EEGFaceSem: processed data, code, models

EEGFaceSem/EEGModels.py

@@ -0,0 +1,402 @@
+"""
+ ARL_EEGModels - A collection of Convolutional Neural Network models for EEG
+ Signal Processing and Classification, using Keras and Tensorflow
+
+ Requirements:
+    (1) tensorflow == 2.X (as of this writing, 2.0 - 2.3 have been verified
+        as working)
+ 
+ To run the EEG/MEG ERP classification sample script, you will also need
+
+    (4) mne >= 0.17.1
+    (5) PyRiemann >= 0.2.5
+    (6) scikit-learn >= 0.20.1
+    (7) matplotlib >= 2.2.3
+    
+ To use:
+    
+    (1) Place this file in the PYTHONPATH variable in your IDE (i.e.: Spyder)
+    (2) Import the model as
+        
+        from EEGModels import EEGNet    
+        
+        model = EEGNet(nb_classes = ..., Chans = ..., Samples = ...)
+        
+    (3) Then compile and fit the model
+    
+        model.compile(loss = ..., optimizer = ..., metrics = ...)
+        fitted    = model.fit(...)
+        predicted = model.predict(...)
+
+ Portions of this project are works of the United States Government and are not
+ subject to domestic copyright protection under 17 USC Sec. 105.  Those 
+ portions are released world-wide under the terms of the Creative Commons Zero 
+ 1.0 (CC0) license.  
+ 
+ Other portions of this project are subject to domestic copyright protection 
+ under 17 USC Sec. 105.  Those portions are licensed under the Apache 2.0 
+ license.  The complete text of the license governing this material is in 
+ the file labeled LICENSE.TXT that is a part of this project's official 
+ distribution. 
+"""
+
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Dense, Activation, Permute, Dropout
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
+from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.layers import SpatialDropout2D
+from tensorflow.keras.regularizers import l1_l2
+from tensorflow.keras.layers import Input, Flatten
+from tensorflow.keras.constraints import max_norm
+from tensorflow.keras import backend as K
+
+
+def EEGNet(nb_classes, Chans = 64, Samples = 128, 
+             dropoutRate = 0.5, kernLength = 64, F1 = 8, 
+             D = 2, F2 = 16, norm_rate = 0.25, dropoutType = 'Dropout'):
+    """ Keras Implementation of EEGNet
+    http://iopscience.iop.org/article/10.1088/1741-2552/aace8c/meta
+
+    Note that this implements the newest version of EEGNet and NOT the earlier
+    version (version v1 and v2 on arxiv). We strongly recommend using this
+    architecture as it performs much better and has nicer properties than
+    our earlier version. For example:
+        
+        1. Depthwise Convolutions to learn spatial filters within a 
+        temporal convolution. The use of the depth_multiplier option maps 
+        exactly to the number of spatial filters learned within a temporal
+        filter. This matches the setup of algorithms like FBCSP which learn 
+        spatial filters within each filter in a filter-bank. This also limits 
+        the number of free parameters to fit when compared to a fully-connected
+        convolution. 
+        
+        2. Separable Convolutions to learn how to optimally combine spatial
+        filters across temporal bands. Separable Convolutions are Depthwise
+        Convolutions followed by (1x1) Pointwise Convolutions. 
+        
+    
+    While the original paper used Dropout, we found that SpatialDropout2D 
+    sometimes produced slightly better results for classification of ERP 
+    signals. However, SpatialDropout2D significantly reduced performance 
+    on the Oscillatory dataset (SMR, BCI-IV Dataset 2A). We recommend using
+    the default Dropout in most cases.
+        
+    Assumes the input signal is sampled at 128Hz. If you want to use this model
+    for any other sampling rate you will need to modify the lengths of temporal
+    kernels and average pooling size in blocks 1 and 2 as needed (double the 
+    kernel lengths for double the sampling rate, etc). Note that we haven't 
+    tested the model performance with this rule so this may not work well. 
+    
+    The model with default parameters gives the EEGNet-8,2 model as discussed
+    in the paper. This model should do pretty well in general, although it is
+	advised to do some model searching to get optimal performance on your
+	particular dataset.
+
+    We set F2 = F1 * D (number of input filters = number of output filters) for
+    the SeparableConv2D layer. We haven't extensively tested other values of this
+    parameter (say, F2 < F1 * D for compressed learning, and F2 > F1 * D for
+    overcomplete). We believe the main parameters to focus on are F1 and D. 
+
+    Inputs:
+        
+      nb_classes      : int, number of classes to classify
+      Chans, Samples  : number of channels and time points in the EEG data
+      dropoutRate     : dropout fraction
+      kernLength      : length of temporal convolution in first layer. We found
+                        that setting this to be half the sampling rate worked
+                        well in practice. For the SMR dataset in particular
+                        since the data was high-passed at 4Hz we used a kernel
+                        length of 32.     
+      F1, F2          : number of temporal filters (F1) and number of pointwise
+                        filters (F2) to learn. Default: F1 = 8, F2 = F1 * D. 
+      D               : number of spatial filters to learn within each temporal
+                        convolution. Default: D = 2
+      dropoutType     : Either SpatialDropout2D or Dropout, passed as a string.
+
+    """
+    
+    if dropoutType == 'SpatialDropout2D':
+        dropoutType = SpatialDropout2D
+    elif dropoutType == 'Dropout':
+        dropoutType = Dropout
+    else:
+        raise ValueError('dropoutType must be one of SpatialDropout2D '
+                         'or Dropout, passed as a string.')
+    
+    input1   = Input(shape = (Chans, Samples, 1))
+
+    ##################################################################
+    block1       = Conv2D(F1, (1, kernLength), padding = 'same',
+                                   input_shape = (Chans, Samples, 1),
+                                   use_bias = False)(input1)
+    block1       = BatchNormalization()(block1)
+    block1       = DepthwiseConv2D((Chans, 1), use_bias = False, 
+                                   depth_multiplier = D,
+                                   depthwise_constraint = max_norm(1.))(block1)
+    block1       = BatchNormalization()(block1)
+    block1       = Activation('elu')(block1)
+    block1       = AveragePooling2D((1, 4))(block1)
+    block1       = dropoutType(dropoutRate)(block1)
+    
+    block2       = SeparableConv2D(F2, (1, 16),
+                                   use_bias = False, padding = 'same')(block1)
+    block2       = BatchNormalization()(block2)
+    block2       = Activation('elu')(block2)
+    block2       = AveragePooling2D((1, 8))(block2)
+    block2       = dropoutType(dropoutRate)(block2)
+        
+    flatten      = Flatten(name = 'flatten')(block2)
+    
+    dense        = Dense(nb_classes, name = 'dense', 
+                         kernel_constraint = max_norm(norm_rate))(flatten)
+    softmax      = Activation('softmax', name = 'softmax')(dense)
+    
+    return Model(inputs=input1, outputs=softmax)
+
+
+
+
+def EEGNet_SSVEP(nb_classes = 12, Chans = 8, Samples = 256, 
+             dropoutRate = 0.5, kernLength = 256, F1 = 96, 
+             D = 1, F2 = 96, dropoutType = 'Dropout'):
+    """ SSVEP Variant of EEGNet, as used in [1]. 
+
+    Inputs:
+        
+      nb_classes      : int, number of classes to classify
+      Chans, Samples  : number of channels and time points in the EEG data
+      dropoutRate     : dropout fraction
+      kernLength      : length of temporal convolution in first layer
+      F1, F2          : number of temporal filters (F1) and number of pointwise
+                        filters (F2) to learn. 
+      D               : number of spatial filters to learn within each temporal
+                        convolution.
+      dropoutType     : Either SpatialDropout2D or Dropout, passed as a string.
+      
+      
+    [1]. Waytowich, N. et. al. (2018). Compact Convolutional Neural Networks
+    for Classification of Asynchronous Steady-State Visual Evoked Potentials.
+    Journal of Neural Engineering vol. 15(6). 
+    http://iopscience.iop.org/article/10.1088/1741-2552/aae5d8
+
+    """
+    
+    if dropoutType == 'SpatialDropout2D':
+        dropoutType = SpatialDropout2D
+    elif dropoutType == 'Dropout':
+        dropoutType = Dropout
+    else:
+        raise ValueError('dropoutType must be one of SpatialDropout2D '
+                         'or Dropout, passed as a string.')
+    
+    input1   = Input(shape = (Chans, Samples, 1))
+
+    ##################################################################
+    block1       = Conv2D(F1, (1, kernLength), padding = 'same',
+                                   input_shape = (Chans, Samples, 1),
+                                   use_bias = False)(input1)
+    block1       = BatchNormalization()(block1)
+    block1       = DepthwiseConv2D((Chans, 1), use_bias = False, 
+                                   depth_multiplier = D,
+                                   depthwise_constraint = max_norm(1.))(block1)
+    block1       = BatchNormalization()(block1)
+    block1       = Activation('elu')(block1)
+    block1       = AveragePooling2D((1, 4))(block1)
+    block1       = dropoutType(dropoutRate)(block1)
+    
+    block2       = SeparableConv2D(F2, (1, 16),
+                                   use_bias = False, padding = 'same')(block1)
+    block2       = BatchNormalization()(block2)
+    block2       = Activation('elu')(block2)
+    block2       = AveragePooling2D((1, 8))(block2)
+    block2       = dropoutType(dropoutRate)(block2)
+        
+    flatten      = Flatten(name = 'flatten')(block2)
+    
+    dense        = Dense(nb_classes, name = 'dense')(flatten)
+    softmax      = Activation('softmax', name = 'softmax')(dense)
+    
+    return Model(inputs=input1, outputs=softmax)
+
+
+
+def EEGNet_old(nb_classes, Chans = 64, Samples = 128, regRate = 0.0001,
+           dropoutRate = 0.25, kernels = [(2, 32), (8, 4)], strides = (2, 4)):
+    """ Keras Implementation of EEGNet_v1 (https://arxiv.org/abs/1611.08024v2)
+
+    This model is the original EEGNet model proposed on arxiv
+            https://arxiv.org/abs/1611.08024v2
+    
+    with a few modifications: we use striding instead of max-pooling as this 
+    helped slightly in classification performance while also providing a 
+    computational speed-up. 
+    
+    Note that we no longer recommend the use of this architecture, as the new
+    version of EEGNet performs much better overall and has nicer properties.
+    
+    Inputs:
+        
+        nb_classes     : total number of final categories
+        Chans, Samples : number of EEG channels and samples, respectively
+        regRate        : regularization rate for L1 and L2 regularizations
+        dropoutRate    : dropout fraction
+        kernels        : the 2nd and 3rd layer kernel dimensions (default is 
+                         the [2, 32] x [8, 4] configuration)
+        strides        : the stride size (note that this replaces the max-pool
+                         used in the original paper)
+    
+    """
+
+    # start the model
+    input_main   = Input((Chans, Samples))
+    layer1       = Conv2D(16, (Chans, 1), input_shape=(Chans, Samples, 1),
+                                 kernel_regularizer = l1_l2(l1=regRate, l2=regRate))(input_main)
+    layer1       = BatchNormalization()(layer1)
+    layer1       = Activation('elu')(layer1)
+    layer1       = Dropout(dropoutRate)(layer1)
+    
+    permute_dims = 2, 1, 3
+    permute1     = Permute(permute_dims)(layer1)
+    
+    layer2       = Conv2D(4, kernels[0], padding = 'same', 
+                            kernel_regularizer=l1_l2(l1=0.0, l2=regRate),
+                            strides = strides)(permute1)
+    layer2       = BatchNormalization()(layer2)
+    layer2       = Activation('elu')(layer2)
+    layer2       = Dropout(dropoutRate)(layer2)
+    
+    layer3       = Conv2D(4, kernels[1], padding = 'same',
+                            kernel_regularizer=l1_l2(l1=0.0, l2=regRate),
+                            strides = strides)(layer2)
+    layer3       = BatchNormalization()(layer3)
+    layer3       = Activation('elu')(layer3)
+    layer3       = Dropout(dropoutRate)(layer3)
+    
+    flatten      = Flatten(name = 'flatten')(layer3)
+    
+    dense        = Dense(nb_classes, name = 'dense')(flatten)
+    softmax      = Activation('softmax', name = 'softmax')(dense)
+    
+    return Model(inputs=input_main, outputs=softmax)
+
+
+
+def DeepConvNet(nb_classes, Chans = 64, Samples = 256,
+                dropoutRate = 0.5):
+    """ Keras implementation of the Deep Convolutional Network as described in
+    Schirrmeister et. al. (2017), Human Brain Mapping.
+    
+    This implementation assumes the input is a 2-second EEG signal sampled at 
+    128Hz, as opposed to signals sampled at 250Hz as described in the original
+    paper. We also perform temporal convolutions of length (1, 5) as opposed
+    to (1, 10) due to this sampling rate difference. 
+    
+    Note that we use the max_norm constraint on all convolutional layers, as 
+    well as the classification layer. We also change the defaults for the
+    BatchNormalization layer. We used this based on a personal communication 
+    with the original authors.
+    
+                      ours        original paper
+    pool_size        1, 2        1, 3
+    strides          1, 2        1, 3
+    conv filters     1, 5        1, 10
+    
+    Note that this implementation has not been verified by the original 
+    authors. 
+    
+    """
+
+    # start the model
+    input_main   = Input((Chans, Samples, 1))
+    block1       = Conv2D(25, (1, 5), 
+                                 input_shape=(Chans, Samples, 1),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(input_main)
+    block1       = Conv2D(25, (Chans, 1),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(block1)
+    block1       = BatchNormalization(epsilon=1e-05, momentum=0.9)(block1)
+    block1       = Activation('elu')(block1)
+    block1       = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block1)
+    block1       = Dropout(dropoutRate)(block1)
+  
+    block2       = Conv2D(50, (1, 5),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(block1)
+    block2       = BatchNormalization(epsilon=1e-05, momentum=0.9)(block2)
+    block2       = Activation('elu')(block2)
+    block2       = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block2)
+    block2       = Dropout(dropoutRate)(block2)
+    
+    block3       = Conv2D(100, (1, 5),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(block2)
+    block3       = BatchNormalization(epsilon=1e-05, momentum=0.9)(block3)
+    block3       = Activation('elu')(block3)
+    block3       = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block3)
+    block3       = Dropout(dropoutRate)(block3)
+    
+    block4       = Conv2D(200, (1, 5),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(block3)
+    block4       = BatchNormalization(epsilon=1e-05, momentum=0.9)(block4)
+    block4       = Activation('elu')(block4)
+    block4       = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block4)
+    block4       = Dropout(dropoutRate)(block4)
+    
+    flatten      = Flatten()(block4)
+    
+    dense        = Dense(nb_classes, kernel_constraint = max_norm(0.5))(flatten)
+    softmax      = Activation('softmax')(dense)
+    
+    return Model(inputs=input_main, outputs=softmax)
+
+
+# need these for ShallowConvNet
+def square(x):
+    return K.square(x)
+
+def log(x):
+    return K.log(K.clip(x, min_value = 1e-7, max_value = 10000))   
+
+
+def ShallowConvNet(nb_classes, Chans = 64, Samples = 128, dropoutRate = 0.5):
+    """ Keras implementation of the Shallow Convolutional Network as described
+    in Schirrmeister et. al. (2017), Human Brain Mapping.
+    
+    Assumes the input is a 2-second EEG signal sampled at 128Hz. Note that in 
+    the original paper, they do temporal convolutions of length 25 for EEG
+    data sampled at 250Hz. We instead use length 13 since the sampling rate is 
+    roughly half of the 250Hz which the paper used. The pool_size and stride
+    in later layers is also approximately half of what is used in the paper.
+    
+    Note that we use the max_norm constraint on all convolutional layers, as 
+    well as the classification layer. We also change the defaults for the
+    BatchNormalization layer. We used this based on a personal communication 
+    with the original authors.
+    
+                     ours        original paper
+    pool_size        1, 35       1, 75
+    strides          1, 7        1, 15
+    conv filters     1, 13       1, 25    
+    
+    Note that this implementation has not been verified by the original 
+    authors. We do note that this implementation reproduces the results in the
+    original paper with minor deviations. 
+    """
+
+    # start the model
+    input_main   = Input((Chans, Samples, 1))
+    block1       = Conv2D(40, (1, 13), 
+                                 input_shape=(Chans, Samples, 1),
+                                 kernel_constraint = max_norm(2., axis=(0,1,2)))(input_main)
+    block1       = Conv2D(40, (Chans, 1), use_bias=False, 
+                          kernel_constraint = max_norm(2., axis=(0,1,2)))(block1)
+    block1       = BatchNormalization(epsilon=1e-05, momentum=0.9)(block1)
+    block1       = Activation(square)(block1)
+    block1       = AveragePooling2D(pool_size=(1, 35), strides=(1, 7))(block1)
+    block1       = Activation(log)(block1)
+    block1       = Dropout(dropoutRate)(block1)
+    flatten      = Flatten()(block1)
+    dense        = Dense(nb_classes, kernel_constraint = max_norm(0.5))(flatten)
+    softmax      = Activation('softmax')(dense)
+    
+    return Model(inputs=input_main, outputs=softmax)
+
+

EEGFaceSem/EEGPT/__init__.py

@@ -0,0 +1 @@
+from .eegpt_linear import EEGPT_InternalModel

EEGFaceSem/EEGPT/eegpt_linear.py

@@ -0,0 +1,207 @@
+
+import torch
+from torch import nn
+from torch.utils.data import TensorDataset, DataLoader
+import numpy as np
+from functools import partial
+import math
+import os
+from itertools import chain
+
+# Imports from copied modules
+from .modules.EEGPT_mcae import Block, trunc_normal_
+from .modules.Network.utils import LinearWithConstraint
+
+# Constants
+EEGPT_ch_names = [
+    'FP1', 'FPZ', 'FP2', 'AF7', 'AF3', 'AF4', 'AF8', 'F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8',
+    'FT7', 'FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6', 'FT8', 'T7', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4',
+    'C6', 'T8', 'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4', 'CP6', 'TP8', 'P7', 'P5', 'P3', 'P1', 'PZ',
+    'P2', 'P4', 'P6', 'P8', 'PO7', "PO5", 'PO3', 'POZ', 'PO4', "PO6", 'PO8', 'O1', 'OZ', 'O2',
+]
+EEGPT_ch_names = [x.upper() for x in EEGPT_ch_names]
+CHANNEL_DICT = {k.upper(): v for v, k in enumerate(EEGPT_ch_names)}
+FACECAT_ch_names_all = [x.upper() for x in ['Fp1', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'FC5', 'FC1', 'FC2', 'FC6', 'T7', 'C3', 'Cz', 'C4', 'T8', 'TP9', 'CP5', 'CP1', 'CP2', 'CP6', 'TP10', 'P7', 'P3', 'Pz', 'P4', 'P8', 'PO9', 'O1', 'Iz', 'O2', 'PO10']]
+FACECAT_ch_names, FACECAT_ch_ids = [], []
+for ch in EEGPT_ch_names:
+    if ch in FACECAT_ch_names_all:
+        FACECAT_ch_names.append(ch)
+        FACECAT_ch_ids.append(FACECAT_ch_names_all.index(ch))
+
+# Helper function from original script
+def seed_torch(seed=1029):
+    import random
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+
+# --- Start of Model-related classes copied from rep_EEGPT_inter.py ---
+
+class Conv2dWithConstraint(nn.Conv2d):
+    def __init__(self, *args, doWeightNorm=True, max_norm=1, **kwargs):
+        self.max_norm = max_norm
+        self.doWeightNorm = doWeightNorm
+        super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        if self.doWeightNorm:
+            self.weight.data = torch.renorm(self.weight.data, p=2, dim=0, maxnorm=self.max_norm)
+        return super(Conv2dWithConstraint, self).forward(x)
+
+class PatchEmbed(nn.Module):
+    def __init__(self, patch_size=16, patch_stride=None, embed_dim=768):
+        super().__init__()
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        self.proj = nn.Conv2d(1, embed_dim, kernel_size=(1, patch_size),
+                              stride=(1, patch_size if patch_stride is None else patch_stride))
+
+    @staticmethod
+    def compute_num_patches(img_size, patch_size, patch_stride):
+        if patch_stride is None:
+            return (img_size[0], (img_size[1] // patch_size))
+        else:
+            return (img_size[0], ((img_size[1] - patch_size) // patch_stride + 1))
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        x = self.proj(x).transpose(1, 3)
+        return x
+
+class ChannelScaleLayer(nn.Module):
+    def __init__(self, ch_nums=1):
+        super().__init__()
+        self.shape = (ch_nums,)
+        init_weights = 10**-6 * torch.rand(self.shape)
+        self.weights = nn.Parameter(init_weights, requires_grad=True)
+
+    def forward(self, x):
+        return x * (1 + self.weights[..., None])
+
+class EEGTransformerMulti(nn.Module):
+    def __init__(self, patch_size=64, patch_stride=None, embed_dim=768, embed_num=1,
+                 depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, drop_rate=0.0,
+                 attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=nn.LayerNorm,
+                 patch_module=PatchEmbed, init_std=0.02, **kwargs):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.embed_num = embed_num
+        self.init_std = init_std
+        self.patch_embed = patch_module(patch_size=patch_size, patch_stride=patch_stride, embed_dim=embed_dim)
+        self.chan_embed = nn.Embedding(len(CHANNEL_DICT), embed_dim)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+        self.blocks = nn.ModuleList([
+            Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+        self.summary_token = nn.Parameter(torch.zeros(1, embed_num, embed_dim))
+        trunc_normal_(self.summary_token, std=init_std)
+        self.apply(self._init_weights)
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=self.init_std)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=self.init_std)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Embedding):
+            torch.nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def prepare_chan_ids(self, channels):
+        chan_ids = [CHANNEL_DICT[ch.upper().strip('.')] for ch in channels]
+        return torch.tensor(chan_ids).unsqueeze_(0).long()
+
+    def forward(self, x, chan_ids):
+        x = self.patch_embed(x)
+        B, N, C, D = x.shape
+        chan_ids = chan_ids.to(x.device)
+        x = x + self.chan_embed(chan_ids).unsqueeze(0)
+        x = x.flatten(0, 1)
+        summary_token = self.summary_token.repeat((x.shape[0], 1, 1))
+        x = torch.cat([x, summary_token], dim=1)
+        for blk in self.blocks:
+            x = blk(x)
+        x = x[:, -summary_token.shape[1]:, :]
+        x = self.norm(x)
+        x = x.flatten(-2)
+        x = x.reshape((B, N, self.embed_num, -1))
+        return x
+
+# --- End of copied classes ---
+
+
+class EEGPT_InternalModel(nn.Module):
+    """A single nn.Module to hold all parts for easier management."""
+    def __init__(self, num_classes, pretrained_weights_path=None, input_shape=None, ids_map=FACECAT_ch_ids, ch_names=FACECAT_ch_names):
+        super().__init__()
+        self.num_classes = num_classes
+        _, self.n_samples = input_shape
+        self.chans_num = len(ids_map)
+        self.ids_map = ids_map
+        self.ch_names = ch_names
+        
+        self.channel_scaler = ChannelScaleLayer(ch_nums=self.chans_num)
+        self.target_encoder = EEGTransformerMulti(patch_size=64, patch_stride=32, embed_num=4, embed_dim=512, depth=8, num_heads=8, mlp_ratio=4.0, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6))
+        self.chans_ids = self.target_encoder.prepare_chan_ids(self.ch_names)
+
+        if pretrained_weights_path:
+            self.initilize_pretrained_ckpt(pretrained_weights_path)
+        
+        # Freeze the encoder
+        for param in self.target_encoder.parameters():
+            param.requires_grad = False
+
+        # Fine-tuning Head
+        self.linear_probe1 = LinearWithConstraint(2048, 16, max_norm=1)
+        mult = (self.n_samples - 64) // 32 + 1
+        self.linear_probe2 = LinearWithConstraint(16*mult, self.num_classes, max_norm=0.25)
+        self.drop = nn.Dropout(p=0.50)
+
+    # add a method to load the model from a checkpoint only once for all instances
+    def initilize_pretrained_ckpt(self, pretrained_weights_path):
+        if not hasattr(EEGPT_InternalModel, 'encoder_state_dict'):
+            pretrain_ckpt = torch.load(pretrained_weights_path, map_location='cpu', weights_only=False)
+            EEGPT_InternalModel.encoder_state_dict = {k.replace('target_encoder.', ''): v for k, v in pretrain_ckpt['state_dict'].items() if k.startswith("target_encoder.")}
+        self.target_encoder.load_state_dict(EEGPT_InternalModel.encoder_state_dict)
+
+    def forward(self, x):
+        x = x.to(torch.float)
+        x = x * 1e3
+        x = x - x.mean(dim=-2, keepdim=True)
+        x = x[:, self.ids_map, :]
+        x = self.channel_scaler(x)
+        
+        self.target_encoder.eval()
+        z = self.target_encoder(x, self.chans_ids.to(x.device))
+        h = z.flatten(2)
+        h = self.linear_probe1(self.drop(h))
+        h = h.flatten(1)
+        h = self.linear_probe2(h)
+        return h
+
+    def trainable_parameters(self):
+        return chain(
+            self.channel_scaler.parameters(),
+            self.linear_probe1.parameters(),
+            self.linear_probe2.parameters()
+        )
+
+    def copy_from(self, other_model):
+        self.load_state_dict(other_model.state_dict())

