TensorNets

High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0).

Guiding principles

• Applicability. Many people already have their own ML workflows, and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes.
• Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point. Also, it is easy to deploy and expand a collection of pre-processing and pre-trained weights.
• Readability. With recent TensorFlow APIs, more factoring and less indenting can be possible. For example, all the inception variants are implemented as about 500 lines of code in TensorNets while 2000+ lines in official TensorFlow models.
• Reproducibility. You can always reproduce the original results with simple APIs including feature extractions. Furthermore, you don't need to care about a version of TensorFlow beacuse compatibilities with various releases of TensorFlow have been checked with Travis.

Installation

You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

A quick example

Each network (see full list) is not a custom class but a function that takes and returns tf.Tensor as its input and output. Here is an example of ResNet50:

import tensorflow as tf
# import tensorflow.compat.v1 as tf  # for TF 2
import tensornets as nets
# tf.disable_v2_behavior()  # for TF 2

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
model = nets.ResNet50(inputs)

assert isinstance(model, tf.Tensor)

You can load an example image by using utils.load_img returning a np.ndarray as the NHWC format:

img = nets.utils.load_img('cat.png', target_size=256, crop_size=224)
assert img.shape == (1, 224, 224, 3)

Once your network is created, you can run with regular TensorFlow APIs 😊 because all the networks in TensorNets always return tf.Tensor. Using pre-trained weights and pre-processing are as easy as pretrained() and preprocess() to reproduce the original results:

with tf.Session() as sess:
    img = model.preprocess(img)  # equivalent to img = nets.preprocess(model, img)
    sess.run(model.pretrained())  # equivalent to nets.pretrained(model)
    preds = sess.run(model, {inputs: img})

You can see the most probable classes:

print(nets.utils.decode_predictions(preds, top=2)[0])
[(u'n02124075', u'Egyptian_cat', 0.28067636), (u'n02127052', u'lynx', 0.16826575)]

You can also easily obtain values of intermediate layers with middles() and outputs():

with tf.Session() as sess:
    img = model.preprocess(img)
    sess.run(model.pretrained())
    middles = sess.run(model.middles(), {inputs: img})
    outputs = sess.run(model.outputs(), {inputs: img})

model.print_middles()
assert middles[0].shape == (1, 56, 56, 256)
assert middles[-1].shape == (1, 7, 7, 2048)

model.print_outputs()
assert sum(sum((outputs[-1] - preds) ** 2)) < 1e-8

With load() and save(), your weight values can be restorable:

with tf.Session() as sess:
    model.init()
    # ... your training ...
    model.save('test.npz')

with tf.Session() as sess:
    model.load('test.npz')
    # ... your deployment ...

TensorNets enables us to deploy well-known architectures and benchmark those results faster ⚡️. For more information, you can check out the lists of utilities, examples, and architectures.

Object detection example

Each object detection model can be coupled with any network in TensorNets (see performance) and takes two arguments: a placeholder and a function acting as a stem layer. Here is an example of YOLOv2 for PASCAL VOC:

import tensorflow as tf
import tensornets as nets

inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv2(inputs, nets.Darknet19)

img = nets.utils.load_img('cat.png')

with tf.Session() as sess:
    sess.run(model.pretrained())
    preds = sess.run(model, {inputs: model.preprocess(img)})
    boxes = model.get_boxes(preds, img.shape[1:3])

Like other models, a detection model also returns tf.Tensor as its output. You can see the bounding box predictions (x1, y1, x2, y2, score) by using model.get_boxes(model_output, original_img_shape) and visualize the results:

from tensornets.datasets import voc
print("%s: %s" % (voc.classnames[7], boxes[7][0]))  # 7 is cat

import numpy as np
import matplotlib.pyplot as plt
box = boxes[7][0]
plt.imshow(img[0].astype(np.uint8))
plt.gca().add_patch(plt.Rectangle(
    (box[0], box[1]), box[2] - box[0], box[3] - box[1],
    fill=False, edgecolor='r', linewidth=2))
plt.show()

