webapp/seed/Lib/site-packages/transformers/models/cvt/modeling_cvt.py

# Copyright 2022 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 CvT model."""

import collections.abc
from dataclasses import dataclass

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ... import initialization as init
from ...modeling_outputs import ImageClassifierOutputWithNoAttention, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from .configuration_cvt import CvtConfig


logger = logging.get_logger(__name__)


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for model's outputs, with potential hidden states and attentions.
    """
)
class BaseModelOutputWithCLSToken(ModelOutput):
    r"""
    cls_token_value (`torch.FloatTensor` of shape `(batch_size, 1, hidden_size)`):
        Classification token at the output of the last layer of the model.
    """

    last_hidden_state: torch.FloatTensor | None = None
    cls_token_value: torch.FloatTensor | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    """
    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 CvtDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: float | None = 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 f"p={self.drop_prob}"


class CvtEmbeddings(nn.Module):
    """
    Construct the CvT embeddings.
    """

    def __init__(self, patch_size, num_channels, embed_dim, stride, padding, dropout_rate):
        super().__init__()
        self.convolution_embeddings = CvtConvEmbeddings(
            patch_size=patch_size, num_channels=num_channels, embed_dim=embed_dim, stride=stride, padding=padding
        )
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, pixel_values):
        hidden_state = self.convolution_embeddings(pixel_values)
        hidden_state = self.dropout(hidden_state)
        return hidden_state


class CvtConvEmbeddings(nn.Module):
    """
    Image to Conv Embedding.
    """

    def __init__(self, patch_size, num_channels, embed_dim, stride, padding):
        super().__init__()
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        self.patch_size = patch_size
        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
        self.normalization = nn.LayerNorm(embed_dim)

    def forward(self, pixel_values):
        pixel_values = self.projection(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)
        if self.normalization:
            pixel_values = self.normalization(pixel_values)
        # rearrange "b (h w) c" -> b c h w"
        pixel_values = pixel_values.permute(0, 2, 1).view(batch_size, num_channels, height, width)
        return pixel_values


class CvtSelfAttentionConvProjection(nn.Module):
    def __init__(self, embed_dim, kernel_size, padding, stride):
        super().__init__()
        self.convolution = nn.Conv2d(
            embed_dim,
            embed_dim,
            kernel_size=kernel_size,
            padding=padding,
            stride=stride,
            bias=False,
            groups=embed_dim,
        )
        self.normalization = nn.BatchNorm2d(embed_dim)

    def forward(self, hidden_state):
        hidden_state = self.convolution(hidden_state)
        hidden_state = self.normalization(hidden_state)
        return hidden_state


class CvtSelfAttentionLinearProjection(nn.Module):
    def forward(self, hidden_state):
        batch_size, num_channels, height, width = hidden_state.shape
        hidden_size = height * width
        # rearrange " b c h w -> b (h w) c"
        hidden_state = hidden_state.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
        return hidden_state


class CvtSelfAttentionProjection(nn.Module):
    def __init__(self, embed_dim, kernel_size, padding, stride, projection_method="dw_bn"):
        super().__init__()
        if projection_method == "dw_bn":
            self.convolution_projection = CvtSelfAttentionConvProjection(embed_dim, kernel_size, padding, stride)
        self.linear_projection = CvtSelfAttentionLinearProjection()

    def forward(self, hidden_state):
        hidden_state = self.convolution_projection(hidden_state)
        hidden_state = self.linear_projection(hidden_state)
        return hidden_state


class CvtSelfAttention(nn.Module):
    def __init__(
        self,
        num_heads,
        embed_dim,
        kernel_size,
        padding_q,
        padding_kv,
        stride_q,
        stride_kv,
        qkv_projection_method,
        qkv_bias,
        attention_drop_rate,
        with_cls_token=True,
        **kwargs,
    ):
        super().__init__()
        self.scale = embed_dim**-0.5
        self.with_cls_token = with_cls_token
        self.embed_dim = embed_dim
        self.num_heads = num_heads

