{"action":"created","comment":{"url":"https://api.github.com/repos/keras-team/keras/pulls/comments/185169498","pull_request_review_id":116504954,"id":185169498,"diff_hunk":"@@ -0,0 +1,535 @@\n+\"\"\"MobileNet v2 models for Keras.\n+\n+MobileNetv2 is a general architecture and can be used for multiple use cases.\n+Depending on the use case, it can use different input layer size and\n+different width factors. This allows different width models to reduce\n+the number of multiply-adds and thereby\n+reduce inference cost on mobile devices.\n+\n+MobileNetv2 is very similar to the original MobileNet,\n+except that it uses inverted residual blocks with\n+bottlenecking features. It has a drastically lower\n+parameter count than the original MobileNet.\n+MobileNets support any input size greater\n+than 32 x 32, with larger image sizes\n+offering better performance.\n+\n+The number of parameters and number of multiply-adds\n+can be modified by using the `alpha` parameter,\n+which increases/decreases the number of filters in each layer.\n+By altering the image size and `alpha` parameter,\n+all 22 models from the paper can be built, with ImageNet weights provided.\n+\n+We currently only support models built with the tensorflow backend\n+\n+The paper demonstrates the performance of MobileNets using `alpha` values of\n+1.0 (also called 100 % MobileNet), 0.35, 0.5, 0.75, 1.0, 1.3, and 1.4\n+\n+For each of these `alpha` values, weights for 5 different input image sizes\n+are provided (224, 192, 160, 128, and 96).\n+\n+\n+The following table describes the performance of\n+MobileNet on various input sizes:\n+------------------------------------------------------------------------\n+MACs stands for Multiply Adds\n+\n+ Classification Checkpoint| MACs (M)   | Parameters (M)| Top 1 Accuracy| Top 5 Accuracy\n+--------------------------|------------|---------------|---------|----|-------------\n+| [mobilenet_v2_1.4_224]  | 582 | 6.06 |          75.0 | 92.5 |\n+| [mobilenet_v2_1.3_224]  | 509 | 5.34 |          74.4 | 92.1 |\n+| [mobilenet_v2_1.0_224]  | 300 | 3.47 |          71.8 | 91.0 |\n+| [mobilenet_v2_1.0_192]  | 221 | 3.47 |          70.7 | 90.1 |\n+| [mobilenet_v2_1.0_160]  | 154 | 3.47 |          68.8 | 89.0 |\n+| [mobilenet_v2_1.0_128]  | 99  | 3.47 |          65.3 | 86.9 |\n+| [mobilenet_v2_1.0_96]   | 56  | 3.47 |          60.3 | 83.2 |\n+| [mobilenet_v2_0.75_224] | 209 | 2.61 |          69.8 | 89.6 |\n+| [mobilenet_v2_0.75_192] | 153 | 2.61 |          68.7 | 88.9 |\n+| [mobilenet_v2_0.75_160] | 107 | 2.61 |          66.4 | 87.3 |\n+| [mobilenet_v2_0.75_128] | 69  | 2.61 |          63.2 | 85.3 |\n+| [mobilenet_v2_0.75_96]  | 39  | 2.61 |          58.8 | 81.6 |\n+| [mobilenet_v2_0.5_224]  | 97  | 1.95 |          65.4 | 86.4 |\n+| [mobilenet_v2_0.5_192]  | 71  | 1.95 |          63.9 | 85.4 |\n+| [mobilenet_v2_0.5_160]  | 50  | 1.95 |          61.0 | 83.2 |\n+| [mobilenet_v2_0.5_128]  | 32  | 1.95 |          57.7 | 80.8 |\n+| [mobilenet_v2_0.5_96]   | 18  | 1.95 |          51.2 | 75.8 |\n+| [mobilenet_v2_0.35_224) | 59  | 1.66 |          60.3 | 82.9 |\n+| [mobilenet_v2_0.35_192] | 43  | 1.66 |          58.2 | 81.2 |\n+| [mobilenet_v2_0.35_160] | 30  | 1.66 |          55.7 | 79.1 |\n+| [mobilenet_v2_0.35_128] | 20  | 1.66 |          50.8 | 75.0 |\n+| [mobilenet_v2_0.35_96]  | 11  | 1.66 |          45.5 | 70.4 |\n+\n+The weights for all 16 models are obtained and translated from the Tensorflow checkpoints\n+from TensorFlow checkpoints found at\n+https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md\n+\n+# Reference\n+This file contains building code for MobileNetV2, based on\n+[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)\n+\n+The weights for this model were extracted, loaded, and tested using this repository:\n+(https://github.com/JonathanCMitchell/mobilenet_v2_keras)\n+\n+Tests comparing this model to the existing Tensorflow model can be\n+found at [mobilenet_v2_keras])(https://github.com/JonathanCMitchell/mobilenet_v2_keras)\n+\"\"\"\n+from __future__ import print_function\n+from __future__ import absolute_import\n+from __future__ import division\n+\n+import os\n+import warnings\n+import h5py\n+import numpy as np\n+\n+from ..models import Model\n+from ..layers import Input\n+from ..layers import Activation\n+from ..layers import Dropout\n+from ..layers import Reshape\n+from ..layers import BatchNormalization\n+from ..layers import Conv2D\n+from ..layers import DepthwiseConv2D\n+from ..layers import AveragePooling2D\n+from ..layers import Add\n+from ..layers import Flatten\n+from ..layers import Dense\n+from .. import initializers\n+from .. import regularizers\n+from .. import constraints\n+from ..utils import conv_utils\n+from ..utils.data_utils import get_file\n+from ..engine import get_source_inputs\n+from ..engine.base_layer import InputSpec\n+from . import imagenet_utils\n+from .imagenet_utils import _obtain_input_shape\n+from .imagenet_utils import decode_predictions\n+from .. import backend as K\n+\n+BASE_WEIGHT_PATH = 'https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.0/'\n+\n+\n+def relu6(x):\n+    return K.relu(x, max_value=6)\n+\n+\n+def preprocess_input(x):\n+    \"\"\"Preprocesses a numpy array encoding a batch of images.\n+\n+    This function applies the \"Inception\" preprocessing which converts\n+    the RGB values from [0, 255] to [-1, 1]. Note that this preprocessing\n+    function is different from `imagenet_utils.preprocess_input()`.\n+\n+    # Arguments\n+        x: a 4D numpy array consists of RGB values within [0, 255].\n+\n+    # Returns\n+        Preprocessed array.\n+    \"\"\"\n+    x /= 128.\n+    x -= 1.\n+    return x.astype(np.float32)\n+\n+\n+# This function is taken from the original tf repo.\n+# It ensures that all layers have a channel number that is divisible by 8\n+# It can be seen here:\n+# https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\n+\n+\n+def _make_divisible(v, divisor, min_value=None):\n+    if min_value is None:\n+        min_value = divisor\n+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n+    # Make sure that round down does not go down by more than 10%.\n+    if new_v < 0.9 * v:\n+        new_v += divisor\n+    return new_v\n+\n+\n+def MobileNetV2(input_shape=None,\n+                alpha=1.0,\n+                depth_multiplier=1,\n+                dropout=1e-3,\n+                include_top=True,\n+                weights='imagenet',\n+                input_tensor=None,\n+                classes=1000):\n+    \"\"\"\n+    Instantiates the MobileNetv2 architecture.\n+\n+    To load a MobileNet model via `load_model`, import the custom\n+    objects `relu6` and pass them to the `custom_objects` parameter.\n+    E.g.\n+    model = load_model('mobilenet.h5', custom_objects={\n+                       'relu6': mobilenet.relu6})\n+\n+    # Arguments\n+        input_shape: optional shape tuple, to be specified if you would\n+            like to use a model with an input img resolution that is not\n+            (224, 224, 3).\n+            It should have exactly 3 inputs channels (224, 224, 3).\n+            You can also omit this option if you would like\n+            to infer input_shape from an input_tensor.\n+            If you choose to include both input_tensor and input_shape then\n+            input_shape will be used if they match, if the shapes\n+            do not match then we will throw an error.\n+            E.g. `(160, 160, 3)` would be one valid value.\n+        alpha: controls the width of the network. This is known as the\n+        width multiplier in the MobileNetv2 paper.\n+            - If `alpha` < 1.0, proportionally decreases the number\n+                of filters in each layer.\n+            - If `alpha` > 1.0, proportionally increases the number\n+                of filters in each layer.\n+            - If `alpha` = 1, default number of filters from the paper\n+                 are used at each layer.\n+        depth_multiplier: depth multiplier for depthwise convolution\n+            (also called the resolution multiplier)\n+        dropout: dropout rate, dropout is currently not in use\n+        include_top: whether to include the fully-connected\n+            layer at the top of the network.\n+        weights: one of `None` (random initialization),\n+              'imagenet' (pre-training on ImageNet),\n+              or the path to the weights file to be loaded.\n+        input_tensor: optional Keras tensor (i.e. output of\n+            `layers.Input()`)\n+            to use as image input for the model.