diff --git a/model/model.py b/model/model.py deleted file mode 100755 index aa8fc27..0000000 --- a/model/model.py +++ /dev/null @@ -1,450 +0,0 @@ -from collections import OrderedDict -from typing import Tuple, Union - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1): - super().__init__() - - # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 - self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - - self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - - self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() - - self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - - self.relu = nn.ReLU(inplace=True) - self.downsample = None - self.stride = stride - - if stride > 1 or inplanes != planes * Bottleneck.expansion: - # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 - self.downsample = nn.Sequential(OrderedDict([ - ("-1", nn.AvgPool2d(stride)), - ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), - ("1", nn.BatchNorm2d(planes * self.expansion)) - ])) - - def forward(self, x: torch.Tensor): - identity = x - - out = self.relu(self.bn1(self.conv1(x))) - out = self.relu(self.bn2(self.conv2(out))) - out = self.avgpool(out) - out = self.bn3(self.conv3(out)) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - return out - - -class AttentionPool2d(nn.Module): - def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): - super().__init__() - self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) - self.k_proj = nn.Linear(embed_dim, embed_dim) - self.q_proj = nn.Linear(embed_dim, embed_dim) - self.v_proj = nn.Linear(embed_dim, embed_dim) - self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) - self.num_heads = num_heads - - def forward(self, x): - x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC - x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC - x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC - x, _ = F.multi_head_attention_forward( - query=x, key=x, value=x, - embed_dim_to_check=x.shape[-1], - num_heads=self.num_heads, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - in_proj_weight=None, - in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), - bias_k=None, - bias_v=None, - add_zero_attn=False, - dropout_p=0, - out_proj_weight=self.c_proj.weight, - out_proj_bias=self.c_proj.bias, - use_separate_proj_weight=True, - training=self.training, - need_weights=False - ) - - return x[0] - - -class ModifiedResNet(nn.Module): - """ - A ResNet class that is similar to torchvision's but contains the following changes: - - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. - - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 - - The final pooling layer is a QKV attention instead of an average pool - """ - - def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): - super().__init__() - self.output_dim = output_dim - self.input_resolution = input_resolution - - # the 3-layer stem - self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(width // 2) - self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(width // 2) - self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) - self.bn3 = nn.BatchNorm2d(width) - self.avgpool = nn.AvgPool2d(2) - self.relu = nn.ReLU(inplace=True) - - # residual layers - self._inplanes = width # this is a *mutable* variable used during construction - self.layer1 = self._make_layer(width, layers[0]) - self.layer2 = self._make_layer(width * 2, layers[1], stride=2) - self.layer3 = self._make_layer(width * 4, layers[2], stride=2) - self.layer4 = self._make_layer(width * 8, layers[3], stride=2) - - embed_dim = width * 32 # the ResNet feature dimension - self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) - - def _make_layer(self, planes, blocks, stride=1): - layers = [Bottleneck(self._inplanes, planes, stride)] - - self._inplanes = planes * Bottleneck.expansion - for _ in range(1, blocks): - layers.append(Bottleneck(self._inplanes, planes)) - - return nn.Sequential(*layers) - - def forward(self, x): - def stem(x): - for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: - x = self.relu(bn(conv(x))) - x = self.avgpool(x) - return x - - x = x.type(self.conv1.weight.dtype) - x = stem(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.attnpool(x) - - return x - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16.""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - ret = super().forward(x.type(torch.float32)) - return ret.type(orig_type) - - -class QuickGELU(nn.Module): - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): - super().__init__() - - self.attn = nn.MultiheadAttention(d_model, n_head) - self.ln_1 = LayerNorm(d_model) - self.mlp = nn.Sequential(OrderedDict([ - ("c_fc", nn.Linear(d_model, d_model * 4)), - ("gelu", QuickGELU()), - ("c_proj", nn.Linear(d_model * 4, d_model)) - ])) - self.ln_2 = LayerNorm(d_model) - self.attn_mask = attn_mask - - def attention(self, x: torch.Tensor): - # self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None - # return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] - attn_mask_ = self.attn_mask - if self.attn_mask is not None and hasattr(self.attn_mask, '__call__'): - attn_mask_ = self.attn_mask(x.size(0)) # LND - - attn_mask_ = attn_mask_.to(dtype=x.dtype, device=x.device) if attn_mask_ is not None else None - return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask_)[0] - - def forward(self, x: torch.Tensor): - # x, video_frame = x_tuple - # print(x.shape) - x = x + self.attention(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - - -class Transformer(nn.Module): - def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): - super().