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import gradio as gr
import os
import cv2
from encoded_video import EncodedVideo, write_video
import torch
import numpy as np
from torchvision.datasets import ImageFolder
from transformers import ViTFeatureExtractor, ViTForImageClassification, AutoFeatureExtractor, ViTMSNForImageClassification
from pathlib import Path
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
from PIL import Image
import PIL
HF_DATASETS_CACHE="./"
class ImageClassificationCollator:
def __init__(self, feature_extractor):
self.feature_extractor = feature_extractor
def __call__(self, batch):
encodings = self.feature_extractor([x[0] for x in batch], return_tensors='pt')
encodings['labels'] = torch.tensor([x[1] for x in batch], dtype=torch.long)
return encodings
class Classifier(pl.LightningModule):
def __init__(self, model, lr: float = 2e-5, **kwargs):
super().__init__()
self.save_hyperparameters('lr', *list(kwargs))
self.model = model
self.forward = self.model.forward
self.val_acc = Accuracy(
task='multiclass' if model.config.num_labels > 2 else 'binary',
num_classes=model.config.num_labels
)
def training_step(self, batch, batch_idx):
outputs = self(**batch)
self.log(f"train_loss", outputs.loss)
return outputs.loss
def validation_step(self, batch, batch_idx):
outputs = self(**batch)
self.log(f"val_loss", outputs.loss)
acc = self.val_acc(outputs.logits.argmax(1), batch['labels'])
self.log(f"val_acc", acc, prog_bar=True)
return outputs.loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
def video_identity(video,user_name,class_name,trainortest,ready):
if ready=='yes':
data_dir = Path(str(user_name)+'/train')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.ConvertImageDtype(torch.float)
])
train_ds = ImageFolder(data_dir, transform=transform)
test_dir = Path(str(user_name)+'/test')
test_ds = ImageFolder(test_dir, transform=transform)
label2id = {}
id2label = {}
for i, class_name in enumerate(train_ds.classes):
label2id[class_name] = str(i)
id2label[str(i)] = class_name
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTForImageClassification.from_pretrained(
'google/vit-base-patch16-224-in21k',
num_labels=len(label2id),
label2id=label2id,
id2label=id2label
)
collator = ImageClassificationCollator(feature_extractor)
train_loader = DataLoader(train_ds, batch_size=2, collate_fn=collator, num_workers=8, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=2, collate_fn=collator, num_workers=7)
for name, param in model.named_parameters():
param.requires_grad = False
if name.startswith("classifier"): # choose whatever you like here
param.requires_grad = True
pl.seed_everything(42)
classifier = Classifier(model, lr=2e-5)
trainer = pl.Trainer(accelerator='gpu', devices=1, precision=16, max_epochs=3)
trainer.fit(classifier, train_loader, test_loader)
for batch_idx, data in enumerate(test_loader):
outputs = model(**data)
img=data['pixel_values'][0][0]
preds=str(outputs.logits.softmax(1).argmax(1))
labels=str(data['labels'])
return img, preds, labels
else:
capture = cv2.VideoCapture(video)
user_d=str(user_name)+'/'+str(trainortest)
class_d=str(user_name)+'/'+str(trainortest)+'/'+str(class_name)
if not os.path.exists(user_d):
os.makedirs(user_d)
if not os.path.exists(class_d):
os.makedirs(class_d)
frameNr = 0
while (True):
success, frame = capture.read()
if success:
cv2.imwrite(f'{class_d}/frame_{frameNr}.jpg', frame)
else:
break
frameNr = frameNr+10
img=cv2.imread(class_d+'/frame_0.jpg')
return img, trainortest, class_d
demo = gr.Interface(video_identity,
inputs=[gr.Video(source='upload'),
gr.Text(),
gr.Text(),
gr.Text(label='Which set is this? (type train or test)'),
gr.Text(label='Are you ready? (type yes or no)')],
outputs=[gr.Image(),
gr.Text(),
gr.Text()],
cache_examples=True)
demo.launch(debug=True) |