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123 lines
3.5 KiB
123 lines
3.5 KiB
import json
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from datetime import datetime
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from typing import Union, Literal
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from io import BytesIO
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from cv2 import cv2
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from fastapi import FastAPI, File, UploadFile
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from PIL import Image
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from nets import Model
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app = FastAPI()
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reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
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reference_pattern = cv2.imread(reference_pattern_path)
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device = torch.device('cuda:0')
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model = None
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def load_model(epoch):
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global model
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epoch = f'epoch-{epoch}.pth' if epoch != 'latest' else 'latest.pth'
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model_path = f"train_log/models/{epoch}"
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model = Model(max_disp=256, mixed_precision=False, test_mode=True)
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model = nn.DataParallel(model, device_ids=[device])
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# model.load_state_dict(torch.load(model_path), strict=False)
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state_dict = torch.load(model_path)['state_dict']
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model.load_state_dict(state_dict, strict=True)
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model.to(device)
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model.eval()
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print(f'loaded model {epoch}')
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return model
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model = load_model('latest')
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class NumpyEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, np.ndarray):
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return obj.tolist()
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return json.JSONEncoder.default(self, obj)
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def inference(left, right, model, n_iter=20):
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print("Model Forwarding...")
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imgL = np.ascontiguousarray(left[None, :, :, :])
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imgR = np.ascontiguousarray(right[None, :, :, :])
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device = torch.device('cuda')
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imgL = torch.tensor(imgL.astype("float32")).to(device)
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imgR = torch.tensor(imgR.astype("float32")).to(device)
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imgR = imgR.transpose(1,2)
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imgL = imgL.transpose(1,2)
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imgL_dw2 = F.interpolate(
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imgL,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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imgR_dw2 = F.interpolate(
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imgR,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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with torch.inference_mode():
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pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None)
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pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
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pred_disp = torch.squeeze(pred_flow[:, 0, :, :]).cpu().detach().numpy()
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return pred_disp
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@app.post("/model/update/{epoch}")
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async def change_model(epoch: Union[int, Literal['latest']]):
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global model
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print(epoch)
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print('updating model')
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model = load_model(epoch)
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return {'status': 'success'}
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@app.put("/ir")
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async def read_ir_input(file: UploadFile = File(...)):
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try:
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img = np.array(Image.open(BytesIO(await file.read())))
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except Exception as e:
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return {'error': 'couldn\'t read file', 'exception': e}
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# img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
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if len(img.shape) == 2:
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img = cv2.merge([img for _ in range(3)])
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if img.shape == (1024, 1280, 3):
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diff = (512 - 480) // 2
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downsampled = cv2.pyrDown(img)
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img = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]]
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img = img.transpose((1,2,0))
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ref_pat = reference_pattern.transpose((1,2,0))
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start = datetime.now()
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pred_disp = inference(img, ref_pat, model, 20)
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duration = (datetime.now() - start).total_seconds()
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return json.dumps({'disp': pred_disp, 'reference': ref_pat, 'input': img, 'duration': duration}, cls=NumpyEncoder)
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# return json.dumps({'disp': pred_disp, 'duration': duration}, cls=NumpyEncoder)
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@app.get('/')
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def main():
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return {'test': 'abc'}
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