Fix LeNet Tensorflow

PBA/LeNet.py

@@ -14,7 +14,7 @@ def bias_variable(shape, name = None):
 
 # 2D convolution
 def conv2d(x, W, name = None):
-    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name = name)
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID', name = name)
 
 # max pooling
 def max_pool_2x2(x, name = None):
@@ -30,44 +30,38 @@ def LeNet(images, num_classes):
      n_n_fc1 = 500; # number of neurons of first fully connected layer (default = 576)
      n_n_fc2 = 500; # number of neurons of first fully connected layer (default = 576)
 
+     #print(images.shape)
      # 1.layer: convolution + max pooling
-     W_conv1_tf = weight_variable([s_f_conv1, s_f_conv1, images.shape[3], n_f_conv1], name = 'W_conv1_tf') # (5,5,1,32)
+     W_conv1_tf = weight_variable([s_f_conv1, s_f_conv1, int(images.shape[3]), n_f_conv1], name = 'W_conv1_tf') # (5,5,1,32)
      b_conv1_tf = bias_variable([n_f_conv1], name = 'b_conv1_tf') # (32)
-     h_conv1_tf = tf.nn.relu(conv2d(images, 
-                                                 W_conv1_tf) + b_conv1_tf, 
-                                     name = 'h_conv1_tf') # (.,28,28,32)
-     h_pool1_tf = max_pool_2x2(h_conv1_tf, 
-                                            name = 'h_pool1_tf') # (.,14,14,32)
-
+     h_conv1_tf = tf.nn.relu(conv2d(images, W_conv1_tf) + b_conv1_tf, name = 'h_conv1_tf') # (.,28,28,32)
+     h_pool1_tf = max_pool_2x2(h_conv1_tf, name = 'h_pool1_tf') # (.,14,14,32)
+     #print(h_conv1_tf.shape)
+     #print(h_pool1_tf.shape)
      # 2.layer: convolution + max pooling
-     W_conv2_tf = weight_variable([s_f_conv2, s_f_conv2, 
-                                                n_f_conv1, n_f_conv2], 
-                                               name = 'W_conv2_tf')
+     W_conv2_tf = weight_variable([s_f_conv2, s_f_conv2, n_f_conv1, n_f_conv2], name = 'W_conv2_tf')
      b_conv2_tf = bias_variable([n_f_conv2], name = 'b_conv2_tf')
-     h_conv2_tf = tf.nn.relu(conv2d(h_pool1_tf, 
-                                                 W_conv2_tf) + b_conv2_tf, 
-                                     name ='h_conv2_tf') #(.,14,14,32)
+     h_conv2_tf = tf.nn.relu(conv2d(h_pool1_tf, W_conv2_tf) + b_conv2_tf, name ='h_conv2_tf') #(.,14,14,32)
      h_pool2_tf = max_pool_2x2(h_conv2_tf, name = 'h_pool2_tf') #(.,7,7,32)
 
+     #print(h_pool2_tf.shape)
+
      # 4.layer: fully connected
-     W_fc1_tf = weight_variable([5*5*n_f_conv2,n_n_fc1], 
-                                             name = 'W_fc1_tf') # (4*4*32, 1024)
+     W_fc1_tf = weight_variable([5*5*n_f_conv2,n_n_fc1], name = 'W_fc1_tf') # (4*4*32, 1024)
      b_fc1_tf = bias_variable([n_n_fc1], name = 'b_fc1_tf') # (1024)
-     h_pool2_flat_tf = tf.reshape(h_pool2_tf, [-1,5*5*n_f_conv2], 
-                                          name = 'h_pool3_flat_tf') # (.,1024)
-     h_fc1_tf = tf.nn.relu(tf.matmul(h_pool2_flat_tf, 
-                                             W_fc1_tf) + b_fc1_tf, 
+     h_pool2_flat_tf = tf.reshape(h_pool2_tf, [int(h_pool2_tf.shape[0]), -1], name = 'h_pool3_flat_tf') # (.,1024)
+     h_fc1_tf = tf.nn.relu(tf.matmul(h_pool2_flat_tf, W_fc1_tf) + b_fc1_tf, 
                                    name = 'h_fc1_tf') # (.,1024)
       
      # add dropout
      #keep_prob_tf = tf.placeholder(dtype=tf.float32, name = 'keep_prob_tf')
      #h_fc1_drop_tf = tf.nn.dropout(h_fc1_tf, keep_prob_tf, name = 'h_fc1_drop_tf')
+     print(h_fc1_tf.shape)
 
      # 5.layer: fully connected
      W_fc2_tf = weight_variable([n_n_fc1, num_classes], name = 'W_fc2_tf')
      b_fc2_tf = bias_variable([num_classes], name = 'b_fc2_tf')
-     z_pred_tf = tf.add(tf.matmul(h_fc1_tf, W_fc2_tf), 
-                                b_fc2_tf, name = 'z_pred_tf')# => (.,10)
+     z_pred_tf = tf.add(tf.matmul(h_fc1_tf, W_fc2_tf), b_fc2_tf, name = 'z_pred_tf')# => (.,10)
      # predicted probabilities in one-hot encoding
      #y_pred_proba_tf = tf.nn.softmax(z_pred_tf, name='y_pred_proba_tf') 
         