EEGFaceSem/EEGPT/modules/EEGPT_mcae.py

@@ -0,0 +1,876 @@
+# EEG Pretrain Transformers
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import math
+from functools import partial
+import numpy as np
+
+import torch
+import torch.nn as nn
+
+import math
+
+import torch
+
+from logging import getLogger
+
+logger = getLogger()
+
+
+CHANNEL_DICT = {k.upper():v for v,k in enumerate(
+                     [      'FP1', 'FPZ', 'FP2', 
+                        "AF7", 'AF3', 'AF4', "AF8", 
+            'F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8', 
+        'FT7', 'FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6', 'FT8', 
+            'T7', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'T8', 
+        'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4', 'CP6', 'TP8',
+             'P7', 'P5', 'P3', 'P1', 'PZ', 'P2', 'P4', 'P6', 'P8', 
+                      'PO7', "PO5", 'PO3', 'POZ', 'PO4', "PO6", 'PO8', 
+                               'O1', 'OZ', 'O2', ])}
+
+################################# Utils ######################################
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def apply_mask(mask, x):
+    """
+    :param x: tensor of shape [B (batch-size), N (num-patches), C, D (feature-dim)]
+    :param mask: tensor [mN, mC] containing indices of patches in [N, C] to keep 
+    """    
+    B, N, C, D = x.shape
+    if len(mask.shape)==2:
+        mN, mC = mask.shape
+        
+        mask_keep = mask.reshape((1,mN*mC,1)).repeat((B, 1, D))
+        masked_x = torch.gather(x.reshape((B, N*C, D)), dim=-2, index=mask_keep)
+        masked_x = masked_x.contiguous().view((B,mN,mC,D))
+    else:
+        mN = mask.shape[0]
+        
+        mask_keep = mask.reshape((1,mN,1)).repeat((B, 1, D))
+        masked_x = torch.gather(x.reshape((B, N*C, D)), dim=-2, index=mask_keep)
+    return masked_x
+
+def apply_mask_t(mask_t, x):
+    """
+    :param x: tensor of shape [B (batch-size), N (num-patches), C, D (feature-dim)]
+    :param mask: tensor [mN, mC] containing indices of patches in [N, C] to keep 
+    """    
+    B, N, D = x.shape
+    mN = mask_t.shape[0]
+    
+    mask_keep = mask_t.reshape((1,mN,1)).repeat((B, 1, D))
+    masked_x = torch.gather(x, dim=1, index=mask_keep)
+    return masked_x
+
+def repeat_interleave_batch(x, B, repeat):
+    N = len(x) // B
+    x = torch.cat([
+        torch.cat([x[i*B:(i+1)*B] for _ in range(repeat)], dim=0)
+        for i in range(N)
+    ], dim=0)
+    return x
+
+# helper functions
+def exists(val):
+    return val is not None
+
+# rotary embedding helper functions
+
+def rotate_half(x):
+    
+    # x = rearrange(x, '... (d r) -> ... d r', r = 2)
+    x = x.reshape((*x.shape[:-1],x.shape[-1]//2, 2))
+    x1, x2 = x.unbind(dim = -1)
+    x = torch.stack((-x2, x1), dim = -1)
+    # return rearrange(x, '... d r -> ... (d r)')
+    return x.flatten(-2)
+
+def apply_rotary_emb(freqs, t, start_index = 0, scale = 1.):
+    freqs = freqs.to(t)
+    rot_dim = freqs.shape[-1]
+    end_index = start_index + rot_dim
+    assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}'
+    t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:]
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+    return torch.cat((t_left, t, t_right), dim = -1)
+
+################################# RoPE Model Begin ######################################
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        theta = 10000,
+        learned_freq = False,
+        interpolate_factor = 1.
+    ):
+        super().__init__()
+        
+        self.cache = dict()
+        self.cache_scale = dict()
+        self.freqs = nn.Parameter(
+            1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)), 
+            requires_grad = learned_freq)
+        
+        # interpolation factors
+
+        assert interpolate_factor >= 1.
+        self.interpolate_factor = interpolate_factor
+
+        self.register_buffer('scale', None)
+        
+    def prepare_freqs(self, num_patches = (1, 8), device='cuda', dtype=torch.float, offset = 0):
+        # num_patches (C, N)
+        C, N = num_patches
+        cache_key = f'freqs:{num_patches}'
+        
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+        
+        seq_pos = torch.arange(N, device = device, dtype = dtype)
+        seq_pos = seq_pos.repeat_interleave(repeats=C, dim=0) # correspond to x (B, N, C, D)
+        seq_pos = (seq_pos + offset) / self.interpolate_factor
+        
+        freqs = self.freqs
+        freqs = torch.outer(seq_pos.type(freqs.dtype), freqs) # (n_seq_pos, n_freqs)
+        freqs = freqs.repeat_interleave(repeats=2, dim=-1)    # (n_seq_pos, n_freqs*2)
+
+        self.cache[cache_key] = freqs
+
+        return freqs
+    
+    
+
+
+################################# EEGPT Model Begin ######################################
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        
+    def drop_path(self, x, drop_prob: float = 0., training: bool = False):
+        if drop_prob == 0. or not training:
+            return x
+        keep_prob = 1 - drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+        random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+        random_tensor.floor_()  # binarize
+        output = x.div(keep_prob) * random_tensor
+        return output
+    
+    def forward(self, x):
+        return self.drop_path(x, self.drop_prob, self.training)
+
+
+class MLP(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features 
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., is_causal=False, use_rope=False, return_attention=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.use_rope = use_rope
+        
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+            
+        self.attn_drop = attn_drop
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.is_causal = is_causal
+        self.return_attention= return_attention
+
+    def forward(self, x, freqs=None):
+        B, T, C = x.shape
+        qkv = self.qkv(x).reshape(B, T, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # 3,B,nh,t,d
+        q, k, v = qkv[0], qkv[1], qkv[2] # B,nh,t,d
+        
+        if self.use_rope:# RoPE
+            q = apply_rotary_emb(freqs, q)
+            k = apply_rotary_emb(freqs, k)
+        if self.return_attention:
+            if self.is_causal:
+                attn_mask = torch.ones(q.size(-2), q.size(-2), dtype=torch.bool).tril(diagonal=0)
+                attn_maak = torch.zeros(q.size(-2), q.size(-2))
+                attn_mask = attn_maak.masked_fill(torch.logical_not(attn_mask), -float('inf'))
+                attn_weight = torch.softmax((q @ k.transpose(-2, -1) / math.sqrt(q.size(-1))) + attn_mask, dim=-1)
+            else:
+                attn_weight = torch.softmax((q @ k.transpose(-2, -1) / math.sqrt(q.size(-1))), dim=-1)
+            return attn_weight
+        # efficient attention using Flash Attention CUDA kernels
+        y = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, dropout_p=self.attn_drop if self.training else 0, is_causal=self.is_causal)
+        x = y.transpose(1, 2).contiguous().view(B, T, C) #(B, nh, T, hs) -> (B, T, hs*nh)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, is_causal=False, use_rope=False, return_attention=False):
+        super().__init__()
+        
+        self.return_attention= return_attention
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, is_causal=is_causal, use_rope=use_rope, return_attention = return_attention)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, freqs=None):
+        y = self.attn(self.norm1(x), freqs)
+        if self.return_attention: return y
+        x = x + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=(64, 1000), patch_size=16, patch_stride=None, embed_dim=768):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        if patch_stride is None:
+            self.num_patches = ((img_size[0]), (img_size[1] // patch_size))
+        else:
+            self.num_patches = ((img_size[0]), ((img_size[1] - patch_size) // patch_stride + 1))
+
+        self.proj = nn.Conv2d(1, embed_dim, kernel_size=(1,patch_size), 
+                              stride=(1, patch_size if patch_stride is None else patch_stride))
+        
+    def forward(self, x):
+        # x: B,C,T
+        x = x.unsqueeze(1)# B, 1, C, T
+        x = self.proj(x).transpose(1,3) # B, T, C, D
+        return x
+class PatchNormEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=(64, 1000), patch_size=16, patch_stride=None, embed_dim=768):
+        super().__init__()
+        
+        assert img_size[1] % patch_size==0
+        
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        
+        if patch_stride is None:
+            self.num_patches = ((img_size[0]), (img_size[1] // patch_size))
+        else:
+            self.num_patches = ((img_size[0]), ((img_size[1] - patch_size) // patch_stride + 1))
+
+        self.unfold = torch.nn.Unfold(kernel_size=(1, patch_size), stride = (1, patch_stride if patch_stride is not None else patch_size))
+
+        self.proj = nn.Linear(patch_size, embed_dim)#+2
+
+    def forward(self, x):
+        # x: B,C,T
+        B,C,T = x.shape
+        x = x.unsqueeze(1) # B 1 C T
+        
+        x = self.unfold(x)
+        
+        x = x.transpose(-1,-2)
+        
+        x = x.view(B, C, -1, self.patch_size).contiguous()
+        x = x.transpose(1,2)
+        
+        # m = torch.mean(x, dim=-1).unsqueeze(-1)
+        # v = torch.std( x, dim=-1).unsqueeze(-1)
+        x = torch.layer_norm(x, (self.patch_size,))
+        # x = torch.cat([x,m,v], dim=-1) # B, T, C, P
+        # print(x)
+        
+        x = self.proj(x) # B, T, C, D
+        
+        return x
+class EEGTransformerReconstructor(nn.Module):
+    """ EEG Transformer """
+    def __init__(
+        self,
+        num_patches,
+        patch_size=64,
+        embed_num=1,
+        use_pos_embed = False,
+        use_inp_embed = True,
+        embed_dim=768,
+        reconstructor_embed_dim=384,
+        depth=6,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=nn.LayerNorm,
+        init_std=0.02,
+        interpolate_factor = 2.,
+        return_attention_layer=-1,
+        **kwargs
+    ):
+        super().__init__()
+        self.use_inp_embed = use_inp_embed
+        self.use_pos_embed = use_pos_embed
+        self.num_patches = num_patches
+        
+        if use_inp_embed:
+            self.reconstructor_embed = nn.Linear(embed_dim, reconstructor_embed_dim, bias=True)
+        
+        if use_pos_embed:
+            self.pos_embed           = nn.Parameter(torch.zeros(1, 1, embed_num, reconstructor_embed_dim))
+            trunc_normal_(self.pos_embed, std=init_std)
+        
+        self.mask_token          = nn.Parameter(torch.zeros(1, 1, reconstructor_embed_dim))
+        
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        # --
+        self.time_embed_dim = (reconstructor_embed_dim//num_heads)//2
+        self.time_embed = RotaryEmbedding(dim=self.time_embed_dim, interpolate_factor=interpolate_factor)
+        self.chan_embed = nn.Embedding(len(CHANNEL_DICT), reconstructor_embed_dim)
+        # --
+        self.reconstructor_blocks = nn.ModuleList([
+            Block(
+                dim=reconstructor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, is_causal=False, use_rope=True, 
+                return_attention=(i+1)==return_attention_layer)
+            for i in range(depth)])
+        self.reconstructor_norm = norm_layer(reconstructor_embed_dim)
+        self.reconstructor_proj = nn.Linear(reconstructor_embed_dim, patch_size, bias=True)
+        # ------
+        self.init_std = init_std
+        trunc_normal_(self.mask_token, std=self.init_std)
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+        
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.reconstructor_blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=self.init_std)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=self.init_std)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Embedding):
+            torch.nn.init.normal_(m.weight, mean=0.0, std=0.02)
+    
+    def forward(self, x, chan_ids=None, mask_x=None, mask_y=None):
+        # conditions: (Nq, D) as qurey for downstream 
+        # mask_x/mask_y: (mN, mC) one number index like (n*C+c) in matrix (N,C)
+        
+        chan_ids = chan_ids.to(x).long()
+        
+        # -- map from encoder-dim to pedictor-dim
+        if self.use_inp_embed:
+            x = self.reconstructor_embed(x)
+
+        C, N        = self.num_patches
+        B, mN, eN, D= x.shape
+        # assert mN == N, f"{mN},{N}"
+        ############## Mask x ###############
+        # -- add channels positional embedding to x
+        chan_embed = self.chan_embed(chan_ids).unsqueeze(0) # (1,C) -> (1,1,C,D)
+        # -- get freqs for RoPE
+        
+        if mask_x is not None:
+            mask_x       = mask_x.to(x.device)
+            mask_x       = torch.floor(mask_x[:,0] / C).long().to(x.device)    # select first as represent
+            
+            freqs_x      = self.time_embed.prepare_freqs((1, N), x.device, x.dtype)
+            freqs_x      = freqs_x.contiguous().view((1,N,self.time_embed_dim))
+            freqs_x      = apply_mask_t(mask_x, freqs_x)                                       # 1, mN, 1, D
+            freqs_x      = freqs_x.contiguous().view((mask_x.shape[0], 1, self.time_embed_dim)) # mN, D//2
+            freqs_x      = freqs_x.repeat((1, eN, 1)).flatten(0,1)
+            
+        else:
+            freqs_x      = self.time_embed.prepare_freqs((eN, N), x.device, x.dtype) # NC, time_dim
+            
+        ############# Mask y ################
+        if mask_y is not None:
+            mask_y       = mask_y.to(x.device)
+            
+            # create query mask_token ys
+            N_y          = mask_y.shape[0]
+            chan_embed   = chan_embed.repeat((1,N,1,1))
+            chan_embed   = apply_mask(mask_y, chan_embed)
+            
+            freqs        = self.time_embed.prepare_freqs((C, N), x.device, x.dtype) # NC, time_dim
+            freqs_y      = freqs.contiguous().view((1, N, C, self.time_embed_dim))
+            freqs_y      = apply_mask(mask_y, freqs_y)        # 1, mN, mC, D
+            freqs_y      = freqs_y.contiguous().view((N_y, self.time_embed_dim))
+            
+            y = self.mask_token.repeat((B, N_y, 1)) + chan_embed
+            
+            
+            if self.use_pos_embed:
+                x        = x + self.pos_embed.repeat((B, x.shape[1], 1, 1)).to(x.device)
+                
+            # -- concat query mask_token ys
+            x           = x.flatten(1,2) # B N E D -> B NE D
+            x           = torch.cat([x,y], dim=1)
+            freqs_x     = torch.cat([freqs_x, freqs_y], dim=0).to(x)
+            
+            
+            # -- fwd prop
+            for blk in self.reconstructor_blocks:
+                x = blk(x, freqs_x) # B, NC, D
+                if blk.return_attention==True: return x
+            
+            
+            x = x[:,-N_y:,:]      # B, N_y, D
+            
+            x = self.reconstructor_norm(x) 
+                
+            x = self.reconstructor_proj(x)
+            
+            return x
+        
+class EEGTransformerPredictor(nn.Module):
+    """ EEG Transformer """
+    def __init__(
+        self,
+        num_patches,
+        embed_dim=768,
+        embed_num=1,
+        use_pos_embed = False,
+        use_inp_embed = True,
+        use_part_pred = False,
+        predictor_embed_dim=384,
+        depth=6,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=nn.LayerNorm,
+        init_std=0.02,
+        interpolate_factor = 2.,
+        return_attention_layer=-1,
+        **kwargs
+    ):
+        super().__init__()
+        self.use_part_pred = use_part_pred
+        self.use_pos_embed = use_pos_embed
+        self.use_inp_embed = use_inp_embed
+        self.num_patches = num_patches
+        self.embed_num = embed_num
+        
+        if use_inp_embed:
+            self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True)
+        
+        if use_pos_embed:
+            self.pos_embed   = nn.Parameter(torch.zeros(1, 1, embed_num, predictor_embed_dim))
+            trunc_normal_(self.pos_embed, std=init_std)
+        
+        self.mask_token      = nn.Parameter(torch.zeros(1, 1, embed_num, predictor_embed_dim))
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        # --
+        self.time_embed_dim = (predictor_embed_dim//num_heads)//2
+        self.time_embed = RotaryEmbedding(dim=self.time_embed_dim, interpolate_factor=interpolate_factor)
+        
+        # --
+        self.predictor_blocks = nn.ModuleList([
+            Block(
+                dim=predictor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, is_causal=False, use_rope=True, 
+                return_attention=(i+1)==return_attention_layer)
+            for i in range(depth)])
+        self.predictor_norm = norm_layer(predictor_embed_dim)
+        self.predictor_proj = nn.Linear(predictor_embed_dim, embed_dim, bias=True)
+        # ------
+        self.init_std = init_std
+        trunc_normal_(self.mask_token, std=self.init_std)
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+        
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.predictor_blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=self.init_std)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=self.init_std)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Embedding):
+            torch.nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def forward(self, x, mask_x=None, mask_t=None):
+        # conditions: (Nq, D) as qurey for downstream 
+        # mask_t: mN one number index like (n*C+c) in matrix (N,1)
+        
+        # -- map from encoder-dim to pedictor-dim
+        if self.use_part_pred:
+            inp_x = x
+            
+        if self.use_inp_embed:
+            x = self.predictor_embed(x)
+
+        C, N        = self.num_patches
+        B, mN, eN, D    = x.shape
+        
+        ############## Mask x ###############
+        # -- get freqs for RoPE
+        freqs = self.time_embed.prepare_freqs((eN, N), x.device, x.dtype) # NC, time_dim
+        
+        if mask_x is not None:
+            mask_x       = mask_x
+            mask_x       = torch.floor(mask_x[:,0] / C).long()
+            ############# Mask y ################
+            if mask_t is None:
+                mask_t       = torch.tensor(list(set(list(range(0,N))) - set(mask_x.tolist()))).long()
+            # -- concat query mask_token ys
+            N_y              = mask_t.shape[0]
+            y                = self.mask_token.repeat((B, N_y, 1, 1))
+            x                = torch.cat([x,y], dim=1)
+            
+            # -- masked index of tensor x rearrange to normal index
+            mask_id          = torch.concat([mask_x.to(x.device), mask_t.to(x.device)], dim=0)            
+            x                = torch.index_select(x, dim=1, index=torch.argsort(mask_id))    
+            
+        if self.use_pos_embed:
+            x                = x + self.pos_embed.repeat((B, x.shape[1], 1, 1)).to(x.device)
+            
+        B, N, eN, D    = x.shape
+        x = x.flatten(1,2)
+        
+        # -- fwd prop
+        for blk in self.predictor_blocks:
+            x = blk(x, freqs) # B, NC, D
+            if blk.return_attention==True: return x
+        
+        # -- reshape back
+        x = x.reshape((B, N, eN, D))
+        
+        x = self.predictor_norm(x) 
+            
+        x = self.predictor_proj(x)
+        
+        if self.use_part_pred and mask_x is not None:
+            cmb_x = torch.index_select(x, dim=1, index=mask_t.to(x.device)) 
+            cmb_x = torch.concat([inp_x, cmb_x], dim=1)
+            cmb_x = torch.index_select(cmb_x, dim=1, index=torch.argsort(mask_id)) 
+            return x, cmb_x
+        return x
+
+class EEGTransformer(nn.Module):
+    """ EEG Transformer """
+    def __init__(
+        self,
+        img_size=(64,1000),
+        patch_size=64,
+        patch_stride=None,
+        embed_dim=768,
+        embed_num=1,
+        predictor_embed_dim=384,
+        depth=12,
+        predictor_depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=nn.LayerNorm,
+        patch_module=PatchEmbed,# PatchNormEmbed
+        init_std=0.02,
+        interpolate_factor = 2.,
+        return_attention_layer=-1,
+        **kwargs
+    ):
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim
+        self.embed_num = embed_num
+        
+        self.num_heads = num_heads
+        
+        # --
+        self.patch_embed = patch_module(
+            img_size=img_size,
+            patch_size=patch_size,
+            patch_stride=patch_stride,
+            embed_dim=embed_dim)
+        self.num_patches = self.patch_embed.num_patches
+        # --
+        
+        self.chan_embed = nn.Embedding(len(CHANNEL_DICT), embed_dim)
+        # --
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                is_causal=False, use_rope= False, return_attention=(i+1)==return_attention_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+        # ------
+        self.init_std = init_std
+        self.summary_token = nn.Parameter(torch.zeros(1, embed_num, embed_dim))
+            
+        trunc_normal_(self.summary_token, std=self.init_std)
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+        
+    def prepare_chan_ids(self, channels):
+        chan_ids = []
+        for ch in channels:
+            ch = ch.upper().strip('.')
+            assert ch in CHANNEL_DICT
+            chan_ids.append(CHANNEL_DICT[ch])
+        return torch.tensor(chan_ids).unsqueeze_(0).long()
+    
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=self.init_std)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+        elif isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=self.init_std)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Embedding):
+            torch.nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def forward(self, x, chan_ids=None, mask_x=None, mask_t=None):
+        # x.shape B, C, T
+        # mask_x.shape mN, mC
+        # mask_t.shape mN
+        
+        # -- patchify x
+        x = self.patch_embed(x) #
+        B, N, C, D = x.shape
+        
+        assert N==self.num_patches[1] and C==self.num_patches[0], f"{N}=={self.num_patches[1]} and {C}=={self.num_patches[0]}"
+        
+        if chan_ids is None:
+            chan_ids = torch.arange(0,C)     
+        chan_ids = chan_ids.to(x)
+        
+        # -- add channels positional embedding to x
+        x = x + self.chan_embed(chan_ids.long()).unsqueeze(0) # (1,C) -> (1,1,C,D)
+        
+        if mask_x is not None:
+            mask_x = mask_x.to(x.device)
+            x = apply_mask(mask_x, x)# B, mN, mC, D
+            B, N, C, D = x.shape
+            
+        
+        x = x.flatten(0, 1) # BmN, mC, D
+        
+        # -- concat summary token
+        summary_token = self.summary_token.repeat((x.shape[0], 1, 1))
+        x = torch.cat([x,summary_token], dim=1)  # BmN, mC+embed_num, D
+        
+        # -- fwd prop
+        for i, blk in enumerate(self.blocks):
+            x = blk(x) # B*N, mC+1, D
+            if blk.return_attention==True: return x
+
+        x = x[:, -summary_token.shape[1]:, :]
+        
+        if self.norm is not None:
+            x = self.norm(x) 
+
+        
+        x = x.flatten(-2)
+        x = x.reshape((B, N, -1))
+        # -- reshape back
+            
+        if mask_t is not None:
+            mask_t = mask_t.to(x.device)
+            x = apply_mask_t(mask_t, x)# B, mN, D        
+        
+        x = x.reshape((B, N, self.embed_num, -1))
+        
+        return x
+    
+
+
+if __name__=="__main__":
+    m = PatchNormEmbed((3,100), 10, 2)
+    print(m(1000*torch.randn((1,3,100))))
+    exit()
+    VIT_EMBED_DIMS = {
+        'vit_little': 32*3,
+        'vit_tiny': 192,
+        'vit_small': 384,
+        'vit_base': 768,
+        'vit_large': 1024,
+        'vit_huge': 1280,
+        'vit_giant': 1408,
+    }
+
+    import random
+    import os
+    def seed_torch(seed=1029):
+        random.seed(seed)
+        os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
+
+    seed_torch(7)
+    model = EEGTransformer(
+        img_size=[2, 12],
+        patch_size=2,
+        embed_dim=4,
+        depth=2,
+        num_heads=1,
+        mlp_ratio=4.0,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        init_std=0.02,
+        qkv_bias=True, 
+        norm_layer=partial(nn.LayerNorm, eps=1e-6))
+    
+    x = torch.rand(1,2,12)
+    out = model(x, mask_x=torch.tensor([[4,5],[0,1],[2,3]]))
+    # out = model(x, mask_t=torch.tensor([0,1,4,5]))
+    print(out.shape) 
+    # exit()
+    
+    
+    model2 = EEGTransformerPredictor(
+        num_patches=model.num_patches,
+        embed_dim=4,
+        predictor_embed_dim=4,
+        depth=2,
+        num_heads=1,
+        mlp_ratio=4.0,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        init_std=0.02,
+        qkv_bias=True, 
+        norm_layer=partial(nn.LayerNorm, eps=1e-6))
+    
+    # a = model2(out, )
+    # print(a) 
+    # out[0,1,0,0]=-1
+    # a = model2(out)
+    # print(a, a.shape) 
+    a = model2(out, mask_x=torch.tensor([[4,5],[0,1],[2,3]]), mask_t=None)
+    
+    model3 = EEGTransformerReconstructor(
+        num_patches=model.num_patches,
+        patch_size=2,
+        embed_dim=4,
+        reconstructor_embed_dim=4,
+        depth=2,
+        num_heads=1,
+        mlp_ratio=4.0,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        init_std=0.02,
+        qkv_bias=True, 
+        norm_layer=partial(nn.LayerNorm, eps=1e-6))
+    
+    b = model3(a, mask_y=torch.tensor([6,7,8,9]))
+    print(b) 

