Merge pull request #18591 from sl-sergei:download_utilities

Scripts for downloading models in DNN samples

* Initial commit. Utility classes and functions for downloading files

* updated download script

* Support YAML parsing, update download script and configs

* Fix problem with archived files

* fix models.yml

* Move download utilities to more appropriate place

* Fix script description

* Update README

* update utilities for broader range of files

* fix loading with no hashsum provided

* remove unnecessary import

* fix for Python2

* Add usage examples for downloadFile function

* Add more secure cache folder selection

* Remove trailing whitespaces

* Fix indentation

* Update function interface

* Change function for temp dir, change entry name in models.yml

* Update getCacheDirectory function call

* Return python implementation for cache directory selection, use more specific env variable

* Fix whitespace

samples/dnn/.gitignore

@@ -0,0 +1,3 @@
+*.caffemodel
+*.pb
+*.weights

samples/dnn/README.md

@@ -19,6 +19,36 @@ Check `-h` option to know which values are used by default:
 python object_detection.py opencv_fd -h
 ```
 
+### Sample models
+
+You can download sample models using ```download_models.py```. For example, the following command will download network weights for OpenCV Face Detector model and store them in FaceDetector folder:
+
+```bash
+python download_models.py --save_dir FaceDetector opencv_fd
+```
+
+You can use default configuration files adopted for OpenCV from [here](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn).
+
+You also can use the script to download necessary files from your code. Assume you have the following code inside ```your_script.py```:
+
+```python
+from download_models import downloadFile
+
+filepath1 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", None, filename="MobileNetSSD_deploy.caffemodel", save_dir="save_dir_1")
+filepath2 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a", filename="MobileNetSSD_deploy.caffemodel")
+print(filepath1)
+print(filepath2)
+# Your code
+```
+
+By running the following commands, you will get **MobileNetSSD_deploy.caffemodel** file:
+```bash
+export OPENCV_DOWNLOAD_DATA_PATH=download_folder
+python your_script.py
+```
+
+**Note** that you can provide a directory using **save_dir** parameter or via **OPENCV_SAVE_DIR** environment variable.
+
 #### Face detection
 [An origin model](https://github.com/opencv/opencv/tree/3.4/samples/dnn/face_detector)
 with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
@@ -48,7 +78,7 @@ AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528     | 0.528          |
 ```
 
 ## References
-* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
+* [Models downloading script](https://github.com/opencv/opencv/samples/dnn/download_models.py)
 * [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
 * [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
 * [Names of classes from different datasets](https://github.com/opencv/opencv/tree/3.4/samples/data/dnn)

