Swin Transformer：层次化视觉 Transformer

参考资料

• 论文：Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

• 官方项目：microsoft/Swin-Transformer

• 才疏学浅，只会写写代码，就不班门弄斧解读这论文了

  

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• 具体详解可以参考 @长风破浪会有时 大佬发布的项目 Swin Transformer，之前大佬写的 RepVGG 和 ReXNet 模型解析太强了

• 模型精度细节：

                 

构建模型

• 依然需要依赖 PPIM 进行模型搭建

安装依赖

# 安装 PPIM!pip install ppim

导入必要的包

import numpy as npimport paddleimport paddle.nn as nnimport paddle.vision.transforms as Tfrom ppim.models.vit import Mlpfrom ppim.models.common import to_2tuplefrom ppim.models.common import DropPath, Identityfrom ppim.models.common import trunc_normal_, zeros_, ones_

模型组网

def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.reshape((B, H // window_size, window_size,
                   W // window_size, window_size, C))
    windows = x.transpose((0, 1, 3, 2, 4, 5)).reshape(
        (-1, window_size, window_size, C))    return windowsdef window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.reshape(
        (B, H // window_size, W // window_size, window_size, window_size, -1))
    x = x.transpose((0, 1, 3, 2, 4, 5)).reshape((B, H, W, -1))    return xclass WindowAttention(nn.Layer):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = self.create_parameter(
            shape=((2 * window_size[0] - 1) *
                   (2 * window_size[1] - 1), num_heads),
            default_initializer=zeros_
        )  # 2*Wh-1 * 2*Ww-1, nH
        self.add_parameter("relative_position_bias_table",
                           self.relative_position_bias_table)        # get pair-wise relative position index for each token inside the window
        coords_h = paddle.arange(self.window_size[0])
        coords_w = paddle.arange(self.window_size[1])
        coords = paddle.stack(paddle.meshgrid(
            [coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = paddle.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten.unsqueeze(-1) - \
            coords_flatten.unsqueeze(1)  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.transpose(
            (1, 2, 0))  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - \            1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1

        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index",
                             relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table)
        self.softmax = nn.Softmax(axis=-1)    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape((B_, N, 3, self.num_heads, C //
                                   self.num_heads)).transpose((2, 0, 3, 1, 4))
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q * self.scale
        attn = q.matmul(k.transpose((0, 1, 3, 2)))

        relative_position_bias = paddle.index_select(
            self.relative_position_bias_table,
            self.relative_position_index.reshape((-1,)),
            axis=0).reshape(
            (self.window_size[0] * self.window_size[1],
             self.window_size[0] * self.window_size[1], -1))
        relative_position_bias = relative_position_bias.transpose((2, 0, 1))

        attn = attn + relative_position_bias.unsqueeze(0)        if mask is not None:
            nW = mask.shape[0]
            attn = attn.reshape(
                (B_ // nW, nW, self.num_heads, N, N)
            ) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.reshape((-1, self.num_heads, N, N))
            attn = self.softmax(attn)        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((B_, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)        return xclass SwinTransformerBlock(nn.Layer):
    r""" Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Layer, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Layer, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio        if min(self.input_resolution) <= self.window_size:            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop)        if self.shift_size > 0:            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = paddle.zeros((1, H, W, 1))  # 1 H W 1

            h_slices = (slice(0, -self.window_size),                        slice(-self.window_size, -self.shift_size),                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),                        slice(-self.window_size, -self.shift_size),                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            # nW, window_size, window_size, 1
            mask_windows = window_partition(img_mask, self.window_size)
            mask_windows = mask_windows.reshape((-1,
                                                 self.window_size * self.window_size))
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)

