Upload CctForImageClassification

config.json

@@ -0,0 +1,2040 @@
+{
+  "architectures": [
+    "CctForImageClassification"
+  ],
+  "attention_drop_rate": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_cct.CctConfig",
+    "AutoModelForImageClassification": "modeling_cct.CctForImageClassification"
+  },
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+  "model_type": "cct",
+  "num_channels": 384,
+  "num_conv_layers": 2,
+  "num_heads": 6,
+  "num_transformer_layers": 14,
+  "out_channels": [
+    64,
+    384
+  ],
+  "output_hidden_states": true,
+  "pool_kernel_size": 3,
+  "pool_padding": 1,
+  "pool_stride": 2,
+  "pos_emb_type": "learnable",
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0.dev0"
+}

configuration_cct.py

@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" CCT model configuration"""
+
+from transformers import PretrainedConfig
+
+CCT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "rishabbala/cct_14_7x2_384": "https://huggingface.co/rishabbala/cct_14_7x2_384/blob/main/config.json",
+}
+
+
+class CctConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`CctModel`]. It is used to instantiate a CCT model
+    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the CCT
+    [rishabbala/cct](https://huggingface.co/rishabbala/cct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        img_size (`int`, *optional*, defaults to 384):
+            The size of the input image
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`List[int]`, *optional*, defaults to [64, 384]):
+            The number of output channels of each conv layer.
+        conv_kernel_size (`int`, *optional*, defaults to 7):
+            The kernel size of convolutional layers in patch embedding.
+        conv_stride (`int`, *optional*, defaults to 2):
+            The stride size of convolutional layers in patch embedding.
+        conv_padding (`int`, *optional*, defaults to 3):
+            The padding size of convolutional layers in patch embedding.
+        conv_bias (`bool`, *optional*, defaults to False):
+            Whether the convolutional layers have bias
+        pool_kernel_size (`int`, *optional*, defaults to 7):
+            The kernel size of max pool layers in patch embedding.
+        pool_stride (`int`, *optional*, defaults to 2):
+            The stride size of max pool layers in patch embedding.
+        pool_padding (`int`, *optional*, defaults to 3):
+            The padding size of max pool layers in patch embedding.
+        num_conv_layers (`int`, *optional*, defaults to 2):
+            Number of convolutional embedding layers
+        embed_dim (`int`, *optional*, defaults to 384):
+            Dimension of each of the encoder blocks.
+        num_heads (`int`, *optional*, defaults to 6):
+            Number of attention heads for each attention layer in each block of the Transformer encoder.
+        mlp_ratio (`float`, *optional*, defaults to 3.0):
+            Ratio of the size of the hidden layer compared to the size of the input layer of the FFNs in the encoder
+            blocks.
+        attention_drop_rate (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        drop_rate (`float`, *optional*, defaults to 0.0):
+            The dropout ratio following linear projections.
+        drop_path_rate (`float`, *optional*, defaults to `0.0`):
+            The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
+        num_transformer_layers(`int`, *optional*, defaults to 14):
+            Number of transformer self-attention layers
+        pos_emb_type (`str`, *optional*, defaults to 'learnable'):
+            Type of positional embedding used. Alternative: 'sinusoidal'
+
+    Example:
+
+    ```python
+    >>> from transformers import CctConfig, CctModel
+
+    >>> # Initializing a Cct msft/cct style configuration
+    >>> configuration = CctConfig()
+
+    >>> # Initializing a model (with random weights) from the msft/cct style configuration
+    >>> model = CctModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "cct"
+
+    def __init__(
+        self,
+        img_size=384,
+        in_channels=3,
+        out_channels=[64, 384],
+        conv_kernel_size=7,
+        conv_stride=2,
+        conv_padding=3,
+        conv_bias=False,
+        pool_kernel_size=3,
+        pool_stride=2,
+        pool_padding=1,
+        num_conv_layers=2,
+        embed_dim=384,
+        num_heads=6,
+        mlp_ratio=3,
+        attention_drop_rate=0.1,
+        drop_rate=0.0,
+        drop_path_rate=0.0,
+        num_transformer_layers=14,
+        pos_emb_type="learnable",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.img_size = img_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_channels = out_channels[-1]
+        self.conv_kernel_size = conv_kernel_size
+        self.conv_stride = conv_stride
+        self.conv_padding = conv_padding
+        self.conv_bias = conv_bias
+        self.pool_kernel_size = pool_kernel_size
+        self.pool_stride = pool_stride
+        self.pool_padding = pool_padding
+        self.num_conv_layers = num_conv_layers
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.attention_drop_rate = attention_drop_rate
+        self.drop_rate = drop_rate
+        self.drop_path_rate = drop_path_rate
+        self.num_transformer_layers = num_transformer_layers
+        self.pos_emb_type = pos_emb_type

modeling_cct.py

@@ -0,0 +1,437 @@
+# coding=utf-8
+# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch CCT model."""