samples/dnn/download_models.py

@@ -0,0 +1,364 @@
+'''
+Helper module to download extra data from Internet
+'''
+from __future__ import print_function
+import os
+import cv2
+import sys
+import yaml
+import argparse
+import tarfile
+import platform
+import tempfile
+import hashlib
+import requests
+import shutil
+from pathlib import Path
+from datetime import datetime
+if sys.version_info[0] < 3:
+    from urllib2 import urlopen
+else:
+    from urllib.request import urlopen
+import xml.etree.ElementTree as ET
+
+__all__ = ["downloadFile"]
+
+class HashMismatchException(Exception):
+    def __init__(self, expected, actual):
+        Exception.__init__(self)
+        self.expected = expected
+        self.actual = actual
+    def __str__(self):
+        return 'Hash mismatch: expected {} vs actual of {}'.format(self.expected, self.actual)
+
+def getHashsumFromFile(filepath):
+    sha = hashlib.sha1()
+    if os.path.exists(filepath):
+        print('  there is already a file with the same name')
+        with open(filepath, 'rb') as f:
+            while True:
+                buf = f.read(10*1024*1024)
+                if not buf:
+                    break
+                sha.update(buf)
+    hashsum = sha.hexdigest()
+    return hashsum
+
+def checkHashsum(expected_sha, filepath, silent=True):
+    print('  expected SHA1: {}'.format(expected_sha))
+    actual_sha = getHashsumFromFile(filepath)
+    print('  actual SHA1:{}'.format(actual_sha))
+    hashes_matched = expected_sha == actual_sha
+    if not hashes_matched and not silent:
+        raise HashMismatchException(expected_sha, actual_sha)
+    return hashes_matched
+
+def isArchive(filepath):
+    return tarfile.is_tarfile(filepath)
+
+class DownloadInstance:
+    def __init__(self, **kwargs):
+        self.name = kwargs.pop('name')
+        self.filename = kwargs.pop('filename')
+        self.loader = kwargs.pop('loader', None)
+        self.save_dir = kwargs.pop('save_dir')
+        self.sha = kwargs.pop('sha', None)
+
+    def __str__(self):
+        return 'DownloadInstance <{}>'.format(self.name)
+
+    def get(self):
+        print("  Working on " + self.name)
+        print("  Getting file " + self.filename)
+        if self.sha is None:
+            print('  No expected hashsum provided, loading file')
+        else:
+            filepath = os.path.join(self.save_dir, self.sha, self.filename)
+            if checkHashsum(self.sha, filepath):
+                print('  hash match - file already exists, skipping')
+                return filepath
+            else:
+                print('  hash didn\'t match, loading file')
+
+        if not os.path.exists(self.save_dir):
+            print('  creating directory: ' + self.save_dir)
+            os.makedirs(self.save_dir)
+
+
+        print('  hash check failed - loading')
+        assert self.loader
+        try:
+            self.loader.load(self.filename, self.sha, self.save_dir)
+            print(' done')
+            print(' file {}'.format(self.filename))
+            if self.sha is None:
+                download_path = os.path.join(self.save_dir, self.filename)
+                self.sha = getHashsumFromFile(download_path)
+                new_dir = os.path.join(self.save_dir, self.sha)
+
+                if not os.path.exists(new_dir):
+                    os.makedirs(new_dir)
+                filepath = os.path.join(new_dir, self.filename)
+                if not (os.path.exists(filepath)):
+                    shutil.move(download_path, new_dir)
+                print('  No expected hashsum provided, actual SHA is {}'.format(self.sha))
+            else:
+                checkHashsum(self.sha, filepath, silent=False)
+        except Exception as e:
+            print("  There was some problem with loading file {} for {}".format(self.filename, self.name))
+            print("  Exception: {}".format(e))
+            return
+
+        print("  Finished " + self.name)
+        return filepath
+
+class Loader(object):
+    MB = 1024*1024
+    BUFSIZE = 10*MB
+    def __init__(self, download_name, download_sha, archive_member = None):
+        self.download_name = download_name
+        self.download_sha = download_sha
+        self.archive_member = archive_member
+
+    def load(self, requested_file, sha, save_dir):
+        if self.download_sha is None:
+            download_dir = save_dir
+        else:
+            # create a new folder in save_dir to avoid possible name conflicts
+            download_dir = os.path.join(save_dir, self.download_sha)
+        if not os.path.exists(download_dir):
+            os.makedirs(download_dir)
+        download_path = os.path.join(download_dir, self.download_name)
+        print("  Preparing to download file " + self.download_name)
