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import os
import json
import torch
import torchvision.transforms as TF
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from model import PredictorModel
class CityAestheticsPipeline:
"""
Demo model pipeline for [image=>score] prediction
Accepts a single model path on initialization.
Resulting object can be called directly with a PIL image as the input
Returns a single float value with the predicted score [0.0;1.0].
"""
clip_ver = "openai/clip-vit-large-patch14-336"
def __init__(self, model_path, device="cpu", clip_dtype=torch.float32):
self.device = device
self.clip_dtype = clip_dtype
self._init_clip()
self.model = self._load_model(model_path)
print("CityAesthetics: Pipeline init ok") # debug
def __call__(self, raw):
emb = self.get_clip_emb(raw)
return self.get_model_pred(self.model, emb)
def get_model_pred(self, model, emb):
with torch.no_grad():
pred = model(emb)
return float(pred.detach().cpu().squeeze(0))
def get_clip_emb(self, raw):
img = self.proc(
images = raw,
return_tensors = "pt"
)["pixel_values"].to(self.clip_dtype).to(self.device)
with torch.no_grad():
emb = self.clip(pixel_values=img)
return emb["image_embeds"].detach().to(torch.float32)
def _init_clip(self):
self.proc = CLIPImageProcessor.from_pretrained(self.clip_ver)
self.clip = CLIPVisionModelWithProjection.from_pretrained(
self.clip_ver,
device_map = self.device,
torch_dtype = self.clip_dtype,
)
def _load_model(self, path):
sd = load_file(path)
assert tuple(sd["up.0.weight"].shape) == (1024, 768) # only allow CLIP ver
model = PredictorModel(outputs=1)
model.eval()
model.load_state_dict(sd)
model.to(self.device)
return model
class CityAestheticsMultiModelPipeline(CityAestheticsPipeline):
"""
Demo multi-model pipeline for [image=>score] prediction
Accepts a list of model paths on initialization.
Resulting object can be called directly with a PIL image as the input.
Returns a dict with the model name as key and the score [0.0;1.0] as a value.
"""
def __init__(self, model_paths, device="cpu", clip_dtype=torch.float32):
self.device = device
self.clip_dtype = clip_dtype
self._init_clip()
self.models = {}
for path in model_paths:
name = os.path.splitext(os.path.basename(path))[0]
self.models[name] = self._load_model(path)
print("CityAesthetics: Pipeline init ok") # debug
def __call__(self, raw):
emb = self.get_clip_emb(raw)
out = {}
for name, model in self.models.items():
pred = model(emb)
out[name] = self.get_model_pred(model, emb)
return out
class CityClassifierPipeline:
"""
Demo model pipeline for [image=>label] prediction
Accepts a single model path and (optionally) a JSON file on initialization.
Resulting object can be called directly with a PIL image as the input
Returns a single float value with the predicted score [0.0;1.0].
"""
clip_ver = "openai/clip-vit-large-patch14-336"
def __init__(self, model_path, config_path=None, device="cpu", clip_dtype=torch.float32):
self.device = device
self.clip_dtype = clip_dtype
self._init_clip()
self.labels, model_args = self._load_config(config_path)
self.model = self._load_model(model_path, model_args)
print("CityClassifier: Pipeline init ok") # debug
def __call__(self, raw, default=True, tiling=True, tile_strat="mean"):
emb = self.get_clip_emb(raw, tiling=tiling)
pred = self.get_model_pred(self.model, emb)
return self.format_pred(
pred,
labels = self.labels,
drop = [] if default else [0],
ts = tile_strat if tiling else "raw",
)
def format_pred(self, pred, labels, drop=[], ts="mean"):
# recombine strategy
if ts == "mean" : vp = lambda x: float(torch.mean(x))
elif ts == "median": vp = lambda x: float(torch.median(x))
elif ts == "max" : vp = lambda x: float(torch.max(x))
elif ts == "min" : vp = lambda x: float(torch.min(x))
elif ts == "raw" : vp = lambda x: float(x)
else: raise NotImplementedError(f"CityClassifier: Invalid combine strategy '{ts}'!")