\n+        classes: optional number of classes to classify images\n+            into, only to be specified if `include_top` is True, and\n+            if no `weights` argument is specified.\n+\n+    # Returns\n+        A Keras model instance.\n+\n+    # Raises\n+        ValueError: in case of invalid argument for `weights`,\n+            or invalid input shape.","path":"keras/applications/mobilenetv2.py","position":206,"original_position":206,"commit_id":"cdf10fe305367d012c45dc4c4bff0aa7aae90274","original_commit_id":"cdf10fe305367d012c45dc4c4bff0aa7aae90274","user":{"login":"taehoonlee","id":4445535,"avatar_url":"https://avatars2.githubusercontent.com/u/4445535?v=4","gravatar_id":"","url":"https://api.github.com/users/taehoonlee","html_url":"https://github.com/taehoonlee","followers_url":"https://api.github.com/users/taehoonlee/followers","following_url":"https://api.github.com/users/taehoonlee/following{/other_user}","gists_url":"https://api.github.com/users/taehoonlee/gists{/gist_id}","starred_url":"https://api.github.com/users/taehoonlee/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/taehoonlee/subscriptions","organizations_url":"https://api.github.com/users/taehoonlee/orgs","repos_url":"https://api.github.com/users/taehoonlee/repos","events_url":"https://api.github.com/users/taehoonlee/events{/privacy}","received_events_url":"https://api.github.com/users/taehoonlee/received_events","type":"User","site_admin":false},"body":"or invalid depth_multiplier, alpha, rows when `weights='imagenet'`.","created_at":"2018-05-01T05:37:56Z","updated_at":"2018-05-01T05:37:56Z","html_url":"https://github.com/keras-team/keras/pull/10047#discussion_r185169498","pull_request_url":"https://api.github.com/repos/keras-team/keras/pulls/10047","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/keras-team/keras/pulls/comments/185169498"},"html":{"href":"https://github.com/keras-team/keras/pull/10047#discussion_r185169498"},"pull_request":{"href":"https://api.github.com/repos/keras-team/keras/pulls/10047"}}},"pull_request":{"url":"https://api.github.com/repos/keras-team/keras/pulls/10047","id":184506475,"html_url":"https://github.com/keras-team/keras/pull/10047","diff_url":"https://github.com/keras-team/keras/pull/10047.diff","patch_url":"https://github.com/keras-team/keras/pull/10047.patch","issue_url":"https://api.github.com/repos/keras-team/keras/issues/10047","number":10047,"state":"open","locked":false,"title":"MobileNetV2 keras implementation for imagenet weights for Tensorflow backend.","user":{"login":"JonathanCMitchell","id":13068956,"avatar_url":"https://avatars1.githubusercontent.com/u/13068956?v=4","gravatar_id":"","url":"https://api.github.com/users/JonathanCMitchell","html_url":"https://github.com/JonathanCMitchell","followers_url":"https://api.github.com/users/JonathanCMitchell/followers","following_url":"https://api.github.com/users/JonathanCMitchell/following{/other_user}","gists_url":"https://api.github.com/users/JonathanCMitchell/gists{/gist_id}","starred_url":"https://api.github.com/users/JonathanCMitchell/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JonathanCMitchell/subscriptions","organizations_url":"https://api.github.com/users/JonathanCMitchell/orgs","repos_url":"https://api.github.com/users/JonathanCMitchell/repos","events_url":"https://api.github.com/users/JonathanCMitchell/events{/privacy}","received_events_url":"https://api.github.com/users/JonathanCMitchell/received_events","type":"User","site_admin":false},"body":"This file enables access to the 23 trained MobileNetV2 models found [here](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet). The design and architecture for MobileNetV2 is explained in this [paper](https://arxiv.org/abs/1801.04381)\r\n\r\nFor a complete view of the weight extraction. loading, and testing process please see this [repository](https://github.com/JonathanCMitchell/mobilenet_v2_keras)\r\n\r\nLink to google doc explaining changes:\r\nhttps://docs.google.com/document/d/1-sRVeWiUMNmM8uZSfX-V0dZu9D_580OOQaTKdoqcJHM/edit?usp=sharing\r\n\r\nPlease let me know if there is something you would like me to add/ change/ etc. 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