Resnet50

CHANGELOG.md

@@ -93,6 +93,7 @@ To release a new version, please update the changelog as followed:
 - Support nested layer customization (#PR 1015)
 - Support string dtype in InputLayer (#PR 1017)
 - Support Dynamic RNN in RNN (#PR 1023)
+- Add ResNet50 static model (#PR 1030)
 
 ### Changed
 
@@ -120,7 +121,7 @@ To release a new version, please update the changelog as followed:
 ### Contributors
 
 - @zsdonghao
-- @ChrisWu1997: #1010 #1015 #1025
+- @ChrisWu1997: #1010 #1015 #1025 #1030
 - @warshallrho: #1017 #1021 #1026 #1029
 - @ArnoldLIULJ: #1023
 - @JingqingZ: #1023

docs/modules/models.rst

@@ -13,6 +13,7 @@ TensorLayer provides many pretrained models, you can easily use the whole or a p
     VGG19
     SqueezeNetV1
     MobileNetV1
+    ResNet50
     Seq2seq
     Seq2seqLuongAttention
 
@@ -41,6 +42,11 @@ MobileNetV1
 
 .. autofunction:: MobileNetV1
 
+ResNet50
+----------------
+
+.. autofunction:: ResNet50
+
 Seq2seq
 ------------------------
 

examples/pretrained_cnn/tutorial_models_resnet50.py

@@ -0,0 +1,34 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+ResNet50 for ImageNet using TL models
+
+"""
+
+import time
+
+import numpy as np
+
+import tensorflow as tf
+import tensorlayer as tl
+from tensorlayer.models.imagenet_classes import class_names
+
+# tf.logging.set_verbosity(tf.logging.DEBUG)
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# get the whole model
+resnet = tl.models.ResNet50(pretrained=True)
+
+img1 = tl.vis.read_image('data/tiger.jpeg')
+img1 = tl.prepro.imresize(img1, (224, 224))[:, :, ::-1]
+img1 = img1 - np.array([103.939, 116.779, 123.68]).reshape((1, 1, 3))
+
+img1 = img1.astype(np.float32)[np.newaxis, ...]
+
+start_time = time.time()
+output = resnet(img1, is_train=False)
+prob = tf.nn.softmax(output)[0].numpy()
+print("  End time : %.5ss" % (time.time() - start_time))
+preds = (np.argsort(prob)[::-1])[0:5]
+for p in preds:
+    print(class_names[p], prob[p])

tensorlayer/models/__init__.py

@@ -4,6 +4,7 @@
 # """A collections of pre-defined well known models."""
 
 from .core import *
+from .resnet import ResNet50
 from .mobilenetv1 import MobileNetV1
 from .squeezenetv1 import SqueezeNetV1
 from .vgg import *

