Spaces:
Runtime error
Runtime error
| # -------------------------------------------------------- | |
| # FocalNets -- Focal Modulation Networks | |
| # Copyright (c) 2022 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # Written by Jianwei Yang (jianwyan@microsoft.com) | |
| # -------------------------------------------------------- | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.checkpoint as checkpoint | |
| from timm.models.layers import DropPath, to_2tuple, trunc_normal_ | |
| from timm.models.registry import register_model | |
| from torchvision import transforms | |
| from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD | |
| from timm.data import create_transform | |
| from timm.data.transforms import _pil_interp | |
| class Mlp(nn.Module): | |
| def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): | |
| super().__init__() | |
| out_features = out_features or in_features | |
| hidden_features = hidden_features or in_features | |
| self.fc1 = nn.Linear(in_features, hidden_features) | |
| self.act = act_layer() | |
| self.fc2 = nn.Linear(hidden_features, out_features) | |
| self.drop = nn.Dropout(drop) | |
| def forward(self, x): | |
| x = self.fc1(x) | |
| x = self.act(x) | |
| x = self.drop(x) | |
| x = self.fc2(x) | |
| x = self.drop(x) | |
| return x | |
| class FocalModulation(nn.Module): | |
| def __init__(self, dim, focal_window, focal_level, focal_factor=2, bias=True, proj_drop=0.): | |
| super().__init__() | |
| self.dim = dim | |
| self.focal_window = focal_window | |
| self.focal_level = focal_level | |
| self.focal_factor = focal_factor | |
| self.f = nn.Linear(dim, 2*dim + (self.focal_level+1), bias=bias) | |
| self.h = nn.Conv2d(dim, dim, kernel_size=1, stride=1, bias=bias) | |
| self.act = nn.GELU() | |
| self.proj = nn.Linear(dim, dim) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| self.focal_layers = nn.ModuleList() | |
| self.kernel_sizes = [] | |
| for k in range(self.focal_level): | |
| kernel_size = self.focal_factor*k + self.focal_window | |
| self.focal_layers.append( | |
| nn.Sequential( | |
| nn.Conv2d(dim, dim, kernel_size=kernel_size, stride=1, | |
| groups=dim, padding=kernel_size//2, bias=False), | |
| nn.GELU(), | |
| ) | |
| ) | |
| self.kernel_sizes.append(kernel_size) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x: input features with shape of (B, H, W, C) | |
| """ | |
| C = x.shape[-1] | |
| # pre linear projection | |
| x = self.f(x).permute(0, 3, 1, 2).contiguous() | |
| q, ctx, self.gates = torch.split(x, (C, C, self.focal_level+1), 1) | |
| # context aggreation | |
| ctx_all = 0 | |
| for l in range(self.focal_level): | |
| ctx = self.focal_layers[l](ctx) | |
| ctx_all = ctx_all + ctx*self.gates[:, l:l+1] | |
| ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True)) | |
| ctx_all = ctx_all + ctx_global*self.gates[:,self.focal_level:] | |
| # focal modulation | |
| self.modulator = self.h(ctx_all) | |
| x_out = q*self.modulator | |
| x_out = x_out.permute(0, 2, 3, 1).contiguous() | |
| # post linear porjection | |
| x_out = self.proj(x_out) | |
| x_out = self.proj_drop(x_out) | |
| return x_out | |
| def extra_repr(self) -> str: | |
| return f'dim={self.dim}' | |
| def flops(self, N): | |
| # calculate flops for 1 window with token length of N | |
| flops = 0 | |
| flops += N * self.dim * (self.dim * 2 + (self.focal_level+1)) | |
| # focal convolution | |
| for k in range(self.focal_level): | |
| flops += N * (self.kernel_sizes[k]**2+1) * self.dim | |
| # global gating | |
| flops += N * 1 * self.dim | |
| # self.linear | |
| flops += N * self.dim * (self.dim + 1) | |
| # x = self.proj(x) | |
| flops += N * self.dim * self.dim | |
| return flops | |
| class FocalNetBlock(nn.Module): | |
| r""" Focal Modulation Network Block. | |
| Args: | |
| dim (int): Number of input channels. | |
| input_resolution (tuple[int]): Input resulotion. | |
| mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. | |
| drop (float, optional): Dropout rate. Default: 0.0 | |
| drop_path (float, optional): Stochastic depth rate. Default: 0.0 | |