More detection examples such as FasterRCNN on VOC2007 are here 😎. Note that:

• APIs of detection models are slightly different:

  • YOLOv3: sess.run(model.preds, {inputs: img}),
  • YOLOv2: sess.run(model, {inputs: img}),
  • FasterRCNN: sess.run(model, {inputs: img, model.scales: scale}),

• FasterRCNN requires roi_pooling:

  • git clone https://github.com/deepsense-io/roi-pooling && cd roi-pooling && vi roi_pooling/Makefile and edit according to here,
  • python setup.py install.

Utilities

Besides pretrained() and preprocess(), the output tf.Tensor provides the following useful methods:

• logits: returns the tf.Tensor logits (the values before the softmax),
• middles() (=get_middles()): returns a list of all the representative tf.Tensor end-points,
• outputs() (=get_outputs()): returns a list of all the tf.Tensor end-points,
• weights() (=get_weights()): returns a list of all the tf.Tensor weight matrices,
• summary() (=print_summary()): prints the numbers of layers, weight matrices, and parameters,
• print_middles(): prints all the representative end-points,
• print_outputs(): prints all the end-points,
• print_weights(): prints all the weight matrices.

Example outputs of print methods are:

>>> model.print_middles()
Scope: resnet50
conv2/block1/out:0 (?, 56, 56, 256)
conv2/block2/out:0 (?, 56, 56, 256)
conv2/block3/out:0 (?, 56, 56, 256)
conv3/block1/out:0 (?, 28, 28, 512)
conv3/block2/out:0 (?, 28, 28, 512)
conv3/block3/out:0 (?, 28, 28, 512)
conv3/block4/out:0 (?, 28, 28, 512)
conv4/block1/out:0 (?, 14, 14, 1024)
...

>>> model.print_outputs()
Scope: resnet50
conv1/pad:0 (?, 230, 230, 3)
conv1/conv/BiasAdd:0 (?, 112, 112, 64)
conv1/bn/batchnorm/add_1:0 (?, 112, 112, 64)
conv1/relu:0 (?, 112, 112, 64)
pool1/pad:0 (?, 114, 114, 64)
pool1/MaxPool:0 (?, 56, 56, 64)
conv2/block1/0/conv/BiasAdd:0 (?, 56, 56, 256)
conv2/block1/0/bn/batchnorm/add_1:0 (?, 56, 56, 256)
conv2/block1/1/conv/BiasAdd:0 (?, 56, 56, 64)
conv2/block1/1/bn/batchnorm/add_1:0 (?, 56, 56, 64)
conv2/block1/1/relu:0 (?, 56, 56, 64)
...

>>> model.print_weights()
Scope: resnet50
conv1/conv/weights:0 (7, 7, 3, 64)
conv1/conv/biases:0 (64,)
conv1/bn/beta:0 (64,)
conv1/bn/gamma:0 (64,)
conv1/bn/moving_mean:0 (64,)
conv1/bn/moving_variance:0 (64,)
conv2/block1/0/conv/weights:0 (1, 1, 64, 256)
conv2/block1/0/conv/biases:0 (256,)
conv2/block1/0/bn/beta:0 (256,)
conv2/block1/0/bn/gamma:0 (256,)
...