tensorlayer/models/resnet.py

@@ -0,0 +1,202 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""ResNet for ImageNet.
+
+# Reference:
+- [Deep Residual Learning for Image Recognition](
+    https://arxiv.org/abs/1512.03385) (CVPR 2016 Best Paper Award)
+
+"""
+
+import os
+
+import tensorflow as tf
+from tensorlayer import logging
+from tensorlayer.files import (assign_weights, load_npz, maybe_download_and_extract)
+from tensorlayer.layers import (BatchNorm, Conv2d, Elementwise, GlobalMeanPool2d, MaxPool2d, Input, Dense)
+from tensorlayer.models import Model
+
+__all__ = [
+    'ResNet50',
+]
+
+
+def identity_block(input, kernel_size, n_filters, stage, block):
+    """The identity block where there is no conv layer at shortcut.
+
+    Parameters
+    ----------
+    input : tf tensor
+        Input tensor from above layer.
+    kernel_size : int
+        The kernel size of middle conv layer at main path.
+    n_filters : list of integers
+        The numbers of filters for 3 conv layer at main path.
+    stage : int
+        Current stage label.
+    block : str
+        Current block label.
+
+    Returns
+    -------
+        Output tensor of this block.
+
+    """
+    filters1, filters2, filters3 = n_filters
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2d(filters1, (1, 1), W_init=tf.initializers.he_normal(), name=conv_name_base + '2a')(input)
+    x = BatchNorm(name=bn_name_base + '2a', act='relu')(x)
+
+    ks = (kernel_size, kernel_size)
+    x = Conv2d(filters2, ks, padding='SAME', W_init=tf.initializers.he_normal(), name=conv_name_base + '2b')(x)
+    x = BatchNorm(name=bn_name_base + '2b', act='relu')(x)
+
+    x = Conv2d(filters3, (1, 1), W_init=tf.initializers.he_normal(), name=conv_name_base + '2c')(x)
+    x = BatchNorm(name=bn_name_base + '2c')(x)
+
+    x = Elementwise(tf.add, act='relu')([x, input])
+    return x
+
+
+def conv_block(input, kernel_size, n_filters, stage, block, strides=(2, 2)):
+    """The conv block where there is a conv layer at shortcut.
+
+    Parameters
+    ----------
+    input : tf tensor
+        Input tensor from above layer.
+    kernel_size : int
+        The kernel size of middle conv layer at main path.
+    n_filters : list of integers
+        The numbers of filters for 3 conv layer at main path.
+    stage : int
+        Current stage label.
+    block : str
+        Current block label.
+    strides : tuple
+        Strides for the first conv layer in the block.
+
+    Returns
+    -------
+        Output tensor of this block.
+
+    """
+    filters1, filters2, filters3 = n_filters
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2d(filters1, (1, 1), strides=strides, W_init=tf.initializers.he_normal(), name=conv_name_base + '2a')(input)
+    x = BatchNorm(name=bn_name_base + '2a', act='relu')(x)
+
+    ks = (kernel_size, kernel_size)
+    x = Conv2d(filters2, ks, padding='SAME', W_init=tf.initializers.he_normal(), name=conv_name_base + '2b')(x)
+    x = BatchNorm(name=bn_name_base + '2b', act='relu')(x)
+
+    x = Conv2d(filters3, (1, 1), W_init=tf.initializers.he_normal(), name=conv_name_base + '2c')(x)
+    x = BatchNorm(name=bn_name_base + '2c')(x)
+
+    shortcut = Conv2d(filters3, (1, 1), strides=strides, W_init=tf.initializers.he_normal(),
+                      name=conv_name_base + '1')(input)
+    shortcut = BatchNorm(name=bn_name_base + '1')(shortcut)
+
+    x = Elementwise(tf.add, act='relu')([x, shortcut])
+    return x
+
+
+block_names = ['2a', '2b', '2c', '3a', '3b', '3c', '3d', '4a', '4b', '4c', '4d', '4e', '4f', '5a', '5b', '5c'
+              ] + ['avg_pool', 'fc1000']
+block_filters = [[64, 64, 256], [128, 128, 512], [256, 256, 1024], [512, 512, 2048]]
+
+
+def ResNet50(pretrained=False, end_with='fc1000', n_classes=1000, name=None):
+    """Pre-trained MobileNetV1 model (static mode). Input shape [?, 224, 224, 3].
+    To use pretrained model, input should be in BGR format and subtracted from ImageNet mean [103.939, 116.779, 123.68].
+
+    Parameters
+    ----------
+    pretrained : boolean
+        Whether to load pretrained weights. Default False.
+    end_with : str
+        The end point of the model [conv, depth1, depth2 ... depth13, globalmeanpool, out].
+        Default ``out`` i.e. the whole model.
+    n_classes : int
+        Number of classes in final prediction.
+    name : None or str
+        Name for this model.
+
+    Examples
+    ---------
+    Classify ImageNet classes, see `tutorial_models_resnet50.py`
+
+    >>> # get the whole model with pretrained weights
+    >>> resnet = tl.models.ResNet50(pretrained=True)
+    >>> # use for inferencing
+    >>> output = resnet(img1, is_train=False)
+    >>> prob = tf.nn.softmax(output)[0].numpy()
+
+    Extract the features before fc layer
+    >>> resnet = tl.models.ResNet50(pretrained=True, end_with='5c')
+    >>> output = resnet(img1, is_train=False)
+
+    Returns
+    -------
+        ResNet50 model.
+
+    """
+    ni = Input([None, 224, 224, 3], name="input")
+    n = Conv2d(64, (7, 7), strides=(2, 2), padding='SAME', W_init=tf.initializers.he_normal(), name='conv1')(ni)
+    n = BatchNorm(name='bn_conv1', act='relu')(n)
+    n = MaxPool2d((3, 3), strides=(2, 2), name='max_pool1')(n)
+
+    for i, name in enumerate(block_names):
+        if len(name) == 2:
+            stage = int(name[0])
+            block = name[1]
+            if block == 'a':
+                strides = (1, 1) if stage == 2 else (2, 2)
+                n = conv_block(n, 3, block_filters[stage - 2], stage=stage, block=block, strides=strides)
+            else:
+                n = identity_block(n, 3, block_filters[stage - 2], stage=stage, block=block)
+        elif name == 'avg_pool':
+            n = GlobalMeanPool2d(name='avg_pool')(n)
+        elif name == 'fc1000':
+            n = Dense(n_classes, name='fc1000')(n)
+
+        if name == end_with:
+            break
+
+    network = Model(inputs=ni, outputs=n, name=name)
+
+    if pretrained:
+        restore_params(network)
+
+    return network
+
+
+def restore_params(network, path='models'):
+    logging.info("Restore pre-trained parameters")
+    maybe_download_and_extract(
+        'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
+        path,
+        'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/',
+    )  # ls -al
+    try:
+        import h5py
+    except Exception:
+        raise ImportError('h5py not imported')
+
+    f = h5py.File(os.path.join(path, 'resnet50_weights_tf_dim_ordering_tf_kernels.h5'), 'r')
+
+    for layer in network.all_layers:
+        if len(layer.all_weights) == 0:
+            continue
+        w_names = list(f[layer.name])
+        params = [f[layer.name][n][:] for n in w_names]
+        if 'bn' in layer.name:
+            params = [x.reshape(1, 1, 1, -1) for x in params]
+        assign_weights(params, layer)
+        del params
+
+    f.close()