extract_feature.py

import argparse, os, json
import numpy as np
from imageio import imread
from PIL import Image

import torch
import torchvision
import ssl
ssl._create_default_https_context = ssl._create_unverified_context


def build_model(model='resnet101', model_stage=3):
    cnn = getattr(torchvision.models, model)(pretrained=True)
    layers = [
    cnn.conv1,
    cnn.bn1,
    cnn.relu,
    cnn.maxpool,
    ]
    for i in range(model_stage):
        name = 'layer%d' % (i + 1)
        layers.append(getattr(cnn, name))
        model = torch.nn.Sequential(*layers)
        # model.cuda()
        model.eval()
    return model


def run_image(img, model):
  mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1)
  std = np.array([0.229, 0.224, 0.224]).reshape(1, 3, 1, 1)

  image = np.concatenate([img], 0).astype(np.float32)
  image = (image / 255.0 - mean) / std
  image = torch.FloatTensor(image)
  image = torch.autograd.Variable(image, volatile=True)

  feats = model(image)
  feats = feats.data.cpu().clone().numpy()

  return feats


def get_img_feat(cnn_model, img, image_height=224, image_width=224):
    img_size = (image_height, image_width)
    img = np.array(Image.fromarray(np.uint8(img)).resize(img_size))
    img = img.transpose(2, 0, 1)[None]
    feats = run_image(img, cnn_model)
    _, C, H, W = feats.shape
    feat_dset = feats.reshape(1, C, H, W)
    return feat_dset