>>> model.summary()
Scope: resnet50
Total layers: 54
Total weights: 320
Total parameters: 25,636,712

Examples

• Comparison of different networks:

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
models = [
    nets.MobileNet75(inputs),
    nets.MobileNet100(inputs),
    nets.SqueezeNet(inputs),
]

img = utils.load_img('cat.png', target_size=256, crop_size=224)
imgs = nets.preprocess(models, img)

with tf.Session() as sess:
    nets.pretrained(models)
    for (model, img) in zip(models, imgs):
        preds = sess.run(model, {inputs: img})
        print(utils.decode_predictions(preds, top=2)[0])

• Transfer learning:

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
outputs = tf.placeholder(tf.float32, [None, 50])
model = nets.DenseNet169(inputs, is_training=True, classes=50)

loss = tf.losses.softmax_cross_entropy(outputs, model.logits)
train = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(loss)

with tf.Session() as sess:
    nets.pretrained(model)
    for (x, y) in your_NumPy_data:  # the NHWC and one-hot format
        sess.run(train, {inputs: x, outputs: y})

• Using multi-GPU:

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
models = []

with tf.device('gpu:0'):
    models.append(nets.ResNeXt50(inputs))

with tf.device('gpu:1'):
    models.append(nets.DenseNet201(inputs))

from tensornets.preprocess import fb_preprocess
img = utils.load_img('cat.png', target_size=256, crop_size=224)
img = fb_preprocess(img)

with tf.Session() as sess:
    nets.pretrained(models)
    preds = sess.run(models, {inputs: img})
    for pred in preds:
        print(utils.decode_predictions(pred, top=2)[0])

Performance

Image classification

• The top-k accuracies were obtained with TensorNets on ImageNet validation set and may slightly differ from the original ones.
  • Input: input size fed into models
  • Top-1: single center crop, top-1 accuracy
  • Top-5: single center crop, top-5 accuracy
  • MAC: rounded the number of float operations by using tf.profiler
  • Size: rounded the number of parameters (w/ fully-connected layers)
  • Stem: rounded the number of parameters (w/o fully-connected layers)
• The computation times were measured on NVIDIA Tesla P100 (3584 cores, 16 GB global memory) with cuDNN 6.0 and CUDA 8.0.
  • Speed: milliseconds for inferences of 100 images
• The summary plot is generated by this script.