+
+
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.modeling_outputs import ImageClassifierOutputWithNoAttention, ModelOutput
+from transformers import PreTrainedModel
+from .configuration_cct import CctConfig
+
+# General docstring
+_CONFIG_FOR_DOC = "CctConfig"
+
+# Base docstring
+_CHECKPOINT_FOR_DOC = "rishabbala/cct_14_7x2_384"
+_EXPECTED_OUTPUT_SHAPE = [1, 384]
+
+# Image classification docstring
+_IMAGE_CLASS_CHECKPOINT = "rishabbala/cct_14_7x2_384"
+_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
+
+
+CCT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "rishabbala/cct_14_7x2_384",
+    "rishabbala/cct_14_7x2_224"
+    # See all CCT models at https://huggingface.co/models?filter=cct
+]
+
+
+@dataclass
+class BaseModelOutputWithSeqPool(ModelOutput):
+    """
+    Base class for model's outputs, with potential hidden states and attentions.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model prior to sequential pooling.
+        hidden_state_post_pool (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model post sequential pooling.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
+            plus the initial embedding outputs.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    hidden_state_post_pool: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+# Copied from transformers.models.beit.modeling_beit.drop_path
+def drop_path(input, drop_prob: float = 0.0, training: bool = False):
+    """
+    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+    argument.
+    """
+    if drop_prob == 0.0 or not training:
+        return input
+    keep_prob = 1 - drop_prob
+    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+    random_tensor.floor_()  # binarize
+    output = input.div(keep_prob) * random_tensor
+    return output
+
+
+# Copied from transformers.models.beit.modeling_beit.BeitDropPath
+class CctDropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob: Optional[float] = None) -> None:
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return "p={}".format(self.drop_prob)
+
+
+class CctConvEmbeddings(nn.Module):
+    """
+    Performs convolutional tokenization of the input image.
+    """
+
+    def __init__(self, config: CctConfig):
+        super().__init__()
+        self.in_channels = config.in_channels
+        self.img_size = config.img_size
+
+        channels_size = [config.in_channels] + config.out_channels
+        assert (
+            len(channels_size) == config.num_conv_layers + 1
+        ), "Ensure that the number output channels matches the number of conv layers"
+
+        self.embedding_layers = nn.ModuleList([])
+        for i in range(config.num_conv_layers):
+            self.embedding_layers.extend(
+                [
+                    nn.Conv2d(
+                        channels_size[i],
+                        channels_size[i + 1],
+                        kernel_size=config.conv_kernel_size,
+                        stride=config.conv_stride,
+                        padding=config.conv_padding,
+                        bias=config.conv_bias,
+                    ),
+                    nn.ReLU(),
+                    nn.MaxPool2d(config.pool_kernel_size, stride=config.pool_stride, padding=config.pool_padding),
+                ]
+            )
+
+    def forward(self, pixel_values):
+        for layer in self.embedding_layers:
+            pixel_values = layer(pixel_values)
+        batch_size, num_channels, height, width = pixel_values.shape
+        hidden_size = height * width
+        # rearrange "b c h w -> b (h w) c"
+        pixel_values = pixel_values.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
+        return pixel_values
+
+    def get_sequence_length(self) -> int:
+        return self.forward(torch.zeros((1, self.in_channels, self.img_size, self.img_size))).shape[1]
+
+
+class CctSelfAttention(nn.Module):
+    """
+    Attention Module that computes self-attention, given an input hidden_state. Q, K, V are computed implicitly from
+    hidden_state
+    """
+