# combine pred w/ labels
out = {}
for k in range(len(pred)):
if k in drop: continue
key = labels.get(str(k), str(k))
out[key] = vp(pred[k])
return out
def get_model_pred(self, model, emb):
with torch.no_grad():
pred = model(emb)
pred = pred.detach().cpu()
return [pred[:, x] for x in range(pred.shape[1])] # split
def get_clip_emb(self, raw, tiling=False):
if tiling and min(raw.size) > self.size*2:
if max(raw.size)>1536:
raw = TF.functional.resize(raw, 1536)
raw = TF.functional.five_crop(raw, self.size*2)
img = self.proc(
images = raw,
return_tensors = "pt"
)["pixel_values"].to(self.clip_dtype).to(self.device)
with torch.no_grad():
emb = self.clip(pixel_values=img)
return emb["image_embeds"].detach().to(torch.float32)
def _init_clip(self):
self.proc = CLIPImageProcessor.from_pretrained(self.clip_ver)
self.size = self.proc.size.get("shortest_edge", 256)
self.clip = CLIPVisionModelWithProjection.from_pretrained(
self.clip_ver,
device_map = self.device,
torch_dtype = self.clip_dtype,
)
def _load_model(self, path, args=None):
sd = load_file(path)
assert tuple(sd["up.0.weight"].shape) == (1024, 768) # only allow CLIP ver
args = args or { # infer from model
"outputs" : int(sd["down.5.bias"].shape[0])
}
model = PredictorModel(**args)
model.eval()
model.load_state_dict(sd)
model.to(self.device)
return model
def _load_config(self, path):
if not path or not os.path.isfile(path):
return ({},None)
with open(path) as f:
data = json.loads(f.read())
return (
data.get("labels", {}),
data.get("model_params", {}),
)
class CityClassifierMultiModelPipeline(CityClassifierPipeline):
"""
Demo model pipeline for [image=>label] prediction
Accepts a list of model paths on initialization.
A matching list of JSON files can also be passed in the same order.
Resulting object can be called directly with a PIL image as the input
Returns a single float value with the predicted score [0.0;1.0].
"""
def __init__(self, model_paths, config_paths=[], device="cpu", clip_dtype=torch.float32):
self.device = device
self.clip_dtype = clip_dtype
self._init_clip()
self.models = {}
self.labels = {}
assert len(model_paths) == len(config_paths) or not config_paths, "CityClassifier: Model and config paths must match!"
for k in range(len(model_paths)):
name = os.path.splitext(os.path.basename(model_paths[k]))[0] # TODO: read from config
self.labels[name], model_args = self._load_config(config_paths[k] if config_paths else None)
self.models[name] = self._load_model(model_paths[k], model_args)
print("CityClassifier: Pipeline init ok") # debug
def __call__(self, raw, default=True, tiling=True, tile_strat="mean"):
emb = self.get_clip_emb(raw, tiling=tiling)
out = {}
for name, model in self.models.items():
pred = self.get_model_pred(model, emb)
out[name] = self.format_pred(
pred,
labels = self.labels[name],
drop = [] if default else [0],
ts = tile_strat if tiling else "raw",
)
if len(out.values()) == 1: return list(out.values())[0] # GRADIO HOTFIX
return list(out.values())
def get_model_path(name, repo, token=True, extension="safetensors", local=False):
"""
Returns local model path or falls back to HF hub if required.
"""
fname = f"{name}.{extension}"
# local path: [models/AesPred-Anime-v1.8.safetensors]
path = os.path.join(os.path.dirname(os.path.realpath(__file__)),"models")
if os.path.isfile(os.path.join(path, fname)):
print(f"Using local model for '{fname}'")
return os.path.join(path, fname)
if local: raise OSError(f"Can't find local model '{fname}'!")
# huggingface hub fallback
print(f"Using HF Hub model for '{fname}'")
return str(hf_hub_download(
token = token,
repo_id = repo,
filename = fname,
))
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