            _h = paddle.full_like(attn_mask, -100.0, dtype='float32')
            _z = paddle.full_like(attn_mask, 0.0, dtype='float32')
            attn_mask = paddle.where(attn_mask != 0, _h, _z)        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.reshape((B, H, W, C))        # cyclic shift
        if self.shift_size > 0:
            shifted_x = paddle.roll(
                x, shifts=(-self.shift_size, -self.shift_size), axis=(1, 2))        else:
            shifted_x = x        # partition windows
        # nW*B, window_size, window_size, C
        x_windows = window_partition(shifted_x, self.window_size)        # nW*B, window_size*window_size, C
        x_windows = x_windows.reshape(
            (-1, self.window_size * self.window_size, C))        # W-MSA/SW-MSA
        # nW*B, window_size*window_size, C
        attn_windows = self.attn(x_windows, mask=self.attn_mask)        # merge windows
        attn_windows = attn_windows.reshape(
            (-1, self.window_size, self.window_size, C))
        shifted_x = window_reverse(
            attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = paddle.roll(shifted_x, shifts=(
                self.shift_size, self.shift_size), axis=(1, 2))        else:
            x = shifted_x
        x = x.reshape((B, H * W, C))        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))        return xclass PatchMerging(nn.Layer):
    r""" Patch Merging Layer.
    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Layer, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias_attr=False)
        self.norm = norm_layer(4 * dim)    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.reshape((B, H, W, C))

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = paddle.concat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.reshape((B, -1, 4 * C))  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)        return xclass BasicLayer(nn.Layer):
    """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Layer | None, optional): Downsample layer at the end of the layer. Default: None
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth        # build blocks
        self.blocks = nn.LayerList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (
                                     i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(
                                     drop_path, np.ndarray) else drop_path,
                                 norm_layer=norm_layer)            for i in range(depth)])        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                input_resolution, dim=dim, norm_layer=norm_layer)        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            x = blk(x)        if self.downsample is not None:
            x = self.downsample(x)        return xclass PatchEmbed(nn.Layer):
    r""" Image to Patch Embedding
    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Layer, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] //
                              patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2D(in_chans, embed_dim,
                              kernel_size=patch_size, stride=patch_size)        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose((0, 2, 1))  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)        return xclass SwinTransformer(nn.Layer):
    r""" Swin Transformer
        A Paddle impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030
    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        class_dim (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Layer): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 class_dim=1000, with_pool=True, **kwargs):
        super().__init__()
        self.class_dim = class_dim
        self.with_pool = with_pool

        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = self.create_parameter(
                shape=(1, num_patches, embed_dim),
                default_initializer=zeros_
            )
            self.add_parameter("absolute_pos_embed", self.absolute_pos_embed)
            trunc_normal_(self.absolute_pos_embed)

        self.pos_drop = nn.Dropout(p=drop_rate)        # stochastic depth
        dpr = np.linspace(0, drop_path_rate, sum(depths))        # build layers
        self.layers = nn.LayerList()        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(
                                   depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (
                                   i_layer < self.num_layers - 1) else None
                               )
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)        if with_pool:
            self.avgpool = nn.AdaptiveAvgPool1D(1)        
        if class_dim > 0:
            self.head = nn.Linear(self.num_features, class_dim)

        self.apply(self._init_weights)    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight)            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)        elif isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)    def forward_features(self, x):
        x = self.patch_embed(x)        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        return x.transpose((0, 2, 1)) # B C 1

    def forward(self, x):
        x = self.forward_features(x)        if self.with_pool:
            x = self.avgpool(x) 

        if self.class_dim > 0:
            x = paddle.flatten(x, 1)
            x = self.head(x)        return x

验证集数据处理

def get_transforms(resize, crop):
    transforms = [T.Resize(resize, interpolation='bicubic')]    if crop:
        transforms.append(T.CenterCrop(crop))
    transforms.append(T.ToTensor())
    transforms.append(T.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
    transforms = T.Compose(transforms)    return transforms


transforms_224 = get_transforms(256, 224)
transforms_384 = get_transforms((384, 384), None)