        self.convolution_projection_query = CvtSelfAttentionProjection(
            embed_dim,
            kernel_size,
            padding_q,
            stride_q,
            projection_method="linear" if qkv_projection_method == "avg" else qkv_projection_method,
        )
        self.convolution_projection_key = CvtSelfAttentionProjection(
            embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method
        )
        self.convolution_projection_value = CvtSelfAttentionProjection(
            embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method
        )

        self.projection_query = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.projection_key = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.projection_value = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)

        self.dropout = nn.Dropout(attention_drop_rate)

    def rearrange_for_multi_head_attention(self, hidden_state):
        batch_size, hidden_size, _ = hidden_state.shape
        head_dim = self.embed_dim // self.num_heads
        # rearrange 'b t (h d) -> b h t d'
        return hidden_state.view(batch_size, hidden_size, self.num_heads, head_dim).permute(0, 2, 1, 3)

    def forward(self, hidden_state, height, width):
        if self.with_cls_token:
            cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
        batch_size, hidden_size, num_channels = hidden_state.shape
        # rearrange "b (h w) c -> b c h w"
        hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)

        key = self.convolution_projection_key(hidden_state)
        query = self.convolution_projection_query(hidden_state)
        value = self.convolution_projection_value(hidden_state)

        if self.with_cls_token:
            query = torch.cat((cls_token, query), dim=1)
            key = torch.cat((cls_token, key), dim=1)
            value = torch.cat((cls_token, value), dim=1)

        head_dim = self.embed_dim // self.num_heads

        query = self.rearrange_for_multi_head_attention(self.projection_query(query))
        key = self.rearrange_for_multi_head_attention(self.projection_key(key))
        value = self.rearrange_for_multi_head_attention(self.projection_value(value))

        attention_score = torch.einsum("bhlk,bhtk->bhlt", [query, key]) * self.scale
        attention_probs = torch.nn.functional.softmax(attention_score, dim=-1)
        attention_probs = self.dropout(attention_probs)

        context = torch.einsum("bhlt,bhtv->bhlv", [attention_probs, value])
        # rearrange"b h t d -> b t (h d)"
        _, _, hidden_size, _ = context.shape
        context = context.permute(0, 2, 1, 3).contiguous().view(batch_size, hidden_size, self.num_heads * head_dim)
        return context


class CvtSelfOutput(nn.Module):
    """
    The residual connection is defined in CvtLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, embed_dim, drop_rate):
        super().__init__()
        self.dense = nn.Linear(embed_dim, embed_dim)
        self.dropout = nn.Dropout(drop_rate)

    def forward(self, hidden_state, input_tensor):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.dropout(hidden_state)
        return hidden_state


class CvtAttention(nn.Module):
    def __init__(
        self,
        num_heads,
        embed_dim,
        kernel_size,
        padding_q,
        padding_kv,
        stride_q,
        stride_kv,
        qkv_projection_method,
        qkv_bias,
        attention_drop_rate,
        drop_rate,
        with_cls_token=True,
    ):
        super().__init__()
        self.attention = CvtSelfAttention(
            num_heads,
            embed_dim,
            kernel_size,
            padding_q,
            padding_kv,
            stride_q,
            stride_kv,
            qkv_projection_method,
            qkv_bias,
            attention_drop_rate,
            with_cls_token,
        )
        self.output = CvtSelfOutput(embed_dim, drop_rate)

    def forward(self, hidden_state, height, width):
        self_output = self.attention(hidden_state, height, width)
        attention_output = self.output(self_output, hidden_state)
        return attention_output


class CvtIntermediate(nn.Module):
    def __init__(self, embed_dim, mlp_ratio):
        super().__init__()
        self.dense = nn.Linear(embed_dim, int(embed_dim * mlp_ratio))
        self.activation = nn.GELU()