EEGFaceSem/EEGPT/modules/Network/utils.py

@@ -0,0 +1,118 @@
+import torch.nn as nn
+import torch
+from torch.autograd import Function
+
+class ReverseLayerF(Function):
+
+    @staticmethod
+    def forward(ctx, x, alpha):
+        ctx.alpha = alpha
+
+        return x.view_as(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        output = grad_output.neg() * ctx.alpha
+
+        return output, None
+
+class Conv2dWithConstraint(nn.Conv2d):
+    '''
+    Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of neural engineering, 2018, 15(5): 056013.
+    '''
+    def __init__(self, *args, doWeightNorm = True, max_norm=1, **kwargs):
+        self.max_norm = max_norm
+        self.doWeightNorm = doWeightNorm
+        super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        if self.doWeightNorm: 
+            self.weight.data = torch.renorm(
+                self.weight.data, p=2, dim=0, maxnorm=self.max_norm
+            )
+        return super(Conv2dWithConstraint, self).forward(x)
+
+class Conv1dWithConstraint(nn.Conv1d):
+    '''
+    Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of neural engineering, 2018, 15(5): 056013.
+    '''
+    def __init__(self, *args, doWeightNorm = True, max_norm=1, **kwargs):
+        self.max_norm = max_norm
+        self.doWeightNorm = doWeightNorm
+        super(Conv1dWithConstraint, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        if self.doWeightNorm: 
+            self.weight.data = torch.renorm(
+                self.weight.data, p=2, dim=0, maxnorm=self.max_norm
+            )
+        return super(Conv1dWithConstraint, self).forward(x)
+
+class LinearWithConstraint(nn.Linear):
+    def __init__(self, *args, doWeightNorm = True, max_norm=1, **kwargs):
+        self.max_norm = max_norm
+        self.doWeightNorm = doWeightNorm
+        super(LinearWithConstraint, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        if self.doWeightNorm: 
+            self.weight.data = torch.renorm(
+                self.weight.data, p=2, dim=0, maxnorm=self.max_norm
+            )
+        return super(LinearWithConstraint, self).forward(x)
+
+
+def SMMDL_marginal(Cs,Ct):
+
+    '''
+    The SMMDL used in the CRGNet.
+    Arg:
+        Cs:The source input which shape is NxdXd.
+        Ct:The target input which shape is Nxdxd.
+    '''
+    
+    Cs = torch.mean(Cs,dim=0)
+    Ct = torch.mean(Ct,dim=0)
+    
+    # loss = torch.mean((Cs-Ct)**2)
+    loss = torch.mean(torch.mul((Cs-Ct), (Cs-Ct)))
+    
+    return loss
+
+def SMMDL_conditional(Cs,s_label,Ct,t_label):
+  
+    '''
+    The Conditional SMMDL of the source and target data.
+    Arg:
+        Cs:The source input which shape is NxdXd.
+        s_label:The label of Cs data.
+        Ct:The target input which shape is Nxdxd.
+        t_label:The label of Ct data.
+    '''
+    s_label = s_label.reshape(-1)
+    t_label = t_label.reshape(-1)
+    
+    class_unique = torch.unique(s_label)
+    
+    class_num = len(class_unique)
+    all_loss = 0.0
+    
+    for c in class_unique:
+        s_index = (s_label == c)
+        t_index = (t_label == c)
+        # print(t_index)
+        if torch.sum(t_index)==0:
+            class_num-=1
+            continue
+        c_Cs = Cs[s_index]
+        c_Ct = Ct[t_index]
+        m_Cs = torch.mean(c_Cs,dim = 0)
+        m_Ct = torch.mean(c_Ct,dim = 0)
+        loss = torch.mean((m_Cs-m_Ct)**2)
+        all_loss +=loss
+        
+    if class_num == 0:
+        return 0
+    
+    return all_loss/class_num   
+

EEGFaceSem/__init__.py

@@ -0,0 +1,144 @@
+"""
+EEGFaceSem - EEG Dataset for Semantic Visual Response
+
+A Python package for the EEGFaceSem brain dataset from CVPR 2024.
+"""
+
+__version__ = "0.1.0"
+
+# ============================================================================
+# TASK DEFINITIONS
+# ============================================================================
+# 8 binary classification tasks in order
+
+ALL_TASKS = [
+    'facecat/female',       # 0
+    'facecat/male',         # 1
+    'facecat/blond',        # 2
+    'facecat/darkhaired',   # 3
+    'facecat/smiles',       # 4
+    'facecat/nosmile',      # 5
+    'facecat/old',          # 6
+    'facecat/young',        # 7
+]
+
+TASK_NAMES = ['female', 'male', 'blond', 'darkhaired', 'smiles', 'nosmile', 'old', 'young']
+
+def get_task_id(task):
+    """Convert task name or id to task_id."""
+    if isinstance(task, int):
+        return task
+    task_lower = task.lower()
+    if task_lower in TASK_NAMES:
+        return TASK_NAMES.index(task_lower)
+    # Try with prefix
+    for i, t in enumerate(ALL_TASKS):
+        if task_lower in t.lower():
+            return i
+    raise ValueError(f"Unknown task: {task}. Valid tasks: {TASK_NAMES}")
+
+# ============================================================================
+# CORE API
+# ============================================================================
+
+# Download functions
+from .download import download, download_models, download_latents, get_data_dir
+
+# Dataset loading - returns (X, Y, ids) tuple
+from .utils import Dataset, load_from_processed, load_latent
+
+# Benchmark
+from .benchmark import benchmark
+
+# Image generation
+from .generation import generate
+
+
+def load_data(data_dir=None, task=None, subjects=None, auto_download=True):
+    """
+    Load EEGFaceSem processed data.
+    
+    Args:
+        data_dir: Path to data directory. Default: ~/.cache/EEGFaceSem
+        task: Task name ('female', 'male', etc.) or task_id (0-7). None for all.
+        subjects: List of subject IDs (1-30) to download. None = all subjects.
+        auto_download: Download data if not found. Default: True
+    
+    Returns:
+        X: numpy array (n_trials, n_channels, n_timesteps) - EEG epochs
+        Y: numpy array (n_trials,) - Binary labels (0 or 1)
+        ids: numpy array (n_trials, 5) - [subject_id, task_id, trial_id, label, image_id]
+    
+    Example:
+        >>> X, Y, ids = EEGFaceSem.load_data(task='female')
+        >>> X, Y, ids = EEGFaceSem.load_data(task='female', subjects=[1])  # One subject only
+    """
+    from pathlib import Path
+    
+    if data_dir is None:
+        data_dir = get_data_dir()
+    data_dir = Path(data_dir)
+    
+    # Auto-download if needed
+    processed_dir = data_dir / "data" / "processed"
+    if auto_download and not processed_dir.exists():
+        download(data_dir=data_dir, data_type="processed", subjects=subjects)
+    
+    # Convert task name to full path if needed
+    task_full = None
+    if task is not None:
+        task_id = get_task_id(task)
+        task_full = ALL_TASKS[task_id]
+    
+    # Load dataset
+    dataset = Dataset(str(processed_dir), cache=True, task=task_full)
+    
+    # Reshape X to (n_trials, n_channels, n_timesteps)
+    X = dataset.X.reshape(dataset.X.shape[0], 32, -1)
+    Y = dataset.Y
+    ids = dataset.ids
+    
+    return X, Y, ids
+
+
+# ============================================================================
+# CONVENIENCE FUNCTIONS
+# ============================================================================
+
+def get_subjects(ids):
+    """Get unique subject IDs from ids array."""
+    import numpy as np
+    return np.unique(ids[:, 0])
+
+
+def split_by_subject(X, Y, ids, test_subject):
+    """
+    Split data by subject for leave-one-subject-out cross-validation.
+    
+    Returns:
+        (X_train, Y_train), (X_test, Y_test)
+    """
+    import numpy as np
+    test_mask = ids[:, 0] == test_subject
+    train_mask = ~test_mask
+    
+    return (X[train_mask], Y[train_mask]), (X[test_mask], Y[test_mask])
+
+
+def split_random(X, Y, test_size=0.2, random_state=42):
+    """
+    Random train/test split.
+    
+    Returns:
+        (X_train, Y_train), (X_test, Y_test)
+    """
+    import numpy as np
+    np.random.seed(random_state)
+    n = len(X)
+    indices = np.random.permutation(n)
+    test_n = int(n * test_size)
+    
+    test_idx = indices[:test_n]
+    train_idx = indices[test_n:]
+    
+    return (X[train_idx], Y[train_idx]), (X[test_idx], Y[test_idx])

EEGFaceSem/benchmark.py

@@ -0,0 +1,300 @@
+# benchmark.py
+import argparse
+import logging
+import sys
+import os
+import pickle
+import numpy as np
+from sklearn.model_selection import KFold
+from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
+from copy import deepcopy
+
+# --- Import all models ---
+from .models import (
+    BaseModel, 
+    LDAModel, 
+    LRModel, 
+    MLPModel, 
+    EEGNetModel,
+    EEGPTLinear
+)
+from .download import get_data_dir
+
+# --- Constants ---
+
+all_tasks = ['facecat/female', 'facecat/male', 'facecat/blond', 'facecat/darkhaired', 'facecat/smiles', 'facecat/nosmile', 'facecat/old', 'facecat/young']
+
+def get_default_data_path():
+    """Get default data path from package cache or current directory."""
+    data_dir = get_data_dir()
+    processed = data_dir / "data" / "processed"
+    if processed.exists():
+        return str(processed)
+    return './data/processed/'
+# N_SUBJECTS = 30
+N_TASKS = len(all_tasks)
+# TRIALS_PER_TASK_SUBJECT = 280
+# TOTAL_TRIALS = N_SUBJECTS * N_TASKS * TRIALS_PER_TASK_SUBJECT # 67200
+N_CHANNELS = 32  # 32 channels
+N_TIMESTEPS = 1101 # 1101 time points [-0.2, 0.9]s with 1000Hz sampling rate
+
+# --- 1. Data Loading ---
+
+from .utils import Dataset
+def load_data(task_id=-1, data_path='./data/processed/'):
+    """
+    Dataset loader will return:
+    X: (n_trials, n_channels, n_timesteps) - The epoch data.
+    Y_binary: (n_trials,) - The binary label (0 or 1).
+    ids: (n_trials, 5) - Metadata array:
+         [subject_idx, task_idx, trial_idx, label, image_idx]
+    """
+    print(f"--- Loading task_id={task_id} ---")
+    # Z, Y, D = load_from_processed(data_path, cache=True)
+    if task_id == -1:
+        task = None
+    else:
+        task = all_tasks[task_id]
+    dataset = Dataset(data_path, cache=True, chs=N_CHANNELS, samples=N_TIMESTEPS, task=task)
+    return dataset.X, dataset.Y, dataset.ids
+
+# --- 2. Logging Setup ---
+
+def setup_logging(log_file):
+    """Configures logging to both file and console."""
+    for handler in logging.root.handlers[:]:
+        logging.root.removeHandler(handler)
+        
+    format='[%(asctime)s %(levelname)-8s %(thread)-6d %(filename)s:%(lineno)d]: %(message)s'
+    logging.basicConfig(
+        level=logging.INFO,
+        format=format,
+        handlers=[
+            logging.FileHandler(log_file, mode='a'),
+            logging.StreamHandler(sys.stdout)
+        ]
+    )
+    return logging.getLogger()
+
+# --- 3. Helper Functions ---
+
+def initialize_model(args, n_classes, input_shape):
+    """Factory function to create the model instance."""
+    
+    model_kwargs = {
+        'lr': args.lr,
+        'batch_size': args.batch_size,
+        'epochs': args.epochs
+    }
+    
+    if args.model == 'LDA':
+        model_class = LDAModel
+    elif args.model == 'LR':
+        model_class = LRModel
+    elif args.model == 'MLP':
+        model_class = MLPModel
+    elif args.model == 'EEGNet':
+        model_class = EEGNetModel
+    elif args.model == 'EEGPT':
+        model_kwargs['eegpt_load_path'] = args.eegpt_load_path
+        model_class = EEGPTLinear
+    else:
+        raise ValueError(f"Unknown model type: {args.model}")
+    return model_class(n_classes=n_classes, input_shape=input_shape, **model_kwargs)
+
+def evaluate_predictions(Y_test, Y_prob, n_classes=2):
+    """Calculates accuracy and AUC from probabilities."""
+    if n_classes == 2:
+        Y_pred = (Y_prob[:, 1] > 0.5).astype(int)
+        try:
+            auc = roc_auc_score(Y_test, Y_prob[:, 1])
+        except ValueError:
+            auc = 0.5 # Handle case where only one class is present
+        f1 = f1_score(Y_test, Y_pred, average='macro')
+    else:
+        Y_pred = np.argmax(Y_prob, axis=1)
+        try:
+            auc = roc_auc_score(Y_test, Y_prob, multi_class='ovr')
+        except ValueError:
+            auc = 0.5
+        f1 = f1_score(Y_test, Y_pred, average='macro')
+    
+    acc = accuracy_score(Y_test, Y_pred)
+    return acc, auc, f1
+
+# --- 4. Core Experiment Logic ---
+
+def run_experiment(args, logger):
+    logger.info(f"--- New Job Started --- \nArgs: {vars(args)}")
+
+    # --- 4.1. Load Data ---
+    X, Y_binary, ids = load_data(task_id=args.task_id, data_path=args.data_path)
+    logger.info(f"Loaded data with shapes: X:{X.shape}, Y:{Y_binary.shape}, ids:{ids.shape}")
+    X = X.reshape(X.shape[0], -1)
+    
+    input_shape = (N_CHANNELS, N_TIMESTEPS) # (n_channels, n_timesteps)
+
+    # --- 4.2. Define Task Labels ---
+    if args.experiment == 'relevance':
+        Y = Y_binary
+        n_classes = 2
+    elif args.experiment == 'task':
+        Y = ids[:, 1]  # task_idx (0-7)
+        n_classes = N_TASKS
+    elif args.experiment == 'joint':
+        Y = ids[:, 1] * 2 + Y_binary  # 16-class (0-15)
+        n_classes = N_TASKS * 2
+    else:
+        logger.error(f"Unknown experiment type: {args.experiment}"); return
+
+    # --- 4.3. Execute Strategy ---
+    
+    subject_ids = np.unique(ids[:, 0])
+    results = {}
+    if args.strategy == 'single_subject':
+        for subject_id in subject_ids:
+            logger.info(f"Running Subject-Specific ({args.k_folds}-fold CV) for subject: {subject_id}")
+            subject_mask = (ids[:, 0] == subject_id)
+            X_subject, Y_subject, ids_subject = X[subject_mask], Y[subject_mask], ids[subject_mask]
+
+            kf = KFold(n_splits=args.k_folds, shuffle=True, random_state=42)
+
+            for fold, (train_idx, test_idx) in enumerate(kf.split(X_subject)):
+                # Get fold data
+                X_train, Y_train = X_subject[train_idx], Y_subject[train_idx]
+                X_test, Y_test, ids_test = X_subject[test_idx], Y_subject[test_idx], ids_subject[test_idx]
+                
+                # # Create validation split from training data
+                # val_split_size = max(1, int(0.15 * len(X_train_valid)))
+                # ids = np.arange(len(X_train_valid))
+                # np.random.shuffle(ids)
+                # X_train, Y_train = X_train_valid[ids[val_split_size:]], Y_train_valid[ids[val_split_size:]]
+                # X_valid, Y_valid = X_train_valid[ids[:val_split_size]], Y_train_valid[ids[:val_split_size]]
+
+                try:
+                    # Initialize a new model for each fold
+                    model = initialize_model(args, n_classes, input_shape)
+                    model.fit(X_train, Y_train)
+                    
+                    y_prob = model.predict_proba(X_test)
+                    acc, auc, f1 = evaluate_predictions(Y_test, y_prob, n_classes)
+                    results[(subject_id, fold)] = (y_prob, test_idx, ids_test, (acc, auc, f1))
+                    
+                    logger.info(f"RESULT, exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{subject_id}, fold:{fold}, model:{args.model}, acc:{acc:.4f}, auc:{auc:.4f}, f1:{f1:.4f}")
+                except Exception as e:
+                    logger.error(f"Failed exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{subject_id}, fold:{fold}, model:{args.model}: {e}", exc_info=True)
+    elif args.strategy == 'cross_subject' or args.strategy == 'subject_adapted':
+        for test_subject_id in subject_ids:
+            if args.k_folds == -1:
+                args.k_folds = 1
+                args.fold = 0
+            if test_subject_id % args.k_folds != args.fold:
+                continue
+            logger.info(f"Running Subject-Independent (LOSO) for test subject: {test_subject_id}")
+
+            test_mask = (ids[:, 0] == test_subject_id)
+            train_mask = (ids[:, 0] != test_subject_id)
+
+            X_train, Y_train = X[train_mask], Y[train_mask]
+            X_test, Y_test, ids_test = X[test_mask], Y[test_mask], ids[test_mask]
+            
+            try:
+                model = initialize_model(args, n_classes, input_shape)
+                model.fit(X_train, Y_train)
+                
+                y_prob = model.predict_proba(X_test)
+                acc, auc, f1 = evaluate_predictions(Y_test, y_prob, n_classes)
+                if args.strategy == 'cross_subject':
+                    results[test_subject_id] = (y_prob, test_mask, ids_test, (acc, auc, f1))
+
+                logger.info(f"RESULT, exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{test_subject_id}, model:{args.model}, acc:{acc:.4f}, auc:{auc:.4f}, f1:{f1:.4f}")
+            except Exception as e:
+                logger.error(f"Failed exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{test_subject_id}, model:{args.model}: {e}", exc_info=True)
+            
+            if args.strategy == 'subject_adapted':
+                kf = KFold(n_splits=args.k_folds, shuffle=True, random_state=42)
+                X_subject, Y_subject, ids_subject = X[test_mask], Y[test_mask], ids[test_mask]
+                for fold, (train_idx, test_idx) in enumerate(kf.split(X_test)):
+                    # Get fold data
+                    X_ft_train, Y_ft_train = X_subject[train_idx], Y_subject[train_idx]
+                    X_ft_test, Y_ft_test, ids_ft_test = X_subject[test_idx], Y_subject[test_idx], ids_subject[test_idx]
+                    
+                    try:
+                        ft_model = initialize_model(args, n_classes, input_shape)
+                        ft_model.copy_from(model)
+                        ft_model.fit(X_ft_train, Y_ft_train)
+                        
+                        y_prob = ft_model.predict_proba(X_ft_test)
+                        acc, auc, f1 = evaluate_predictions(Y_ft_test, y_prob, n_classes)
+                        results[(test_subject_id, fold)] = (y_prob, test_idx, ids_ft_test, (acc, auc, f1))
+                        logger.info(f"RESULT, exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{test_subject_id}, fold:{fold}, model:{args.model}, acc:{acc:.4f}, auc:{auc:.4f}, f1:{f1:.4f}")
+
+                    except Exception as e:
+                        logger.error(f"Failed exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, subject:{test_subject_id}, fold:{fold}, model:{args.model}: {e}", exc_info=True)
+                        continue
+
+    
+    os.makedirs(f'./results/', exist_ok=True)
+    pickle.dump(results, open(f'./results/results_{args.prefix}_{args.experiment}_{args.strategy}_{args.model}_task{args.task_id}_fold{args.fold}.pkl', 'wb'))
+    # summarize results
+    metrics = np.array([v[3] for v in results.values()])
+    if args.strategy == 'single_subject':
+        metrics = metrics.reshape(-1, args.k_folds, 3)
+        metrics = np.mean(metrics, axis=1)
+    mean_acc, mean_auc, mean_f1 = np.mean(metrics, axis=0)
+    std_acc, std_auc, std_f1 = np.std(metrics, axis=0)
+    logger.info(f"SUMMARY, exp:{args.experiment}, strat:{args.strategy}, task_id:{args.task_id}, model:{args.model}, acc:{mean_acc:.4f} +/- {std_acc:.4f}, auc:{mean_auc:.4f} +/- {std_auc:.4f}, f1:{mean_f1:.4f} +/- {std_f1:.4f}")
+    logger.info(f"--- Job Finished ---")
+
+def get_args():
+    parser = argparse.ArgumentParser(description="Run EEG Dataset Benchmark Experiments")
+    
+    # --- Experiment Selection ---
+    parser.add_argument('--experiment', type=str, default='relevance',
+                        choices=['relevance', 'task', 'joint'],
+                        help="The benchmark task to run.")
+    
+    parser.add_argument('--strategy', type=str, default='single_subject',
+                        choices=['single_subject', 'cross_subject', 'subject_adapted'],
+                        help="The training strategy to use.")
+    
+    parser.add_argument('--model', type=str, default='LDA', choices=['LDA', 'LR', 'MLP', 'EEGNet', 'EEGPT'], help="The classifier model to evaluate.")
+    
+    # --- Configuration ---
+    parser.add_argument("--prefix", type=str, default="", help="dir_prefix")
+    parser.add_argument('--task_id', type=int, default=-1, help="Task ID (0-7) to run. Or -1 for all tasks combined.")
+    parser.add_argument('--k_folds', type=int, default=-1, help="Number of folds for cross-validation. ")
+    parser.add_argument('--fold', type=int, default=-1, help="index of fold for cross-validation.")
+    parser.add_argument('--data_path', type=str, default=None, help="Path to the preprocessed dataset. Default: auto-detect.")
+    parser.add_argument('--epochs', type=int, default=100, help="Max epochs.")
+    parser.add_argument('--batch_size', type=int, default=32, help="Batch size.")
+    parser.add_argument('--lr', type=float, default=0.001, help="Learning rate.")
+    parser.add_argument('--eegpt_load_path', type=str, default='models/eegpt_mcae_58chs_4s_large4E.ckpt', help="Path to the pre-trained EEGPT checkpoint.")
+
+    args = parser.parse_args()
+    return args
+
+def benchmark(model, **kwargs):
+    args = get_args()
+    args.model = model
+    for k, v in kwargs.items():
+        setattr(args, k, v)
+    # Use default data path if not specified
+    if args.data_path is None:
+        args.data_path = get_default_data_path()
+    os.makedirs(f'./logs/', exist_ok=True)
+    logger = setup_logging(f'./logs/log_{args.prefix}_{args.experiment}_{args.strategy}_{args.model}.log')
+    run_experiment(args, logger)
+
+if __name__ == "__main__":
+    args = get_args()
+
+    # --- Setup and Run ---
+    os.makedirs('./logs/', exist_ok=True)
+    log_file = f'./logs/log_{args.prefix}_{args.experiment}_{args.strategy}_{args.model}.log'
+    logger = setup_logging(log_file)
+
+    if args.strategy == 'subject_adapted' and args.model in ['LDA', 'LR']:
+        logger.warning(f"Subject adaption not supported for {args.model}. Skipping.")
+    else:
+        run_experiment(args, logger)