+    def __init__(self, embed_dim, num_heads=6, attention_drop_rate=0.1, drop_rate=0.0):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = embed_dim // self.num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(embed_dim, embed_dim * 3, bias=False)
+        self.attn_drop = nn.Dropout(attention_drop_rate)
+        self.proj = nn.Linear(embed_dim, embed_dim)
+        self.proj_drop = nn.Dropout(drop_rate)
+
+    def forward(self, hidden_state):
+        B, N, C = hidden_state.shape
+        qkv = self.qkv(hidden_state).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        hidden_state = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        hidden_state = self.proj(hidden_state)
+        hidden_state = self.proj_drop(hidden_state)
+        return hidden_state
+
+
+class CctStage(nn.Module):
+    """
+    CCT stage composed of stacked transformer layers
+    """
+
+    def __init__(
+        self, embed_dim=384, num_heads=6, mlp_ratio=3, drop_rate=0.0, attention_drop_rate=0.1, drop_path_rate=0.0
+    ):
+        super().__init__()
+        dim_feedforward = mlp_ratio * embed_dim
+        self.pre_norm = nn.LayerNorm(embed_dim)
+
+        self.linear1 = nn.Linear(embed_dim, dim_feedforward)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.linear2 = nn.Linear(dim_feedforward, embed_dim)
+        self.self_attn = CctSelfAttention(
+            embed_dim=embed_dim, num_heads=num_heads, attention_drop_rate=attention_drop_rate, drop_rate=drop_rate
+        )
+        self.dropout1 = nn.Dropout(drop_rate)
+        self.dropout2 = nn.Dropout(drop_rate)
+        self.drop_path = CctDropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
+        self.activation = F.gelu
+
+    def forward(self, hidden_state):
+        hidden_state = hidden_state + self.drop_path(self.self_attn(self.pre_norm(hidden_state)))
+        hidden_state = self.norm1(hidden_state)
+        hidden_state = hidden_state + self.drop_path(
+            self.dropout2(self.linear2(self.dropout1(self.activation(self.linear1(hidden_state)))))
+        )
+
+        return hidden_state
+
+
+class CctEncoder(nn.Module):
+    """
+    Class that combines CctConvEmbeddings and CctStage. Output is of type BaseModelOutputWithSeqPool if return_dict is
+    set to True, else the output is a Tuple
+    """
+
+    def __init__(self, config: CctConfig, sequence_length: int):
+        super().__init__()
+        assert sequence_length is not None, "Sequence Length required to initialize positional embedding"
+
+        int(config.embed_dim * config.mlp_ratio)
+        self.attention_pool = nn.Linear(config.embed_dim, 1)
+
+        if config.pos_emb_type == "learnable":
+            self.positional_emb = nn.Parameter(
+                self.learnable_embedding(sequence_length, config.embed_dim), requires_grad=True
+            )
+        else:
+            self.positional_emb = nn.Parameter(
+                self.sinusoidal_embedding(sequence_length, config.embed_dim), requires_grad=False
+            )
+
+        self.dropout = nn.Dropout(config.drop_rate)
+        stochastic_dropout_rate = [
+            x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_transformer_layers)
+        ]
+
+        self.blocks = nn.ModuleList(
+            [
+                CctStage(
+                    config.embed_dim,
+                    config.num_heads,
+                    config.mlp_ratio,
+                    config.drop_rate,
+                    config.attention_drop_rate,
+                    stochastic_dropout_rate[i],
+                )
+                for i in range(config.num_transformer_layers)
+            ]
+        )
+        self.norm = nn.LayerNorm(config.embed_dim)
+
+    def forward(self, pixel_values, output_hidden_states=False, return_dict=True) -> BaseModelOutputWithSeqPool:
+        all_hidden_states = ()
+
+        hidden_state = pixel_values + self.positional_emb
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_state,)
+        hidden_state = self.dropout(hidden_state)
+
+        for blk in self.blocks:
+            hidden_state = blk(hidden_state)
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_state,)