    def forward(self, hidden_state):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.activation(hidden_state)
        return hidden_state


class CvtOutput(nn.Module):
    def __init__(self, embed_dim, mlp_ratio, drop_rate):
        super().__init__()
        self.dense = nn.Linear(int(embed_dim * mlp_ratio), embed_dim)
        self.dropout = nn.Dropout(drop_rate)

    def forward(self, hidden_state, input_tensor):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.dropout(hidden_state)
        hidden_state = hidden_state + input_tensor
        return hidden_state


class CvtLayer(nn.Module):
    """
    CvtLayer composed by attention layers, normalization and multi-layer perceptrons (mlps).
    """

    def __init__(
        self,
        num_heads,
        embed_dim,
        kernel_size,
        padding_q,
        padding_kv,
        stride_q,
        stride_kv,
        qkv_projection_method,
        qkv_bias,
        attention_drop_rate,
        drop_rate,
        mlp_ratio,
        drop_path_rate,
        with_cls_token=True,
    ):
        super().__init__()
        self.attention = CvtAttention(
            num_heads,
            embed_dim,
            kernel_size,
            padding_q,
            padding_kv,
            stride_q,
            stride_kv,
            qkv_projection_method,
            qkv_bias,
            attention_drop_rate,
            drop_rate,
            with_cls_token,
        )

        self.intermediate = CvtIntermediate(embed_dim, mlp_ratio)
        self.output = CvtOutput(embed_dim, mlp_ratio, drop_rate)
        self.drop_path = CvtDropPath(drop_prob=drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.layernorm_before = nn.LayerNorm(embed_dim)
        self.layernorm_after = nn.LayerNorm(embed_dim)

    def forward(self, hidden_state, height, width):
        self_attention_output = self.attention(
            self.layernorm_before(hidden_state),  # in Cvt, layernorm is applied before self-attention
            height,
            width,
        )
        attention_output = self_attention_output
        attention_output = self.drop_path(attention_output)

        # first residual connection
        hidden_state = attention_output + hidden_state

        # in Cvt, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_state)
        layer_output = self.intermediate(layer_output)

        # second residual connection is done here
        layer_output = self.output(layer_output, hidden_state)
        layer_output = self.drop_path(layer_output)
        return layer_output


class CvtStage(nn.Module):
    def __init__(self, config, stage):
        super().__init__()
        self.config = config
        self.stage = stage
        if self.config.cls_token[self.stage]:
            self.cls_token = nn.Parameter(torch.randn(1, 1, self.config.embed_dim[-1]))

        self.embedding = CvtEmbeddings(
            patch_size=config.patch_sizes[self.stage],
            stride=config.patch_stride[self.stage],
            num_channels=config.num_channels if self.stage == 0 else config.embed_dim[self.stage - 1],
            embed_dim=config.embed_dim[self.stage],
            padding=config.patch_padding[self.stage],
            dropout_rate=config.drop_rate[self.stage],
        )

        drop_path_rates = [
            x.item() for x in torch.linspace(0, config.drop_path_rate[self.stage], config.depth[stage], device="cpu")
        ]

        self.layers = nn.Sequential(
            *[
                CvtLayer(
                    num_heads=config.num_heads[self.stage],
                    embed_dim=config.embed_dim[self.stage],
                    kernel_size=config.kernel_qkv[self.stage],
                    padding_q=config.padding_q[self.stage],
                    padding_kv=config.padding_kv[self.stage],
                    stride_kv=config.stride_kv[self.stage],
                    stride_q=config.stride_q[self.stage],
                    qkv_projection_method=config.qkv_projection_method[self.stage],
                    qkv_bias=config.qkv_bias[self.stage],
                    attention_drop_rate=config.attention_drop_rate[self.stage],
                    drop_rate=config.drop_rate[self.stage],
                    drop_path_rate=drop_path_rates[self.stage],
                    mlp_ratio=config.mlp_ratio[self.stage],
                    with_cls_token=config.cls_token[self.stage],
                )
                for _ in range(config.depth[self.stage])
            ]
        )

