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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,27 +1,23 @@ | ||
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | ||
| ## Created by: Yaoyao Liu | ||
| ## NUS School of Computing | ||
| ## Email: [email protected] | ||
| ## Copyright (c) 2019 | ||
| ## This file tests few shot learning | ||
| ## | ||
| ## This source code is licensed under the MIT-style license found in the | ||
| ## LICENSE file in the root directory | ||
| ## of https://github.com/y2l/mini-imagenet-tools | ||
| ## Prototype learning with tensorflow.keras by D. Schmicker | ||
| ## | ||
| ## This file is modified for tensorflow.keras usage by D. Schmicker | ||
| ## | ||
| ## original file from https://github.com/y2l/mini-imagenet-tools | ||
| ## using https://github.com/y2l/mini-imagenet-tools | ||
| ## | ||
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | ||
|
|
||
| import os | ||
| import random | ||
| import numpy as np | ||
| from tqdm import trange | ||
| import imageio | ||
| import ast | ||
|
|
||
| from mini_imagenet_dataloader import MiniImageNetDataLoader | ||
| from tensorflow.keras.models import Model | ||
| from tensorflow.keras.layers import Activation, Dense, Input, Flatten, Conv2D, Lambda, TimeDistributed, MaxPooling2D | ||
| from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard | ||
| import tensorflow.keras.backend as K | ||
| import tensorflow.keras | ||
| from tensorflow.keras import optimizers as op | ||
| import tensorflow as tf | ||
| import argparse | ||
|
|
||
| parser = argparse.ArgumentParser(description='train recurrent net.') | ||
| parser.add_argument('--pretrained_name', dest='pretrained_name', type=str, default=None) | ||
| parser.add_argument('--dataset', dest='dataset', type=str, default='train') | ||
|
|
@@ -42,7 +38,6 @@ | |
| #os.environ["CUDA_VISIBLE_DEVICES"] = "-1" | ||
| ########################################### | ||
|
|
||
| from mini_imagenet_dataloader import MiniImageNetDataLoader | ||
|
|
||
| class OurMiniImageNetDataLoader(MiniImageNetDataLoader): | ||
| # adding functions we need | ||
|
|
@@ -89,21 +84,12 @@ def idx_to_big(self, phase, idx): | |
| print("epoch training size:", train_epoch_size, base_train_label.shape[0], "epoch testing size", test_epoch_size) | ||
|
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| class KerasBatchGenerator(object): | ||
|
|
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| # def __init__(self): | ||
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| def generate(self, phase='train'): | ||
| # idx = 0 | ||
| while True: | ||
| # episode_train_img, episode_train_label, episode_test_img, episode_test_label = \ | ||
| # dataloader.get_batch(phase='train', idx=idx) | ||
| if phase == 'train': | ||
| #print(episode_train_img.shape[0]) | ||
| for i in range(train_epoch_size): | ||
| yield base_train_img[i:i+1], base_train_label[i:i+1] | ||
| else: | ||
| #print(episode_test_img.shape[0]) | ||
| for i in range(test_epoch_size): | ||
| yield base_test_img[i:i+1], base_test_label[i:i+1] | ||
|
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@@ -140,24 +126,12 @@ def generate_add_samples(self, phase = 'train'): | |
| print("all data used, starting from beginning") | ||
| print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!") | ||
|
|
||
| #print(episode_train_img.shape[0]) | ||
| #assert(episode_train_img.shape[0] == 25) | ||
| for i in range(train_epoch_size): | ||
| yield [[episode_train_img[i:i+1]], [network_base_img], [network_base_label]], episode_train_label[i:i+1] | ||
| else: | ||
| #print(episode_test_img.shape[0]) | ||
| #assert(0) | ||
| #assert(episode_test_img.shape[0] == 75) | ||
| #assert(self.idx < 50) | ||
| for i in range(test_epoch_size): | ||
| #print('i',i) | ||
| yield [[episode_test_img[i:i+1]], [network_base_img], [network_base_label]], episode_test_label[i:i+1] | ||
|
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| keras_gen_train = KerasBatchGenerator() | ||
| gen_train = keras_gen_train.generate() | ||
|
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|
@@ -172,7 +146,6 @@ def generate_add_samples(self, phase = 'train'): | |
| img, l = next(gen_test) | ||
| print(img.shape,l.shape) | ||
|
|
||
| import tensorflow as tf | ||
| if tf.__version__ < "2.0": | ||
| from tensorflow.keras.backend import set_session | ||
| config = tf.ConfigProto() | ||
|
|
@@ -193,12 +166,6 @@ def generate_add_samples(self, phase = 'train'): | |
| # Memory growth must be set before GPUs have been initialized | ||
| print(e) | ||
|
|
||
| from tensorflow.keras.models import Model | ||
| from tensorflow.keras.layers import Activation, Dense, Input, Flatten, Conv2D, Lambda, TimeDistributed, MaxPooling2D | ||
| from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard | ||
| import tensorflow.keras.backend as K | ||
| import tensorflow.keras | ||
|
|
||
| inputs = Input(shape=(None,84,84,3)) | ||