__init__() - self.width = width - self.layers = layers - self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) - - def forward(self, x: torch.Tensor): - return self.resblocks(x) - - -class VisionTransformer(nn.Module): - def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): - super().__init__() - self.input_resolution = input_resolution - self.output_dim = output_dim - self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) - - scale = width ** -0.5 - self.class_embedding = nn.Parameter(scale * torch.randn(width)) - self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) - self.ln_pre = LayerNorm(width) - - self.transformer = Transformer(width, layers, heads) - - self.ln_post = LayerNorm(width) - self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) - - def forward(self, x: torch.Tensor): - # print(x.shape) - # print(x.shape) - x = self.conv1(x) # shape = [*, width, grid, grid] - # print("image feature map:", x.shape) - x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] - # print(x.shape) - x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] - # print(x.shape) - x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] - x = x + self.positional_embedding.to(x.dtype) - x = self.ln_pre(x) - # print(x.shape) - - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x) - # print(x.shape) - x = x.permute(1, 0, 2) # LND -> NLD - - x = self.ln_post(x[:, 0, :]) - - if self.proj is not None: - x = x @ self.proj - - return x - - -class CLIP(nn.Module): - def __init__(self, - embed_dim: int, - # vision - image_resolution: int, - vision_layers: Union[Tuple[int, int, int, int], int], - vision_width: int, - vision_patch_size: int, - # text - context_length: int, - vocab_size: int, - transformer_width: int, - transformer_heads: int, - transformer_layers: int - ): - super().__init__() - - self.context_length = context_length - - if isinstance(vision_layers, (tuple, list)): - vision_heads = vision_width * 32 // 64 - self.visual = ModifiedResNet( - layers=vision_layers, - output_dim=embed_dim, - heads=vision_heads, - input_resolution=image_resolution, - width=vision_width - ) - else: - vision_heads = vision_width // 64 - self.visual = VisionTransformer( - input_resolution=image_resolution, - patch_size=vision_patch_size, - width=vision_width, - layers=vision_layers, - heads=vision_heads, - output_dim=embed_dim - ) - - self.transformer = Transformer( - width=transformer_width, - layers=transformer_layers, - heads=transformer_heads, - attn_mask=self.build_attention_mask - ) - - self.vocab_size = vocab_size - self.token_embedding = nn.Embedding(vocab_size, transformer_width) - self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) - self.ln_final = LayerNorm(transformer_width) - - self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) - self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) - - self.initialize_parameters() - - def initialize_parameters(self): - nn.init.normal_(self.token_embedding.weight, std=0.02) - nn.init.normal_(self.positional_embedding, std=0.01) - - if isinstance(self.visual, ModifiedResNet): - if self.visual.attnpool is not None: - std = self.visual.attnpool.c_proj.in_features ** -0.5 - nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) - nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) - - for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: - for name, param in resnet_block.named_parameters(): - if name.endswith("bn3.weight"): - nn.init.zeros_(param) - - proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) - attn_std = self.transformer.width ** -0.5 - fc_std = (2 * self.transformer.width) ** -0.5 - for block in self.transformer.resblocks: - nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - - if self.text_projection is not None: - nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) - - def build_attention_mask(self, context_length): - # lazily create causal attention mask, with full attention between the vision tokens - # pytorch uses additive attention mask; fill with -inf - mask = torch.empty(context_length, context_length) - mask.fill_(float("-inf")) - mask.triu_(1) # zero out the lower diagonal - return mask - - @property - def dtype(self): - return self.visual.conv1.weight.dtype - - def encode_image(self, image): - return self.visual(image.type(self.dtype)) - - def encode_text(self, text): - x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] - - x = x + self.positional_embedding[:x.size(1), :].type(self.dtype) - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x) - x = x.permute(1, 0, 2) # LND -> NLD - x = self.ln_final(x).type(self.dtype) - - # x.shape = [batch_size, n_ctx, transformer.width] - # take