| act_layer (nn.Module, optional): Activation layer. Default: nn.GELU | |
| norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm | |
| focal_level (int): Number of focal levels. | |
| focal_window (int): Focal window size at first focal level | |
| use_layerscale (bool): Whether use layerscale | |
| layerscale_value (float): Initial layerscale value | |
| use_postln (bool): Whether use layernorm after modulation | |
| """ | |
| def __init__(self, dim, input_resolution, mlp_ratio=4., drop=0., drop_path=0., | |
| act_layer=nn.GELU, norm_layer=nn.LayerNorm, | |
| focal_level=1, focal_window=3, | |
| use_layerscale=False, layerscale_value=1e-4, | |
| use_postln=False): | |
| super().__init__() | |
| self.dim = dim | |
| self.input_resolution = input_resolution | |
| self.mlp_ratio = mlp_ratio | |
| self.focal_window = focal_window | |
| self.focal_level = focal_level | |
| self.use_postln = use_postln | |
| self.norm1 = norm_layer(dim) | |
| self.modulation = FocalModulation(dim, proj_drop=drop, focal_window=focal_window, focal_level=self.focal_level) | |
| self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() | |
| self.norm2 = norm_layer(dim) | |
| mlp_hidden_dim = int(dim * mlp_ratio) | |
| self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) | |
| self.alpha = 3.0 if self.use_postln else 1.0 | |
| self.gamma_1 = 1.0 | |
| self.gamma_2 = 1.0 | |
| if use_layerscale: | |
| self.gamma_1 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True) | |
| self.gamma_2 = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True) | |
| self.H = None | |
| self.W = None | |
| def forward(self, x): | |
| H, W = self.H, self.W | |
| B, L, C = x.shape | |
| shortcut = x | |
| # Focal Modulation | |
| if not self.use_postln: | |
| x = self.norm1(x) | |
| x = x.view(B, H, W, C) | |
| x = self.modulation(x).view(B, H * W, C) | |
| # FFN | |
| x = shortcut*self.alpha + self.drop_path(self.gamma_1 * x) | |
| if self.use_postln: | |
| x = self.norm1(x) | |
| if not self.use_postln: | |
| x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) | |
| else: | |
| x = x*self.alpha + self.drop_path(self.gamma_2 * self.mlp(x)) | |
| x = self.norm2(x) | |
| return x | |
| def extra_repr(self) -> str: | |
| return f"dim={self.dim}, input_resolution={self.input_resolution}, " \ | |
| f"mlp_ratio={self.mlp_ratio}" | |
| def flops(self): | |
| flops = 0 | |
| H, W = self.input_resolution | |
| # norm1 | |
| flops += self.dim * H * W | |
| # W-MSA/SW-MSA | |
| flops += self.modulation.flops(H*W) | |
| # mlp | |
| flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio | |
| # norm2 | |
| flops += self.dim * H * W | |
| return flops | |
| class BasicLayer(nn.Module): | |
| """ A basic Focal Transformer layer for one stage. | |
| Args: | |
| dim (int): Number of input channels. | |
| input_resolution (tuple[int]): Input resolution. | |
| depth (int): Number of blocks. | |
| window_size (int): Local window size. | |
| mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. | |
| qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True | |
| qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. | |
| drop (float, optional): Dropout rate. Default: 0.0 | |
| drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 | |
| norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm | |
| downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None | |
| use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
| focal_level (int): Number of focal levels | |
| focal_window (int): Focal window size at first focal level | |
| use_layerscale (bool): Whether use layerscale | |
| layerscale_value (float): Initial layerscale value | |
| use_postln (bool): Whether use layernorm after modulation | |
| """ | |
| def __init__(self, dim, out_dim, input_resolution, depth, | |
| mlp_ratio=4., drop=0., drop_path=0., norm_layer=nn.LayerNorm, | |
| downsample=None, use_checkpoint=False, | |
| focal_level=1, focal_window=1, | |
| use_conv_embed=False, | |
| use_layerscale=False, layerscale_value=1e-4, use_postln=False): | |
| super().__init__() | |
| self.dim = dim | |
| self.input_resolution = input_resolution | |
