Ross Wightman
3 years ago
commit
25d52ea71d
@ -1,4 +1,4 @@
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from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
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from .binary_cross_entropy import DenseBinaryCrossEntropy
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from .binary_cross_entropy import BinaryCrossEntropy
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from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
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from .jsd import JsdCrossEntropy
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@ -1,23 +1,47 @@
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""" Binary Cross Entropy w/ a few extras
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class DenseBinaryCrossEntropy(nn.Module):
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""" BCE using one-hot from dense targets w/ label smoothing
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class BinaryCrossEntropy(nn.Module):
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""" BCE with optional one-hot from dense targets, label smoothing, thresholding
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NOTE for experiments comparing CE to BCE /w label smoothing, may remove
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"""
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def __init__(self, smoothing=0.1):
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super(DenseBinaryCrossEntropy, self).__init__()
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def __init__(
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self, smoothing=0.1, target_threshold: Optional[float] = None, weight: Optional[torch.Tensor] = None,
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reduction: str = 'mean', pos_weight: Optional[torch.Tensor] = None):
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super(BinaryCrossEntropy, self).__init__()
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assert 0. <= smoothing < 1.0
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self.smoothing = smoothing
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self.bce = nn.BCEWithLogitsLoss()
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self.target_threshold = target_threshold
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self.reduction = reduction
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self.register_buffer('weight', weight)
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self.register_buffer('pos_weight', pos_weight)
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def forward(self, x, target):
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num_classes = x.shape[-1]
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off_value = self.smoothing / num_classes
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on_value = 1. - self.smoothing + off_value
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target = target.long().view(-1, 1)
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target = torch.full(
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(target.size()[0], num_classes), off_value, device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
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return self.bce(x, target)
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def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
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assert x.shape[0] == target.shape[0]
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if target.shape != x.shape:
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# NOTE currently assume smoothing or other label softening is applied upstream if targets are already sparse
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num_classes = x.shape[-1]
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# FIXME should off/on be different for smoothing w/ BCE? Other impl out there differ
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off_value = self.smoothing / num_classes
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on_value = 1. - self.smoothing + off_value
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target = target.long().view(-1, 1)
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target = torch.full(
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(target.size()[0], num_classes),
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off_value,
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device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
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if self.target_threshold is not None:
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# Make target 0, or 1 if threshold set
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target = target.gt(self.target_threshold).to(dtype=target.dtype)
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return F.binary_cross_entropy_with_logits(
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x, target,
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self.weight,
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pos_weight=self.pos_weight,
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reduction=self.reduction)
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@ -0,0 +1,420 @@
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""" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
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Model from official source: https://github.com/microsoft/unilm/tree/master/beit
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At this point only the 1k fine-tuned classification weights and model configs have been added,
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see original source above for pre-training models and procedure.
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Modifications by / Copyright 2021 Ross Wightman, original copyrights below
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"""
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# --------------------------------------------------------
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# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
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# Github source: https://github.com/microsoft/unilm/tree/master/beit
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# By Hangbo Bao
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# Based on timm and DeiT code bases
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# https://github.com/facebookresearch/deit/
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# https://github.com/facebookresearch/dino
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# --------------------------------------------------------'
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import math
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from functools import partial
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .helpers import build_model_with_cfg
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from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
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from .registry import register_model
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from .vision_transformer import checkpoint_filter_fn
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'beit_base_patch16_224': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'),
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'beit_base_patch16_384': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
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input_size=(3, 384, 384), crop_pct=1.0,
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),
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'beit_base_patch16_224_in22k': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22k.pth',
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num_classes=21841,
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),
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'beit_large_patch16_224': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'),
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'beit_large_patch16_384': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
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input_size=(3, 384, 384), crop_pct=1.0,
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),
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'beit_large_patch16_512': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
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input_size=(3, 512, 512), crop_pct=1.0,
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),
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'beit_large_patch16_224_in22k': _cfg(
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url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22k.pth',
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num_classes=21841,
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),
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}
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class Attention(nn.Module):
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def __init__(
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self, dim, num_heads=8, qkv_bias=False, attn_drop=0.,
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proj_drop=0., window_size=None, attn_head_dim=None):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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if attn_head_dim is not None:
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head_dim = attn_head_dim
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all_head_dim = head_dim * self.num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
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self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
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else:
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self.q_bias = None
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self.v_bias = None
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if window_size:
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self.window_size = window_size
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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else:
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self.window_size = None
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self.relative_position_bias_table = None
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self.relative_position_index = None
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(all_head_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
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B, N, C = x.shape
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qkv_bias = None
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if self.q_bias is not None:
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if torch.jit.is_scripting():
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# FIXME requires_grad breaks w/ torchscript
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias), self.v_bias))
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else:
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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if self.relative_position_bias_table is not None:
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if rel_pos_bias is not None:
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attn = attn + rel_pos_bias
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
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drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
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window_size=None, attn_head_dim=None):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
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window_size=window_size, attn_head_dim=attn_head_dim)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if init_values:
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
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else:
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self.gamma_1, self.gamma_2 = None, None
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def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
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if self.gamma_1 is None:
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x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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else:
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
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return x
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class RelativePositionBias(nn.Module):
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def __init__(self, window_size, num_heads):
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super().__init__()
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self.window_size = window_size
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self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# cls to token & token 2 cls & cls to cls
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(window_size[0])
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coords_w = torch.arange(window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * window_size[1] - 1
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relative_position_index = \
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torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
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relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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relative_position_index[0, 0:] = self.num_relative_distance - 3
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relative_position_index[0:, 0] = self.num_relative_distance - 2
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relative_position_index[0, 0] = self.num_relative_distance - 1
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self.register_buffer("relative_position_index", relative_position_index)
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# trunc_normal_(self.relative_position_bias_table, std=.02)
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def forward(self):
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relative_position_bias = \
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self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1] + 1,
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self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
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return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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class Beit(nn.Module):
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""" Vision Transformer with support for patch or hybrid CNN input stage
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=None,
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use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
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use_mean_pooling=True, init_scale=0.001):
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super().__init__()
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.patch_embed = PatchEmbed(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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if use_abs_pos_emb:
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
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else:
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self.pos_embed = None
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self.pos_drop = nn.Dropout(p=drop_rate)
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if use_shared_rel_pos_bias:
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self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.grid_size, num_heads=num_heads)
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else:
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self.rel_pos_bias = None
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.use_rel_pos_bias = use_rel_pos_bias
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self.blocks = nn.ModuleList([
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Block(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
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init_values=init_values, window_size=self.patch_embed.grid_size if use_rel_pos_bias else None)
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for i in range(depth)])
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self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
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self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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self.apply(self._init_weights)
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if self.pos_embed is not None:
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trunc_normal_(self.pos_embed, std=.02)
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trunc_normal_(self.cls_token, std=.02)
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# trunc_normal_(self.mask_token, std=.02)
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self.fix_init_weight()
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if isinstance(self.head, nn.Linear):
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trunc_normal_(self.head.weight, std=.02)
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self.head.weight.data.mul_(init_scale)
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self.head.bias.data.mul_(init_scale)
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def fix_init_weight(self):
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def rescale(param, layer_id):
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param.div_(math.sqrt(2.0 * layer_id))
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|
||||
for layer_id, layer in enumerate(self.blocks):
|
||||
rescale(layer.attn.proj.weight.data, layer_id + 1)
|
||||
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
def get_num_layers(self):
|
||||
return len(self.blocks)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed', 'cls_token'}
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.patch_embed(x)
|
||||
batch_size, seq_len, _ = x.size()
|
||||
|
||||
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
if self.pos_embed is not None:
|
||||
x = x + self.pos_embed
|
||||
x = self.pos_drop(x)
|
||||
|
||||
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
|
||||
for blk in self.blocks:
|
||||
x = blk(x, rel_pos_bias=rel_pos_bias)
|
||||
|
||||
x = self.norm(x)
|
||||
if self.fc_norm is not None:
|
||||
t = x[:, 1:, :]
|
||||
return self.fc_norm(t.mean(1))
|
||||
else:
|
||||
return x[:, 0]
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
|
||||
default_cfg = default_cfg or default_cfgs[variant]
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Beit models.')