PBA/search.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+export PYTHONPATH="$(pwd)"
+
+cifar10_LeNet_search() {
+    local_dir="$PWD/results/"
+    data_path="$PWD/datasets/cifar-10-batches-py"
+
+    python pba/search.py \
+    --local_dir "$local_dir" \
+    --model_name LeNet \
+    --data_path "$data_path" --dataset cifar10 \
+    --train_size 4000 --val_size 46000 \
+    --checkpoint_freq 0 \
+    --name "cifar10_search" --gpu 0.15 --cpu 2 \
+    --num_samples 16 --perturbation_interval 3 --epochs 150 \
+    --explore cifar10 --aug_policy cifar10 \
+    --lr 0.1 --wd 0.0005
+}
+
+cifar10_search() {
+    local_dir="$PWD/results/"
+    data_path="$PWD/datasets/cifar-10-batches-py"
+
+    python pba/search.py \
+    --local_dir "$local_dir" \
+    --model_name wrn_40_2 \
+    --data_path "$data_path" --dataset cifar10 \
+    --train_size 4000 --val_size 46000 \
+    --checkpoint_freq 0 \
+    --name "cifar10_search" --gpu 0.15 --cpu 2 \
+    --num_samples 16 --perturbation_interval 3 --epochs 200 \
+    --explore cifar10 --aug_policy cifar10 \
+    --lr 0.1 --wd 0.0005
+}
+
+svhn_search() {
+    local_dir="$PWD/results/"
+    data_path="$PWD/datasets/"
+
+    python pba/search.py \
+    --local_dir "$local_dir" --data_path "$data_path" \
+    --model_name wrn_40_2 --dataset svhn \
+    --train_size 1000 --val_size 7325 \
+    --checkpoint_freq 0 \
+    --name "svhn_search" --gpu 0.19 --cpu 2 \
+    --num_samples 16 --perturbation_interval 3 --epochs 160 \
+    --explore cifar10 --aug_policy cifar10 --no_cutout \
+    --lr 0.1 --wd 0.005
+}
+
+if [ "$1" = "rcifar10" ]; then
+    cifar10_search
+elif [ "$1" = "rsvhn" ]; then
+    svhn_search
+elif [ "$1" = "LeNet" ]; then
+    cifar10_LeNet_search
+else
+    echo "invalid args"
+fi

higher/datasets.py

@@ -39,8 +39,9 @@ data_test = torchvision.datasets.CIFAR10(
 )
 #'''
 train_subset_indices=range(int(len(data_train)/2))
-#train_subset_indices=range(BATCH_SIZE*10)
 val_subset_indices=range(int(len(data_train)/2),len(data_train))
+#train_subset_indices=range(BATCH_SIZE*10)
+#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
 
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
 dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))

higher/test_dataug.py

@@ -6,11 +6,11 @@ from train_utils import *
 tf_names = [
     ## Geometric TF ##
     'Identity',
-    'FlipUD',
+    #'FlipUD',
     'FlipLR',
     'Rotate',
-    'TranslateX',
-    'TranslateY',
+    #'TranslateX',
+    #'TranslateY',
     'ShearX',
     'ShearY',
 
@@ -20,7 +20,7 @@ tf_names = [
     'Brightness',
     'Sharpness',
     'Posterize',
-    'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
+    #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
 
     #Non fonctionnel
     #'Auto_Contrast', #Pas opti pour des batch (Super lent)
@@ -37,14 +37,14 @@ else:
 ##########################################
 if __name__ == "__main__":
 
-    n_inner_iter = 10
-    epochs = 200
+    n_inner_iter = 0
+    epochs = 100
     dataug_epoch_start=0
 
     #### Classic ####
     '''
-    #model = LeNet(3,10).to(device)
-    model = WideResNet(num_classes=10, wrn_size=16).to(device)
+    model = LeNet(3,10).to(device)
+    #model = WideResNet(num_classes=10, wrn_size=16).to(device)
     #model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
     #model.augment(mode=False)
 
@@ -68,11 +68,11 @@ if __name__ == "__main__":
     t0 = time.process_time()
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     #tf_dict = TF.TF_dict
-    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
-    #aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
+    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
+    #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
     print(str(aug_model), 'on', device_name)
     #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
-    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=10)
+    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)
 
     ####
     print('-'*9)
@@ -91,16 +91,16 @@ if __name__ == "__main__":
     '''
     #### TF tests ####
     #'''
-    res_folder="res/brutus-tests/"
-    epochs= 150
-    inner_its = [0, 1, 10]
+    res_folder="res/good_TF_tests/"
+    epochs= 100
+    inner_its = [0, 10]
     dist_mix = [0.0, 0.5]
     dataug_epoch_starts= [0]
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
-    N_seq_TF= [1,2,3,4]#[2, 3, 4, 6]
-    mag_setup = [(True,True), (False,True), (False, False)]
-    nb_run= 3
+    N_seq_TF= [1]#[1, 2, 3, 4]
+    mag_setup = [(True,True)]#[(True,True), (False,True), (False, False)]
+    nb_run= 1
     
     try:
         os.mkdir(res_folder)