EEGFaceSem/download.py

@@ -0,0 +1,205 @@
+"""
+Download EEGFaceSem data from HuggingFace.
+"""
+
+import os
+from pathlib import Path
+
+REPO_ID = "yefllower/EEGFaceSem"
+DEFAULT_DATA_DIR = Path.home() / ".cache" / "EEGFaceSem"
+
+
+def get_data_dir(data_dir=None):
+    """Get the data directory, creating if needed."""
+    if data_dir is None:
+        data_dir = DEFAULT_DATA_DIR
+    data_dir = Path(data_dir)
+    data_dir.mkdir(parents=True, exist_ok=True)
+    return data_dir
+
+
+def is_downloaded(data_dir, data_type="processed"):
+    """Check if data is already downloaded."""
+    data_dir = Path(data_dir)
+    if data_type == "raw":
+        # Check for at least one raw file
+        return (data_dir / "data" / "raw" / "01.vhdr").exists()
+    else:  # processed
+        return (data_dir / "data" / "processed" / "01-epo.fif").exists()
+
+
+def download(data_dir=None, data_type="processed", subjects=None, force=False):
+    """
+    Download EEGFaceSem data from HuggingFace.
+    
+    Args:
+        data_dir: Where to save data. Default: ~/.cache/EEGFaceSem
+        data_type: "raw", "processed", or "both". Default: "processed"
+        subjects: List of subject IDs (1-30) to download. None = all subjects.
+                  Example: subjects=[1] downloads only subject 01.
+        force: Re-download even if files exist. Default: False
+    
+    Returns:
+        Path to the data directory
+    
+    Example:
+        >>> import EEGFaceSem
+        >>> EEGFaceSem.download()  # Downloads all processed data
+        >>> EEGFaceSem.download(subjects=[1, 2])  # Only subjects 01 and 02
+    """
+    try:
+        from huggingface_hub import snapshot_download
+    except ImportError:
+        raise ImportError(
+            "huggingface_hub is required for downloading. "
+            "Install with: pip install huggingface_hub"
+        )
+    
+    data_dir = get_data_dir(data_dir)
+    
+    # Check if already downloaded (only if downloading all subjects)
+    if subjects is None and not force and is_downloaded(data_dir, data_type):
+        print(f"Data already exists at {data_dir}. Use force=True to re-download.")
+        return data_dir
+    
+    # Build patterns for which files to download
+    allow_patterns = []
+    
+    # If specific subjects requested, build patterns for those subjects
+    if subjects is not None:
+        subject_patterns = [f"{s:02d}" for s in subjects]
+        
+        if data_type in ["raw", "both"]:
+            for sp in subject_patterns:
+                allow_patterns.extend([
+                    f"data/raw/{sp}.eeg",
+                    f"data/raw/{sp}.vhdr", 
+                    f"data/raw/{sp}.vmrk"
+                ])
+        
+        if data_type in ["processed", "both"]:
+            for sp in subject_patterns:
+                allow_patterns.extend([
+                    f"data/processed/{sp}-epo.fif",
+                    f"data/processed/{sp}-ev2img.pkl"
+                ])
+        
+        print(f"Downloading EEGFaceSem {data_type} data for subjects {subjects}...")
+    else:
+        # Download all subjects
+        if data_type in ["raw", "both"]:
+            allow_patterns.extend([
+                "data/raw/*.eeg",
+                "data/raw/*.vhdr", 
+                "data/raw/*.vmrk"
+            ])
+        
+        if data_type in ["processed", "both"]:
+            allow_patterns.extend([
+                "data/processed/*-epo.fif",
+                "data/processed/*-ev2img.pkl"
+            ])
+        
+        print(f"Downloading EEGFaceSem {data_type} data (all subjects)...")
+    
+    print(f"Destination: {data_dir}")
+    
+    snapshot_download(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        local_dir=str(data_dir),
+        allow_patterns=allow_patterns,
+    )
+    
+    print(f"Download complete! Data saved to: {data_dir}")
+    return data_dir
+
+
+def download_models(data_dir=None, force=False):
+    """
+    Download pretrained models (Progressive GAN, EEGPT) from HuggingFace.
+    
+    Args:
+        data_dir: Where to save models. Default: ~/.cache/EEGFaceSem
+        force: Re-download even if files exist.
+    
+    Returns:
+        Path to the models directory
+    """
+    try:
+        from huggingface_hub import snapshot_download
+    except ImportError:
+        raise ImportError(
+            "huggingface_hub is required. Install with: pip install huggingface_hub"
+        )
+    
+    data_dir = get_data_dir(data_dir)
+    models_dir = data_dir / "models"
+    
+    # Check if already downloaded
+    if not force and (models_dir / "karras2018iclr-celebahq-1024x1024.pkl").exists():
+        print(f"Models already exist at {models_dir}. Use force=True to re-download.")
+        return models_dir
+    
+    print("Downloading pretrained models from HuggingFace...")
+    
+    snapshot_download(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        local_dir=str(data_dir),
+        allow_patterns=["models/*"],
+    )
+    
+    print(f"Models saved to: {models_dir}")
+    return models_dir
+
+
+def download_latents(data_dir=None, force=False):
+    """
+    Download latent vectors for stimulus images from HuggingFace.
+    
+    Args:
+        data_dir: Where to save. Default: ~/.cache/EEGFaceSem
+        force: Re-download even if files exist.
+    
+    Returns:
+        Path to the latents directory
+    """
+    try:
+        from huggingface_hub import snapshot_download
+    except ImportError:
+        raise ImportError(
+            "huggingface_hub is required. Install with: pip install huggingface_hub"
+        )
+    
+    data_dir = get_data_dir(data_dir)
+    latents_dir = data_dir / "data" / "latents"
+    
+    # Check if already downloaded
+    if not force and (latents_dir / "latent.pkl").exists():
+        print(f"Latents already exist at {latents_dir}. Use force=True to re-download.")
+        return latents_dir
+    
+    print("Downloading latent vectors from HuggingFace...")
+    
+    snapshot_download(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        local_dir=str(data_dir),
+        allow_patterns=["data/latents/*"],
+    )
+    
+    print(f"Latents saved to: {latents_dir}")
+    return latents_dir
+
+
+if __name__ == "__main__":
+    # Quick test
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_dir", default=None)
+    parser.add_argument("--type", default="processed", choices=["raw", "processed", "both"])
+    parser.add_argument("--force", action="store_true")
+    args = parser.parse_args()
+    
+    download(data_dir=args.data_dir, data_type=args.type, force=args.force)

EEGFaceSem/generation.py

@@ -0,0 +1,112 @@
+import os
+import sys
+import pickle
+import numpy as np
+import PIL.Image
+
+# --- Globals ---
+_G = None
+_D = None
+_Gs = None
+
+def load_pgan_model(model_path):
+    """
+    Loads the pretrained Progressive GAN model from a pickle file.
+
+    This function temporarily modifies sys.path to allow the pickle file
+    to find its required modules (networks, legacy, etc.) which are
+    encapsulated inside the .pgan submodule.
+    """
+    global _G, _D, _Gs
+    if _Gs is not None:
+        return _G, _D, _Gs
+
+    pgan_dir = os.path.dirname(os.path.abspath(__file__)) + '/pgan'
+    
+    # Temporarily add the pgan directory to sys.path
+    need_to_remove = False
+    if pgan_dir not in sys.path:
+        sys.path.insert(0, pgan_dir)
+        need_to_remove = True
+    
+    try:
+        print("Loading Progressive GAN model...")
+        import tensorflow.compat.v1 as tf
+        tf.disable_v2_behavior() 
+        # tf.InteractiveSession()
+        sess = tf.compat.v1.InteractiveSession()
+        _G, _D, _Gs = pickle.load(open(model_path, 'rb'))
+    finally:
+        # Always remove the path modification, even if an error occurs.
+        if pgan_dir in sys.path and need_to_remove:
+            sys.path.remove(pgan_dir)
+            
+    return _G, _D, _Gs
+
+def generate_images(latent_vectors, Gs_model, batch_size=10):
+    """Generates images from a batch of latent vectors."""
+    imgs = []
+    for i in range(0, latent_vectors.shape[0], batch_size):
+        batch_vectors = latent_vectors[i:i+batch_size]
+        labels = np.zeros([batch_vectors.shape[0]] + Gs_model.input_shapes[1][1:])
+        
+        images = Gs_model.run(batch_vectors, labels)
+        images = np.clip(np.rint((images + 1.0) / 2.0 * 255.0), 0.0, 255.0).astype(np.uint8)
+        images = images.transpose(0, 2, 3, 1) # NCHW => NHWC
+        
+        imgs.extend([PIL.Image.fromarray(img) for img in images])
+    return imgs
+
+def generate_image(latent_vector, Gs_model):
+    """Generates a single image from a 512-dimensional latent vector."""
+    if latent_vector.ndim == 1:
+        latent_vector = np.expand_dims(latent_vector, axis=0)
+    images = generate_images(latent_vector, Gs_model, batch_size=1)
+    return images[0]
+
+def generate(vector, model_path=None):
+    """
+    Generate face images from latent vectors using Progressive GAN.
+    
+    Args:
+        vector: numpy array of shape (n, 512) or (512,) for single image
+        model_path: Path to PGAN model. Default: auto-detect from package.
+    
+    Returns:
+        List of PIL.Image objects
+    """
+    if model_path is None:
+        # Try to find model in package cache
+        from .download import get_data_dir
+        data_dir = get_data_dir()
+        model_path = data_dir / 'models' / 'karras2018iclr-celebahq-1024x1024.pkl'
+        if not model_path.exists():
+            # Try local path
+            model_path = 'models/karras2018iclr-celebahq-1024x1024.pkl'
+    
+    _, _, Gs = load_pgan_model(str(model_path))
+    if vector.ndim == 1:
+        vector = np.expand_dims(vector, axis=0)
+    images = generate_images(vector, Gs, batch_size=1)
+    return images
+
+if __name__ == "__main__":
+    # Example of how to use the generation functions as a script.
+    model_file = 'models/karras2018iclr-celebahq-1024x1024.pkl'
+    _, _, Gs = load_pgan_model(model_file)
+    
+    random_vector = np.random.randn(1, 512)
+    image = generate_image(random_vector, Gs)
+    
+    output_path = "generated_sample_from_package.png"
+    image.save(output_path)
+    print(f"Image saved to {output_path}")
+
+    random_vector = np.random.randn(4, 512)
+    images = generate_images(random_vector, Gs)
+    for i, img in enumerate(images):
+        output_path = f"generated_sample_from_package_{i}.png"
+        img.save(output_path)
+        print(f"Image {i} saved to {output_path}")
+
+

EEGFaceSem/models.py

@@ -0,0 +1,414 @@
+# models.py
+import numpy as np
+from abc import ABC, abstractmethod
+
+# --- Sklearn Imports ---
+from sklearn.preprocessing import StandardScaler
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.linear_model import LogisticRegression
+from sklearn.decomposition import PCA
+
+# --- 1. Abstract Base Class ---
+
+class BaseModel(ABC):
+    """
+    Abstract Base Class for all benchmark models.
+    It ensures that every model has a .fit() and .predict_proba() method,
+    allowing the main benchmark script to treat all models polymorphically.
+    """
+    def __init__(self, n_classes, input_shape, **kwargs):
+        """
+        Initialize the model.
+        :param n_classes: Number of output classes (e.g., 2 for binary, 8 for task-ID).
+        :param input_shape: Shape of a single sample, e.g., (n_channels, n_timesteps).
+        :param kwargs: Model-specific hyperparameters (e.g., lr, batch_size, C).
+        """
+        self.n_classes = n_classes
+        self.input_shape = input_shape # e.g., (32, 500)
+        self.n_chans, self.n_samples = self.input_shape
+        self.model = None
+        self.kwargs = kwargs # Store all other hyperparams
+        self.scaler = None # For models that need scaling
+
+    @abstractmethod
+    def fit(self, X_train, Y_train):
+        """
+        Train the model.
+        :param X_train: (n_samples, n_channels, n_timesteps)
+        :param Y_train: (n_samples,) - integer labels
+        """
+        pass
+
+    @abstractmethod
+    def predict_proba(self, X_test):
+        """
+        Get class probabilities for test data.
+        :param X_test: (n_test_samples, n_channels, n_timesteps)
+        :return: (n_test_samples, n_classes) array of probabilities.
+        """
+        pass
+
+    @abstractmethod
+    def get_model_name(self):
+        """Return a string name for logging."""
+        pass
+
+    @abstractmethod
+    def copy_from(self, other_model):
+        """Copy weights from another model."""
+        pass
+
+    @staticmethod
+    def validation_split(X, Y, val_size=0.15):
+        """Split data into train and validation sets."""
+        val_split_size = max(1, int(val_size * len(X)))
+        ids = np.arange(len(X))
+        np.random.shuffle(ids)
+        X_train, Y_train = X[ids[val_split_size:]], Y[ids[val_split_size:]]
+        X_valid, Y_valid = X[ids[:val_split_size]], Y[ids[:val_split_size]]
+        return X_train, Y_train, X_valid, Y_valid
+
+
+# --- 2. Sklearn Model Implementations ---
+
+class LDAModel(BaseModel):
+    """Linear Discriminant Analysis Implementation"""
+
+    def fit(self, X_train, Y_train):
+        # 1. Flatten data
+        n_samples = X_train.shape[0]
+        X_train_flat = X_train.reshape(n_samples, -1)
+        
+        # 2. Scale data
+        self.scaler = StandardScaler()
+        X_train_scaled = self.scaler.fit_transform(X_train_flat)
+        
+        # 3. Fit model
+        self.model = LinearDiscriminantAnalysis()
+        self.model.fit(X_train_scaled, Y_train) # Y_train is (n_samples,)
+
+    def predict_proba(self, X_test):
+        # 1. Flatten
+        n_samples = X_test.shape[0]
+        X_test_flat = X_test.reshape(n_samples, -1)
+        
+        # 2. Scale
+        X_test_scaled = self.scaler.transform(X_test_flat)
+        
+        # 3. Predict
+        probs = self.model.predict_proba(X_test_scaled)
+        
+        # Handle binary case where sklearn returns (n, 1)
+        if self.n_classes == 2 and probs.shape[1] == 1:
+            probs = np.hstack([1 - probs, probs])
+        return probs
+
+    def get_model_name(self):
+        return "LDA"
+    
+    def copy_from(self, other_model):
+        self.model = other_model.model
+        self.scaler = other_model.scaler
+
+class LRModel(BaseModel):
+    """Logistic Regression Implementation"""
+    
+    def fit(self, X_train, Y_train):
+        n_samples = X_train.shape[0]
+        X_train_flat = X_train.reshape(n_samples, -1)
+        
+        self.scaler = StandardScaler()
+        X_train_scaled = self.scaler.fit_transform(X_train_flat)
+
+        self.pca = PCA(n_components=0.95, svd_solver='auto', random_state=42)
+        X_train_pca = self.pca.fit_transform(X_train_scaled)
+        
+        self.model = LogisticRegression(C=self.kwargs.get('C', 1.0), solver='lbfgs', max_iter=1000, random_state=42)
+        self.model.fit(X_train_pca, Y_train)
+
+    def predict_proba(self, X_test):
+        n_samples = X_test.shape[0]
+        X_test_flat = X_test.reshape(n_samples, -1)
+        X_test_scaled = self.scaler.transform(X_test_flat)
+        X_test_pca = self.pca.transform(X_test_scaled)
+        return self.model.predict_proba(X_test_pca)
+
+    def get_model_name(self):
+        return "LogisticRegression"
+    
+    def copy_from(self, other_model):
+        self.model = other_model.model
+        self.pca = other_model.pca
+        self.scaler = other_model.scaler
+
+# --- 3. Deep Learning Model Implementations ---
+
+class MLPModel(BaseModel):
+    """Simple Multi-Layer Perceptron (MLP) Implementation"""
+
+    def __init__(self, n_classes, input_shape, **kwargs):
+        import tensorflow as tf
+        from tensorflow.keras.models import Sequential
+        from tensorflow.keras.layers import Dense, Dropout, Input
+        super().__init__(n_classes, input_shape, **kwargs)
+        self.scaler = StandardScaler()
+        self.lr = kwargs.get('lr', 0.001)
+        self.batch_size = kwargs.get('batch_size', 32)
+        self.epochs = kwargs.get('epochs', 100)
+        flat_shape = np.prod(self.input_shape)
+        
+        self.model = Sequential([
+            Input(shape=(flat_shape,)),
+            Dense(128, activation='relu'),
+            Dropout(0.5),
+            Dense(64, activation='relu'),
+            Dropout(0.3),
+            Dense(self.n_classes, activation='softmax')
+        ])
+        
+        loss = 'categorical_crossentropy' if self.n_classes > 2 else 'binary_crossentropy'
+        if self.n_classes == 2:
+            loss = 'binary_crossentropy'
+            self.model.pop()
+            self.model.add(Dense(1, activation='sigmoid'))
+        else:
+            loss = 'categorical_crossentropy'
+
+        self.model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=self.lr),
+            loss=loss,
+            metrics=['accuracy']
+        )
+    
+    def fit(self, X_train, Y_train):
+        import tensorflow as tf
+        from tensorflow.keras.utils import to_categorical
+        from tensorflow.keras.callbacks import EarlyStopping
+        from EEGModels import EEGNet # From the eegmodels package
+        X_train, Y_train, X_valid, Y_valid = self.validation_split(X_train, Y_train)
+        X_train_flat = X_train.reshape(X_train.shape[0], -1)
+        X_train_scaled = self.scaler.fit_transform(X_train_flat)
+        if self.n_classes == 2:
+            Y_train_fmt = Y_train # (n_samples,)
+        else:
+            Y_train_fmt = to_categorical(Y_train, num_classes=self.n_classes)
+            
+        callbacks = [EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)]
+        
+        fit_kwargs = {
+            "batch_size": self.batch_size,
+            "epochs": self.epochs,
+            "verbose": 0,
+            "callbacks": callbacks
+        }
+        
+        X_valid_flat = X_valid.reshape(X_valid.shape[0], -1)
+        X_valid_scaled = self.scaler.transform(X_valid_flat)
+        if self.n_classes == 2:
+            Y_valid_fmt = Y_valid
+        else:
+            Y_valid_fmt = to_categorical(Y_valid, num_classes=self.n_classes)
+        fit_kwargs["validation_data"] = (X_valid_scaled, Y_valid_fmt)
+        
+        self.model.fit(X_train_scaled, Y_train_fmt, **fit_kwargs)
+
+    def predict_proba(self, X_test):
+        X_test_flat = X_test.reshape(X_test.shape[0], -1)
+        X_test_scaled = self.scaler.transform(X_test_flat)
+        probs = self.model.predict(X_test_scaled)
+        
+        if self.n_classes == 2:
+            # probs is (n, 1), reshape to (n, 2)
+            return np.hstack([1 - probs, probs])
+        else:
+            return probs
+
+    def get_model_name(self):
+        return "MLP"
+    
+    def copy_from(self, other_model):
+        # copy model weights
+        self.model.set_weights(other_model.model.get_weights())
+        # copy scalers
+        self.scaler = other_model.scaler
+
+class EEGNetModel(BaseModel):
+    """EEGNet"""
+    
+    def __init__(self, n_classes, input_shape, **kwargs):
+        import tensorflow as tf
+        from EEGModels import EEGNet
+        super().__init__(n_classes, input_shape, **kwargs)
+        self.lr = kwargs.get('lr', 0.001)
+        self.n_chans, self.n_samples = self.input_shape
+        self.scaler = StandardScaler()
+        self.model = EEGNet(
+            nb_classes=self.n_classes,
+            Chans=self.n_chans,
+            Samples=self.n_samples,
+            dropoutRate=self.kwargs.get('dropoutRate', 0.5),
+            kernLength=self.kwargs.get('kernLength', 64),
+            F1=self.kwargs.get('F1', 8),
+            D=self.kwargs.get('D', 2),
+            F2=self.kwargs.get('F2', 16),
+            dropoutType='Dropout'
+        )
+
+    def fit(self, X_train, Y_train):
+        import tensorflow as tf
+        from tensorflow.keras.utils import to_categorical
+        from tensorflow.keras.callbacks import EarlyStopping
+        X_train, Y_train, X_valid, Y_valid = self.validation_split(X_train, Y_train)
+        loss = 'categorical_crossentropy'
+        # EEGNet multiclass output is softmax. For binary, we must also use categorical.
+        Y_train_fmt = to_categorical(Y_train, num_classes=self.n_classes)
+        # for i in range(X_train.shape[1]):
+        #     X_train[:, i] = self.scalers[i].fit_transform(X_train[:, i])
+        #     X_valid[:, i] = self.scalers[i].transform(X_valid[:, i])
+        X_train = self.scaler.fit_transform(X_train)
+        
+        self.model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=self.lr),
+            loss=loss,
+            metrics=['accuracy'] # Use 'accuracy' for categorical
+        )
+        
+        # Reshape X to (batch, chans, samples, 1) for EEGNet
+        X_train_fmt = X_train.reshape(X_train.shape[0], self.n_chans, self.n_samples, 1)
+        
+        callbacks = [EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=True)]
+        
+        fit_kwargs = {
+            "batch_size": self.kwargs.get('batch_size', 32),
+            "epochs": self.kwargs.get('epochs', 100),
+            "verbose": 0,
+            "callbacks": callbacks
+        }
+        
+        X_valid = self.scaler.transform(X_valid)
+        X_valid_fmt = X_valid.reshape(X_valid.shape[0], self.n_chans, self.n_samples, 1)
+        Y_valid_fmt = to_categorical(Y_valid, num_classes=self.n_classes)
+        fit_kwargs["validation_data"] = (X_valid_fmt, Y_valid_fmt)
+        
+        self.model.fit(X_train_fmt, Y_train_fmt, **fit_kwargs)
+
+    def predict_proba(self, X_test):# (32, 1101)
+        X_test = self.scaler.transform(X_test)
+        # for i in range(X_test.shape[1]):
+        #     X_test[:, i] = self.scalers[i].transform(X_test[:, i])
+        X_test_fmt = X_test.reshape(X_test.shape[0], self.n_chans, self.n_samples, 1)
+        return self.model.predict(X_test_fmt)
+
+    def get_model_name(self):
+        return "EEGNet"
+    
+    def copy_from(self, other_model):
+        # copy model weights
+        self.model.set_weights(other_model.model.get_weights())
+        # copy scalers
+        self.scaler = other_model.scaler
+
+# --- 4. EEGPT ---
+
+class EEGPTLinear(BaseModel):
+    """
+    EEGPT Model with a Linear Head.
+    The EEGPT model is loaded ONCE.
+    """
+    def __init__(self, n_classes, input_shape, **kwargs):
+        super().__init__(n_classes, input_shape, **kwargs)
+        self.lr = kwargs.get('lr', 0.001)
+        self.batch_size = kwargs.get('batch_size', 32)
+        self.num_epochs = kwargs.get('epochs', 100)
+        from .EEGPT import EEGPT_InternalModel
+        import torch
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = EEGPT_InternalModel(n_classes, kwargs.get('eegpt_load_path', None), input_shape).to(self.device)
+        
+        # 3. We also need a scaler for the embeddings
+        self.scaler = StandardScaler()
+
+    def fit(self, X_train, Y_train, verbose=False, validation=False):
+        import torch
+        if validation:
+            X_train, Y_train, X_valid, Y_valid = self.validation_split(X_train, Y_train)
+        # Y_train_fmt = to_categorical(Y_train, num_classes=self.n_classes)
+        X_train = self.scaler.fit_transform(X_train)
+        X_train = X_train.reshape(X_train.shape[0], self.n_chans, self.n_samples)
+        train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(Y_train).long())
+        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
+
+        if validation:
+            X_valid = self.scaler.transform(X_valid)
+            X_valid = X_valid.reshape(X_valid.shape[0], self.n_chans, self.n_samples)
+            valid_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_valid).float(), torch.from_numpy(Y_valid).long())
+            valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=self.batch_size, shuffle=False)
+
+        optimizer = torch.optim.AdamW(self.model.trainable_parameters(), lr=self.lr, weight_decay=0.01)
+        scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=self.lr, steps_per_epoch=len(train_loader), epochs=self.num_epochs, pct_start=0.2)
+        loss_fn = torch.nn.CrossEntropyLoss()
+
+        self.model.train()
+        for epoch in range(self.num_epochs):
+            train_loss, train_acc = 0, 0
+            for X_batch, Y_batch in train_loader:
+                X_batch, Y_batch = X_batch.to(self.device), Y_batch.to(self.device)
+                
+                optimizer.zero_grad()
+                logits = self.model(X_batch)
+                loss = loss_fn(logits, Y_batch)
+                loss.backward()
+                optimizer.step()
+                scheduler.step()
+                train_loss += loss.item()
+                preds = torch.argmax(logits, dim=1)
+                train_acc += (preds == Y_batch).sum().item()
+            train_loss /= len(train_loader)
+            train_acc /= len(train_dataset)
+            
+            logstr = f"Epoch {epoch+1}/{self.num_epochs} | Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f}"
+            
+            if validation and (X_valid is not None and Y_valid is not None):
+                valid_loss, valid_acc = 0, 0
+                for X_batch, Y_batch in valid_loader:
+                    X_batch, Y_batch = X_batch.to(self.device), Y_batch.to(self.device)
+                    logits = self.model(X_batch)
+                    loss = loss_fn(logits, Y_batch)
+                    valid_loss += loss.item()
+                    preds = torch.argmax(logits, dim=1)
+                    valid_acc += (preds == Y_batch).sum().item()
+                valid_loss /= len(valid_loader)
+                valid_acc /= len(valid_dataset)
+                logstr += f" | Valid Loss: {valid_loss:.4f} | Valid Acc: {valid_acc:.4f}"
+
+            if verbose:
+                print(logstr)
+
+    def predict_proba(self, X_test):
+        import torch
+        X_test = self.scaler.transform(X_test)
+        X_test = X_test.reshape(X_test.shape[0], self.n_chans, self.n_samples)
+        X_test_tensor = torch.from_numpy(X_test).float().to(self.device)
+        test_dataset = torch.utils.data.TensorDataset(X_test_tensor)
+        test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=self.batch_size, shuffle=False)
+        self.model.eval()
+        all_logits = []
+        with torch.no_grad():
+            for X_batch in test_loader:
+                X_batch = X_batch[0].to(self.device)
+                logits = self.model(X_batch)
+                all_logits.append(logits.cpu().numpy())
+        
+        return np.concatenate(all_logits, axis=0)
+        # X_test_fmt = X_test.reshape(X_test.shape[0], self.n_chans, self.n_samples, 1)
+        # return self.model.predict(X_test_fmt)
+
+    def get_model_name(self):
+        return "EEGPT-Linear"
+    
+    def copy_from(self, other_model):
+        # copy model weights
+        # self.model.set_weights(other_model.model.get_weights())
+        self.model.copy_from(other_model.model)
+        # copy scalers
+        self.scaler = other_model.scaler

