,
-                keep_aspect_ratio=False,
-                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',
-                    'resize_shape', 'img_norm_cfg', 'ori_filename'
-                ]),
-        ])
-]

configs/_base_/schedules/schedule_adam_step_20e.py

@@ -5,6 +5,8 @@ val_cfg = dict(type='ValLoop')
 test_cfg = dict(type='TestLoop')
 # learning policy
 param_scheduler = [
-    dict(type='LinearLR', end=1, start_factor=0.001),
+    dict(
+        type='LinearLR', end=2, start_factor=0.001,
+        convert_to_iter_based=True),
     dict(type='MultiStepLR', milestones=[16, 18], end=20),
 ]

configs/textrecog/abinet/abinet_academic.py

@@ -1,35 +1,3 @@
-_base_ = [
+_base_ = ['../../_base_/recog_models/abinet.py', 'base.py']
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_adam_step_20e.py',
-    '../../_base_/recog_pipelines/abinet_pipeline.py',
-    '../../_base_/recog_models/abinet.py',
-    # '../../_base_/recog_datasets/ST_MJ_alphanumeric_train.py',
-    '../../_base_/recog_datasets/toy_data.py'
-    # '../../_base_/recog_datasets/academic_test.py'
-]
 
-train_list = {{_base_.train_list}}
+load_from = 'abinet_pretrain-1bed979b.pth'
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
-    samples_per_gpu=192,
-    workers_per_gpu=8,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')

configs/textrecog/abinet/abinet_vision_only_academic.py

@@ -1,81 +1 @@
-_base_ = [
+_base_ = ['../../_base_/recog_models/abinet_vision_only.py', 'base.py']
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_adam_step_20e.py',
-    '../../_base_/recog_pipelines/abinet_pipeline.py',
-    '../../_base_/recog_datasets/toy_data.py'
-    # '../../_base_/recog_datasets/ST_MJ_alphanumeric_train.py',
-    # '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-# Model
-num_chars = 37
-max_seq_len = 26
-label_convertor = dict(
-    type='ABIConvertor',
-    dict_type='DICT36',
-    with_unknown=False,
-    with_padding=False,
-    lower=True,
-)
-
-model = dict(
-    type='ABINet',
-    backbone=dict(type='ResNetABI'),
-    encoder=dict(
-        type='ABIVisionModel',
-        encoder=dict(
-            type='TransformerEncoder',
-            n_layers=3,
-            n_head=8,
-            d_model=512,
-            d_inner=2048,
-            dropout=0.1,
-            max_len=8 * 32,
-        ),
-        decoder=dict(
-            type='ABIVisionDecoder',
-            in_channels=512,
-            num_channels=64,
-            attn_height=8,
-            attn_width=32,
-            attn_mode='nearest',
-            use_result='feature',
-            num_chars=num_chars,
-            max_seq_len=max_seq_len,
-            init_cfg=dict(type='Xavier', layer='Conv2d')),
-    ),
-    loss=dict(
-        type='ABILoss',
-        enc_weight=1.0,
-        dec_weight=1.0,
-        fusion_weight=1.0,
-        num_classes=num_chars),
-    label_convertor=label_convertor,
-    max_seq_len=max_seq_len,
-    iter_size=1)
-
-data = dict(
-    samples_per_gpu=192,
-    workers_per_gpu=8,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')

configs/textrecog/abinet/base.py

@@ -0,0 +1,121 @@
+_base_ = [
+    '../../_base_/default_runtime.py',
+    '../../_base_/schedules/schedule_adam_step_20e.py',
+]
+
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
+
+# dataset settings
+dataset_type = 'OCRDataset'
+data_root = 'data/recog/'
+file_client_args = dict(backend='disk')
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadOCRAnnotations', with_text=True),
+    dict(type='Resize', scale=(128, 32)),
+    dict(
+        type='RandomApply',
+        prob=0.5,
+        transforms=[
+            dict(
+                type='RandomChoice',
+                transforms=[
+                    dict(
+                        type='RandomRotate',
+                        max_angle=15,
+                    ),
+                    dict(
+                        type='TorchVisionWrapper',
+                        op='RandomAffine',
+                        degrees=15,
+                        translate=(0.3, 0.3),
+                        scale=(0.5, 2.),
+                        shear=(-45, 45),
+                    ),
+                    dict(
+                        type='TorchVisionWrapper',
+                        op='RandomPerspective',
+                        distortion_scale=0.5,
+                        p=1,
+                    ),
+                ])
+        ],
+    ),
+    dict(
+        type='RandomApply',
+        prob=0.25,
+        transforms=[
+            dict(type='PyramidRescale'),
+            dict(
+                type='mmdet.Albu',
+                transforms=[
+                    dict(type='GaussNoise', var_limit=(20, 20), p=0.5),
+                    dict(type='MotionBlur', blur_limit=6, p=0.5),
+                ]),
+        ]),
+    dict(
+        type='RandomApply',
+        prob=0.25,
+        transforms=[
+            dict(
+                type='TorchVisionWrapper',
+                op='ColorJitter',
+                brightness=0.5,
+                saturation=0.5,
+                contrast=0.5,
+                hue=0.1),
+        ]),
+    dict(
+        type='PackTextRecogInputs',
+        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadOCRAnnotations', with_text=True),
+    dict(type='Resize', scale=(128, 32)),
+    dict(
+        type='PackTextRecogInputs',
+        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio',
+                   'instances'))
+]
+
+dataset_mj = dict(
+    type=dataset_type,
+    data_root=data_root,
+    data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px/'),
+    ann_file='data/MJ/label.json',
+    pipeline=train_pipeline)
+dataset_st = dict(
+    type=dataset_type,
+    data_root=data_root,
+    data_prefix=dict(
+        img_path='SynthText/synthtext/SynthText_patch_horizontal/'),
+    ann_file='data/ST/alphanumeric_labels.json',
+    pipeline=train_pipeline)
+
+train_dataloader = dict(
+    batch_size=192 * 4,
+    num_workers=32,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(type='ConcatDataset', datasets=[dataset_mj, dataset_st]))
+
+val_dataloader = dict(
+    batch_size=192,
+    num_workers=16,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='testset/testset/IIIT5K/'),
+        ann_file='label.json',
+        test_mode=True,
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='WordMetric', mode=['ignore_case_symbol'])
+test_evaluator = val_evaluator
+visualizer = dict(type='TextRecogLocalVisualizer', name='visualizer')