预设模型

def swin_ti(pretrained=False, **kwargs):
    model = SwinTransformer(**kwargs)    if pretrained:
        model.set_dict(paddle.load('data/data80934/swin_tiny_patch4_window7_224.pdparams'))    return model, transforms_224def swin_s(pretrained=False, **kwargs):
    model = SwinTransformer(
        depths=[2, 2, 18, 2],
        num_heads=[3, 6, 12, 24]
        ** kwargs
    )    if pretrained:
        model.set_dict(paddle.load('data/data80934/swin_small_patch4_window7_224.pdparams'))    return model, transforms_224def swin_b(pretrained=False, **kwargs):
    model = SwinTransformer(
        embed_dim=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32]
        ** kwargs
    )    if pretrained:
        model.set_dict(paddle.load('data/data80934/swin_base_patch4_window7_224.pdparams'))    return model, transforms_224def swin_b_384(pretrained=False, **kwargs):
    model = SwinTransformer(
        img_size=384,
        embed_dim=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32],
        window_size=12,
        **kwargs
    )    if pretrained:
        model.set_dict(paddle.load('data/data80934/swin_base_patch4_window12_384.pdparams'))    return model, transforms_384

精度验证

解压数据集

# 解压数据集!mkdir ~/data/ILSVRC2012
!tar -xf ~/data/data68594/ILSVRC2012_img_val.tar -C ~/data/ILSVRC2012

模型验证

import osimport cv2import numpy as npimport paddle# from ppim import pit_b_distilledfrom PIL import Image# 构建数据集class ILSVRC2012(paddle.io.Dataset):
    def __init__(self, root, label_list, transform, backend='pil'):
        self.transform = transform
        self.root = root
        self.label_list = label_list
        self.backend = backend
        self.load_datas()    def load_datas(self):
        self.imgs = []
        self.labels = []        with open(self.label_list, 'r') as f:            for line in f:
                img, label = line[:-1].split(' ')
                self.imgs.append(os.path.join(self.root, img))
                self.labels.append(int(label))    def __getitem__(self, idx):
        label = self.labels[idx]
        image = self.imgs[idx]        if self.backend=='cv2':
            image = cv2.imread(image)        else:
            image = Image.open(image).convert('RGB')
        image = self.transform(image)        return image.astype('float32'), np.array(label).astype('int64')    def __len__(self):
        return len(self.imgs)# 配置模型model, val_transforms = swin_ti(pretrained=True)
model = paddle.Model(model)
model.prepare(metrics=paddle.metric.Accuracy(topk=(1, 5)))# 配置数据集val_dataset = ILSVRC2012('data/ILSVRC2012', transform=val_transforms, label_list='data/data68594/val_list.txt')# 模型验证model.evaluate(val_dataset, batch_size=512)

Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  return (isinstance(seq, collections.Sequence) and

step 10/98 - acc_top1: 0.8164 - acc_top5: 0.9547 - 7s/step
step 20/98 - acc_top1: 0.8155 - acc_top5: 0.9549 - 7s/step
step 30/98 - acc_top1: 0.8113 - acc_top5: 0.9542 - 7s/step
step 40/98 - acc_top1: 0.8113 - acc_top5: 0.9543 - 7s/step
step 50/98 - acc_top1: 0.8115 - acc_top5: 0.9547 - 7s/step
step 60/98 - acc_top1: 0.8115 - acc_top5: 0.9547 - 7s/step
step 70/98 - acc_top1: 0.8107 - acc_top5: 0.9550 - 7s/step
step 80/98 - acc_top1: 0.8116 - acc_top5: 0.9549 - 7s/step
step 90/98 - acc_top1: 0.8113 - acc_top5: 0.9549 - 6s/step
step 98/98 - acc_top1: 0.8119 - acc_top5: 0.9551 - 6s/step
Eval samples: 50000

{'acc_top1': 0.81186, 'acc_top5': 0.9551}