features from the eot embedding (eot_token is the highest number in each sequence) - x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection - - return x - - def forward(self, image, text): - image_features = self.encode_image(image) - text_features = self.encode_text(text) - - # normalized features - image_features = image_features / image_features.norm(dim=-1, keepdim=True) - text_features = text_features / text_features.norm(dim=-1, keepdim=True) - - # cosine similarity as logits - logit_scale = self.logit_scale.exp() - logits_per_image = logit_scale * image_features @ text_features.t() - logits_per_text = logits_per_image.t() - - # shape = [global_batch_size, global_batch_size] - return logits_per_image, logits_per_text - - -def convert_weights(model: nn.Module): - """Convert applicable model parameters to fp16""" - - def _convert_weights_to_fp16(l): - if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): - l.weight.data = l.weight.data.half() - if l.bias is not None: - l.bias.data = l.bias.data.half() - - if isinstance(l, nn.MultiheadAttention): - for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: - tensor = getattr(l, attr) - if tensor is not None: - tensor.data = tensor.data.half() - - for name in ["text_projection", "proj"]: - if hasattr(l, name): - attr = getattr(l, name) - if attr is not None: - attr.data = attr.data.half() - - model.apply(_convert_weights_to_fp16) - - -def build_model(state_dict: dict): - vit = "visual.proj" in state_dict - - if vit: - vision_width = state_dict["visual.conv1.weight"].shape[0] - vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) - vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] - grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) - image_resolution = vision_patch_size * grid_size - else: - counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] - vision_layers = tuple(counts) - vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] - output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) - vision_patch_size = None - assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] - image_resolution = output_width * 32 - - embed_dim = state_dict["text_projection"].shape[1] - context_length = state_dict["positional_embedding"].shape[0] - vocab_size = state_dict["token_embedding.weight"].shape[0] - transformer_width = state_dict["ln_final.weight"].shape[0] - transformer_heads = transformer_width // 64 - transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) - - model = CLIP( - embed_dim, - image_resolution, vision_layers, vision_width, vision_patch_size, - context_length, vocab_size, transformer_width, transformer_heads, transformer_layers - ) - # print("vision width:", vision_width) - # print("vision patch size", vision_patch_size) - - for key in ["input_resolution", "context_length", "vocab_size"]: - if key in state_dict: - del state_dict[key] - - convert_weights(model) - model.load_state_dict(state_dict) - # return model.eval() - return model diff --git a/utils/utils.py b/utils/utils.py index d243a4d..7864994 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -169,64 +169,7 @@ def compute_hash_similarity(visual_embed, text_embed, use_softmax_hash=False, al tv_similarity = calcHammingDist(hash_text, hash_visual) return vt_similarity, tv_similarity -class CrossEn(nn.Module): - def __init__(self, mode="cosine"): - super(CrossEn, self).__init__() - # if mode == "euclidean": - # self.compute_func = F.softmax - # else: - # self.compute_func = F.log_softmax - self.mode = mode - def forward(self, sim_matrix): - # if self.mode == "cosine": - # logpt = F.log_softmax(sim_matrix, dim=-1) - # logpt = torch.diag(logpt) - # nce_loss = -logpt - # sim_loss = nce_loss.mean() - # elif self.mode == "euclidean": - # logpt = F.softmax(sim_matrix, dim=-1) - # logpt = torch.diag(sim_matrix) - # sim_loss = logpt.mean() - # else: - # raise ValueError("mode paramater is not support.[cosine, euclidean]") - if self.mode == "euclidean": - sim_matrix = -sim_matrix - logpt = F.log_softmax(sim_matrix, dim=-1) - logpt = torch.diag(logpt) - nce_loss = -logpt - sim_loss = nce_loss.mean() - return sim_loss - -class CrossEn_mean(nn.Module): - def __init__(self, mode="cosine"): - super(CrossEn_mean, self).__init__() - # if mode == "euclidean": - # self.compute_func = F.softmax - # else: - # self.compute_func = F.log_softmax - self.mode = mode - - def forward(self, sim_matrix): - # if self.mode == "cosine": - # logpt = F.log_softmax(sim_matrix, dim=-1) - # logpt = torch.diag(logpt) - # nce_loss = -logpt - # sim_loss = nce_loss.mean() - # elif self.mode == "euclidean": - # logpt = F.softmax(sim_matrix, dim=-1) - # logpt = torch.diag(sim_matrix) - # sim_loss = logpt.mean() - # else: - # raise ValueError("mode paramater is not support.[cosine, euclidean]") - # if self.mode == "euclidean": - # sim_matrix = -sim_matrix - # print(sim_matrix.max(), sim_matrix.min()) - # logpt = F.log_softmax(sim_matrix, dim=-1) - # logpt = torch.diag(logpt) - # print(logpt.max()) - sim_loss = sim_matrix.mean() - return sim_loss def find_indices(array, b): # Create a boolean mask where the first dimension of the array equals b