EEGFaceSem/pgan/config.py

@@ -0,0 +1,140 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+#----------------------------------------------------------------------------
+# Convenience class that behaves exactly like dict(), but allows accessing
+# the keys and values using the attribute syntax, i.e., "mydict.key = value".
+
+class EasyDict(dict):
+    def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
+    def __getattr__(self, name): return self[name]
+    def __setattr__(self, name, value): self[name] = value
+    def __delattr__(self, name): del self[name]
+
+#----------------------------------------------------------------------------
+# Paths.
+
+data_dir = 'datasets'
+result_dir = 'results'
+
+#----------------------------------------------------------------------------
+# TensorFlow options.
+
+tf_config = EasyDict()  # TensorFlow session config, set by tfutil.init_tf().
+env = EasyDict()        # Environment variables, set by the main program in train.py.
+
+tf_config['graph_options.place_pruned_graph']   = True      # False (default) = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
+#tf_config['gpu_options.allow_growth']          = False     # False (default) = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
+#env.CUDA_VISIBLE_DEVICES                       = '0'       # Unspecified (default) = Use all available GPUs. List of ints = CUDA device numbers to use.
+env.TF_CPP_MIN_LOG_LEVEL                        = '1'       # 0 (default) = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
+
+#----------------------------------------------------------------------------
+# Official training configs, targeted mainly for CelebA-HQ.
+# To run, comment/uncomment the lines as appropriate and launch train.py.
+
+desc        = 'pgan'                                        # Description string included in result subdir name.
+random_seed = 1000                                          # Global random seed.
+dataset     = EasyDict()                                    # Options for dataset.load_dataset().
+train       = EasyDict(func='train.train_progressive_gan')  # Options for main training func.
+G           = EasyDict(func='networks.G_paper')             # Options for generator network.
+D           = EasyDict(func='networks.D_paper')             # Options for discriminator network.
+G_opt       = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
+D_opt       = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
+G_loss      = EasyDict(func='loss.G_wgan_acgan')            # Options for generator loss.
+D_loss      = EasyDict(func='loss.D_wgangp_acgan')          # Options for discriminator loss.
+sched       = EasyDict()                                    # Options for train.TrainingSchedule.
+grid        = EasyDict(size='1080p', layout='random')       # Options for train.setup_snapshot_image_grid().
+
+# Dataset (choose one).
+desc += '-celebahq';            dataset = EasyDict(tfrecord_dir='celebahq'); train.mirror_augment = True
+#desc += '-celeba';              dataset = EasyDict(tfrecord_dir='celeba'); train.mirror_augment = True
+#desc += '-cifar10';             dataset = EasyDict(tfrecord_dir='cifar10')
+#desc += '-cifar100';            dataset = EasyDict(tfrecord_dir='cifar100')
+#desc += '-svhn';                dataset = EasyDict(tfrecord_dir='svhn')
+#desc += '-mnist';               dataset = EasyDict(tfrecord_dir='mnist')
+#desc += '-mnistrgb';            dataset = EasyDict(tfrecord_dir='mnistrgb')
+#desc += '-syn1024rgb';          dataset = EasyDict(class_name='dataset.SyntheticDataset', resolution=1024, num_channels=3)
+#desc += '-lsun-airplane';       dataset = EasyDict(tfrecord_dir='lsun-airplane-100k');       train.mirror_augment = True
+#desc += '-lsun-bedroom';        dataset = EasyDict(tfrecord_dir='lsun-bedroom-100k');        train.mirror_augment = True
+#desc += '-lsun-bicycle';        dataset = EasyDict(tfrecord_dir='lsun-bicycle-100k');        train.mirror_augment = True
+#desc += '-lsun-bird';           dataset = EasyDict(tfrecord_dir='lsun-bird-100k');           train.mirror_augment = True
+#desc += '-lsun-boat';           dataset = EasyDict(tfrecord_dir='lsun-boat-100k');           train.mirror_augment = True
+#desc += '-lsun-bottle';         dataset = EasyDict(tfrecord_dir='lsun-bottle-100k');         train.mirror_augment = True
+#desc += '-lsun-bridge';         dataset = EasyDict(tfrecord_dir='lsun-bridge-100k');         train.mirror_augment = True
+#desc += '-lsun-bus';            dataset = EasyDict(tfrecord_dir='lsun-bus-100k');            train.mirror_augment = True
+#desc += '-lsun-car';            dataset = EasyDict(tfrecord_dir='lsun-car-100k');            train.mirror_augment = True
+#desc += '-lsun-cat';            dataset = EasyDict(tfrecord_dir='lsun-cat-100k');            train.mirror_augment = True
+#desc += '-lsun-chair';          dataset = EasyDict(tfrecord_dir='lsun-chair-100k');          train.mirror_augment = True
+#desc += '-lsun-churchoutdoor';  dataset = EasyDict(tfrecord_dir='lsun-churchoutdoor-100k');  train.mirror_augment = True
+#desc += '-lsun-classroom';      dataset = EasyDict(tfrecord_dir='lsun-classroom-100k');      train.mirror_augment = True
+#desc += '-lsun-conferenceroom'; dataset = EasyDict(tfrecord_dir='lsun-conferenceroom-100k'); train.mirror_augment = True
+#desc += '-lsun-cow';            dataset = EasyDict(tfrecord_dir='lsun-cow-100k');            train.mirror_augment = True
+#desc += '-lsun-diningroom';     dataset = EasyDict(tfrecord_dir='lsun-diningroom-100k');     train.mirror_augment = True
+#desc += '-lsun-diningtable';    dataset = EasyDict(tfrecord_dir='lsun-diningtable-100k');    train.mirror_augment = True
+#desc += '-lsun-dog';            dataset = EasyDict(tfrecord_dir='lsun-dog-100k');            train.mirror_augment = True
+#desc += '-lsun-horse';          dataset = EasyDict(tfrecord_dir='lsun-horse-100k');          train.mirror_augment = True
+#desc += '-lsun-kitchen';        dataset = EasyDict(tfrecord_dir='lsun-kitchen-100k');        train.mirror_augment = True
+#desc += '-lsun-livingroom';     dataset = EasyDict(tfrecord_dir='lsun-livingroom-100k');     train.mirror_augment = True
+#desc += '-lsun-motorbike';      dataset = EasyDict(tfrecord_dir='lsun-motorbike-100k');      train.mirror_augment = True
+#desc += '-lsun-person';         dataset = EasyDict(tfrecord_dir='lsun-person-100k');         train.mirror_augment = True
+#desc += '-lsun-pottedplant';    dataset = EasyDict(tfrecord_dir='lsun-pottedplant-100k');    train.mirror_augment = True
+#desc += '-lsun-restaurant';     dataset = EasyDict(tfrecord_dir='lsun-restaurant-100k');     train.mirror_augment = True
+#desc += '-lsun-sheep';          dataset = EasyDict(tfrecord_dir='lsun-sheep-100k');          train.mirror_augment = True
+#desc += '-lsun-sofa';           dataset = EasyDict(tfrecord_dir='lsun-sofa-100k');           train.mirror_augment = True
+#desc += '-lsun-tower';          dataset = EasyDict(tfrecord_dir='lsun-tower-100k');          train.mirror_augment = True
+#desc += '-lsun-train';          dataset = EasyDict(tfrecord_dir='lsun-train-100k');          train.mirror_augment = True
+#desc += '-lsun-tvmonitor';      dataset = EasyDict(tfrecord_dir='lsun-tvmonitor-100k');      train.mirror_augment = True
+
+# Conditioning & snapshot options.
+#desc += '-cond'; dataset.max_label_size = 'full' # conditioned on full label
+#desc += '-cond1'; dataset.max_label_size = 1 # conditioned on first component of the label
+#desc += '-g4k'; grid.size = '4k'
+#desc += '-grpc'; grid.layout = 'row_per_class'
+
+# Config presets (choose one).
+#desc += '-preset-v1-1gpu'; num_gpus = 1; D.mbstd_group_size = 16; sched.minibatch_base = 16; sched.minibatch_dict = {256: 14, 512: 6, 1024: 3}; sched.lod_training_kimg = 800; sched.lod_transition_kimg = 800; train.total_kimg = 19000
+desc += '-preset-v2-1gpu'; num_gpus = 1; sched.minibatch_base = 4; sched.minibatch_dict = {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {1024: 0.0015}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
+#desc += '-preset-v2-2gpus'; num_gpus = 2; sched.minibatch_base = 8; sched.minibatch_dict = {4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8}; sched.G_lrate_dict = {512: 0.0015, 1024: 0.002}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
+#desc += '-preset-v2-4gpus'; num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}; sched.G_lrate_dict = {256: 0.0015, 512: 0.002, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
+#desc += '-preset-v2-8gpus'; num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}; sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
+
+# Numerical precision (choose one).
+desc += '-fp32'; sched.max_minibatch_per_gpu = {256: 16, 512: 8, 1024: 4}
+#desc += '-fp16'; G.dtype = 'float16'; D.dtype = 'float16'; G.pixelnorm_epsilon=1e-4; G_opt.use_loss_scaling = True; D_opt.use_loss_scaling = True; sched.max_minibatch_per_gpu = {512: 16, 1024: 8}
+
+# Disable individual features.
+#desc += '-nogrowing'; sched.lod_initial_resolution = 1024; sched.lod_training_kimg = 0; sched.lod_transition_kimg = 0; train.total_kimg = 10000
+#desc += '-nopixelnorm'; G.use_pixelnorm = False
+#desc += '-nowscale'; G.use_wscale = False; D.use_wscale = False
+#desc += '-noleakyrelu'; G.use_leakyrelu = False
+#desc += '-nosmoothing'; train.G_smoothing = 0.0
+#desc += '-norepeat'; train.minibatch_repeats = 1
+#desc += '-noreset'; train.reset_opt_for_new_lod = False
+
+# Special modes.
+#desc += '-BENCHMARK'; sched.lod_initial_resolution = 4; sched.lod_training_kimg = 3; sched.lod_transition_kimg = 3; train.total_kimg = (8*2+1)*3; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
+#desc += '-BENCHMARK0'; sched.lod_initial_resolution = 1024; train.total_kimg = 10; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
+#desc += '-VERBOSE'; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1; train.network_snapshot_ticks = 100
+#desc += '-GRAPH'; train.save_tf_graph = True
+#desc += '-HIST'; train.save_weight_histograms = True
+
+#----------------------------------------------------------------------------
+# Utility scripts.
+# To run, uncomment the appropriate line and launch train.py.
+
+#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, num_pngs=1000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, grid_size=[15,8], num_pngs=10, image_shrink=4); num_gpus = 1; desc = 'fake-grids-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.generate_interpolation_video', run_id=23, grid_size=[1,1], duration_sec=60.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.generate_training_video', run_id=23, duration_sec=20.0); num_gpus = 1; desc = 'training-video-' + str(train.run_id)
+
+#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-swd-16k.txt', metrics=['swd'], num_images=16384, real_passes=2); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-10k.txt', metrics=['fid'], num_images=10000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-50k.txt', metrics=['fid'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-is-50k.txt', metrics=['is'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
+#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-msssim-20k.txt', metrics=['msssim'], num_images=20000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/dataset.py

@@ -0,0 +1,241 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import os
+import glob
+import numpy as np
+import tensorflow as tf
+import tfutil
+
+#----------------------------------------------------------------------------
+# Parse individual image from a tfrecords file.
+
+def parse_tfrecord_tf(record):
+    features = tf.parse_single_example(record, features={
+        'shape': tf.FixedLenFeature([3], tf.int64),
+        'data': tf.FixedLenFeature([], tf.string)})
+    data = tf.decode_raw(features['data'], tf.uint8)
+    return tf.reshape(data, features['shape'])
+
+def parse_tfrecord_np(record):
+    ex = tf.train.Example()
+    ex.ParseFromString(record)
+    shape = ex.features.feature['shape'].int64_list.value
+    data = ex.features.feature['data'].bytes_list.value[0]
+    return np.fromstring(data, np.uint8).reshape(shape)
+
+#----------------------------------------------------------------------------
+# Dataset class that loads data from tfrecords files.
+
+class TFRecordDataset:
+    def __init__(self,
+        tfrecord_dir,               # Directory containing a collection of tfrecords files.
+        resolution      = None,     # Dataset resolution, None = autodetect.
+        label_file      = None,     # Relative path of the labels file, None = autodetect.
+        max_label_size  = 0,        # 0 = no labels, 'full' = full labels, <int> = N first label components.
+        repeat          = True,     # Repeat dataset indefinitely.
+        shuffle_mb      = 4096,     # Shuffle data within specified window (megabytes), 0 = disable shuffling.
+        prefetch_mb     = 2048,     # Amount of data to prefetch (megabytes), 0 = disable prefetching.
+        buffer_mb       = 256,      # Read buffer size (megabytes).
+        num_threads     = 2):       # Number of concurrent threads.
+
+        self.tfrecord_dir       = tfrecord_dir
+        self.resolution         = None
+        self.resolution_log2    = None
+        self.shape              = []        # [channel, height, width]
+        self.dtype              = 'uint8'
+        self.dynamic_range      = [0, 255]
+        self.label_file         = label_file
+        self.label_size         = None      # [component]
+        self.label_dtype        = None
+        self._np_labels         = None
+        self._tf_minibatch_in   = None
+        self._tf_labels_var     = None
+        self._tf_labels_dataset = None
+        self._tf_datasets       = dict()
+        self._tf_iterator       = None
+        self._tf_init_ops       = dict()
+        self._tf_minibatch_np   = None
+        self._cur_minibatch     = -1
+        self._cur_lod           = -1
+
+        # List tfrecords files and inspect their shapes.
+        assert os.path.isdir(self.tfrecord_dir)
+        tfr_files = sorted(glob.glob(os.path.join(self.tfrecord_dir, '*.tfrecords')))
+        assert len(tfr_files) >= 1
+        tfr_shapes = []
+        for tfr_file in tfr_files:
+            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
+            for record in tf.python_io.tf_record_iterator(tfr_file, tfr_opt):
+                tfr_shapes.append(parse_tfrecord_np(record).shape)
+                break
+
+        # Autodetect label filename.
+        if self.label_file is None:
+            guess = sorted(glob.glob(os.path.join(self.tfrecord_dir, '*.labels')))
+            if len(guess):
+                self.label_file = guess[0]
+        elif not os.path.isfile(self.label_file):
+            guess = os.path.join(self.tfrecord_dir, self.label_file)
+            if os.path.isfile(guess):
+                self.label_file = guess
+
+        # Determine shape and resolution.
+        max_shape = max(tfr_shapes, key=lambda shape: np.prod(shape))
+        self.resolution = resolution if resolution is not None else max_shape[1]
+        self.resolution_log2 = int(np.log2(self.resolution))
+        self.shape = [max_shape[0], self.resolution, self.resolution]
+        tfr_lods = [self.resolution_log2 - int(np.log2(shape[1])) for shape in tfr_shapes]
+        assert all(shape[0] == max_shape[0] for shape in tfr_shapes)
+        assert all(shape[1] == shape[2] for shape in tfr_shapes)
+        assert all(shape[1] == self.resolution // (2**lod) for shape, lod in zip(tfr_shapes, tfr_lods))
+        assert all(lod in tfr_lods for lod in range(self.resolution_log2 - 1))
+
+        # Load labels.
+        assert max_label_size == 'full' or max_label_size >= 0
+        self._np_labels = np.zeros([1<<20, 0], dtype=np.float32)
+        if self.label_file is not None and max_label_size != 0:
+            self._np_labels = np.load(self.label_file)
+            assert self._np_labels.ndim == 2
+        if max_label_size != 'full' and self._np_labels.shape[1] > max_label_size:
+            self._np_labels = self._np_labels[:, :max_label_size]
+        self.label_size = self._np_labels.shape[1]
+        self.label_dtype = self._np_labels.dtype.name
+
+        # Build TF expressions.
+        with tf.name_scope('Dataset'), tf.device('/cpu:0'):
+            self._tf_minibatch_in = tf.placeholder(tf.int64, name='minibatch_in', shape=[])
+            tf_labels_init = tf.zeros(self._np_labels.shape, self._np_labels.dtype)
+            self._tf_labels_var = tf.Variable(tf_labels_init, name='labels_var')
+            tfutil.set_vars({self._tf_labels_var: self._np_labels})
+            self._tf_labels_dataset = tf.data.Dataset.from_tensor_slices(self._tf_labels_var)
+            for tfr_file, tfr_shape, tfr_lod in zip(tfr_files, tfr_shapes, tfr_lods):
+                if tfr_lod < 0:
+                    continue
+                dset = tf.data.TFRecordDataset(tfr_file, compression_type='', buffer_size=buffer_mb<<20)
+                dset = dset.map(parse_tfrecord_tf, num_parallel_calls=num_threads)
+                dset = tf.data.Dataset.zip((dset, self._tf_labels_dataset))
+                bytes_per_item = np.prod(tfr_shape) * np.dtype(self.dtype).itemsize
+                if shuffle_mb > 0:
+                    dset = dset.shuffle(((shuffle_mb << 20) - 1) // bytes_per_item + 1)
+                if repeat:
+                    dset = dset.repeat()
+                if prefetch_mb > 0:
+                    dset = dset.prefetch(((prefetch_mb << 20) - 1) // bytes_per_item + 1)
+                dset = dset.batch(self._tf_minibatch_in)
+                self._tf_datasets[tfr_lod] = dset
+            self._tf_iterator = tf.data.Iterator.from_structure(self._tf_datasets[0].output_types, self._tf_datasets[0].output_shapes)
+            self._tf_init_ops = {lod: self._tf_iterator.make_initializer(dset) for lod, dset in self._tf_datasets.items()}
+
+    # Use the given minibatch size and level-of-detail for the data returned by get_minibatch_tf().
+    def configure(self, minibatch_size, lod=0):
+        lod = int(np.floor(lod))
+        assert minibatch_size >= 1 and lod in self._tf_datasets
+        if self._cur_minibatch != minibatch_size or self._cur_lod != lod:
+            self._tf_init_ops[lod].run({self._tf_minibatch_in: minibatch_size})
+            self._cur_minibatch = minibatch_size
+            self._cur_lod = lod
+
+    # Get next minibatch as TensorFlow expressions.
+    def get_minibatch_tf(self): # => images, labels
+        return self._tf_iterator.get_next()
+
+    # Get next minibatch as NumPy arrays.
+    def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
+        self.configure(minibatch_size, lod)
+        if self._tf_minibatch_np is None:
+            self._tf_minibatch_np = self.get_minibatch_tf()
+        return tfutil.run(self._tf_minibatch_np)
+
+    # Get random labels as TensorFlow expression.
+    def get_random_labels_tf(self, minibatch_size): # => labels
+        if self.label_size > 0:
+            return tf.gather(self._tf_labels_var, tf.random_uniform([minibatch_size], 0, self._np_labels.shape[0], dtype=tf.int32))
+        else:
+            return tf.zeros([minibatch_size, 0], self.label_dtype)
+
+    # Get random labels as NumPy array.
+    def get_random_labels_np(self, minibatch_size): # => labels
+        if self.label_size > 0:
+            return self._np_labels[np.random.randint(self._np_labels.shape[0], size=[minibatch_size])]
+        else:
+            return np.zeros([minibatch_size, 0], self.label_dtype)
+
+#----------------------------------------------------------------------------
+# Base class for datasets that are generated on the fly.
+
+class SyntheticDataset:
+    def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
+        self.resolution         = resolution
+        self.resolution_log2    = int(np.log2(resolution))
+        self.shape              = [num_channels, resolution, resolution]
+        self.dtype              = dtype
+        self.dynamic_range      = dynamic_range
+        self.label_size         = label_size
+        self.label_dtype        = label_dtype
+        self._tf_minibatch_var  = None
+        self._tf_lod_var        = None
+        self._tf_minibatch_np   = None
+        self._tf_labels_np      = None
+
+        assert self.resolution == 2 ** self.resolution_log2
+        with tf.name_scope('Dataset'):
+            self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
+            self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var')
+
+    def configure(self, minibatch_size, lod=0):
+        lod = int(np.floor(lod))
+        assert minibatch_size >= 1 and lod >= 0 and lod <= self.resolution_log2
+        tfutil.set_vars({self._tf_minibatch_var: minibatch_size, self._tf_lod_var: lod})
+
+    def get_minibatch_tf(self): # => images, labels
+        with tf.name_scope('SyntheticDataset'):
+            shrink = tf.cast(2.0 ** tf.cast(self._tf_lod_var, tf.float32), tf.int32)
+            shape = [self.shape[0], self.shape[1] // shrink, self.shape[2] // shrink]
+            images = self._generate_images(self._tf_minibatch_var, self._tf_lod_var, shape)
+            labels = self._generate_labels(self._tf_minibatch_var)
+            return images, labels
+
+    def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
+        self.configure(minibatch_size, lod)
+        if self._tf_minibatch_np is None:
+            self._tf_minibatch_np = self.get_minibatch_tf()
+        return tfutil.run(self._tf_minibatch_np)
+
+    def get_random_labels_tf(self, minibatch_size): # => labels
+        with tf.name_scope('SyntheticDataset'):
+            return self._generate_labels(minibatch_size)
+
+    def get_random_labels_np(self, minibatch_size): # => labels
+        self.configure(minibatch_size)
+        if self._tf_labels_np is None:
+            self._tf_labels_np = self.get_random_labels_tf()
+        return tfutil.run(self._tf_labels_np)
+
+    def _generate_images(self, minibatch, lod, shape): # to be overridden by subclasses
+        return tf.zeros([minibatch] + shape, self.dtype)
+
+    def _generate_labels(self, minibatch): # to be overridden by subclasses
+        return tf.zeros([minibatch, self.label_size], self.label_dtype)
+
+#----------------------------------------------------------------------------
+# Helper func for constructing a dataset object using the given options.
+
+def load_dataset(class_name='dataset.TFRecordDataset', data_dir=None, verbose=False, **kwargs):
+    adjusted_kwargs = dict(kwargs)
+    if 'tfrecord_dir' in adjusted_kwargs and data_dir is not None:
+        adjusted_kwargs['tfrecord_dir'] = os.path.join(data_dir, adjusted_kwargs['tfrecord_dir'])
+    if verbose:
+        print('Streaming data using %s...' % class_name)
+    dataset = tfutil.import_obj(class_name)(**adjusted_kwargs)
+    if verbose:
+        print('Dataset shape =', np.int32(dataset.shape).tolist())
+        print('Dynamic range =', dataset.dynamic_range)
+        print('Label size    =', dataset.label_size)
+    return dataset
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/dataset_tool.py

