Add some vit configs (#217)

* add vit_base_patch32_384_finetune.py

* add vit_base_patch32_384_finetune_imagenet.py to vision_transformer

* add vit_large_patch16_384_finetune.py to models

* add vit_large_patch16_384_finetune_imagenet.py to vision_transformer

* add vit_large_patch32_384_finetune to models

* add vit_large_patch32_384_finetune_imagenet to vision_transformer

* add vit_large_patch16_224_finetune.py to models

* add vit_large_patch16_224_finetune_imagenet.py to vision_transformer

* delete some useless comments

configs/_base_/models/vit_base_patch32_384_finetune.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=12,
+        embed_dim=768,
+        num_heads=12,
+        img_size=384,
+        patch_size=32,
+        in_channels=3,
+        feedforward_channels=3072,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vit_large_patch16_224_finetune.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=24,
+        embed_dim=1024,
+        num_heads=16,
+        img_size=224,
+        patch_size=16,
+        in_channels=3,
+        feedforward_channels=4096,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vit_large_patch16_384_finetune.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=24,
+        embed_dim=1024,
+        num_heads=16,
+        img_size=384,
+        patch_size=16,
+        in_channels=3,
+        feedforward_channels=4096,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vit_large_patch32_384_finetune.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=24,
+        embed_dim=1024,
+        num_heads=16,
+        img_size=384,
+        patch_size=32,
+        in_channels=3,
+        feedforward_channels=4096,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/vision_transformer/vit_base_patch32_384_finetune_imagenet.py

@@ -0,0 +1,21 @@
+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_base_patch32_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

configs/vision_transformer/vit_large_patch16_224_finetune_imagenet.py

@@ -0,0 +1,21 @@
+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_224_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py

@@ -0,0 +1,21 @@
+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py

@@ -0,0 +1,21 @@
+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch32_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

mmcls/models/backbones/vision_transformer.py

@@ -8,7 +8,6 @@ from ..utils import to_2tuple
 from .base_backbone import BaseBackbone
 
 
-# Modified from mmdet
 class FFN(nn.Module):
     """Implements feed-forward networks (FFNs) with residual connection.
 
@@ -87,7 +86,6 @@ class FFN(nn.Module):
         return repr_str
 
 
-# Modified from mmdet
 class MultiheadAttention(nn.Module):
     """A warpper for torch.nn.MultiheadAttention.
 
@@ -172,7 +170,6 @@ class MultiheadAttention(nn.Module):
         return residual + self.dropout(out)
 
 
-# Modified from mmdet
 class TransformerEncoderLayer(nn.Module):
     """Implements one encoder layer in Vision Transformer.
 
@@ -240,7 +237,6 @@ class TransformerEncoderLayer(nn.Module):
         return x
 
 
-# Modified from pytorch-image-models
 class PatchEmbed(nn.Module):
     """Image to Patch Embedding.
 
@@ -262,11 +258,9 @@ class PatchEmbed(nn.Module):
         super(PatchEmbed, self).__init__()
         if isinstance(img_size, int):
             img_size = to_2tuple(img_size)
-            # img_size = tuple(repeat(img_size, 2))
         elif isinstance(img_size, tuple):
             if len(img_size) == 1:
                 img_size = to_2tuple(img_size[0])
-                # img_size = tuple(repeat(img_size[0], 2))
             assert len(img_size) == 2, \
                 f'The size of image should have length 1 or 2, ' \
                 f'but got {len(img_size)}'
@@ -378,7 +372,6 @@ class HybridEmbed(nn.Module):
         return x
 
 
-# Modified from pytorch-image-models and mmdet
 @BACKBONES.register_module()
 class VisionTransformer(BaseBackbone):
     """ Vision Transformer