+
+        hidden_state_pre_pool = self.norm(hidden_state)
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states[:-1] + (hidden_state_pre_pool,)
+
+        seq_pool_attn = F.softmax(self.attention_pool(hidden_state_pre_pool), dim=1)
+        hidden_state_post_pool = torch.matmul(seq_pool_attn.transpose(-1, -2), hidden_state_pre_pool).squeeze(-2)
+        seq_pool_attn = seq_pool_attn.squeeze()
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_state_post_pool,)
+
+        if not return_dict:
+            if output_hidden_states:
+                return (hidden_state_pre_pool, hidden_state_post_pool, all_hidden_states)
+            else:
+                return (hidden_state_pre_pool, hidden_state_post_pool)
+
+        return BaseModelOutputWithSeqPool(
+            last_hidden_state=hidden_state_pre_pool,
+            hidden_state_post_pool=hidden_state_post_pool,
+            hidden_states=all_hidden_states if output_hidden_states else None,
+        )
+
+    @staticmethod
+    def learnable_embedding(sequence_length, embed_dim):
+        pe = torch.zeros(1, sequence_length, embed_dim)
+        return nn.init.trunc_normal_(pe, std=0.2)
+
+    @staticmethod
+    def sinusoidal_embedding(sequence_length, embed_dim):
+        pe = torch.FloatTensor(
+            [[p / (10000 ** (2 * (i // 2) / embed_dim)) for i in range(embed_dim)] for p in range(sequence_length)]
+        )
+        pe[:, 0::2] = torch.sin(pe[:, 0::2])
+        pe[:, 1::2] = torch.cos(pe[:, 1::2])
+        return pe.unsqueeze(0)
+
+
+class CctPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = CctConfig
+    base_model_prefix = "cct"
+    main_input_name = "pixel_values"
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.ModuleList):
+            for module_child in module:
+                self._init_weights(module_child)
+        elif isinstance(module, nn.Module) and len(list(module.children())) > 0:
+            for module_child in module.children():
+                self._init_weights(module_child)
+        elif isinstance(module, nn.Linear):
+            nn.init.trunc_normal_(module.weight, std=0.02)
+            if module.bias is not None:
+                nn.init.constant_(module.bias, 0.0)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.constant_(module.bias, 0.0)
+            nn.init.constant_(module.weight, 1.0)
+        elif isinstance(module, nn.Conv2d):
+            nn.init.kaiming_normal_(module.weight)
+
+
+class CctModel(CctPreTrainedModel):
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+        self.embedder = CctConvEmbeddings(config)
+        self.encoder = CctEncoder(config, self.embedder.get_sequence_length())
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithSeqPool]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values (input image)")
+
+        embedder_outputs = self.embedder(pixel_values)
+        encoder_outputs = self.encoder(
+            embedder_outputs,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        return encoder_outputs
+
+class CctForImageClassification(CctPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.cct = CctModel(config, add_pooling_layer=False)
+        # Classifier head
+        self.classifier = nn.Linear(config.embed_dim, config.num_labels) if config.num_labels > 0 else nn.Identity()
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        outputs = self.cct(
+            pixel_values,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs.hidden_state_post_pool if return_dict else outputs[1]
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.config.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            out = (logits, outputs[2]) if output_hidden_states else (logits,)
+            return (loss,) + out if loss is not None else out
+
+        return ImageClassifierOutputWithNoAttention(
+            loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None
+        )

pytorch_model.bin

@@ -0,0 +1,3 @@
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+oid sha256:b3131ecbb1273a6d9eb03edb78a2f2d5a871c73e4e7c94bf70db98b870ff565b
+size 90104217