| self.depth = depth | |
| self.use_checkpoint = use_checkpoint | |
| # build blocks | |
| self.blocks = nn.ModuleList([ | |
| FocalNetBlock( | |
| dim=dim, | |
| input_resolution=input_resolution, | |
| mlp_ratio=mlp_ratio, | |
| drop=drop, | |
| drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, | |
| norm_layer=norm_layer, | |
| focal_level=focal_level, | |
| focal_window=focal_window, | |
| use_layerscale=use_layerscale, | |
| layerscale_value=layerscale_value, | |
| use_postln=use_postln, | |
| ) | |
| for i in range(depth)]) | |
| if downsample is not None: | |
| self.downsample = downsample( | |
| img_size=input_resolution, | |
| patch_size=2, | |
| in_chans=dim, | |
| embed_dim=out_dim, | |
| use_conv_embed=use_conv_embed, | |
| norm_layer=norm_layer, | |
| is_stem=False | |
| ) | |
| else: | |
| self.downsample = None | |
| def forward(self, x, H, W): | |
| for blk in self.blocks: | |
| blk.H, blk.W = H, W | |
| if self.use_checkpoint: | |
| x = checkpoint.checkpoint(blk, x) | |
| else: | |
| x = blk(x) | |
| if self.downsample is not None: | |
| x = x.transpose(1, 2).reshape(x.shape[0], -1, H, W) | |
| x, Ho, Wo = self.downsample(x) | |
| else: | |
| Ho, Wo = H, W | |
| return x, Ho, Wo | |
| def extra_repr(self) -> str: | |
| return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" | |
| def flops(self): | |
| flops = 0 | |
| for blk in self.blocks: | |
| flops += blk.flops() | |
| if self.downsample is not None: | |
| flops += self.downsample.flops() | |
| return flops | |
| class PatchEmbed(nn.Module): | |
| r""" Image to Patch Embedding | |
| Args: | |
| img_size (int): Image size. Default: 224. | |
| patch_size (int): Patch token size. Default: 4. | |
| in_chans (int): Number of input image channels. Default: 3. | |
| embed_dim (int): Number of linear projection output channels. Default: 96. | |
| norm_layer (nn.Module, optional): Normalization layer. Default: None | |
| """ | |
| def __init__(self, img_size=(224, 224), patch_size=4, in_chans=3, embed_dim=96, use_conv_embed=False, norm_layer=None, is_stem=False): | |
| super().__init__() | |
| patch_size = to_2tuple(patch_size) | |
| patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] | |
| self.img_size = img_size | |
| self.patch_size = patch_size | |
| self.patches_resolution = patches_resolution | |
| self.num_patches = patches_resolution[0] * patches_resolution[1] | |
| self.in_chans = in_chans | |
| self.embed_dim = embed_dim | |
| if use_conv_embed: | |
| # if we choose to use conv embedding, then we treat the stem and non-stem differently | |
| if is_stem: | |
| kernel_size = 7; padding = 2; stride = 4 | |
| else: | |
| kernel_size = 3; padding = 1; stride = 2 | |
| self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding) | |
| else: | |
| self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) | |
| if norm_layer is not None: | |
| self.norm = norm_layer(embed_dim) | |
| else: | |
| self.norm = None | |
| def forward(self, x): | |
| B, C, H, W = x.shape | |
| x = self.proj(x) | |
| H, W = x.shape[2:] | |
| x = x.flatten(2).transpose(1, 2) # B Ph*Pw C | |
| if self.norm is not None: | |
| x = self.norm(x) | |
| return x, H, W | |
| def flops(self): | |
| Ho, Wo = self.patches_resolution | |
| flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) | |
| if self.norm is not None: | |
| flops += Ho * Wo * self.embed_dim | |
| return flops | |
| class FocalNet(nn.Module): | |
| r""" Focal Modulation Networks (FocalNets) | |
| Args: | |
| img_size (int | tuple(int)): Input image size. Default 224 | |
| patch_size (int | tuple(int)): Patch size. Default: 4 | |
| in_chans (int): Number of input image channels. Default: 3 | |
| num_classes (int): Number of classes for classification head. Default: 1000 | |
| embed_dim (int): Patch embedding dimension. Default: 96 | |
| depths (tuple(int)): Depth of each Focal Transformer layer. | |
| mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 | |
| drop_rate (float): Dropout rate. Default: 0 | |
| drop_path_rate (float): Stochastic depth rate. Default: 0.1 | |
| norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. | |
| patch_norm (bool): If True, add normalization after patch embedding. Default: True | |
| use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False | |
| focal_levels (list): How many focal levels at all stages. Note that this excludes the finest-grain level. Default: [1, 1, 1, 1] | |
| focal_windows (list): The focal window size at all stages. Default: [7, 5, 3, 1] | |
| use_conv_embed (bool): Whether use convolutional embedding. We noted that using convolutional embedding usually improve the performance, but we do not use it by default. Default: False | |
| use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False | |
| layerscale_value (float): Value for layer scale. Default: 1e-4 | |
| use_postln (bool): Whether use layernorm after modulation (it helps stablize training of large models) | |
| """ | |
| def __init__(self, | |
| img_size=224, | |
| patch_size=4, | |
| in_chans=3, | |
| num_classes=1000, | |
| embed_dim=96, | |
| depths=[2, 2, 6, 2], | |
| mlp_ratio=4., | |
| drop_rate=0., | |
| drop_path_rate=0.1, | |
| norm_layer=nn.LayerNorm, | |
| patch_norm=True, | |
| use_checkpoint=False, | |
| focal_levels=[2, 2, 2, 2], | |
| focal_windows=[3, 3, 3, 3], | |
| use_conv_embed=False, | |
| use_layerscale=False, | |
| layerscale_value=1e-4, | |
| use_postln=False, | |
| **kwargs): | |
| super().__init__() | |
| self.num_layers = len(depths) | |
| embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)] | |
| self.num_classes = num_classes | |
| self.embed_dim = embed_dim | |
| self.patch_norm = patch_norm | |
| self.num_features = embed_dim[-1] | |
| self.mlp_ratio = mlp_ratio | |
| # split image into patches using either non-overlapped embedding or overlapped embedding | |
| self.patch_embed = PatchEmbed( | |
| img_size=to_2tuple(img_size), | |
| patch_size=patch_size, | |
| in_chans=in_chans, | |
| embed_dim=embed_dim[0], | |
| use_conv_embed=use_conv_embed, | |
| norm_layer=norm_layer if self.patch_norm else None, | |
| is_stem=True) | |
| num_patches = self.patch_embed.num_patches | |
| patches_resolution = self.patch_embed.patches_resolution | |
| self.patches_resolution = patches_resolution | |
| self.pos_drop = nn.Dropout(p=drop_rate) | |
| # stochastic depth | |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule | |
| # build layers | |
| self.layers = nn.ModuleList() | |
| for i_layer in range(self.num_layers): | |
| layer = BasicLayer(dim=embed_dim[i_layer], | |
| out_dim=embed_dim[i_layer+1] if (i_layer < self.num_layers - 1) else None, | |
| input_resolution=(patches_resolution[0] // (2 ** i_layer), | |
| patches_resolution[1] // (2 ** i_layer)), | |
| depth=depths[i_layer], | |
| mlp_ratio=self.mlp_ratio, | |
| drop=drop_rate, | |
| drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], | |
| norm_layer=norm_layer, | |
| downsample=PatchEmbed if (i_layer < self.num_layers - 1) else None, | |
| focal_level=focal_levels[i_layer], | |
| focal_window=focal_windows[i_layer], | |
| use_conv_embed=use_conv_embed, | |
| use_checkpoint=use_checkpoint, | |
| use_layerscale=use_layerscale, | |
| layerscale_value=layerscale_value, | |
| use_postln=use_postln, | |
| ) | |
| self.layers.append(layer) | |
| self.norm = norm_layer(self.num_features) | |
| self.avgpool = nn.AdaptiveAvgPool1d(1) | |
| self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() | |
| self.dim_out = self.num_features | |
| 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) | |
| def no_weight_decay(self): | |
| return {''} | |
| def no_weight_decay_keywords(self): | |
| return {''} | |
| def forward_features(self, x): | |
| x, H, W = self.patch_embed(x) | |
| x = self.pos_drop(x) | |
| for layer in self.layers: | |
| x, H, W = layer(x, H, W) | |
| x = self.norm(x) # B L C | |
| x = self.avgpool(x.transpose(1, 2)) # B C 1 | |
| x = torch.flatten(x, 1) | |
| return x | |
| def forward(self, x): | |
| x = self.forward_features(x) | |
| x = self.head(x) | |
| return x | |
| def flops(self): | |
| flops = 0 | |
| flops += self.patch_embed.flops() | |
| for i, layer in enumerate(self.layers): | |
