#! /usr/bin/python # -*- coding: utf-8 -*- """ VGG for ImageNet. Introduction ---------------- VGG is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition" . The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes. Download Pre-trained Model ---------------------------- - Model weights in this example - vgg16_weights.npz : http://www.cs.toronto.edu/~frossard/post/vgg16/ - Model weights in this example - vgg19.npy : https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/ - Caffe VGG 16 model : https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md - Tool to convert the Caffe models to TensorFlow's : https://github.com/ethereon/caffe-tensorflow Note ------ - For simplified CNN layer see "Convolutional layer (Simplified)" in read the docs website. - When feeding other images to the model be sure to properly resize or crop them beforehand. Distorted images might end up being misclassified. One way of safely feeding images of multiple sizes is by doing center cropping. """ import os import numpy as np import tensorlayer as tl from tensorlayer import logging from tensorlayer.files import assign_weights, maybe_download_and_extract from tensorlayer.layers import (BatchNorm, Conv2d, Dense, Flatten, Input, SequentialLayer, MaxPool2d) from tensorlayer.layers import Module __all__ = [ 'VGG', 'vgg16', 'vgg19', 'VGG16', 'VGG19', # 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', # 'vgg19_bn', 'vgg19', ] layer_names = [ ['conv1_1', 'conv1_2'], 'pool1', ['conv2_1', 'conv2_2'], 'pool2', ['conv3_1', 'conv3_2', 'conv3_3', 'conv3_4'], 'pool3', ['conv4_1', 'conv4_2', 'conv4_3', 'conv4_4'], 'pool4', ['conv5_1', 'conv5_2', 'conv5_3', 'conv5_4'], 'pool5', 'flatten', 'fc1_relu', 'fc2_relu', 'outputs' ] cfg = { 'A': [[64], 'M', [128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'], 'B': [[64, 64], 'M', [128, 128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'], 'D': [ [64, 64], 'M', [128, 128], 'M', [256, 256, 256], 'M', [512, 512, 512], 'M', [512, 512, 512], 'M', 'F', 'fc1', 'fc2', 'O' ], 'E': [ [64, 64], 'M', [128, 128], 'M', [256, 256, 256, 256], 'M', [512, 512, 512, 512], 'M', [512, 512, 512, 512], 'M', 'F', 'fc1', 'fc2', 'O' ], } mapped_cfg = { 'vgg11': 'A', 'vgg11_bn': 'A', 'vgg13': 'B', 'vgg13_bn': 'B', 'vgg16': 'D', 'vgg16_bn': 'D', 'vgg19': 'E', 'vgg19_bn': 'E' } model_urls = { 'vgg16': 'http://www.cs.toronto.edu/~frossard/vgg16/', 'vgg19': 'https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/' } model_saved_name = {'vgg16': 'vgg16_weights.npz', 'vgg19': 'vgg19.npy'} class VGG(Module): def __init__(self, layer_type, batch_norm=False, end_with='outputs', name=None): super(VGG, self).__init__(name=name) self.end_with = end_with config = cfg[mapped_cfg[layer_type]] self.make_layer = make_layers(config, batch_norm, end_with) def forward(self, inputs): """ inputs : tensor Shape [None, 224, 224, 3], value range [0, 1]. """ inputs = inputs * 255 - np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape([1, 1, 1, 3]) out = self.make_layer(inputs) return out def make_layers(config, batch_norm=False, end_with='outputs'): layer_list = [] is_end = False for layer_group_idx, layer_group in enumerate(config): if isinstance(layer_group, list): for idx, layer in enumerate(layer_group): layer_name = layer_names[layer_group_idx][idx] n_filter = layer if idx == 0: if layer_group_idx > 0: in_channels = config[layer_group_idx - 2][-1] else: in_channels = 3 else: in_channels = layer_group[idx - 1] layer_list.append( Conv2d( n_filter=n_filter, filter_size=(3, 3), strides=(1, 1), act=tl.ReLU, padding='SAME', in_channels=in_channels, name=layer_name ) ) if batch_norm: layer_list.append(BatchNorm(num_features=n_filter)) if layer_name == end_with: is_end = True break else: layer_name = layer_names[layer_group_idx] if layer_group == 'M': layer_list.append(MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding='SAME', name=layer_name)) elif layer_group == 'O': layer_list.append(Dense(n_units=1000, in_channels=4096, name=layer_name)) elif layer_group == 'F': layer_list.append(Flatten(name='flatten')) elif layer_group == 'fc1': layer_list.append(Dense(n_units=4096, act=tl.ReLU, in_channels=512 * 7 * 7, name=layer_name)) elif layer_group == 'fc2': layer_list.append(Dense(n_units=4096, act=tl.ReLU, in_channels=4096, name=layer_name)) if layer_name == end_with: is_end = True if is_end: break return SequentialLayer(layer_list) def restore_model(model, layer_type): logging.info("Restore pre-trained weights") # download weights maybe_download_and_extract(model_saved_name[layer_type], 'model', model_urls[layer_type]) weights = [] if layer_type == 'vgg16': npz = np.load(os.path.join('model', model_saved_name[layer_type]), allow_pickle=True) # get weight list for val in sorted(npz.items()): logging.info(" Loading weights %s in %s" % (str(val[1].shape), val[0])) weights.append(val[1]) if len(model.all_weights) == len(weights): break elif layer_type == 'vgg19': npz = np.load(os.path.join('model', model_saved_name[layer_type]), allow_pickle=True, encoding='latin1').item() # get weight list for val in sorted(npz.items()): logging.info(" Loading %s in %s" % (str(val[1][0].shape), val[0])) logging.info(" Loading %s in %s" % (str(val[1][1].shape), val[0])) weights.extend(val[1]) if len(model.all_weights) == len(weights): break # assign weight values assign_weights(weights, model) del weights def vgg16(pretrained=False, end_with='outputs', mode='dynamic', name=None): """Pre-trained VGG16 model. Parameters ------------ pretrained : boolean Whether to load pretrained weights. Default False. end_with : str The end point of the model. Default ``fc3_relu`` i.e. the whole model. mode : str. Model building mode, 'dynamic' or 'static'. Default 'dynamic'. name : None or str A unique layer name. Examples --------- Classify ImageNet classes with VGG16, see `tutorial_models_vgg.py `__ With TensorLayer TODO Modify the usage example according to the model storage location >>> # get the whole model, without pre-trained VGG parameters >>> vgg = vgg16() >>> # get the whole model, restore pre-trained VGG parameters >>> vgg = vgg16(pretrained=True) >>> # use for inferencing >>> output = vgg(img) >>> probs = tl.ops.softmax(output)[0].numpy() """ if mode == 'dynamic': model = VGG(layer_type='vgg16', batch_norm=False, end_with=end_with, name=name) elif mode == 'static': raise NotImplementedError else: raise Exception("No such mode %s" % mode) if pretrained: restore_model(model, layer_type='vgg16') return model def vgg19(pretrained=False, end_with='outputs', mode='dynamic', name=None): """Pre-trained VGG19 model. Parameters ------------ pretrained : boolean Whether to load pretrained weights. Default False. end_with : str The end point of the model. Default ``fc3_relu`` i.e. the whole model. mode : str. Model building mode, 'dynamic' or 'static'. Default 'dynamic'. name : None or str A unique layer name. Examples --------- Classify ImageNet classes with VGG19, see `tutorial_models_vgg.py `__ With TensorLayer >>> # get the whole model, without pre-trained VGG parameters >>> vgg = vgg19() >>> # get the whole model, restore pre-trained VGG parameters >>> vgg = vgg19(pretrained=True) >>> # use for inferencing >>> output = vgg(img) >>> probs = tl.ops.softmax(output)[0].numpy() """ if mode == 'dynamic': model = VGG(layer_type='vgg19', batch_norm=False, end_with=end_with, name=name) elif mode == 'static': raise NotImplementedError else: raise Exception("No such mode %s" % mode) if pretrained: restore_model(model, layer_type='vgg19') return model VGG16 = vgg16 VGG19 = vgg19 # models without pretrained parameters # def vgg11(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg11', batch_norm=False, end_with=end_with) # if pretrained: # model.restore_weights() # return model # # # def vgg11_bn(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg11_bn', batch_norm=True, end_with=end_with) # if pretrained: # model.restore_weights() # return model # # # def vgg13(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg13', batch_norm=False, end_with=end_with) # if pretrained: # model.restore_weights() # return model # # # def vgg13_bn(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg13_bn', batch_norm=True, end_with=end_with) # if pretrained: # model.restore_weights() # return model # # # def vgg16_bn(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg16_bn', batch_norm=True, end_with=end_with) # if pretrained: # model.restore_weights() # return model # # # def vgg19_bn(pretrained=False, end_with='outputs'): # model = VGG(layer_type='vgg19_bn', batch_norm=True, end_with=end_with) # if pretrained: # model.restore_weights() # return model