+        if checkHashsum(self.download_sha, download_path):
+            print('  hash match - file already exists, no need to download')
+        else:
+            filesize = self.download(download_path)
+            print('  Downloaded {} with size {} Mb'.format(self.download_name, filesize/self.MB))
+            if self.download_sha is not None:
+                checkHashsum(self.download_sha, download_path, silent=False)
+        if self.download_name == requested_file:
+            return
+        else:
+            if isArchive(download_path):
+                if sha is not None:
+                    extract_dir = os.path.join(save_dir, sha)
+                else:
+                    extract_dir = save_dir
+                if not os.path.exists(extract_dir):
+                    os.makedirs(extract_dir)
+                self.extract(requested_file, download_path, extract_dir)
+            else:
+                raise Exception("Downloaded file has different name")
+
+    def download(self, filepath):
+        print("Warning: download is not implemented, this is a base class")
+        return 0
+
+    def extract(self, requested_file, archive_path, save_dir):
+        filepath = os.path.join(save_dir, requested_file)
+        try:
+            with tarfile.open(archive_path) as f:
+                if self.archive_member is None:
+                    pathDict = dict((os.path.split(elem)[1], os.path.split(elem)[0]) for elem in f.getnames())
+                    self.archive_member = pathDict[requested_file]
+                assert self.archive_member in f.getnames()
+                self.save(filepath, f.extractfile(self.archive_member))
+        except Exception as e:
+            print('  catch {}'.format(e))
+
+    def save(self, filepath, r):
+        with open(filepath, 'wb') as f:
+            print('  progress ', end="")
+            sys.stdout.flush()
+            while True:
+                buf = r.read(self.BUFSIZE)
+                if not buf:
+                    break
+                f.write(buf)
+                print('>', end="")
+                sys.stdout.flush()
+
+class URLLoader(Loader):
+    def __init__(self, download_name, download_sha, url, archive_member = None):
+        super(URLLoader, self).__init__(download_name, download_sha, archive_member)
+        self.download_name = download_name
+        self.download_sha = download_sha
+        self.url = url
+
+    def download(self, filepath):
+        r = urlopen(self.url, timeout=60)
+        self.printRequest(r)
+        self.save(filepath, r)
+        return os.path.getsize(filepath)
+
+    def printRequest(self, r):
+        def getMB(r):
+            d = dict(r.info())
+            for c in ['content-length', 'Content-Length']:
+                if c in d:
+                    return int(d[c]) / self.MB
+            return '<unknown>'
+        print('  {} {} [{} Mb]'.format(r.getcode(), r.msg, getMB(r)))
+
+class GDriveLoader(Loader):
+    BUFSIZE = 1024 * 1024
+    PROGRESS_SIZE = 10 * 1024 * 1024
+    def __init__(self, download_name, download_sha, gid, archive_member = None):
+        super(GDriveLoader, self).__init__(download_name, download_sha, archive_member)
+        self.download_name = download_name
+        self.download_sha = download_sha
+        self.gid = gid
+
+    def download(self, filepath):
+        session = requests.Session()  # re-use cookies
+
+        URL = "https://docs.google.com/uc?export=download"
+        response = session.get(URL, params = { 'id' : self.gid }, stream = True)
+
+        def get_confirm_token(response):  # in case of large files
+            for key, value in response.cookies.items():
+                if key.startswith('download_warning'):
+                    return value
+            return None
+        token = get_confirm_token(response)
+
+        if token:
+            params = { 'id' : self.gid, 'confirm' : token }
+            response = session.get(URL, params = params, stream = True)
+
+        sz = 0
+        progress_sz = self.PROGRESS_SIZE
+        with open(filepath, "wb") as f:
+            for chunk in response.iter_content(self.BUFSIZE):
+                if not chunk:
+                    continue  # keep-alive
+
+                f.write(chunk)
+                sz += len(chunk)
+                if sz >= progress_sz:
+                    progress_sz += self.PROGRESS_SIZE
+                    print('>', end='')
+                    sys.stdout.flush()
+        print('')
+        return sz
+
+def produceDownloadInstance(instance_name, filename, sha, url, save_dir, download_name=None, download_sha=None, archive_member=None):
+    spec_param = url
+    loader = URLLoader
+    if download_name is None:
+        download_name = filename
+    if download_sha is None:
+        download_sha = sha
+    if "drive.google.com" in url:
+        token = ""
+        token_part = url.rsplit('/', 1)[-1]
+        if "&id=" not in token_part:
+            token_part = url.rsplit('/', 1)[-2]
+        for param in token_part.split("&"):
+            if param.startswith("id="):
+                token = param[3:]
+        if token:
+            loader = GDriveLoader
+            spec_param = token
+        else:
+            print("Warning: possibly wrong Google Drive link")
+    return DownloadInstance(
+        name=instance_name,
+        filename=filename,
+        sha=sha,
+        save_dir=save_dir,
+        loader=loader(download_name, download_sha, spec_param, archive_member)
+    )
+
+def getSaveDir():
+    env_path = os.environ.get("OPENCV_DOWNLOAD_DATA_PATH", None)
+    if env_path:
+        save_dir = env_path
+    else:
+        # TODO reuse binding function cv2.utils.fs.getCacheDirectory when issue #19011 is fixed
+        if platform.system() == "Darwin":
+            #On Apple devices
+            temp_env = os.environ.get("TMPDIR", None)
+            if temp_env is None or not os.path.isdir(temp_env):
+                temp_dir = Path("/tmp")
+                print("Using world accessible cache directory. This may be not secure: ", temp_dir)
+            else:
+                temp_dir = temp_env
+        elif platform.system() == "Windows":
+            temp_dir = tempfile.gettempdir()
+        else:
+            xdg_cache_env = os.environ.get("XDG_CACHE_HOME", None)
+            if (xdg_cache_env and xdg_cache_env[0] and os.path.isdir(xdg_cache_env)):
+                temp_dir = xdg_cache_env
+            else:
+                home_env = os.environ.get("HOME", None)
+                if (home_env and home_env[0] and os.path.isdir(home_env)):
+                    home_path = os.path.join(home_env, ".cache/")
+                    if os.path.isdir(home_path):
+                        temp_dir = home_path
+                else:
+                    temp_dir = tempfile.gettempdir()
+                    print("Using world accessible cache directory. This may be not secure: ", temp_dir)
+
+        save_dir = os.path.join(temp_dir, "downloads")
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    return save_dir
+
+def downloadFile(url, sha=None, save_dir=None, filename=None):
+    if save_dir is None:
+        save_dir = getSaveDir()
+    if filename is None:
+        filename = "download_" + datetime.now().__str__()
+    name = filename
+    return produceDownloadInstance(name, filename, sha, url, save_dir).get()
+
+def parseMetalinkFile(metalink_filepath, save_dir):
+    NS = {'ml': 'urn:ietf:params:xml:ns:metalink'}
+    models = []
+    for file_elem in ET.parse(metalink_filepath).getroot().findall('ml:file', NS):
+        url = file_elem.find('ml:url', NS).text
+        fname = file_elem.attrib['name']
+        name = file_elem.find('ml:identity', NS).text
+        hash_sum = file_elem.find('ml:hash', NS).text
+        models.append(produceDownloadInstance(name, fname, hash_sum, url, save_dir))
+    return models
+
+def parseYAMLFile(yaml_filepath, save_dir):
+    models = []
+    with open(yaml_filepath, 'r') as stream:
+        data_loaded = yaml.safe_load(stream)
+        for name, params in data_loaded.items():
+            load_info = params.get("load_info", None)
+            if load_info:
+                fname = os.path.basename(params.get("model"))
+                hash_sum = load_info.get("sha1")
+                url = load_info.get("url")
+                download_sha = load_info.get("download_sha")
+                download_name = load_info.get("download_name")
+                archive_member = load_info.get("member")
+                models.append(produceDownloadInstance(name, fname, hash_sum, url, save_dir,
+                    download_name=download_name, download_sha=download_sha, archive_member=archive_member))
+
+    return models
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='This is a utility script for downloading DNN models for samples.')
+
+    parser.add_argument('--save_dir', action="store", default=os.getcwd(),
+                        help='Path to the directory to store downloaded files')
+    parser.add_argument('model_name', type=str, default="", nargs='?', action="store",
+                        help='name of the model to download')
+    args = parser.parse_args()
+    models = []
+    save_dir = args.save_dir
+    selected_model_name = args.model_name
+    models.extend(parseMetalinkFile('face_detector/weights.meta4', save_dir))
+    models.extend(parseYAMLFile('models.yml', save_dir))
+    for m in models:
+        print(m)
+        if selected_model_name and not m.name.startswith(selected_model_name):
+            continue
+        print('Model: ' + selected_model_name)
+        m.get()