	Input	Top-1	Top-5	MAC	Size	Stem	Speed	References
ResNet50	224	74.874	92.018	51.0M	25.6M	23.6M	195.4	[paper] [tf-slim] [torch-fb] [caffe] [keras]
ResNet101	224	76.420	92.786	88.9M	44.7M	42.7M	311.7	[paper] [tf-slim] [torch-fb] [caffe]
ResNet152	224	76.604	93.118	120.1M	60.4M	58.4M	439.1	[paper] [tf-slim] [torch-fb] [caffe]
ResNet50v2	299	75.960	93.034	51.0M	25.6M	23.6M	209.7	[paper] [tf-slim] [torch-fb]
ResNet101v2	299	77.234	93.816	88.9M	44.7M	42.6M	326.2	[paper] [tf-slim] [torch-fb]
ResNet152v2	299	78.032	94.162	120.1M	60.4M	58.3M	455.2	[paper] [tf-slim] [torch-fb]
ResNet200v2	224	78.286	94.152	129.0M	64.9M	62.9M	618.3	[paper] [tf-slim] [torch-fb]
ResNeXt50c32	224	77.740	93.810	49.9M	25.1M	23.0M	267.4	[paper] [torch-fb]
ResNeXt101c32	224	78.730	94.294	88.1M	44.3M	42.3M	427.9	[paper] [torch-fb]
ResNeXt101c64	224	79.494	94.592	0.0M	83.7M	81.6M	877.8	[paper] [torch-fb]
WideResNet50	224	78.018	93.934	137.6M	69.0M	66.9M	358.1	[paper] [torch]
Inception1	224	66.840	87.676	14.0M	7.0M	6.0M	165.1	[paper] [tf-slim] [caffe-zoo]
Inception2	224	74.680	92.156	22.3M	11.2M	10.2M	134.3	[paper] [tf-slim]
Inception3	299	77.946	93.758	47.6M	23.9M	21.8M	314.6	[paper] [tf-slim] [keras]
Inception4	299	80.120	94.978	85.2M	42.7M	41.2M	582.1	[paper] [tf-slim]
InceptionResNet2	299	80.256	95.252	111.5M	55.9M	54.3M	656.8	[paper] [tf-slim]
NASNetAlarge	331	82.498	96.004	186.2M	93.5M	89.5M	2081	[paper] [tf-slim]
NASNetAmobile	224	74.366	91.854	15.3M	7.7M	6.7M	165.8	[paper] [tf-slim]
PNASNetlarge	331	82.634	96.050	171.8M	86.2M	81.9M	1978	[paper] [tf-slim]
VGG16	224	71.268	90.050	276.7M	138.4M	14.7M	348.4	[paper] [keras]
VGG19	224	71.256	89.988	287.3M	143.7M	20.0M	399.8	[paper] [keras]
DenseNet121	224	74.972	92.258	15.8M	8.1M	7.0M	202.9	[paper] [torch]
DenseNet169	224	76.176	93.176	28.0M	14.3M	12.6M	219.1	[paper] [torch]
DenseNet201	224	77.320	93.620	39.6M	20.2M	18.3M	272.0	[paper] [torch]
MobileNet25	224	51.582	75.792	0.9M	0.5M	0.2M	34.46	[paper] [tf-slim]
MobileNet50	224	64.292	85.624	2.6M	1.3M	0.8M	52.46	[paper] [tf-slim]
MobileNet75	224	68.412	88.242	5.1M	2.6M	1.8M	70.11	[paper] [tf-slim]
MobileNet100	224	70.424	89.504	8.4M	4.3M	3.2M	83.41	[paper] [tf-slim]
MobileNet35v2	224	60.086	82.432	3.3M	1.7M	0.4M	57.04	[paper] [tf-slim]
MobileNet50v2	224	65.194	86.062	3.9M	2.0M	0.7M	64.35	[paper] [tf-slim]
MobileNet75v2	224	69.532	89.176	5.2M	2.7M	1.4M	88.68	[paper] [tf-slim]
MobileNet100v2	224	71.336	90.142	6.9M	3.5M	2.3M	93.82	[paper] [tf-slim]
MobileNet130v2	224	74.680	92.122	10.7M	5.4M	3.8M	130.4	[paper] [tf-slim]
MobileNet140v2	224	75.230	92.422	12.1M	6.2M	4.4M	132.9	[paper] [tf-slim]
75v3large	224	73.754	91.618	7.9M	4.0M	2.7M	79.73	[paper] [tf-slim]
100v3large	224	75.790	92.840	27.3M	5.5M	4.2M	94.71	[paper] [tf-slim]
100v3largemini	224	72.706	90.930	7.8M	3.9M	2.7M	70.57	[paper] [tf-slim]
75v3small	224	66.138	86.534	4.1M	2.1M	1.0M	37.78	[paper] [tf-slim]
100v3small	224	68.318	87.942	5.1M	2.6M	1.5M	42.00	[paper] [tf-slim]
100v3smallmini	224	63.440	84.646	4.1M	2.1M	1.0M	29.65	[paper] [tf-slim]
EfficientNetB0	224	77.012	93.338	26.2M	5.3M	4.0M	147.1	[paper] [tf-tpu]
EfficientNetB1	240	79.040	94.284	15.4M	7.9M	6.6M	217.3	[paper] [tf-tpu]
EfficientNetB2	260	80.064	94.862	18.1M	9.2M	7.8M	296.4	[paper] [tf-tpu]
EfficientNetB3	300	81.384	95.586	24.2M	12.3M	10.8M	482.7	[paper] [tf-tpu]
EfficientNetB4	380	82.588	96.094	38.4M	19.5M	17.7M	959.5	[paper] [tf-tpu]
EfficientNetB5	456	83.496	96.590	60.4M	30.6M	28.5M	1872	[paper] [tf-tpu]
EfficientNetB6	528	83.772	96.762	85.5M	43.3M	41.0M	3503	[paper] [tf-tpu]
EfficientNetB7	600	84.088	96.740	131.9M	66.7M	64.1M	6149	[paper] [tf-tpu]
SqueezeNet	224	54.434	78.040	2.5M	1.2M	0.7M	71.43	[paper] [caffe]