@@ -0,0 +1,740 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import os
+import sys
+import glob
+import argparse
+import threading
+import six.moves.queue as Queue
+import traceback
+import numpy as np
+import tensorflow as tf
+import PIL.Image
+
+import tfutil
+import dataset
+
+#----------------------------------------------------------------------------
+
+def error(msg):
+    print('Error: ' + msg)
+    exit(1)
+
+#----------------------------------------------------------------------------
+
+class TFRecordExporter:
+    def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):
+        self.tfrecord_dir       = tfrecord_dir
+        self.tfr_prefix         = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))
+        self.expected_images    = expected_images
+        self.cur_images         = 0
+        self.shape              = None
+        self.resolution_log2    = None
+        self.tfr_writers        = []
+        self.print_progress     = print_progress
+        self.progress_interval  = progress_interval
+        if self.print_progress:
+            print('Creating dataset "%s"' % tfrecord_dir)
+        if not os.path.isdir(self.tfrecord_dir):
+            os.makedirs(self.tfrecord_dir)
+        assert(os.path.isdir(self.tfrecord_dir))
+        
+    def close(self):
+        if self.print_progress:
+            print('%-40s\r' % 'Flushing data...', end='', flush=True)
+        for tfr_writer in self.tfr_writers:
+            tfr_writer.close()
+        self.tfr_writers = []
+        if self.print_progress:
+            print('%-40s\r' % '', end='', flush=True)
+            print('Added %d images.' % self.cur_images)
+
+    def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.
+        order = np.arange(self.expected_images)
+        np.random.RandomState(123).shuffle(order)
+        return order
+
+    def add_image(self, img):
+        if self.print_progress and self.cur_images % self.progress_interval == 0:
+            print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
+        if self.shape is None:
+            self.shape = img.shape
+            self.resolution_log2 = int(np.log2(self.shape[1]))
+            assert self.shape[0] in [1, 3]
+            assert self.shape[1] == self.shape[2]
+            assert self.shape[1] == 2**self.resolution_log2
+            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
+            for lod in range(self.resolution_log2 - 1):
+                tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
+                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
+        assert img.shape == self.shape
+        for lod, tfr_writer in enumerate(self.tfr_writers):
+            if lod:
+                img = img.astype(np.float32)
+                img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
+            quant = np.rint(img).clip(0, 255).astype(np.uint8)
+            ex = tf.train.Example(features=tf.train.Features(feature={
+                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
+                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
+            tfr_writer.write(ex.SerializeToString())
+        self.cur_images += 1
+
+    def add_labels(self, labels):
+        if self.print_progress:
+            print('%-40s\r' % 'Saving labels...', end='', flush=True)
+        assert labels.shape[0] == self.cur_images
+        with open(self.tfr_prefix + '-rxx.labels', 'wb') as f:
+            np.save(f, labels.astype(np.float32))
+            
+    def __enter__(self):
+        return self
+    
+    def __exit__(self, *args):
+        self.close()
+
+#----------------------------------------------------------------------------
+
+class ExceptionInfo(object):
+    def __init__(self):
+        self.value = sys.exc_info()[1]
+        self.traceback = traceback.format_exc()
+
+#----------------------------------------------------------------------------
+
+class WorkerThread(threading.Thread):
+    def __init__(self, task_queue):
+        threading.Thread.__init__(self)
+        self.task_queue = task_queue
+
+    def run(self):
+        while True:
+            func, args, result_queue = self.task_queue.get()
+            if func is None:
+                break
+            try:
+                result = func(*args)
+            except:
+                result = ExceptionInfo()
+            result_queue.put((result, args))
+
+#----------------------------------------------------------------------------
+
+class ThreadPool(object):
+    def __init__(self, num_threads):
+        assert num_threads >= 1
+        self.task_queue = Queue.Queue()
+        self.result_queues = dict()
+        self.num_threads = num_threads
+        for idx in range(self.num_threads):
+            thread = WorkerThread(self.task_queue)
+            thread.daemon = True
+            thread.start()
+
+    def add_task(self, func, args=()):
+        assert hasattr(func, '__call__') # must be a function
+        if func not in self.result_queues:
+            self.result_queues[func] = Queue.Queue()
+        self.task_queue.put((func, args, self.result_queues[func]))
+
+    def get_result(self, func): # returns (result, args)
+        result, args = self.result_queues[func].get()
+        if isinstance(result, ExceptionInfo):
+            print('\n\nWorker thread caught an exception:\n' + result.traceback)
+            raise result.value
+        return result, args
+
+    def finish(self):
+        for idx in range(self.num_threads):
+            self.task_queue.put((None, (), None))
+
+    def __enter__(self): # for 'with' statement
+        return self
+
+    def __exit__(self, *excinfo):
+        self.finish()
+
+    def process_items_concurrently(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flight=None):
+        if max_items_in_flight is None: max_items_in_flight = self.num_threads * 4
+        assert max_items_in_flight >= 1
+        results = []
+        retire_idx = [0]
+
+        def task_func(prepared, idx):
+            return process_func(prepared)
+           
+        def retire_result():
+            processed, (prepared, idx) = self.get_result(task_func)
+            results[idx] = processed
+            while retire_idx[0] < len(results) and results[retire_idx[0]] is not None:
+                yield post_func(results[retire_idx[0]])
+                results[retire_idx[0]] = None
+                retire_idx[0] += 1
+    
+        for idx, item in enumerate(item_iterator):
+            prepared = pre_func(item)
+            results.append(None)
+            self.add_task(func=task_func, args=(prepared, idx))
+            while retire_idx[0] < idx - max_items_in_flight + 2:
+                for res in retire_result(): yield res
+        while retire_idx[0] < len(results):
+            for res in retire_result(): yield res
+
+#----------------------------------------------------------------------------
+
+def display(tfrecord_dir):
+    print('Loading dataset "%s"' % tfrecord_dir)
+    tfutil.init_tf({'gpu_options.allow_growth': True})
+    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size='full', repeat=False, shuffle_mb=0)
+    tfutil.init_uninited_vars()
+    
+    idx = 0
+    while True:
+        try:
+            images, labels = dset.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            break
+        if idx == 0:
+            print('Displaying images')
+            import cv2 # pip install opencv-python
+            cv2.namedWindow('dataset_tool')
+            print('Press SPACE or ENTER to advance, ESC to exit')
+        print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
+        cv2.imshow('dataset_tool', images[0].transpose(1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
+        idx += 1
+        if cv2.waitKey() == 27:
+            break
+    print('\nDisplayed %d images.' % idx)
+
+#----------------------------------------------------------------------------
+
+def extract(tfrecord_dir, output_dir):
+    print('Loading dataset "%s"' % tfrecord_dir)
+    tfutil.init_tf({'gpu_options.allow_growth': True})
+    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0)
+    tfutil.init_uninited_vars()
+    
+    print('Extracting images to "%s"' % output_dir)
+    if not os.path.isdir(output_dir):
+        os.makedirs(output_dir)
+    idx = 0
+    while True:
+        if idx % 10 == 0:
+            print('%d\r' % idx, end='', flush=True)
+        try:
+            images, labels = dset.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            break
+        if images.shape[1] == 1:
+            img = PIL.Image.fromarray(images[0][0], 'L')
+        else:
+            img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
+        img.save(os.path.join(output_dir, 'img%08d.png' % idx))
+        idx += 1
+    print('Extracted %d images.' % idx)
+
+#----------------------------------------------------------------------------
+
+def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
+    max_label_size = 0 if ignore_labels else 'full'
+    print('Loading dataset "%s"' % tfrecord_dir_a)
+    tfutil.init_tf({'gpu_options.allow_growth': True})
+    dset_a = dataset.TFRecordDataset(tfrecord_dir_a, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
+    print('Loading dataset "%s"' % tfrecord_dir_b)
+    dset_b = dataset.TFRecordDataset(tfrecord_dir_b, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
+    tfutil.init_uninited_vars()
+    
+    print('Comparing datasets')
+    idx = 0
+    identical_images = 0
+    identical_labels = 0
+    while True:
+        if idx % 100 == 0:
+            print('%d\r' % idx, end='', flush=True)
+        try:
+            images_a, labels_a = dset_a.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            images_a, labels_a = None, None
+        try:
+            images_b, labels_b = dset_b.get_minibatch_np(1)
+        except tf.errors.OutOfRangeError:
+            images_b, labels_b = None, None
+        if images_a is None or images_b is None:
+            if images_a is not None or images_b is not None:
+                print('Datasets contain different number of images')
+            break
+        if images_a.shape == images_b.shape and np.all(images_a == images_b):
+            identical_images += 1
+        else:
+            print('Image %d is different' % idx)
+        if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
+            identical_labels += 1
+        else:
+            print('Label %d is different' % idx)
+        idx += 1
+    print('Identical images: %d / %d' % (identical_images, idx))
+    if not ignore_labels:
+        print('Identical labels: %d / %d' % (identical_labels, idx))
+
+#----------------------------------------------------------------------------
+
+def create_mnist(tfrecord_dir, mnist_dir):
+    print('Loading MNIST from "%s"' % mnist_dir)
+    import gzip
+    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
+        images = np.frombuffer(file.read(), np.uint8, offset=16)
+    with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
+        labels = np.frombuffer(file.read(), np.uint8, offset=8)
+    images = images.reshape(-1, 1, 28, 28)
+    images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
+    assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (60000,) and labels.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+    
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
+    print('Loading MNIST from "%s"' % mnist_dir)
+    import gzip
+    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
+        images = np.frombuffer(file.read(), np.uint8, offset=16)
+    images = images.reshape(-1, 28, 28)
+    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
+    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    
+    with TFRecordExporter(tfrecord_dir, num_images) as tfr:
+        rnd = np.random.RandomState(random_seed)
+        for idx in range(num_images):
+            tfr.add_image(images[rnd.randint(images.shape[0], size=3)])
+
+#----------------------------------------------------------------------------
+
+def create_cifar10(tfrecord_dir, cifar10_dir):
+    print('Loading CIFAR-10 from "%s"' % cifar10_dir)
+    import pickle
+    images = []
+    labels = []
+    for batch in range(1, 6):
+        with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file:
+            data = pickle.load(file, encoding='latin1')
+        images.append(data['data'].reshape(-1, 3, 32, 32))
+        labels.append(data['labels'])
+    images = np.concatenate(images)
+    labels = np.concatenate(labels)
+    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (50000,) and labels.dtype == np.int32
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_cifar100(tfrecord_dir, cifar100_dir):
+    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
+    import pickle
+    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
+        data = pickle.load(file, encoding='latin1')
+    images = data['data'].reshape(-1, 3, 32, 32)
+    labels = np.array(data['fine_labels'])
+    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (50000,) and labels.dtype == np.int32
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 99
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_svhn(tfrecord_dir, svhn_dir):
+    print('Loading SVHN from "%s"' % svhn_dir)
+    import pickle
+    images = []
+    labels = []
+    for batch in range(1, 4):
+        with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file:
+            data = pickle.load(file, encoding='latin1')
+        images.append(data[0])
+        labels.append(data[1])
+    images = np.concatenate(images)
+    labels = np.concatenate(labels)
+    assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8
+    assert labels.shape == (73257,) and labels.dtype == np.uint8
+    assert np.min(images) == 0 and np.max(images) == 255
+    assert np.min(labels) == 0 and np.max(labels) == 9
+    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
+    onehot[np.arange(labels.size), labels] = 1.0
+
+    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            tfr.add_image(images[order[idx]])
+        tfr.add_labels(onehot[order])
+
+#----------------------------------------------------------------------------
+
+def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
+    print('Loading LSUN dataset from "%s"' % lmdb_dir)
+    import lmdb # pip install lmdb
+    import cv2 # pip install opencv-python
+    import io
+    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
+        total_images = txn.stat()['entries']
+        if max_images is None:
+            max_images = total_images
+        with TFRecordExporter(tfrecord_dir, max_images) as tfr:
+            for idx, (key, value) in enumerate(txn.cursor()):
+                try:
+                    try:
+                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
+                        if img is None:
+                            raise IOError('cv2.imdecode failed')
+                        img = img[:, :, ::-1] # BGR => RGB
+                    except IOError:
+                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
+                    crop = np.min(img.shape[:2])
+                    img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
+                    img = PIL.Image.fromarray(img, 'RGB')
+                    img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
+                    img = np.asarray(img)
+                    img = img.transpose(2, 0, 1) # HWC => CHW
+                    tfr.add_image(img)
+                except:
+                    print(sys.exc_info()[1])
+                if tfr.cur_images == max_images:
+                    break
+        
+#----------------------------------------------------------------------------
+
+def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
+    print('Loading CelebA from "%s"' % celeba_dir)
+    glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
+    image_filenames = sorted(glob.glob(glob_pattern))
+    expected_images = 202599
+    if len(image_filenames) != expected_images:
+        error('Expected to find %d images' % expected_images)
+    
+    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
+        order = tfr.choose_shuffled_order()
+        for idx in range(order.size):
+            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
+            assert img.shape == (218, 178, 3)
+            img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
+            img = img.transpose(2, 0, 1) # HWC => CHW
+            tfr.add_image(img)
+
+#----------------------------------------------------------------------------
+
+def create_celebahq(tfrecord_dir, celeba_dir, delta_dir, num_threads=4, num_tasks=100):
+    print('Loading CelebA from "%s"' % celeba_dir)
+    expected_images = 202599
+    if len(glob.glob(os.path.join(celeba_dir, 'img_celeba', '*.jpg'))) != expected_images:
+        error('Expected to find %d images' % expected_images)
+    with open(os.path.join(celeba_dir, 'Anno', 'list_landmarks_celeba.txt'), 'rt') as file:
+        landmarks = [[float(value) for value in line.split()[1:]] for line in file.readlines()[2:]]
+        landmarks = np.float32(landmarks).reshape(-1, 5, 2)
+    
+    print('Loading CelebA-HQ deltas from "%s"' % delta_dir)
+    import scipy.ndimage
+    import hashlib
+    import bz2
+    import zipfile
+    import base64
+    import cryptography.hazmat.primitives.hashes
+    import cryptography.hazmat.backends
+    import cryptography.hazmat.primitives.kdf.pbkdf2
+    import cryptography.fernet
+    expected_zips = 30
+    if len(glob.glob(os.path.join(delta_dir, 'delta*.zip'))) != expected_zips:
+        error('Expected to find %d zips' % expected_zips)
+    with open(os.path.join(delta_dir, 'image_list.txt'), 'rt') as file:
+        lines = [line.split() for line in file]
+        fields = dict()
+        for idx, field in enumerate(lines[0]):
+            type = int if field.endswith('idx') else str
+            fields[field] = [type(line[idx]) for line in lines[1:]]
+    indices = np.array(fields['idx'])
+
+    # Must use pillow version 3.1.1 for everything to work correctly.
+    if getattr(PIL, 'PILLOW_VERSION', '') != '3.1.1':
+        error('create_celebahq requires pillow version 3.1.1') # conda install pillow=3.1.1
+        
+    # Must use libjpeg version 8d for everything to work correctly.
+    img = np.array(PIL.Image.open(os.path.join(celeba_dir, 'img_celeba', '000001.jpg')))
+    md5 = hashlib.md5()
+    md5.update(img.tobytes())
+    if md5.hexdigest() != '9cad8178d6cb0196b36f7b34bc5eb6d3':
+        error('create_celebahq requires libjpeg version 8d') # conda install jpeg=8d
+
+    def rot90(v):
+        return np.array([-v[1], v[0]])
+
+    def process_func(idx):
+        # Load original image.
+        orig_idx = fields['orig_idx'][idx]
+        orig_file = fields['orig_file'][idx]
+        orig_path = os.path.join(celeba_dir, 'img_celeba', orig_file)
+        img = PIL.Image.open(orig_path)
+
+        # Choose oriented crop rectangle.
+        lm = landmarks[orig_idx]
+        eye_avg = (lm[0] + lm[1]) * 0.5 + 0.5
+        mouth_avg = (lm[3] + lm[4]) * 0.5 + 0.5
+        eye_to_eye = lm[1] - lm[0]
+        eye_to_mouth = mouth_avg - eye_avg
+        x = eye_to_eye - rot90(eye_to_mouth)
+        x /= np.hypot(*x)
+        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
+        y = rot90(x)
+        c = eye_avg + eye_to_mouth * 0.1
+        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+        zoom = 1024 / (np.hypot(*x) * 2)
+
+        # Shrink.
+        shrink = int(np.floor(0.5 / zoom))
+        if shrink > 1:
+            size = (int(np.round(float(img.size[0]) / shrink)), int(np.round(float(img.size[1]) / shrink)))
+            img = img.resize(size, PIL.Image.ANTIALIAS)
+            quad /= shrink
+            zoom *= shrink
+
+        # Crop.
+        border = max(int(np.round(1024 * 0.1 / zoom)), 3)
+        crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
+        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
+        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
+            img = img.crop(crop)
+            quad -= crop[0:2]
+
+        # Simulate super-resolution.
+        superres = int(np.exp2(np.ceil(np.log2(zoom))))
+        if superres > 1:
+            img = img.resize((img.size[0] * superres, img.size[1] * superres), PIL.Image.ANTIALIAS)
+            quad *= superres
+            zoom /= superres
+
+        # Pad.
+        pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
+        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
+        if max(pad) > border - 4:
+            pad = np.maximum(pad, int(np.round(1024 * 0.3 / zoom)))
+            img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+            h, w, _ = img.shape
+            y, x, _ = np.mgrid[:h, :w, :1]
+            mask = 1.0 - np.minimum(np.minimum(np.float32(x) / pad[0], np.float32(y) / pad[1]), np.minimum(np.float32(w-1-x) / pad[2], np.float32(h-1-y) / pad[3]))
+            blur = 1024 * 0.02 / zoom
+            img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+            img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
+            img = PIL.Image.fromarray(np.uint8(np.clip(np.round(img), 0, 255)), 'RGB')
+            quad += pad[0:2]
+            
+        # Transform.
+        img = img.transform((4096, 4096), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
+        img = img.resize((1024, 1024), PIL.Image.ANTIALIAS)
+        img = np.asarray(img).transpose(2, 0, 1)
+        
+        # Verify MD5.
+        md5 = hashlib.md5()
+        md5.update(img.tobytes())
+        assert md5.hexdigest() == fields['proc_md5'][idx]
+        
+        # Load delta image and original JPG.
+        with zipfile.ZipFile(os.path.join(delta_dir, 'deltas%05d.zip' % (idx - idx % 1000)), 'r') as zip:
+            delta_bytes = zip.read('delta%05d.dat' % idx)
+        with open(orig_path, 'rb') as file:
+            orig_bytes = file.read()
+        
+        # Decrypt delta image, using original JPG data as decryption key.
+        algorithm = cryptography.hazmat.primitives.hashes.SHA256()
+        backend = cryptography.hazmat.backends.default_backend()
+        salt = bytes(orig_file, 'ascii')
+        kdf = cryptography.hazmat.primitives.kdf.pbkdf2.PBKDF2HMAC(algorithm=algorithm, length=32, salt=salt, iterations=100000, backend=backend)
+        key = base64.urlsafe_b64encode(kdf.derive(orig_bytes))
+        delta = np.frombuffer(bz2.decompress(cryptography.fernet.Fernet(key).decrypt(delta_bytes)), dtype=np.uint8).reshape(3, 1024, 1024)
+        
+        # Apply delta image.
+        img = img + delta
+        
+        # Verify MD5.
+        md5 = hashlib.md5()
+        md5.update(img.tobytes())
+        assert md5.hexdigest() == fields['final_md5'][idx]
+        return img
+
+    with TFRecordExporter(tfrecord_dir, indices.size) as tfr:
+        order = tfr.choose_shuffled_order()
+        with ThreadPool(num_threads) as pool:
+            for img in pool.process_items_concurrently(indices[order].tolist(), process_func=process_func, max_items_in_flight=num_tasks):
+                tfr.add_image(img)
+
+#----------------------------------------------------------------------------
+
+def create_from_images(tfrecord_dir, image_dir, shuffle):
+    print('Loading images from "%s"' % image_dir)
+    image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
+    if len(image_filenames) == 0:
+        error('No input images found')
+        
+    img = np.asarray(PIL.Image.open(image_filenames[0]))
+    resolution = img.shape[0]
+    channels = img.shape[2] if img.ndim == 3 else 1
+    if img.shape[1] != resolution:
+        error('Input images must have the same width and height')
+    if resolution != 2 ** int(np.floor(np.log2(resolution))):
+        error('Input image resolution must be a power-of-two')
+    if channels not in [1, 3]:
+        error('Input images must be stored as RGB or grayscale')
+    
+    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
+        order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
+        for idx in range(order.size):
+            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
+            if channels == 1:
+                img = img[np.newaxis, :, :] # HW => CHW
+            else:
+                img = img.transpose(2, 0, 1) # HWC => CHW
+            tfr.add_image(img)
+
+#----------------------------------------------------------------------------
+
+def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
+    print('Loading HDF5 archive from "%s"' % hdf5_filename)
+    import h5py # conda install h5py
+    with h5py.File(hdf5_filename, 'r') as hdf5_file:
+        hdf5_data = max([value for key, value in hdf5_file.items() if key.startswith('data')], key=lambda lod: lod.shape[3])
+        with TFRecordExporter(tfrecord_dir, hdf5_data.shape[0]) as tfr:
+            order = tfr.choose_shuffled_order() if shuffle else np.arange(hdf5_data.shape[0])
+            for idx in range(order.size):
+                tfr.add_image(hdf5_data[order[idx]])
+            npy_filename = os.path.splitext(hdf5_filename)[0] + '-labels.npy'
+            if os.path.isfile(npy_filename):
+                tfr.add_labels(np.load(npy_filename)[order])
+
+#----------------------------------------------------------------------------
+
+def execute_cmdline(argv):
+    prog = argv[0]
+    parser = argparse.ArgumentParser(
+        prog        = prog,
+        description = 'Tool for creating, extracting, and visualizing Progressive GAN datasets.',
+        epilog      = 'Type "%s <command> -h" for more information.' % prog)
+        
+    subparsers = parser.add_subparsers(dest='command')
+    subparsers.required = True
+    def add_command(cmd, desc, example=None):
+        epilog = 'Example: %s %s' % (prog, example) if example is not None else None
+        return subparsers.add_parser(cmd, description=desc, help=desc, epilog=epilog)
+
+    p = add_command(    'display',          'Display images in dataset.',
+                                            'display datasets/mnist')
+    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
+  
+    p = add_command(    'extract',          'Extract images from dataset.',
+                                            'extract datasets/mnist mnist-images')
+    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
+    p.add_argument(     'output_dir',       help='Directory to extract the images into')
+
+    p = add_command(    'compare',          'Compare two datasets.',
+                                            'compare datasets/mydataset datasets/mnist')
+    p.add_argument(     'tfrecord_dir_a',   help='Directory containing first dataset')
+    p.add_argument(     'tfrecord_dir_b',   help='Directory containing second dataset')
+    p.add_argument(     '--ignore_labels',  help='Ignore labels (default: 0)', type=int, default=0)
+
+    p = add_command(    'create_mnist',     'Create dataset for MNIST.',
+                                            'create_mnist datasets/mnist ~/downloads/mnist')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
+
+    p = add_command(    'create_mnistrgb',  'Create dataset for MNIST-RGB.',
+                                            'create_mnistrgb datasets/mnistrgb ~/downloads/mnist')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
+    p.add_argument(     '--num_images',     help='Number of composite images to create (default: 1000000)', type=int, default=1000000)
+    p.add_argument(     '--random_seed',    help='Random seed (default: 123)', type=int, default=123)
+
+    p = add_command(    'create_cifar10',   'Create dataset for CIFAR-10.',
+                                            'create_cifar10 datasets/cifar10 ~/downloads/cifar10')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'cifar10_dir',      help='Directory containing CIFAR-10')
+
+    p = add_command(    'create_cifar100',  'Create dataset for CIFAR-100.',
+                                            'create_cifar100 datasets/cifar100 ~/downloads/cifar100')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'cifar100_dir',     help='Directory containing CIFAR-100')
+
+    p = add_command(    'create_svhn',      'Create dataset for SVHN.',
+                                            'create_svhn datasets/svhn ~/downloads/svhn')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'svhn_dir',         help='Directory containing SVHN')
+
+    p = add_command(    'create_lsun',      'Create dataset for single LSUN category.',
+                                            'create_lsun datasets/lsun-car-100k ~/downloads/lsun/car_lmdb --resolution 256 --max_images 100000')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'lmdb_dir',         help='Directory containing LMDB database')
+    p.add_argument(     '--resolution',     help='Output resolution (default: 256)', type=int, default=256)
+    p.add_argument(     '--max_images',     help='Maximum number of images (default: none)', type=int, default=None)
+
+    p = add_command(    'create_celeba',    'Create dataset for CelebA.',
+                                            'create_celeba datasets/celeba ~/downloads/celeba')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'celeba_dir',       help='Directory containing CelebA')
+    p.add_argument(     '--cx',             help='Center X coordinate (default: 89)', type=int, default=89)
+    p.add_argument(     '--cy',             help='Center Y coordinate (default: 121)', type=int, default=121)
+
+    p = add_command(    'create_celebahq',  'Create dataset for CelebA-HQ.',
+                                            'create_celebahq datasets/celebahq ~/downloads/celeba ~/downloads/celeba-hq-deltas')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'celeba_dir',       help='Directory containing CelebA')
+    p.add_argument(     'delta_dir',        help='Directory containing CelebA-HQ deltas')
+    p.add_argument(     '--num_threads',    help='Number of concurrent threads (default: 4)', type=int, default=4)
+    p.add_argument(     '--num_tasks',      help='Number of concurrent processing tasks (default: 100)', type=int, default=100)
+
+    p = add_command(    'create_from_images', 'Create dataset from a directory full of images.',
+                                            'create_from_images datasets/mydataset myimagedir')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'image_dir',        help='Directory containing the images')
+    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
+
+    p = add_command(    'create_from_hdf5', 'Create dataset from legacy HDF5 archive.',
+                                            'create_from_hdf5 datasets/celebahq ~/downloads/celeba-hq-1024x1024.h5')
+    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
+    p.add_argument(     'hdf5_filename',    help='HDF5 archive containing the images')
+    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
+
+    args = parser.parse_args(argv[1:] if len(argv) > 1 else ['-h'])
+    func = globals()[args.command]
+    del args.command
+    func(**vars(args))
+
+#----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    execute_cmdline(sys.argv)
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/legacy.py

