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619 lines
24 KiB
619 lines
24 KiB
import hashlib
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import math
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import os
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import urllib
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import warnings
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from collections import OrderedDict
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from typing import Tuple
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from typing import Union, List
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
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try:
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from torchvision.transforms import InterpolationMode
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BICUBIC = InterpolationMode.BICUBIC
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except ImportError:
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BICUBIC = Image.BICUBIC
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from torch import nn
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from tqdm import tqdm
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from models.utils import one_hot_to_index
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_MODELS = {
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"RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
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"RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
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"RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
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"RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
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"ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
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"ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
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}
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def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
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os.makedirs(root, exist_ok=True)
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filename = os.path.basename(url)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, filename)
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
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return download_target
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else:
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warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
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raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
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return download_target
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def _transform(n_px):
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return Compose([
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Resize(n_px, interpolation=BICUBIC),
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CenterCrop(n_px),
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lambda image: image.convert("RGB"),
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ToTensor(),
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Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
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])
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def available_models() -> List[str]:
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"""Returns the names of available CLIP models"""
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return list(_MODELS.keys())
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def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=True):
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"""Load a CLIP model
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Parameters
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----------
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name : str
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A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
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device : Union[str, torch.device]
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The device to put the loaded model
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jit : bool
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Whether to load the optimized JIT model (default) or more hackable non-JIT model.
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Returns
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-------
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model : torch.nn.Module
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The CLIP model
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preprocess : Callable[[PIL.Image], torch.Tensor]
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A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
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"""
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if name in _MODELS:
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model_path = _download(_MODELS[name])
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elif os.path.isfile(name):
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model_path = name
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else:
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raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
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try:
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# loading JIT archive
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model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
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state_dict = None
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except RuntimeError:
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# loading saved state dict
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if jit:
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warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
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jit = False
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state_dict = torch.load(model_path, map_location="cpu")
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if not jit:
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model = build_model(state_dict or model.state_dict()).to(device)
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if str(device) == "cpu":
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model.float()
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return model, _transform(model.visual.input_resolution)
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# patch the device names
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device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
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device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
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def patch_device(module):
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graphs = [module.graph] if hasattr(module, "graph") else []
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if hasattr(module, "forward1"):
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graphs.append(module.forward1.graph)
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for graph in graphs:
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for node in graph.findAllNodes("prim::Constant"):
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if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
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node.copyAttributes(device_node)
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model.apply(patch_device)
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patch_device(model.encode_image)
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patch_device(model.encode_text)
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# patch dtype to float32 on CPU
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if str(device) == "cpu":
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float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
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float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
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float_node = float_input.node()
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def patch_float(module):
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graphs = [module.graph] if hasattr(module, "graph") else []
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if hasattr(module, "forward1"):
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graphs.append(module.forward1.graph)
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for graph in graphs:
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for node in graph.findAllNodes("aten::to"):
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inputs = list(node.inputs())
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for i in [1, 2]: # dtype can be the second or third argument to aten::to()
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if inputs[i].node()["value"] == 5:
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inputs[i].node().copyAttributes(float_node)
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model.apply(patch_float)
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patch_float(model.encode_image)
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patch_float(model.encode_text)
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model.float()
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return model, _transform(model.input_resolution.item())
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class LayerNorm(nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor):
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1):
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super().__init__()
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# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
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self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = None
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self.stride = stride
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if stride > 1 or inplanes != planes * Bottleneck.expansion:
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# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
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self.downsample = nn.Sequential(OrderedDict([
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("-1", nn.AvgPool2d(stride)),
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("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
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("1", nn.BatchNorm2d(planes * self.expansion))
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]))
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def forward(self, x: torch.Tensor):
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identity = x
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out = self.relu(self.bn1(self.conv1(x)))
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out = self.relu(self.bn2(self.conv2(out)))
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out = self.avgpool(out)
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out = self.bn3(self.conv3(out))
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class AttentionPool2d(nn.Module):
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def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
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super().__init__()
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self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
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self.k_proj = nn.Linear(embed_dim, embed_dim)
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self.q_proj = nn.Linear(embed_dim, embed_dim)
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self.v_proj = nn.Linear(embed_dim, embed_dim)
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self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
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self.num_heads = num_heads
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def forward(self, x):
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x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
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x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
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x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
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x, _ = F.multi_head_attention_forward(
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query=x, key=x, value=x,
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embed_dim_to_check=x.shape[-1],
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num_heads=self.num_heads,
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q_proj_weight=self.q_proj.weight,
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k_proj_weight=self.k_proj.weight,
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v_proj_weight=self.v_proj.weight,
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in_proj_weight=None,
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in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
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bias_k=None,
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bias_v=None,
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add_zero_attn=False,
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dropout_p=0,
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out_proj_weight=self.c_proj.weight,
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out_proj_bias=self.c_proj.bias,