Object detection

• The object detection models can be coupled with any network but mAPs could be measured only for the models with pre-trained weights. Note that:
  • YOLOv3VOC was trained by taehoonlee with this recipe modified as max_batches=70000, steps=40000,60000,
  • YOLOv2VOC is equivalent to YOLOv2(inputs, Darknet19),
  • TinyYOLOv2VOC: TinyYOLOv2(inputs, TinyDarknet19),
  • FasterRCNN_ZF_VOC: FasterRCNN(inputs, ZF),
  • FasterRCNN_VGG16_VOC: FasterRCNN(inputs, VGG16, stem_out='conv5/3').
• The mAPs were obtained with TensorNets and may slightly differ from the original ones. The test input sizes were the numbers reported as the best in the papers:
  • YOLOv3, YOLOv2: 416x416
  • FasterRCNN: min_shorter_side=600, max_longer_side=1000
• The computation times were measured on NVIDIA Tesla P100 (3584 cores, 16 GB global memory) with cuDNN 6.0 and CUDA 8.0.
  • Size: rounded the number of parameters
  • Speed: milliseconds only for network inferences of a 416x416 or 608x608 single image
  • FPS: 1000 / speed

PASCAL VOC2007 test	mAP	Size	Speed	FPS	References
YOLOv3VOC (416)	0.7423	62M	24.09	41.51	[paper] [darknet] [darkflow]
YOLOv2VOC (416)	0.7320	51M	14.75	67.80	[paper] [darknet] [darkflow]
TinyYOLOv2VOC (416)	0.5303	16M	6.534	153.0	[paper] [darknet] [darkflow]
FasterRCNN_ZF_VOC	0.4466	59M	241.4	3.325	[paper] [caffe] [roi-pooling]
FasterRCNN_VGG16_VOC	0.6872	137M	300.7	4.143	[paper] [caffe] [roi-pooling]

MS COCO val2014	mAP	Size	Speed	FPS	References
YOLOv3COCO (608)	0.6016	62M	60.66	16.49	[paper] [darknet] [darkflow]
YOLOv3COCO (416)	0.6028	62M	40.23	24.85	[paper] [darknet] [darkflow]
YOLOv2COCO (608)	0.5189	51M	45.88	21.80	[paper] [darknet] [darkflow]
YOLOv2COCO (416)	0.4922	51M	21.66	46.17	[paper] [darknet] [darkflow]

News 📰

• The six variants of MobileNetv3 are released, 12 Mar 2020.
• The eight variants of EfficientNet are released, 28 Jan 2020.
• It is available to use TensorNets on TF 2, 23 Jan 2020.
• MS COCO utils are released, 9 Jul 2018.
• PNASNetlarge is released, 12 May 2018.
• The six variants of MobileNetv2 are released, 5 May 2018.
• YOLOv3 for COCO and VOC are released, 4 April 2018.
• Generic object detection models for YOLOv2 and FasterRCNN are released, 26 March 2018.

Future work 🔥

• Add training codes.
• Add image classification models.
  • PolyNet: A Pursuit of Structural Diversity in Very Deep Networks, CVPR 2017, Top-5 4.25%
  • Squeeze-and-Excitation Networks, CVPR 2018, Top-5 3.79%
  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism, arXiv 2018, Top-5 3.0%
• Add object detection models (MaskRCNN, SSD).
• Add image segmentation models (FCN, UNet).
• Add image datasets (OpenImages).
• Add style transfer examples which can be coupled with any network in TensorNets.
• Add speech and language models with representative datasets (WaveNet, ByteNet).