|
||||
|
||||
model = build_model_with_cfg(
|
||||
Beit, variant, pretrained,
|
||||
default_cfg=default_cfg,
|
||||
# FIXME an updated filter fn needed to interpolate rel pos emb if fine tuning to diff model sizes
|
||||
pretrained_filter_fn=checkpoint_filter_fn,
|
||||
**kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_base_patch16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
|
||||
model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_base_patch16_384(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
|
||||
model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
|
||||
model = _create_beit('beit_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_large_patch16_224(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
|
||||
model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_large_patch16_384(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
|
||||
model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_large_patch16_512(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
|
||||
model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
|
||||
model_kwargs = dict(
|
||||
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
|
||||
model = _create_beit('beit_large_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
@ -0,0 +1,497 @@
|
||||
""" CrossViT Model
|
||||
|
||||
@inproceedings{
|
||||
chen2021crossvit,
|
||||
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
|
||||
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
|
||||
booktitle={International Conference on Computer Vision (ICCV)},
|
||||
year={2021}
|
||||
}
|
||||
|
||||
Paper link: https://arxiv.org/abs/2103.14899
|
||||
Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
|
||||
|
||||
NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
|
||||
"""
|
||||
|
||||
# Copyright IBM All Rights Reserved.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
|
||||
"""
|
||||
Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
|
||||
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.hub
|
||||
from functools import partial
|
||||
from typing import List
|
||||
|
||||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||
from .helpers import build_model_with_cfg
|
||||
from .layers import DropPath, to_2tuple, trunc_normal_
|
||||
from .registry import register_model
|
||||
from .vision_transformer import Mlp, Block
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None, 'crop_pct': 0.875,
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
|
||||
'first_conv': ('patch_embed.0.proj', 'patch_embed.1.proj'),
|
||||
'classifier': ('head.0', 'head.1'),
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = {
|
||||
'crossvit_15_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'),
|
||||
'crossvit_15_dagger_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth',
|
||||
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
|
||||
),
|
||||
'crossvit_15_dagger_408': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth',
|
||||
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
|
||||
),
|
||||
'crossvit_18_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
|
||||
'crossvit_18_dagger_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth',
|
||||
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
|
||||
),
|
||||
'crossvit_18_dagger_408': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth',
|
||||
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
|
||||
),
|
||||
'crossvit_9_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
|
||||
'crossvit_9_dagger_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth',
|
||||
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
|
||||
),
|
||||
'crossvit_base_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'),
|
||||
'crossvit_small_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'),
|
||||
'crossvit_tiny_240': _cfg(
|
||||
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_tiny_224.pth'),
|
||||
}
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, multi_conv=False):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.num_patches = num_patches
|
||||
if multi_conv:
|
||||
if patch_size[0] == 12:
|
||||
self.proj = nn.Sequential(
|
||||
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
|
||||
)
|
||||
elif patch_size[0] == 16:
|
||||
self.proj = nn.Sequential(
|
||||
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1),
|
||||
)
|
||||
else:
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
|
||||
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(1, 2)
|
||||
return x
|
||||
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.wq = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.wk = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.wv = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x):
|
||||
B, N, C = x.shape
|
||||
# B1C -> B1H(C/H) -> BH1(C/H)
|
||||