samples/dnn/face_detector/download_weights.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import print_function
-import hashlib
-import time
-import sys
-import xml.etree.ElementTree as ET
-if sys.version_info[0] < 3:
-    from urllib2 import urlopen
-else:
-    from urllib.request import urlopen
-
-class HashMismatchException(Exception):
-    def __init__(self, expected, actual):
-        Exception.__init__(self)
-        self.expected = expected
-        self.actual = actual
-    def __str__(self):
-        return 'Hash mismatch: {} vs {}'.format(self.expected, self.actual)
-
-class MetalinkDownloader(object):
-    BUFSIZE = 10*1024*1024
-    NS = {'ml': 'urn:ietf:params:xml:ns:metalink'}
-    tick = 0
-
-    def download(self, metalink_file):
-        status = True
-        for file_elem in ET.parse(metalink_file).getroot().findall('ml:file', self.NS):
-            url = file_elem.find('ml:url', self.NS).text
-            fname = file_elem.attrib['name']
-            hash_sum = file_elem.find('ml:hash', self.NS).text
-            print('*** {}'.format(fname))
-            try:
-                self.verify(hash_sum, fname)
-            except Exception as ex:
-                print('  {}'.format(ex))
-                try:
-                    print('  {}'.format(url))
-                    with open(fname, 'wb') as file_stream:
-                        self.buffered_read(urlopen(url), file_stream.write)
-                    self.verify(hash_sum, fname)
-                except Exception as ex:
-                    print('  {}'.format(ex))
-                    print('  FAILURE')
-                    status = False
-                    continue
-            print('  SUCCESS')
-        return status
-
-    def print_progress(self, msg, timeout = 0):
-        if time.time() - self.tick > timeout:
-            print(msg, end='')
-            sys.stdout.flush()
-            self.tick = time.time()
-
-    def buffered_read(self, in_stream, processing):
-        self.print_progress('  >')
-        while True:
-            buf = in_stream.read(self.BUFSIZE)
-            if not buf:
-                break
-            processing(buf)
-            self.print_progress('>', 5)
-        print(' done')
-
-    def verify(self, hash_sum, fname):
-        sha = hashlib.sha1()
-        with open(fname, 'rb') as file_stream:
-            self.buffered_read(file_stream, sha.update)
-        if hash_sum != sha.hexdigest():
-            raise HashMismatchException(hash_sum, sha.hexdigest())
-
-if __name__ == '__main__':
-    sys.exit(0 if MetalinkDownloader().download('weights.meta4') else 1)