    def forward(self, hidden_state):
        cls_token = None
        hidden_state = self.embedding(hidden_state)
        batch_size, num_channels, height, width = hidden_state.shape
        # rearrange b c h w -> b (h w) c"
        hidden_state = hidden_state.view(batch_size, num_channels, height * width).permute(0, 2, 1)
        if self.config.cls_token[self.stage]:
            cls_token = self.cls_token.expand(batch_size, -1, -1)
            hidden_state = torch.cat((cls_token, hidden_state), dim=1)

        for layer in self.layers:
            layer_outputs = layer(hidden_state, height, width)
            hidden_state = layer_outputs

        if self.config.cls_token[self.stage]:
            cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
        hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)
        return hidden_state, cls_token


class CvtEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.stages = nn.ModuleList([])
        for stage_idx in range(len(config.depth)):
            self.stages.append(CvtStage(config, stage_idx))

    def forward(self, pixel_values, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        hidden_state = pixel_values

        cls_token = None
        for _, (stage_module) in enumerate(self.stages):
            hidden_state, cls_token = stage_module(hidden_state)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_state,)

        if not return_dict:
            return tuple(v for v in [hidden_state, cls_token, all_hidden_states] if v is not None)

        return BaseModelOutputWithCLSToken(
            last_hidden_state=hidden_state,
            cls_token_value=cls_token,
            hidden_states=all_hidden_states,
        )


@auto_docstring
class CvtPreTrainedModel(PreTrainedModel):
    config: CvtConfig
    base_model_prefix = "cvt"
    main_input_name = "pixel_values"
    _no_split_modules = ["CvtLayer"]

    @torch.no_grad()
    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            init.trunc_normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
            init.zeros_(module.bias)
            init.ones_(module.weight)
            if getattr(module, "running_mean", None) is not None:
                init.zeros_(module.running_mean)
                init.ones_(module.running_var)
                init.zeros_(module.num_batches_tracked)
        elif isinstance(module, CvtStage):
            if self.config.cls_token[module.stage]:
                init.trunc_normal_(module.cls_token, mean=0.0, std=self.config.initializer_range)


@auto_docstring
class CvtModel(CvtPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        r"""
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        """
        super().__init__(config)
        self.config = config
        self.encoder = CvtEncoder(config)
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | BaseModelOutputWithCLSToken:
        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")

        encoder_outputs = self.encoder(
            pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]

        if not return_dict:
            return (sequence_output,) + encoder_outputs[1:]

        return BaseModelOutputWithCLSToken(
            last_hidden_state=sequence_output,
            cls_token_value=encoder_outputs.cls_token_value,
            hidden_states=encoder_outputs.hidden_states,
        )


@auto_docstring(
    custom_intro="""
    Cvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.
    """
)
class CvtForImageClassification(CvtPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.cvt = CvtModel(config, add_pooling_layer=False)
        self.layernorm = nn.LayerNorm(config.embed_dim[-1])
        # Classifier head
        self.classifier = (
            nn.Linear(config.embed_dim[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> 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
        outputs = self.cvt(
            pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        cls_token = outputs[1]
        if self.config.cls_token[-1]:
            sequence_output = self.layernorm(cls_token)
        else:
            batch_size, num_channels, height, width = sequence_output.shape
            # rearrange "b c h w -> b (h w) c"
            sequence_output = sequence_output.view(batch_size, num_channels, height * width).permute(0, 2, 1)
            sequence_output = self.layernorm(sequence_output)

        sequence_output_mean = sequence_output.mean(dim=1)
        logits = self.classifier(sequence_output_mean)

        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:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)


__all__ = ["CvtForImageClassification", "CvtModel", "CvtPreTrainedModel"]