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use_separate_proj_weight=True,
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training=self.training,
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need_weights=False
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)
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return x[0]
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class ModifiedResNet(nn.Module):
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"""
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A ResNet class that is similar to torchvision's but contains the following changes:
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- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
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- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
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- The final pooling layer is a QKV attention instead of an average pool
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"""
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def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
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super().__init__()
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self.output_dim = output_dim
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self.input_resolution = input_resolution
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# the 3-layer stem
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self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(width // 2)
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self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(width // 2)
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self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
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self.bn3 = nn.BatchNorm2d(width)
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self.avgpool = nn.AvgPool2d(2)
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self.relu = nn.ReLU(inplace=True)
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# residual layers
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self._inplanes = width # this is a *mutable* variable used during construction
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self.layer1 = self._make_layer(width, layers[0])
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self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
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self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
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self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
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self.embed_dim = width * 32 # the ResNet feature dimension
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self.attnpool = AttentionPool2d(input_resolution // 32, self.embed_dim, heads, output_dim)
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def _make_layer(self, planes, blocks, stride=1):
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layers = [Bottleneck(self._inplanes, planes, stride)]
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self._inplanes = planes * Bottleneck.expansion
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for _ in range(1, blocks):
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layers.append(Bottleneck(self._inplanes, planes))
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return nn.Sequential(*layers)
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def intermediate_features(self, x):
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def stem(x):
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for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
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x = self.relu(bn(conv(x)))
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x = self.avgpool(x)
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return x
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x = x.type(self.conv1.weight.dtype)
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x = stem(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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b, c = x.shape[:2]
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return x.view(b, c, -1).permute(0, 2, 1)
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def forward(self, x):
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x = self.intermediate_features(x)
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x = x.permute(0, 2, 1)
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l = int(math.sqrt(x.shape[-1]))
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x = x.view(x.shape[0], x.shape[1], l, l)
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x = self.attnpool(x)
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return x
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class QuickGELU(nn.Module):
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def forward(self, x: torch.Tensor):
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return x * torch.sigmoid(1.702 * x)
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class ResidualAttentionBlock(nn.Module):
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
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super().__init__()
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self.attn = nn.MultiheadAttention(d_model, n_head)
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self.ln_1 = LayerNorm(d_model)
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self.mlp = nn.Sequential(OrderedDict([
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("c_fc", nn.Linear(d_model, d_model * 4)),
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("gelu", QuickGELU()),
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("c_proj", nn.Linear(d_model * 4, d_model))
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]))
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self.ln_2 = LayerNorm(d_model)
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self.attn_mask = attn_mask
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def attention(self, x: torch.Tensor):
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self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
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return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
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def forward(self, x: torch.Tensor):
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x = x + self.attention(self.ln_1(x))
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x = x + self.mlp(self.ln_2(x))
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return x
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class Transformer(nn.Module):
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def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
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super().__init__()
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self.width = width
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self.layers = layers
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self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
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def forward(self, x: torch.Tensor):
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return self.resblocks(x)
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class VisualTransformer(nn.Module):
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def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
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super().__init__()
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self.input_resolution = input_resolution
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self.embed_dim = width
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self.output_dim = output_dim
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
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scale = width ** -0.5
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self.class_embedding = nn.Parameter(scale * torch.randn(width))
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self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
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self.ln_pre = LayerNorm(width)
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self.transformer = Transformer(width, layers, heads)
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self.ln_post = LayerNorm(width)
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self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
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def intermediate_features(self, x):
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x = self.conv1(x) # shape = [*, width, grid, grid]
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x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
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x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
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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]
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x = x + self.positional_embedding.to(x.dtype)
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x = self.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_post(x)
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return x
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def forward(self, x: torch.Tensor):
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x = self.intermediate_features(x)
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x_cls = x[:, 0, :]
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if self.proj is not None:
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x_cls = x_cls @ self.proj
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return x_cls
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class CLIP(nn.Module):
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def __init__(self,
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embed_dim: int,
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# vision
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image_resolution: int,
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vision_layers: Union[Tuple[int, int, int, int], int],
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vision_width: int,
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vision_patch_size: int,
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# text
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context_length: int,
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vocab_size: int,
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transformer_width: int,
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transformer_heads: int,
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transformer_layers: int
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):
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super().__init__()
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self.context_length = context_length
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if isinstance(vision_layers, (tuple, list)):
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vision_heads = vision_width * 32 // 64
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self.visual = ModifiedResNet(
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layers=vision_layers,
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output_dim=embed_dim,
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|
heads=vision_heads,
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|
input_resolution=image_resolution,
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|
width=vision_width
|
|
)
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|
else:
|
|
vision_heads = vision_width // 64
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|
self.visual = VisualTransformer(
|
|
input_resolution=image_resolution,
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|
patch_size=vision_patch_size,
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|
width=vision_width,
|
|
layers=vision_layers,
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|
heads=vision_heads,
|
|
output_dim=embed_dim
|
|
)
|
|
|
|
self.transformer = Transformer(
|
|
width=transformer_width,
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|
layers=transformer_layers,
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|
heads=transformer_heads,
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|
attn_mask=self.build_attention_mask()
|
|
)
|
|
|
|
self.vocab_size = vocab_size
|
|
self.transformer_width = transformer_width
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|
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)) # todo remove
|
|
|
|
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):
|
|
# lazily create causal attention mask, with full attention between the vision tokens
|
|
# pytorch uses additive attention mask; fill with -inf
|
|
mask = torch.empty(self.context_length, self.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
|
|
|
|
@property
|
|
def device(self):
|
|
return self.visual.conv1.weight.device
|
|
|
|
def encode_image(self, image):
|
|
return self.visual(image.type(self.dtype))
|
|
|
|
def encode_text(self, text):
|
|
if text.dtype in [torch.long, torch.int]:
|
|
text_idxs = text
|
|
x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
|
|
else:
|
|
text_idxs = one_hot_to_index(text)
|
|
x = (text @ self.token_embedding.weight).type(self.dtype)
|
|
|
|
x = x + self.positional_embedding.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_idxs.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 = logit_scale * text_features @ image_features.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
|
|
)
|
|
|
|
for key in ["input_resolution", "context_length", "vocab_size"]:
|
|
if key in state_dict:
|
|
del state_dict[key]
|
|
|
|
# convert_weights(model) todo remove
|
|
model.load_state_dict(state_dict, strict=False)
|
|
return model.eval()
|