@@ -0,0 +1,117 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import pickle
+import inspect
+import numpy as np
+
+import tfutil
+import networks
+
+#----------------------------------------------------------------------------
+# Custom unpickler that is able to load network pickles produced by
+# the old Theano implementation.
+
+class LegacyUnpickler(pickle.Unpickler):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def find_class(self, module, name):
+        if module == 'network' and name == 'Network':
+            return tfutil.Network
+        return super().find_class(module, name)
+
+#----------------------------------------------------------------------------
+# Import handler for tfutil.Network that silently converts networks produced
+# by the old Theano implementation to a suitable format.
+
+theano_gan_remap = {
+    'G_paper':          'G_paper',
+    'G_progressive_8':  'G_paper',
+    'D_paper':          'D_paper',
+    'D_progressive_8':  'D_paper'}
+
+def patch_theano_gan(state):
+    if 'version' in state or state['build_func_spec']['func'] not in theano_gan_remap:
+        return state
+
+    spec = dict(state['build_func_spec'])
+    func = spec.pop('func')
+    resolution = spec.get('resolution', 32)
+    resolution_log2 = int(np.log2(resolution))
+    use_wscale = spec.get('use_wscale', True)
+
+    assert spec.pop('label_size',       0)          == 0
+    assert spec.pop('use_batchnorm',    False)      == False
+    assert spec.pop('tanh_at_end',      None)       is None
+    assert spec.pop('mbstat_func',      'Tstdeps')  == 'Tstdeps'
+    assert spec.pop('mbstat_avg',       'all')      == 'all'
+    assert spec.pop('mbdisc_kernels',   None)       is None
+    spec.pop(       'use_gdrop',        True)       # doesn't make a difference
+    assert spec.pop('use_layernorm',    False)      == False
+    spec[           'fused_scale']                  = False
+    spec[           'mbstd_group_size']             = 16
+
+    vars = []
+    param_iter = iter(state['param_values'])
+    relu = np.sqrt(2); linear = 1.0
+    def flatten2(w): return w.reshape(w.shape[0], -1)
+    def he_std(gain, w): return gain / np.sqrt(np.prod(w.shape[:-1]))
+    def wscale(gain, w): return w * next(param_iter) / he_std(gain, w) if use_wscale else w
+    def layer(name, gain, w): return [(name + '/weight', wscale(gain, w)), (name + '/bias', next(param_iter))]
+    
+    if func.startswith('G'):
+        vars += layer('4x4/Dense', relu/4, flatten2(next(param_iter).transpose(1,0,2,3)))
+        vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+        for res in range(3, resolution_log2 + 1):
+            vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+            vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+        for lod in range(0, resolution_log2 - 1):
+            vars += layer('ToRGB_lod%d' % lod, linear, next(param_iter)[np.newaxis, np.newaxis])
+
+    if func.startswith('D'):
+        vars += layer('FromRGB_lod0', relu, next(param_iter)[np.newaxis, np.newaxis])
+        for res in range(resolution_log2, 2, -1):
+            vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+            vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+            vars += layer('FromRGB_lod%d' % (resolution_log2 - (res - 1)), relu, next(param_iter)[np.newaxis, np.newaxis])
+        vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1])
+        vars += layer('4x4/Dense0', relu, flatten2(next(param_iter)[:,:,::-1,::-1]).transpose())
+        vars += layer('4x4/Dense1', linear, next(param_iter))
+
+    vars += [('lod', state['toplevel_params']['cur_lod'])]
+
+    return {
+        'version':          2,
+        'name':             func,
+        'build_module_src': inspect.getsource(networks),
+        'build_func_name':  theano_gan_remap[func],
+        'static_kwargs':    spec,
+        'variables':        vars}
+
+tfutil.network_import_handlers.append(patch_theano_gan)
+
+#----------------------------------------------------------------------------
+# Import handler for tfutil.Network that ignores unsupported/deprecated
+# networks produced by older versions of the code.
+
+def ignore_unknown_theano_network(state):
+    if 'version' in state:
+        return state
+
+    print('Ignoring unknown Theano network:', state['build_func_spec']['func'])
+    return {
+        'version':          2,
+        'name':             'Dummy',
+        'build_module_src': 'def dummy(input, **kwargs): input.set_shape([None, 1]); return input',
+        'build_func_name':  'dummy',
+        'static_kwargs':    {},
+        'variables':        []}
+
+tfutil.network_import_handlers.append(ignore_unknown_theano_network)
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/loss.py

@@ -0,0 +1,82 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import numpy as np
+import tensorflow as tf
+
+import tfutil
+
+#----------------------------------------------------------------------------
+# Convenience func that casts all of its arguments to tf.float32.
+
+def fp32(*values):
+    if len(values) == 1 and isinstance(values[0], tuple):
+        values = values[0]
+    values = tuple(tf.cast(v, tf.float32) for v in values)
+    return values if len(values) >= 2 else values[0]
+
+#----------------------------------------------------------------------------
+# Generator loss function used in the paper (WGAN + AC-GAN).
+
+def G_wgan_acgan(G, D, opt, training_set, minibatch_size,
+    cond_weight = 1.0): # Weight of the conditioning term.
+
+    latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
+    labels = training_set.get_random_labels_tf(minibatch_size)
+    fake_images_out = G.get_output_for(latents, labels, is_training=True)
+    fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
+    loss = -fake_scores_out
+
+    if D.output_shapes[1][1] > 0:
+        with tf.name_scope('LabelPenalty'):
+            label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
+        loss += label_penalty_fakes * cond_weight
+    return loss
+
+#----------------------------------------------------------------------------
+# Discriminator loss function used in the paper (WGAN-GP + AC-GAN).
+
+def D_wgangp_acgan(G, D, opt, training_set, minibatch_size, reals, labels,
+    wgan_lambda     = 10.0,     # Weight for the gradient penalty term.
+    wgan_epsilon    = 0.001,    # Weight for the epsilon term, \epsilon_{drift}.
+    wgan_target     = 1.0,      # Target value for gradient magnitudes.
+    cond_weight     = 1.0):     # Weight of the conditioning terms.
+
+    latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
+    fake_images_out = G.get_output_for(latents, labels, is_training=True)
+    real_scores_out, real_labels_out = fp32(D.get_output_for(reals, is_training=True))
+    fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
+    real_scores_out = tfutil.autosummary('Loss/real_scores', real_scores_out)
+    fake_scores_out = tfutil.autosummary('Loss/fake_scores', fake_scores_out)
+    loss = fake_scores_out - real_scores_out
+
+    with tf.name_scope('GradientPenalty'):
+        mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype)
+        mixed_images_out = tfutil.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
+        mixed_scores_out, mixed_labels_out = fp32(D.get_output_for(mixed_images_out, is_training=True))
+        mixed_scores_out = tfutil.autosummary('Loss/mixed_scores', mixed_scores_out)
+        mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out))
+        mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0]))
+        mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3]))
+        mixed_norms = tfutil.autosummary('Loss/mixed_norms', mixed_norms)
+        gradient_penalty = tf.square(mixed_norms - wgan_target)
+    loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
+
+    with tf.name_scope('EpsilonPenalty'):
+        epsilon_penalty = tfutil.autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
+    loss += epsilon_penalty * wgan_epsilon
+
+    if D.output_shapes[1][1] > 0:
+        with tf.name_scope('LabelPenalty'):
+            label_penalty_reals = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=real_labels_out)
+            label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
+            label_penalty_reals = tfutil.autosummary('Loss/label_penalty_reals', label_penalty_reals)
+            label_penalty_fakes = tfutil.autosummary('Loss/label_penalty_fakes', label_penalty_fakes)
+        loss += (label_penalty_reals + label_penalty_fakes) * cond_weight
+    return loss
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/misc.py

@@ -0,0 +1,344 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import os
+import sys
+import glob
+import datetime
+import pickle
+import re
+import numpy as np
+from collections import OrderedDict 
+import scipy.ndimage
+import PIL.Image
+
+import config
+import dataset
+import legacy
+
+#----------------------------------------------------------------------------
+# Convenience wrappers for pickle that are able to load data produced by
+# older versions of the code.
+
+def load_pkl(filename):
+    with open(filename, 'rb') as file:
+        return legacy.LegacyUnpickler(file, encoding='latin1').load()
+
+def save_pkl(obj, filename):
+    with open(filename, 'wb') as file:
+        pickle.dump(obj, file, protocol=pickle.HIGHEST_PROTOCOL)
+
+#----------------------------------------------------------------------------
+# Image utils.
+
+def adjust_dynamic_range(data, drange_in, drange_out):
+    if drange_in != drange_out:
+        scale = (np.float32(drange_out[1]) - np.float32(drange_out[0])) / (np.float32(drange_in[1]) - np.float32(drange_in[0]))
+        bias = (np.float32(drange_out[0]) - np.float32(drange_in[0]) * scale)
+        data = data * scale + bias
+    return data
+
+def create_image_grid(images, grid_size=None):
+    assert images.ndim == 3 or images.ndim == 4
+    num, img_w, img_h = images.shape[0], images.shape[-1], images.shape[-2]
+
+    if grid_size is not None:
+        grid_w, grid_h = tuple(grid_size)
+    else:
+        grid_w = max(int(np.ceil(np.sqrt(num))), 1)
+        grid_h = max((num - 1) // grid_w + 1, 1)
+
+    grid = np.zeros(list(images.shape[1:-2]) + [grid_h * img_h, grid_w * img_w], dtype=images.dtype)
+    for idx in range(num):
+        x = (idx % grid_w) * img_w
+        y = (idx // grid_w) * img_h
+        grid[..., y : y + img_h, x : x + img_w] = images[idx]
+    return grid
+
+def convert_to_pil_image(image, drange=[0,1]):
+    assert image.ndim == 2 or image.ndim == 3
+    if image.ndim == 3:
+        if image.shape[0] == 1:
+            image = image[0] # grayscale CHW => HW
+        else:
+            image = image.transpose(1, 2, 0) # CHW -> HWC
+
+    image = adjust_dynamic_range(image, drange, [0,255])
+    image = np.rint(image).clip(0, 255).astype(np.uint8)
+    format = 'RGB' if image.ndim == 3 else 'L'
+    return PIL.Image.fromarray(image, format)
+
+def save_image(image, filename, drange=[0,1], quality=95):
+    img = convert_to_pil_image(image, drange)
+    if '.jpg' in filename:
+        img.save(filename,"JPEG", quality=quality, optimize=True)
+    else:
+        img.save(filename)
+
+def save_image_grid(images, filename, drange=[0,1], grid_size=None):
+    convert_to_pil_image(create_image_grid(images, grid_size), drange).save(filename)
+
+#----------------------------------------------------------------------------
+# Logging of stdout and stderr to a file.
+
+class OutputLogger(object):
+    def __init__(self):
+        self.file = None
+        self.buffer = ''
+
+    def set_log_file(self, filename, mode='wt'):
+        assert self.file is None
+        self.file = open(filename, mode)
+        if self.buffer is not None:
+            self.file.write(self.buffer)
+            self.buffer = None
+
+    def write(self, data):
+        if self.file is not None:
+            self.file.write(data)
+        if self.buffer is not None:
+            self.buffer += data
+
+    def flush(self):
+        if self.file is not None:
+            self.file.flush()
+
+class TeeOutputStream(object):
+    def __init__(self, child_streams, autoflush=False):
+        self.child_streams = child_streams
+        self.autoflush = autoflush
+ 
+    def write(self, data):
+        for stream in self.child_streams:
+            stream.write(data)
+        if self.autoflush:
+            self.flush()
+
+    def flush(self):
+        for stream in self.child_streams:
+            stream.flush()
+
+output_logger = None
+
+def init_output_logging():
+    global output_logger
+    if output_logger is None:
+        output_logger = OutputLogger()
+        sys.stdout = TeeOutputStream([sys.stdout, output_logger], autoflush=True)
+        sys.stderr = TeeOutputStream([sys.stderr, output_logger], autoflush=True)
+
+def set_output_log_file(filename, mode='wt'):
+    if output_logger is not None:
+        output_logger.set_log_file(filename, mode)
+
+#----------------------------------------------------------------------------
+# Reporting results.
+
+def create_result_subdir(result_dir, desc):
+
+    # Select run ID and create subdir.
+    while True:
+        run_id = 0
+        for fname in glob.glob(os.path.join(result_dir, '*')):
+            try:
+                fbase = os.path.basename(fname)
+                ford = int(fbase[:fbase.find('-')])
+                run_id = max(run_id, ford + 1)
+            except ValueError:
+                pass
+
+        result_subdir = os.path.join(result_dir, '%03d-%s' % (run_id, desc))
+        try:
+            os.makedirs(result_subdir)
+            break
+        except OSError:
+            if os.path.isdir(result_subdir):
+                continue
+            raise
+
+    print("Saving results to", result_subdir)
+    set_output_log_file(os.path.join(result_subdir, 'log.txt'))
+
+    # Export config.
+    try:
+        with open(os.path.join(result_subdir, 'config.txt'), 'wt') as fout:
+            for k, v in sorted(config.__dict__.items()):
+                if not k.startswith('_'):
+                    fout.write("%s = %s\n" % (k, str(v)))
+    except:
+        pass
+
+    return result_subdir
+
+def format_time(seconds):
+    s = int(np.rint(seconds))
+    if s < 60:         return '%ds'                % (s)
+    elif s < 60*60:    return '%dm %02ds'          % (s // 60, s % 60)
+    elif s < 24*60*60: return '%dh %02dm %02ds'    % (s // (60*60), (s // 60) % 60, s % 60)
+    else:              return '%dd %02dh %02dm'    % (s // (24*60*60), (s // (60*60)) % 24, (s // 60) % 60)
+
+#----------------------------------------------------------------------------
+# Locating results.
+
+def locate_result_subdir(run_id_or_result_subdir):
+    if isinstance(run_id_or_result_subdir, str) and os.path.isdir(run_id_or_result_subdir):
+        return run_id_or_result_subdir
+
+    searchdirs = []
+    searchdirs += ['']
+    searchdirs += ['results']
+    searchdirs += ['networks']
+
+    for searchdir in searchdirs:
+        dir = config.result_dir if searchdir == '' else os.path.join(config.result_dir, searchdir)
+        dir = os.path.join(dir, str(run_id_or_result_subdir))
+        if os.path.isdir(dir):
+            return dir
+        prefix = '%03d' % run_id_or_result_subdir if isinstance(run_id_or_result_subdir, int) else str(run_id_or_result_subdir)
+        dirs = sorted(glob.glob(os.path.join(config.result_dir, searchdir, prefix + '-*')))
+        dirs = [dir for dir in dirs if os.path.isdir(dir)]
+        if len(dirs) == 1:
+            return dirs[0]
+    raise IOError('Cannot locate result subdir for run', run_id_or_result_subdir)
+
+def list_network_pkls(run_id_or_result_subdir, include_final=True):
+    result_subdir = locate_result_subdir(run_id_or_result_subdir)
+    pkls = sorted(glob.glob(os.path.join(result_subdir, 'network-*.pkl')))
+    if len(pkls) >= 1 and os.path.basename(pkls[0]) == 'network-final.pkl':
+        if include_final:
+            pkls.append(pkls[0])
+        del pkls[0]
+    return pkls
+
+def locate_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=None):
+    if isinstance(run_id_or_result_subdir_or_network_pkl, str) and os.path.isfile(run_id_or_result_subdir_or_network_pkl):
+        return run_id_or_result_subdir_or_network_pkl
+
+    pkls = list_network_pkls(run_id_or_result_subdir_or_network_pkl)
+    if len(pkls) >= 1 and snapshot is None:
+        return pkls[-1]
+    for pkl in pkls:
+        try:
+            name = os.path.splitext(os.path.basename(pkl))[0]
+            number = int(name.split('-')[-1])
+            if number == snapshot:
+                return pkl
+        except ValueError: pass
+        except IndexError: pass
+    raise IOError('Cannot locate network pkl for snapshot', snapshot)
+
+def get_id_string_for_network_pkl(network_pkl):
+    p = network_pkl.replace('.pkl', '').replace('\\', '/').split('/')
+    return '-'.join(p[max(len(p) - 2, 0):])
+
+#----------------------------------------------------------------------------
+# Loading and using trained networks.
+
+def load_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot=None):
+    return load_pkl(locate_network_pkl(run_id_or_result_subdir_or_network_pkl, snapshot))
+
+def random_latents(num_latents, G, random_state=None):
+    if random_state is not None:
+        return random_state.randn(num_latents, *G.input_shape[1:]).astype(np.float32)
+    else:
+        return np.random.randn(num_latents, *G.input_shape[1:]).astype(np.float32)
+
+def load_dataset_for_previous_run(run_id, **kwargs): # => dataset_obj, mirror_augment
+    result_subdir = locate_result_subdir(run_id)
+
+    # Parse config.txt.
+    parsed_cfg = dict()
+    with open(os.path.join(result_subdir, 'config.txt'), 'rt') as f:
+        for line in f:
+            if line.startswith('dataset =') or line.startswith('train ='):
+                exec(line, parsed_cfg, parsed_cfg)
+    dataset_cfg = parsed_cfg.get('dataset', dict())
+    train_cfg = parsed_cfg.get('train', dict())
+    mirror_augment = train_cfg.get('mirror_augment', False)
+
+    # Handle legacy options.
+    if 'h5_path' in dataset_cfg:
+        dataset_cfg['tfrecord_dir'] = dataset_cfg.pop('h5_path').replace('.h5', '')
+    if 'mirror_augment' in dataset_cfg:
+        mirror_augment = dataset_cfg.pop('mirror_augment')
+    if 'max_labels' in dataset_cfg:
+        v = dataset_cfg.pop('max_labels')
+        if v is None: v = 0
+        if v == 'all': v = 'full'
+        dataset_cfg['max_label_size'] = v
+    if 'max_images' in dataset_cfg:
+        dataset_cfg.pop('max_images')
+
+    # Handle legacy dataset names.
+    v = dataset_cfg['tfrecord_dir']
+    v = v.replace('-32x32', '').replace('-32', '')
+    v = v.replace('-128x128', '').replace('-128', '')
+    v = v.replace('-256x256', '').replace('-256', '')
+    v = v.replace('-1024x1024', '').replace('-1024', '')
+    v = v.replace('celeba-hq', 'celebahq')
+    v = v.replace('cifar-10', 'cifar10')
+    v = v.replace('cifar-100', 'cifar100')
+    v = v.replace('mnist-rgb', 'mnistrgb')
+    v = re.sub('lsun-100k-([^-]*)', 'lsun-\\1-100k', v)
+    v = re.sub('lsun-full-([^-]*)', 'lsun-\\1-full', v)
+    dataset_cfg['tfrecord_dir'] = v
+
+    # Load dataset.
+    dataset_cfg.update(kwargs)
+    dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **dataset_cfg)
+    return dataset_obj, mirror_augment
+
+def apply_mirror_augment(minibatch):
+    mask = np.random.rand(minibatch.shape[0]) < 0.5
+    minibatch = np.array(minibatch)
+    minibatch[mask] = minibatch[mask, :, :, ::-1]
+    return minibatch
+
+#----------------------------------------------------------------------------
+# Text labels.
+
+_text_label_cache = OrderedDict()
+
+def draw_text_label(img, text, x, y, alignx=0.5, aligny=0.5, color=255, opacity=1.0, glow_opacity=1.0, **kwargs):
+    color = np.array(color).flatten().astype(np.float32)
+    assert img.ndim == 3 and img.shape[2] == color.size or color.size == 1
+    alpha, glow = setup_text_label(text, **kwargs)
+    xx, yy = int(np.rint(x - alpha.shape[1] * alignx)), int(np.rint(y - alpha.shape[0] * aligny))
+    xb, yb = max(-xx, 0), max(-yy, 0)
+    xe, ye = min(alpha.shape[1], img.shape[1] - xx), min(alpha.shape[0], img.shape[0] - yy)
+    img = np.array(img)
+    slice = img[yy+yb : yy+ye, xx+xb : xx+xe, :]
+    slice[:] = slice * (1.0 - (1.0 - (1.0 - alpha[yb:ye, xb:xe]) * (1.0 - glow[yb:ye, xb:xe] * glow_opacity)) * opacity)[:, :, np.newaxis]
+    slice[:] = slice + alpha[yb:ye, xb:xe, np.newaxis] * (color * opacity)[np.newaxis, np.newaxis, :]
+    return img
+
+def setup_text_label(text, font='Calibri', fontsize=32, padding=6, glow_size=2.0, glow_coef=3.0, glow_exp=2.0, cache_size=100): # => (alpha, glow)
+    # Lookup from cache.
+    key = (text, font, fontsize, padding, glow_size, glow_coef, glow_exp)
+    if key in _text_label_cache:
+        value = _text_label_cache[key]
+        del _text_label_cache[key] # LRU policy
+        _text_label_cache[key] = value
+        return value
+
+    # Limit cache size.
+    while len(_text_label_cache) >= cache_size:
+        _text_label_cache.popitem(last=False)
+
+    # Render text.
+    import moviepy.editor # pip install moviepy
+    alpha = moviepy.editor.TextClip(text, font=font, fontsize=fontsize).mask.make_frame(0)
+    alpha = np.pad(alpha, padding, mode='constant', constant_values=0.0)
+    glow = scipy.ndimage.gaussian_filter(alpha, glow_size)
+    glow = 1.0 - np.maximum(1.0 - glow * glow_coef, 0.0) ** glow_exp
+
+    # Add to cache.
+    value = (alpha, glow)
+    _text_label_cache[key] = value
+    return value
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/networks.py