q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
# BNC -> BNH(C/H) -> BHN(C/H)
|
||||
k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
# BNC -> BNH(C/H) -> BHN(C/H)
|
||||
v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, 1, C) # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class CrossAttentionBlock(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = CrossAttention(
|
||||
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MultiScaleBlock(nn.Module):
|
||||
|
||||
def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
|
||||
num_branches = len(dim)
|
||||
self.num_branches = num_branches
|
||||
# different branch could have different embedding size, the first one is the base
|
||||
self.blocks = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
tmp = []
|
||||
for i in range(depth[d]):
|
||||
tmp.append(Block(
|
||||
dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer))
|
||||
if len(tmp) != 0:
|
||||
self.blocks.append(nn.Sequential(*tmp))
|
||||
|
||||
if len(self.blocks) == 0:
|
||||
self.blocks = None
|
||||
|
||||
self.projs = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
if dim[d] == dim[(d + 1) % num_branches] and False:
|
||||
tmp = [nn.Identity()]
|
||||
else:
|
||||
tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d + 1) % num_branches])]
|
||||
self.projs.append(nn.Sequential(*tmp))
|
||||
|
||||
self.fusion = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
d_ = (d + 1) % num_branches
|
||||
nh = num_heads[d_]
|
||||
if depth[-1] == 0: # backward capability:
|
||||
self.fusion.append(
|
||||
CrossAttentionBlock(
|
||||
dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer))
|
||||
else:
|
||||
tmp = []
|
||||
for _ in range(depth[-1]):
|
||||
tmp.append(CrossAttentionBlock(
|
||||
dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer))
|
||||
self.fusion.append(nn.Sequential(*tmp))
|
||||
|
||||
self.revert_projs = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
if dim[(d + 1) % num_branches] == dim[d] and False:
|
||||
tmp = [nn.Identity()]
|
||||
else:
|
||||
tmp = [norm_layer(dim[(d + 1) % num_branches]), act_layer(),
|
||||
nn.Linear(dim[(d + 1) % num_branches], dim[d])]
|
||||
self.revert_projs.append(nn.Sequential(*tmp))
|
||||
|
||||
def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
|
||||
|
||||
outs_b = []
|
||||
for i, block in enumerate(self.blocks):
|
||||
outs_b.append(block(x[i]))
|
||||
|
||||
# only take the cls token out
|
||||
proj_cls_token = torch.jit.annotate(List[torch.Tensor], [])
|
||||
for i, proj in enumerate(self.projs):
|
||||
proj_cls_token.append(proj(outs_b[i][:, 0:1, ...]))
|
||||
|
||||
# cross attention
|
||||
outs = []
|
||||
for i, (fusion, revert_proj) in enumerate(zip(self.fusion, self.revert_projs)):
|
||||
tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
|
||||
tmp = fusion(tmp)
|
||||
reverted_proj_cls_token = revert_proj(tmp[:, 0:1, ...])
|
||||
tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1)
|
||||
outs.append(tmp)
|
||||
return outs
|
||||
|
||||
|
||||
def _compute_num_patches(img_size, patches):
|
||||
return [i[0] // p * i[1] // p for i, p in zip(img_size, patches)]
|
||||
|
||||
|
||||
class CrossViT(nn.Module):
|
||||
""" Vision Transformer with support for patch or hybrid CNN input stage
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, img_size=224, img_scale=(1.0, 1.0), patch_size=(8, 16), in_chans=3, num_classes=1000,
|
||||
embed_dim=(192, 384), depth=((1, 3, 1), (1, 3, 1), (1, 3, 1)), num_heads=(6, 12), mlp_ratio=(2., 2., 4.),
|
||||
qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False, crop_scale=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.num_classes = num_classes
|
||||
self.img_size = to_2tuple(img_size)
|
||||
img_scale = to_2tuple(img_scale)
|
||||
self.img_size_scaled = [tuple([int(sj * si) for sj in self.img_size]) for si in img_scale]
|
||||
self.crop_scale = crop_scale # crop instead of interpolate for scale
|
||||
num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
|
||||
self.num_branches = len(patch_size)
|
||||
self.embed_dim = embed_dim
|
||||
self.num_features = embed_dim[0] # to pass the tests
|
||||
self.patch_embed = nn.ModuleList()
|
||||
|
||||
# hard-coded for torch jit script
|
||||
for i in range(self.num_branches):
|
||||
setattr(self, f'pos_embed_{i}', nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i])))
|
||||
setattr(self, f'cls_token_{i}', nn.Parameter(torch.zeros(1, 1, embed_dim[i])))
|
||||
|
||||
for im_s, p, d in zip(self.img_size_scaled, patch_size, embed_dim):
|
||||
self.patch_embed.append(
|
||||
PatchEmbed(img_size=im_s, patch_size=p, in_chans=in_chans, embed_dim=d, multi_conv=multi_conv))
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
total_depth = sum([sum(x[-2:]) for x in depth])
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule
|
||||
dpr_ptr = 0
|
||||