| flops += layer.flops() | |
| flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) | |
| flops += self.num_features * self.num_classes | |
| return flops | |
| def build_transforms(img_size, center_crop=False): | |
| t = [] | |
| if center_crop: | |
| size = int((256 / 224) * img_size) | |
| t.append( | |
| transforms.Resize(size, interpolation=_pil_interp('bicubic')) | |
| ) | |
| t.append( | |
| transforms.CenterCrop(img_size) | |
| ) | |
| else: | |
| t.append( | |
| transforms.Resize(img_size, interpolation=_pil_interp('bicubic')) | |
| ) | |
| t.append(transforms.ToTensor()) | |
| t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)) | |
| return transforms.Compose(t) | |
| def build_transforms4display(img_size, center_crop=False): | |
| t = [] | |
| if center_crop: | |
| size = int((256 / 224) * img_size) | |
| t.append( | |
| transforms.Resize(size, interpolation=_pil_interp('bicubic')) | |
| ) | |
| t.append( | |
| transforms.CenterCrop(img_size) | |
| ) | |
| else: | |
| t.append( | |
| transforms.Resize(img_size, interpolation=_pil_interp('bicubic')) | |
| ) | |
| t.append(transforms.ToTensor()) | |
| return transforms.Compose(t) | |
| model_urls = { | |
| "focalnet_tiny_srf": "", | |
| "focalnet_small_srf": "", | |
| "focalnet_base_srf": "", | |
| "focalnet_tiny_lrf": "", | |
| "focalnet_small_lrf": "", | |
| "focalnet_base_lrf": "", | |
| } | |
| def focalnet_tiny_srf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_tiny_srf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_small_srf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 18, 2], embed_dim=96, **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_small_srf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_base_srf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 18, 2], embed_dim=128, **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_base_srf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_tiny_lrf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_tiny_lrf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_small_lrf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 18, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_small_lrf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_base_lrf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_base_lrf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_giant_lrf(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[2, 2, 42, 2], embed_dim=512, focal_levels=[3, 3, 3, 3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_giant_lrf'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_tiny_iso_16(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[12], patch_size=16, embed_dim=192, focal_levels=[3], focal_windows=[3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_tiny_iso_16'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_small_iso_16(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[12], patch_size=16, embed_dim=384, focal_levels=[3], focal_windows=[3], **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_small_iso_16'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def focalnet_base_iso_16(pretrained=False, **kwargs): | |
| model = FocalNet(depths=[12], patch_size=16, embed_dim=768, focal_levels=[3], focal_windows=[3], use_layerscale=True, use_postln=True, **kwargs) | |
| if pretrained: | |
| url = model_urls['focalnet_base_iso_16'] | |
| checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu") | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| if __name__ == '__main__': | |
| img_size = 224 | |
| x = torch.rand(16, 3, img_size, img_size).cuda() | |
| # model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96) | |
| # model = FocalNet(depths=[12], patch_size=16, embed_dim=768, focal_levels=[3], focal_windows=[3], focal_factors=[2]) | |
| model = FocalNet(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3]).cuda() | |
| print(model); model(x) | |
| flops = model.flops() | |
| print(f"number of GFLOPs: {flops / 1e9}") | |
| n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| print(f"number of params: {n_parameters}") | |