| print('the shape', inputs.shape) | ||
| conv1 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(inputs) | ||
|
|
@@ -212,7 +179,6 @@ def generate_add_samples(self, phase = 'train'): | |
| conv5 = TimeDistributed(Conv2D(64, 3, padding='same', activation = 'relu'))(pool4) | ||
| pool5 = TimeDistributed(MaxPooling2D(pool_size = 2))(conv5) | ||
|
|
||
| #conv3 = TimeDistributed(Conv2D(5, 5, (3,3) , padding='same', activation = 'relu'))(conv2) | ||
| flat = TimeDistributed(Flatten())(pool5) | ||
| #x = TimeDistributed(Dense(100, activation = 'relu'))(flat) | ||
| #predictions = Activation('softmax')(x) | ||
|
|
@@ -267,15 +233,11 @@ def call(x): | |
|
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| lambda_model = Model(inputs = [input_lambda1, input_lambda2, input_lambda3], outputs = call_lambda_softmax) | ||
|
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| from tensorflow.keras import optimizers as op | ||
|
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||
| if args.pretrained_name is not None: | ||
| from tensorflow.keras.models import load_model | ||
| lambda_model = load_model(args.pretrained_name, custom_objects = { "keras": tensorflow.keras , "args":args}) | ||
| print("loaded model",lambda_model) | ||
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| # models in models forget the layer name, therefore one must use the automatically given layer name and iterate throught the models by hand | ||
| # here we can try setting the layer not trainable | ||
| def all_layers(model): | ||
|
|
@@ -335,7 +297,7 @@ def all_layers(model): | |
| #workers = 0 is a work around to correct the number of calls to the validation_data generator | ||
| lambda_model.save(args.final_name+'.hdf5') | ||
|
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| # tools for debugging | ||
| def get_weight_grad(model, inputs, outputs): | ||
| """ Gets gradient of model for given inputs and outputs for all weights""" | ||
| grads = model.optimizer.get_gradients(model.total_loss, model.trainable_weights) | ||
|
|
@@ -354,57 +316,3 @@ def get_layer_output_grad(model, inputs, outputs, layer=-1): | |
| x, y, sample_weight = model._standardize_user_data(inputs, outputs) | ||
| output_grad = f(x + y + sample_weight) | ||
| return output_grad | ||
|
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| #weight_grads = get_layer_output_grad(lambda_model, [[episode_train_img[0:1]], [episode_train_img[:]], [episode_train_label[:]]], [episode_train_label[0:1]]) | ||
|
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| #weight_grads = get_layer_output_grad(siamese_net, [episode_train_img[0:1],episode_train_img[0:1]], episode_train_label[0:1]) | ||
|
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||
| #print(weight_grads) | ||
| # | ||
| #input_few = Input(shape=(84,84,3)) | ||
| #input_labels = Input(shape=(84,84,3)) | ||
| # | ||
| #output_few = Lambda(call)([input_few,K.variable(episode_train_img), K.variable(episode_train_label)]) | ||
| # | ||
| #model_few = Model(inputs = [input_few, input_labels], outputs = output_few) | ||
| # | ||
| #print('test few', K.eval(model_few([K.variable(episode_train_img[0:0+1]),K.variable(episode_train_label)]))) | ||
|
|
||
| ##sum_few = np.zeros(episode_train_label[0:1].shape) | ||
| #input_few = Input(shape=(84,84,3)) | ||
| #for i in range(0,2): | ||
| # a = siamese_net([input_few, K.variable(episode_train_img[i:i+1])]) | ||
| # if i == 0: | ||
| # sum_few = K.variable(episode_train_label[i:i+1]) * a | ||
| # else: | ||
| # sum_few += K.variable(episode_train_label[i:i+1]) * a | ||
| # print(i) | ||
| #sum_few_softmax = Activation('softmax')(sum_few) | ||
| #full_few_shot = Model(inputs = input_few, outputs = sum_few_softmax) | ||
| # | ||
| ##print('net_ready') | ||
| ##aa = K.variable(episode_train_img[0:1]) | ||
| ##a = full_few_shot(aa) | ||
| ##print('net ready', K.eval(a)) | ||
| ## | ||
| #from tensorflow.keras.optimizers import Adam | ||
| #full_few_shot.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['categorical_accuracy']) | ||
| # | ||
| #print(full_few_shot.summary()) | ||
| # | ||
| # | ||
| #print("eval", episode_train_img[0:1]) | ||
| #sum_a = np.zeros(episode_train_label[0:1].shape) | ||
| #for i in range(0,train_epoch_size): | ||
| # a = siamese_net([K.variable(episode_train_img[0:1]), K.variable(episode_train_img[i:i+1])]) | ||
| # sum_a += K.variable(episode_train_label[i:i+1]) * a | ||
| # print('aaaaa',i,K.eval(a), episode_train_label[0:1], episode_train_label[i:i+1]) | ||
| # | ||
| # | ||
| ##print('suma',episode_train_label[0:1], K.eval(K.softmax(sum_a)), K.eval(full_few_shot(K.variable(episode_train_img[0:1])))) | ||
| # | ||
| #checkpointer = ModelCheckpoint(filepath='checkpoints/model-{epoch:02d}.hdf5', verbose=1) | ||
| # | ||
| #history = full_few_shot.fit_generator(gen_train.generate(), train_epoch_size, 100, validation_data=gen_test, validation_steps=test_epoch_size) | ||
| # | ||