self.blocks = nn.ModuleList()
|
||||
for idx, block_cfg in enumerate(depth):
|
||||
curr_depth = max(block_cfg[:-1]) + block_cfg[-1]
|
||||
dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth]
|
||||
blk = MultiScaleBlock(
|
||||
embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr_, norm_layer=norm_layer)
|
||||
dpr_ptr += curr_depth
|
||||
self.blocks.append(blk)
|
||||
|
||||
self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)])
|
||||
self.head = nn.ModuleList([
|
||||
nn.Linear(embed_dim[i], num_classes) if num_classes > 0 else nn.Identity()
|
||||
for i in range(self.num_branches)])
|
||||
|
||||
for i in range(self.num_branches):
|
||||
trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
|
||||
trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
out = set()
|
||||
for i in range(self.num_branches):
|
||||
out.add(f'cls_token_{i}')
|
||||
pe = getattr(self, f'pos_embed_{i}', None)
|
||||
if pe is not None and pe.requires_grad:
|
||||
out.add(f'pos_embed_{i}')
|
||||
return out
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.ModuleList(
|
||||
[nn.Linear(self.embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in
|
||||
range(self.num_branches)])
|
||||
|
||||
def forward_features(self, x):
|
||||
B, C, H, W = x.shape
|
||||
xs = []
|
||||
for i, patch_embed in enumerate(self.patch_embed):
|
||||
x_ = x
|
||||
ss = self.img_size_scaled[i]
|
||||
if H != ss[0] or W != ss[1]:
|
||||
if self.crop_scale and ss[0] <= H and ss[1] <= W:
|
||||
cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
|
||||
x_ = x_[:, :, cu:cu + ss[0], cl:cl + ss[1]]
|
||||
else:
|
||||
x_ = torch.nn.functional.interpolate(x_, size=ss, mode='bicubic', align_corners=False)
|
||||
x_ = patch_embed(x_)
|
||||
cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script
|
||||
cls_tokens = cls_tokens.expand(B, -1, -1)
|
||||
x_ = torch.cat((cls_tokens, x_), dim=1)
|
||||
pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1 # hard-coded for torch jit script
|
||||
x_ = x_ + pos_embed
|
||||
x_ = self.pos_drop(x_)
|
||||
xs.append(x_)
|
||||
|
||||
for i, blk in enumerate(self.blocks):
|
||||
xs = blk(xs)
|
||||
|
||||
# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
|
||||
xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
|
||||
return [xo[:, 0] for xo in xs]
|
||||
|
||||
def forward(self, x):
|
||||
xs = self.forward_features(x)
|
||||
ce_logits = [head(xs[i]) for i, head in enumerate(self.head)]
|
||||
if not isinstance(self.head[0], nn.Identity):
|
||||
ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
|
||||
return ce_logits
|
||||
|
||||
|
||||
def _create_crossvit(variant, pretrained=False, **kwargs):
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||
|
||||
def pretrained_filter_fn(state_dict):
|
||||
new_state_dict = {}
|
||||
for key in state_dict.keys():
|
||||
if 'pos_embed' in key or 'cls_token' in key:
|
||||
new_key = key.replace(".", "_")
|
||||
else:
|
||||
new_key = key
|
||||
new_state_dict[new_key] = state_dict[key]
|
||||
return new_state_dict
|
||||
|
||||
return build_model_with_cfg(
|
||||
CrossViT, variant, pretrained,
|
||||
default_cfg=default_cfgs[variant],
|
||||
pretrained_filter_fn=pretrained_filter_fn,
|
||||
**kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_tiny_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[3, 3], mlp_ratio=[4, 4, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_tiny_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_small_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[4, 4, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_small_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_base_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[384, 768], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[12, 12], mlp_ratio=[4, 4, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_base_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_15_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_18_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224 / 240), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_dagger_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224 / 240), patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_dagger_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_408(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 384/408), patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_408', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_240(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 224/240), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_240', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_408(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_scale=(1.0, 384/408), patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_408', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
Loading…
Reference in new issue