samples/dnn/face_detector/weights.meta4

@@ -1,12 +1,12 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <metalink xmlns="urn:ietf:params:xml:ns:metalink">
     <file name="res10_300x300_ssd_iter_140000_fp16.caffemodel">
-        <identity>OpenCV face detector FP16 weights</identity>
+        <identity>opencv_face_detector_fp16</identity>
         <hash type="sha-1">31fc22bfdd907567a04bb45b7cfad29966caddc1</hash>
         <url>https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel</url>
     </file>
     <file name="opencv_face_detector_uint8.pb">
-        <identity>OpenCV face detector UINT8 weights</identity>
+        <identity>opencv_face_detector_uint8</identity>
         <hash type="sha-1">4f2fdf6f231d759d7bbdb94353c5a68690f3d2ae</hash>
         <url>https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180220_uint8/opencv_face_detector_uint8.pb</url>
     </file>

samples/dnn/models.yml

@@ -1,11 +1,14 @@
-%YAML:1.0
+%YAML 1.0
-
+---
 ################################################################################
 # Object detection models.
 ################################################################################
 
 # OpenCV's face detection network
 opencv_fd:
+  load_info:
+    url: "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
+    sha1: "15aa726b4d46d9f023526d85537db81cbc8dd566"
   model: "opencv_face_detector.caffemodel"
   config: "opencv_face_detector.prototxt"
   mean: [104, 177, 123]
@@ -19,6 +22,9 @@ opencv_fd:
 # YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/)
 # Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4
 yolo:
+  load_info:
+    url: "https://pjreddie.com/media/files/yolov3.weights"
+    sha1: "520878f12e97cf820529daea502acca380f1cb8e"
   model: "yolov3.weights"
   config: "yolov3.cfg"
   mean: [0, 0, 0]
@@ -30,6 +36,9 @@ yolo:
   sample: "object_detection"
 
 tiny-yolo-voc:
+  load_info:
+    url: "https://pjreddie.com/media/files/yolov2-tiny-voc.weights"
+    sha1: "24b4bd049fc4fa5f5e95f684a8967e65c625dff9"
   model: "tiny-yolo-voc.weights"
   config: "tiny-yolo-voc.cfg"
   mean: [0, 0, 0]
@@ -42,6 +51,9 @@ tiny-yolo-voc:
 
 # Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD
 ssd_caffe:
+  load_info:
+    url: "https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc"
+    sha1: "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a"
   model: "MobileNetSSD_deploy.caffemodel"
   config: "MobileNetSSD_deploy.prototxt"
   mean: [127.5, 127.5, 127.5]
@@ -54,6 +66,12 @@ ssd_caffe:
 
 # TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection
 ssd_tf:
+  load_info:
+    url: "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
+    sha1: "9e4bcdd98f4c6572747679e4ce570de4f03a70e2"
+    download_sha: "6157ddb6da55db2da89dd561eceb7f944928e317"
+    download_name: "ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
+    member: "ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb"
   model: "ssd_mobilenet_v1_coco_2017_11_17.pb"
   config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt"
   mean: [0, 0, 0]
@@ -66,6 +84,12 @@ ssd_tf:
 
 # TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection
 faster_rcnn_tf:
+  load_info:
+    url: "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
+    sha1: "f2e4bf386b9bb3e25ddfcbbd382c20f417e444f3"
+    download_sha: "c710f25e5c6a3ce85fe793d5bf266d581ab1c230"
+    download_name: "faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
+    member: "faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb"
   model: "faster_rcnn_inception_v2_coco_2018_01_28.pb"
   config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt"
   mean: [0, 0, 0]
@@ -81,6 +105,9 @@ faster_rcnn_tf:
 
 # SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet
 squeezenet:
+  load_info:
+    url: "https://raw.githubusercontent.com/DeepScale/SqueezeNet/b5c3f1a23713c8b3fd7b801d229f6b04c64374a5/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel"
+    sha1: "3397f026368a45ae236403ccc81cfcbe8ebe1bd0"
   model: "squeezenet_v1.1.caffemodel"
   config: "squeezenet_v1.1.prototxt"
   mean: [0, 0, 0]
@@ -93,6 +120,9 @@ squeezenet:
 
 # Googlenet from https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
 googlenet:
+  load_info:
+    url: "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel"
+    sha1: "405fc5acd08a3bb12de8ee5e23a96bec22f08204"
   model: "bvlc_googlenet.caffemodel"
   config: "bvlc_googlenet.prototxt"
   mean: [104, 117, 123]
@@ -110,6 +140,9 @@ googlenet:
 # ENet road scene segmentation network from https://github.com/e-lab/ENet-training
 # Works fine for different input sizes.
 enet:
+  load_info:
+    url: "https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1"
+    sha1: "b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315"
   model: "Enet-model-best.net"
   mean: [0, 0, 0]
   scale: 0.00392
@@ -120,6 +153,9 @@ enet:
   sample: "segmentation"
 
 fcn8s:
+  load_info:
+    url: "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel"
+    sha1: "c449ea74dd7d83751d1357d6a8c323fcf4038962"
   model: "fcn8s-heavy-pascal.caffemodel"
   config: "fcn8s-heavy-pascal.prototxt"
   mean: [0, 0, 0]