@@ -0,0 +1,316 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import numpy as np
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
+
+# NOTE: Do not import any application-specific modules here!
+
+#----------------------------------------------------------------------------
+
+def lerp(a, b, t): return a + (b - a) * t
+def lerp_clip(a, b, t): return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
+def cset(cur_lambda, new_cond, new_lambda): return lambda: tf.cond(new_cond, new_lambda, cur_lambda)
+
+#----------------------------------------------------------------------------
+# Get/create weight tensor for a convolutional or fully-connected layer.
+
+def get_weight(shape, gain=np.sqrt(2), use_wscale=False, fan_in=None):
+    if fan_in is None: fan_in = np.prod(shape[:-1])
+    std = gain / np.sqrt(fan_in) # He init
+    if use_wscale:
+        wscale = tf.constant(np.float32(std), name='wscale')
+        return tf.compat.v1.get_variable('weight', shape=shape, initializer=tf.initializers.random_normal()) * wscale
+    else:
+        return tf.compat.v1.get_variable('weight', shape=shape, initializer=tf.initializers.random_normal(0, std))
+
+#----------------------------------------------------------------------------
+# Fully-connected layer.
+
+def dense(x, fmaps, gain=np.sqrt(2), use_wscale=False):
+    if len(x.shape) > 2:
+        x = tf.reshape(x, [-1, np.prod([d.value for d in x.shape[1:]])])
+    w = get_weight([x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
+    w = tf.cast(w, x.dtype)
+    return tf.matmul(x, w)
+
+#----------------------------------------------------------------------------
+# Convolutional layer.
+
+def conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
+    assert kernel >= 1 and kernel % 2 == 1
+    w = get_weight([kernel, kernel, x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
+    w = tf.cast(w, x.dtype)
+    return tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='SAME', data_format='NCHW')
+
+#----------------------------------------------------------------------------
+# Apply bias to the given activation tensor.
+
+def apply_bias(x):
+    b = tf.compat.v1.get_variable('bias', shape=[x.shape[1]], initializer=tf.initializers.zeros())
+    b = tf.cast(b, x.dtype)
+    if len(x.shape) == 2:
+        return x + b
+    else:
+        return x + tf.reshape(b, [1, -1, 1, 1])
+
+#----------------------------------------------------------------------------
+# Leaky ReLU activation. Same as tf.nn.leaky_relu, but supports FP16.
+
+def leaky_relu(x, alpha=0.2):
+    with tf.name_scope('LeakyRelu'):
+        alpha = tf.constant(alpha, dtype=x.dtype, name='alpha')
+        return tf.maximum(x * alpha, x)
+
+#----------------------------------------------------------------------------
+# Nearest-neighbor upscaling layer.
+
+def upscale2d(x, factor=2):
+    assert isinstance(factor, int) and factor >= 1
+    if factor == 1: return x
+    with tf.variable_scope('Upscale2D'):
+        s = x.shape
+        x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
+        x = tf.tile(x, [1, 1, 1, factor, 1, factor])
+        x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
+        return x
+
+#----------------------------------------------------------------------------
+# Fused upscale2d + conv2d.
+# Faster and uses less memory than performing the operations separately.
+
+def upscale2d_conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
+    assert kernel >= 1 and kernel % 2 == 1
+    w = get_weight([kernel, kernel, fmaps, x.shape[1].value], gain=gain, use_wscale=use_wscale, fan_in=(kernel**2)*x.shape[1].value)
+    w = tf.pad(w, [[1,1], [1,1], [0,0], [0,0]], mode='CONSTANT')
+    w = tf.add_n([w[1:, 1:], w[:-1, 1:], w[1:, :-1], w[:-1, :-1]])
+    w = tf.cast(w, x.dtype)
+    os = [tf.shape(x)[0], fmaps, x.shape[2] * 2, x.shape[3] * 2]
+    return tf.nn.conv2d_transpose(x, w, os, strides=[1,1,2,2], padding='SAME', data_format='NCHW')
+
+#----------------------------------------------------------------------------
+# Box filter downscaling layer.
+
+def downscale2d(x, factor=2):
+    assert isinstance(factor, int) and factor >= 1
+    if factor == 1: return x
+    with tf.variable_scope('Downscale2D'):
+        ksize = [1, 1, factor, factor]
+        return tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding='VALID', data_format='NCHW') # NOTE: requires tf_config['graph_options.place_pruned_graph'] = True
+
+#----------------------------------------------------------------------------
+# Fused conv2d + downscale2d.
+# Faster and uses less memory than performing the operations separately.
+
+def conv2d_downscale2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
+    assert kernel >= 1 and kernel % 2 == 1
+    w = get_weight([kernel, kernel, x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
+    w = tf.pad(w, [[1,1], [1,1], [0,0], [0,0]], mode='CONSTANT')
+    w = tf.add_n([w[1:, 1:], w[:-1, 1:], w[1:, :-1], w[:-1, :-1]]) * 0.25
+    w = tf.cast(w, x.dtype)
+    return tf.nn.conv2d(x, w, strides=[1,1,2,2], padding='SAME', data_format='NCHW')
+
+#----------------------------------------------------------------------------
+# Pixelwise feature vector normalization.
+
+def pixel_norm(x, epsilon=1e-8):
+    with tf.variable_scope('PixelNorm'):
+        return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=1, keepdims=True) + epsilon)
+
+#----------------------------------------------------------------------------
+# Minibatch standard deviation.
+
+def minibatch_stddev_layer(x, group_size=4):
+    with tf.variable_scope('MinibatchStddev'):
+        group_size = tf.minimum(group_size, tf.shape(x)[0])     # Minibatch must be divisible by (or smaller than) group_size.
+        s = x.shape                                             # [NCHW]  Input shape.
+        y = tf.reshape(x, [group_size, -1, s[1], s[2], s[3]])   # [GMCHW] Split minibatch into M groups of size G.
+        y = tf.cast(y, tf.float32)                              # [GMCHW] Cast to FP32.
+        y -= tf.reduce_mean(y, axis=0, keepdims=True)           # [GMCHW] Subtract mean over group.
+        y = tf.reduce_mean(tf.square(y), axis=0)                # [MCHW]  Calc variance over group.
+        y = tf.sqrt(y + 1e-8)                                   # [MCHW]  Calc stddev over group.
+        y = tf.reduce_mean(y, axis=[1,2,3], keepdims=True)      # [M111]  Take average over fmaps and pixels.
+        y = tf.cast(y, x.dtype)                                 # [M111]  Cast back to original data type.
+        y = tf.tile(y, [group_size, 1, s[2], s[3]])             # [N1HW]  Replicate over group and pixels.
+        return tf.concat([x, y], axis=1)                        # [NCHW]  Append as new fmap.
+
+#----------------------------------------------------------------------------
+# Generator network used in the paper.
+
+def G_paper(
+    latents_in,                         # First input: Latent vectors [minibatch, latent_size].
+    labels_in,                          # Second input: Labels [minibatch, label_size].
+    num_channels        = 1,            # Number of output color channels. Overridden based on dataset.
+    resolution          = 32,           # Output resolution. Overridden based on dataset.
+    label_size          = 0,            # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
+    fmap_base           = 8192,         # Overall multiplier for the number of feature maps.
+    fmap_decay          = 1.0,          # log2 feature map reduction when doubling the resolution.
+    fmap_max            = 512,          # Maximum number of feature maps in any layer.
+    latent_size         = None,         # Dimensionality of the latent vectors. None = min(fmap_base, fmap_max).
+    normalize_latents   = True,         # Normalize latent vectors before feeding them to the network?
+    use_wscale          = True,         # Enable equalized learning rate?
+    use_pixelnorm       = True,         # Enable pixelwise feature vector normalization?
+    pixelnorm_epsilon   = 1e-8,         # Constant epsilon for pixelwise feature vector normalization.
+    use_leakyrelu       = True,         # True = leaky ReLU, False = ReLU.
+    dtype               = 'float32',    # Data type to use for activations and outputs.
+    fused_scale         = True,         # True = use fused upscale2d + conv2d, False = separate upscale2d layers.
+    structure           = None,         # 'linear' = human-readable, 'recursive' = efficient, None = select automatically.
+    is_template_graph   = False,        # True = template graph constructed by the Network class, False = actual evaluation.
+    **kwargs):                          # Ignore unrecognized keyword args.
+    # print(":!!!")
+    resolution_log2 = int(np.log2(resolution))
+    assert resolution == 2**resolution_log2 and resolution >= 4
+    def nf(stage): return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
+    def PN(x): return pixel_norm(x, epsilon=pixelnorm_epsilon) if use_pixelnorm else x
+    if latent_size is None: latent_size = nf(0)
+    if structure is None: structure = 'linear' if is_template_graph else 'recursive'
+    act = leaky_relu if use_leakyrelu else tf.nn.relu
+    
+    latents_in.set_shape([None, latent_size])
+    labels_in.set_shape([None, label_size])
+    combo_in = tf.cast(tf.concat([latents_in, labels_in], axis=1), dtype)
+    lod_in = tf.cast(tf.compat.v1.get_variable('lod', initializer=np.float32(0.0), trainable=False), dtype)
+
+    # Building blocks.
+    def block(x, res): # res = 2..resolution_log2
+        with tf.variable_scope('%dx%d' % (2**res, 2**res)):
+            if res == 2: # 4x4
+                if normalize_latents: x = pixel_norm(x, epsilon=pixelnorm_epsilon)
+                with tf.variable_scope('Dense'):
+                    x = dense(x, fmaps=nf(res-1)*16, gain=np.sqrt(2)/4, use_wscale=use_wscale) # override gain to match the original Theano implementation
+                    x = tf.reshape(x, [-1, nf(res-1), 4, 4])
+                    x = PN(act(apply_bias(x)))
+                with tf.variable_scope('Conv'):
+                    x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
+            else: # 8x8 and up
+                if fused_scale:
+                    with tf.variable_scope('Conv0_up'):
+                        x = PN(act(apply_bias(upscale2d_conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
+                else:
+                    x = upscale2d(x)
+                    with tf.variable_scope('Conv0'):
+                        x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
+                with tf.variable_scope('Conv1'):
+                    x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
+            return x
+    def torgb(x, res): # res = 2..resolution_log2
+        lod = resolution_log2 - res
+        with tf.variable_scope('ToRGB_lod%d' % lod):
+            return apply_bias(conv2d(x, fmaps=num_channels, kernel=1, gain=1, use_wscale=use_wscale))
+
+    # Linear structure: simple but inefficient.
+    if structure == 'linear':
+        x = block(combo_in, 2)
+        images_out = torgb(x, 2)
+        for res in range(3, resolution_log2 + 1):
+            lod = resolution_log2 - res
+            x = block(x, res)
+            img = torgb(x, res)
+            images_out = upscale2d(images_out)
+            with tf.variable_scope('Grow_lod%d' % lod):
+                images_out = lerp_clip(img, images_out, lod_in - lod)
+
+    # Recursive structure: complex but efficient.
+    if structure == 'recursive':
+        def grow(x, res, lod):
+            y = block(x, res)
+            img = lambda: upscale2d(torgb(y, res), 2**lod)
+            if res > 2: img = cset(img, (lod_in > lod), lambda: upscale2d(lerp(torgb(y, res), upscale2d(torgb(x, res - 1)), lod_in - lod), 2**lod))
+            if lod > 0: img = cset(img, (lod_in < lod), lambda: grow(y, res + 1, lod - 1))
+            return img()
+        images_out = grow(combo_in, 2, resolution_log2 - 2)
+        
+    assert images_out.dtype == tf.as_dtype(dtype)
+    images_out = tf.identity(images_out, name='images_out')
+    return images_out
+
+#----------------------------------------------------------------------------
+# Discriminator network used in the paper.
+
+def D_paper(
+    images_in,                          # Input: Images [minibatch, channel, height, width].
+    num_channels        = 1,            # Number of input color channels. Overridden based on dataset.
+    resolution          = 32,           # Input resolution. Overridden based on dataset.
+    label_size          = 0,            # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
+    fmap_base           = 8192,         # Overall multiplier for the number of feature maps.
+    fmap_decay          = 1.0,          # log2 feature map reduction when doubling the resolution.
+    fmap_max            = 512,          # Maximum number of feature maps in any layer.
+    use_wscale          = True,         # Enable equalized learning rate?
+    mbstd_group_size    = 4,            # Group size for the minibatch standard deviation layer, 0 = disable.
+    dtype               = 'float32',    # Data type to use for activations and outputs.
+    fused_scale         = True,         # True = use fused conv2d + downscale2d, False = separate downscale2d layers.
+    structure           = None,         # 'linear' = human-readable, 'recursive' = efficient, None = select automatically
+    is_template_graph   = False,        # True = template graph constructed by the Network class, False = actual evaluation.
+    **kwargs):                          # Ignore unrecognized keyword args.
+    
+    resolution_log2 = int(np.log2(resolution))
+    assert resolution == 2**resolution_log2 and resolution >= 4
+    def nf(stage): return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
+    if structure is None: structure = 'linear' if is_template_graph else 'recursive'
+    act = leaky_relu
+
+    images_in.set_shape([None, num_channels, resolution, resolution])
+    images_in = tf.cast(images_in, dtype)
+    lod_in = tf.cast(tf.compat.v1.get_variable('lod', initializer=np.float32(0.0), trainable=False), dtype)
+
+    # Building blocks.
+    def fromrgb(x, res): # res = 2..resolution_log2
+        with tf.variable_scope('FromRGB_lod%d' % (resolution_log2 - res)):
+            return act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=1, use_wscale=use_wscale)))
+    def block(x, res): # res = 2..resolution_log2
+        with tf.variable_scope('%dx%d' % (2**res, 2**res)):
+            if res >= 3: # 8x8 and up
+                with tf.variable_scope('Conv0'):
+                    x = act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale)))
+                if fused_scale:
+                    with tf.variable_scope('Conv1_down'):
+                        x = act(apply_bias(conv2d_downscale2d(x, fmaps=nf(res-2), kernel=3, use_wscale=use_wscale)))
+                else:
+                    with tf.variable_scope('Conv1'):
+                        x = act(apply_bias(conv2d(x, fmaps=nf(res-2), kernel=3, use_wscale=use_wscale)))
+                    x = downscale2d(x)
+            else: # 4x4
+                if mbstd_group_size > 1:
+                    x = minibatch_stddev_layer(x, mbstd_group_size)
+                with tf.variable_scope('Conv'):
+                    x = act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale)))
+                with tf.variable_scope('Dense0'):
+                    x = act(apply_bias(dense(x, fmaps=nf(res-2), use_wscale=use_wscale)))
+                with tf.variable_scope('Dense1'):
+                    x = apply_bias(dense(x, fmaps=1+label_size, gain=1, use_wscale=use_wscale))
+            return x
+    
+    # Linear structure: simple but inefficient.
+    if structure == 'linear':
+        img = images_in
+        x = fromrgb(img, resolution_log2)
+        for res in range(resolution_log2, 2, -1):
+            lod = resolution_log2 - res
+            x = block(x, res)
+            img = downscale2d(img)
+            y = fromrgb(img, res - 1)
+            with tf.variable_scope('Grow_lod%d' % lod):
+                x = lerp_clip(x, y, lod_in - lod)
+        combo_out = block(x, 2)
+
+    # Recursive structure: complex but efficient.
+    if structure == 'recursive':
+        def grow(res, lod):
+            x = lambda: fromrgb(downscale2d(images_in, 2**lod), res)
+            if lod > 0: x = cset(x, (lod_in < lod), lambda: grow(res + 1, lod - 1))
+            x = block(x(), res); y = lambda: x
+            if res > 2: y = cset(y, (lod_in > lod), lambda: lerp(x, fromrgb(downscale2d(images_in, 2**(lod+1)), res - 1), lod_in - lod))
+            return y()
+        combo_out = grow(2, resolution_log2 - 2)
+
+    assert combo_out.dtype == tf.as_dtype(dtype)
+    scores_out = tf.identity(combo_out[:, :1], name='scores_out')
+    labels_out = tf.identity(combo_out[:, 1:], name='labels_out')
+    return scores_out, labels_out
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/tfutil.py

@@ -0,0 +1,770 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import os
+import sys
+import inspect
+import importlib
+import imp
+import numpy as np
+from collections import OrderedDict
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
+import networks
+
+#----------------------------------------------------------------------------
+# Convenience.
+
+def run(*args, **kwargs): # Run the specified ops in the default session.
+    return tf.get_default_session().run(*args, **kwargs)
+
+def is_tf_expression(x):
+    return isinstance(x, tf.Tensor) or isinstance(x, tf.Variable) or isinstance(x, tf.Operation)
+
+def shape_to_list(shape):
+    return [dim.value for dim in shape]
+
+def flatten(x):
+    with tf.name_scope('Flatten'):
+        return tf.reshape(x, [-1])
+
+def log2(x):
+    with tf.name_scope('Log2'):
+        return tf.log(x) * np.float32(1.0 / np.log(2.0))
+
+def exp2(x):
+    with tf.name_scope('Exp2'):
+        return tf.exp(x * np.float32(np.log(2.0)))
+
+def lerp(a, b, t):
+    with tf.name_scope('Lerp'):
+        return a + (b - a) * t
+
+def lerp_clip(a, b, t):
+    with tf.name_scope('LerpClip'):
+        return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
+
+def absolute_name_scope(scope): # Forcefully enter the specified name scope, ignoring any surrounding scopes.
+    return tf.name_scope(scope + '/')
+
+#----------------------------------------------------------------------------
+# Initialize TensorFlow graph and session using good default settings.
+
+def init_tf(config_dict=dict()):
+    if tf.get_default_session() is None:
+        tf.set_random_seed(np.random.randint(1 << 31))
+        create_session(config_dict, force_as_default=True)
+
+#----------------------------------------------------------------------------
+# Create tf.Session based on config dict of the form
+# {'gpu_options.allow_growth': True}
+
+def create_session(config_dict=dict(), force_as_default=False):
+    config = tf.ConfigProto()
+    for key, value in config_dict.items():
+        fields = key.split('.')
+        obj = config
+        for field in fields[:-1]:
+            obj = getattr(obj, field)
+        setattr(obj, fields[-1], value)
+    session = tf.Session(config=config)
+    if force_as_default:
+        session._default_session = session.as_default()
+        session._default_session.enforce_nesting = False
+        session._default_session.__enter__()
+    return session
+
+#----------------------------------------------------------------------------
+# Initialize all tf.Variables that have not already been initialized.
+# Equivalent to the following, but more efficient and does not bloat the tf graph:
+#   tf.variables_initializer(tf.report_unitialized_variables()).run()
+
+def init_uninited_vars(vars=None):
+    if vars is None: vars = tf.global_variables()
+    test_vars = []; test_ops = []
+    with tf.control_dependencies(None): # ignore surrounding control_dependencies
+        for var in vars:
+            assert is_tf_expression(var)
+            try:
+                tf.get_default_graph().get_tensor_by_name(var.name.replace(':0', '/IsVariableInitialized:0'))
+            except KeyError:
+                # Op does not exist => variable may be uninitialized.
+                test_vars.append(var)
+                with absolute_name_scope(var.name.split(':')[0]):
+                    test_ops.append(tf.is_variable_initialized(var))
+    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
+    run([var.initializer for var in init_vars])
+
+#----------------------------------------------------------------------------
+# Set the values of given tf.Variables.
+# Equivalent to the following, but more efficient and does not bloat the tf graph:
+#   tfutil.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
+
+def set_vars(var_to_value_dict):
+    ops = []
+    feed_dict = {}
+    for var, value in var_to_value_dict.items():
+        assert is_tf_expression(var)
+        try:
+            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(':0', '/setter:0')) # look for existing op
+        except KeyError:
+            with absolute_name_scope(var.name.split(':')[0]):
+                with tf.control_dependencies(None): # ignore surrounding control_dependencies
+                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, 'new_value'), name='setter') # create new setter
+        ops.append(setter)
+        feed_dict[setter.op.inputs[1]] = value
+    run(ops, feed_dict)
+
+#----------------------------------------------------------------------------
+# Autosummary creates an identity op that internally keeps track of the input
+# values and automatically shows up in TensorBoard. The reported value
+# represents an average over input components. The average is accumulated
+# constantly over time and flushed when save_summaries() is called.
+#
+# Notes:
+# - The output tensor must be used as an input for something else in the
+#   graph. Otherwise, the autosummary op will not get executed, and the average
+#   value will not get accumulated.
+# - It is perfectly fine to include autosummaries with the same name in
+#   several places throughout the graph, even if they are executed concurrently.
+# - It is ok to also pass in a python scalar or numpy array. In this case, it
+#   is added to the average immediately.
+
+_autosummary_vars = OrderedDict() # name => [var, ...]
+_autosummary_immediate = OrderedDict() # name => update_op, update_value
+_autosummary_finalized = False
+
+def autosummary(name, value):
+    id = name.replace('/', '_')
+    if is_tf_expression(value):
+        with tf.name_scope('summary_' + id), tf.device(value.device):
+            update_op = _create_autosummary_var(name, value)
+            with tf.control_dependencies([update_op]):
+                return tf.identity(value)
+    else: # python scalar or numpy array
+        if name not in _autosummary_immediate:
+            with absolute_name_scope('Autosummary/' + id), tf.device(None), tf.control_dependencies(None):
+                update_value = tf.placeholder(tf.float32)
+                update_op = _create_autosummary_var(name, update_value)
+                _autosummary_immediate[name] = update_op, update_value
+        update_op, update_value = _autosummary_immediate[name]
+        run(update_op, {update_value: np.float32(value)})
+        return value
+
+# Create the necessary ops to include autosummaries in TensorBoard report.
+# Note: This should be done only once per graph.
+def finalize_autosummaries():
+    global _autosummary_finalized
+    if _autosummary_finalized:
+        return
+    _autosummary_finalized = True
+    init_uninited_vars([var for vars in _autosummary_vars.values() for var in vars])
+    with tf.device(None), tf.control_dependencies(None):
+        for name, vars in _autosummary_vars.items():
+            id = name.replace('/', '_')
+            with absolute_name_scope('Autosummary/' + id):
+                sum = tf.add_n(vars)
+                avg = sum[0] / sum[1]
+                with tf.control_dependencies([avg]): # read before resetting
+                    reset_ops = [tf.assign(var, tf.zeros(2)) for var in vars]
+                    with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting
+                        tf.summary.scalar(name, avg)
+
+# Internal helper for creating autosummary accumulators.
+def _create_autosummary_var(name, value_expr):
+    assert not _autosummary_finalized
+    v = tf.cast(value_expr, tf.float32)
+    if v.shape.ndims is 0:
+        v = [v, np.float32(1.0)]
+    elif v.shape.ndims is 1:
+        v = [tf.reduce_sum(v), tf.cast(tf.shape(v)[0], tf.float32)]
+    else:
+        v = [tf.reduce_sum(v), tf.reduce_prod(tf.cast(tf.shape(v), tf.float32))]
+    v = tf.cond(tf.is_finite(v[0]), lambda: tf.stack(v), lambda: tf.zeros(2))
+    with tf.control_dependencies(None):
+        var = tf.Variable(tf.zeros(2)) # [numerator, denominator]
+    update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
+    if name in _autosummary_vars:
+        _autosummary_vars[name].append(var)
+    else:
+        _autosummary_vars[name] = [var]
+    return update_op
+
+#----------------------------------------------------------------------------
+# Call filewriter.add_summary() with all summaries in the default graph,
+# automatically finalizing and merging them on the first call.
+
+_summary_merge_op = None
+
+def save_summaries(filewriter, global_step=None):
+    global _summary_merge_op
+    if _summary_merge_op is None:
+        finalize_autosummaries()
+        with tf.device(None), tf.control_dependencies(None):
+            _summary_merge_op = tf.summary.merge_all()
+    filewriter.add_summary(_summary_merge_op.eval(), global_step)
+
+#----------------------------------------------------------------------------
+# Utilities for importing modules and objects by name.
+
+def import_module(module_or_obj_name):
+    parts = module_or_obj_name.split('.')
+    parts[0] = {'np': 'numpy', 'tf': 'tensorflow'}.get(parts[0], parts[0])
+    for i in range(len(parts), 0, -1):
+        try:
+            module = importlib.import_module('.'.join(parts[:i]))
+            relative_obj_name = '.'.join(parts[i:])
+            return module, relative_obj_name
+        except ImportError:
+            pass
+    raise ImportError(module_or_obj_name)
+
+def find_obj_in_module(module, relative_obj_name):
+    obj = module
+    for part in relative_obj_name.split('.'):
+        obj = getattr(obj, part)
+    return obj
+
+def import_obj(obj_name):
+    module, relative_obj_name = import_module(obj_name)
+    return find_obj_in_module(module, relative_obj_name)
+
+def call_func_by_name(*args, func=None, **kwargs):
+    assert func is not None
+    return import_obj(func)(*args, **kwargs)
+
+#----------------------------------------------------------------------------
+# Wrapper for tf.train.Optimizer that automatically takes care of:
+# - Gradient averaging for multi-GPU training.
+# - Dynamic loss scaling and typecasts for FP16 training.
+# - Ignoring corrupted gradients that contain NaNs/Infs.
+# - Reporting statistics.
+# - Well-chosen default settings.
+
+class Optimizer:
+    def __init__(
+        self,
+        name                = 'Train',
+        tf_optimizer        = 'tf.train.AdamOptimizer',
+        learning_rate       = 0.001,
+        use_loss_scaling    = False,
+        loss_scaling_init   = 64.0,
+        loss_scaling_inc    = 0.0005,
+        loss_scaling_dec    = 1.0,
+        **kwargs):
+
+        # Init fields.
+        self.name               = name
+        self.learning_rate      = tf.convert_to_tensor(learning_rate)
+        self.id                 = self.name.replace('/', '.')
+        self.scope              = tf.get_default_graph().unique_name(self.id)
+        self.optimizer_class    = import_obj(tf_optimizer)
+        self.optimizer_kwargs   = dict(kwargs)
+        self.use_loss_scaling   = use_loss_scaling
+        self.loss_scaling_init  = loss_scaling_init
+        self.loss_scaling_inc   = loss_scaling_inc
+        self.loss_scaling_dec   = loss_scaling_dec
+        self._grad_shapes       = None          # [shape, ...]
+        self._dev_opt           = OrderedDict() # device => optimizer
+        self._dev_grads         = OrderedDict() # device => [[(grad, var), ...], ...]
+        self._dev_ls_var        = OrderedDict() # device => variable (log2 of loss scaling factor)
+        self._updates_applied   = False
+
+    # Register the gradients of the given loss function with respect to the given variables.
+    # Intended to be called once per GPU.
+    def register_gradients(self, loss, vars):
+        assert not self._updates_applied
+
+        # Validate arguments.
+        if isinstance(vars, dict):
+            vars = list(vars.values()) # allow passing in Network.trainables as vars
+        assert isinstance(vars, list) and len(vars) >= 1
+        assert all(is_tf_expression(expr) for expr in vars + [loss])
+        if self._grad_shapes is None:
+            self._grad_shapes = [shape_to_list(var.shape) for var in vars]
+        assert len(vars) == len(self._grad_shapes)
+        assert all(shape_to_list(var.shape) == var_shape for var, var_shape in zip(vars, self._grad_shapes))
+        dev = loss.device
+        assert all(var.device == dev for var in vars)
+
+        # Register device and compute gradients.
+        with tf.name_scope(self.id + '_grad'), tf.device(dev):
+            if dev not in self._dev_opt:
+                opt_name = self.scope.replace('/', '_') + '_opt%d' % len(self._dev_opt)
+                self._dev_opt[dev] = self.optimizer_class(name=opt_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
+                self._dev_grads[dev] = []
+            loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
+            grads = self._dev_opt[dev].compute_gradients(loss, vars, gate_gradients=tf.train.Optimizer.GATE_NONE) # disable gating to reduce memory usage
+            grads = [(g, v) if g is not None else (tf.zeros_like(v), v) for g, v in grads] # replace disconnected gradients with zeros
+            self._dev_grads[dev].append(grads)
+
+    # Construct training op to update the registered variables based on their gradients.
+    def apply_updates(self):
+        assert not self._updates_applied
+        self._updates_applied = True
+        devices = list(self._dev_grads.keys())
+        total_grads = sum(len(grads) for grads in self._dev_grads.values())
+        assert len(devices) >= 1 and total_grads >= 1
+        ops = []
+        with absolute_name_scope(self.scope):
+
+            # Cast gradients to FP32 and calculate partial sum within each device.
+            dev_grads = OrderedDict() # device => [(grad, var), ...]
+            for dev_idx, dev in enumerate(devices):
+                with tf.name_scope('ProcessGrads%d' % dev_idx), tf.device(dev):
+                    sums = []
+                    for gv in zip(*self._dev_grads[dev]):
+                        assert all(v is gv[0][1] for g, v in gv)
+                        g = [tf.cast(g, tf.float32) for g, v in gv]
+                        g = g[0] if len(g) == 1 else tf.add_n(g)
+                        sums.append((g, gv[0][1]))
+                    dev_grads[dev] = sums
+
+            # Sum gradients across devices.
+            if len(devices) > 1:
+                with tf.name_scope('SumAcrossGPUs'), tf.device(None):
+                    for var_idx, grad_shape in enumerate(self._grad_shapes):
+                        g = [dev_grads[dev][var_idx][0] for dev in devices]
+                        if np.prod(grad_shape): # nccl does not support zero-sized tensors
+                            g = tf.contrib.nccl.all_sum(g)
+                        for dev, gg in zip(devices, g):
+                            dev_grads[dev][var_idx] = (gg, dev_grads[dev][var_idx][1])
+
+            # Apply updates separately on each device.
+            for dev_idx, (dev, grads) in enumerate(dev_grads.items()):
+                with tf.name_scope('ApplyGrads%d' % dev_idx), tf.device(dev):
+
+                    # Scale gradients as needed.
+                    if self.use_loss_scaling or total_grads > 1:
+                        with tf.name_scope('Scale'):
+                            coef = tf.constant(np.float32(1.0 / total_grads), name='coef')
+                            coef = self.undo_loss_scaling(coef)
+                            grads = [(g * coef, v) for g, v in grads]
+
+                    # Check for overflows.
+                    with tf.name_scope('CheckOverflow'):
+                        grad_ok = tf.reduce_all(tf.stack([tf.reduce_all(tf.is_finite(g)) for g, v in grads]))
+
+                    # Update weights and adjust loss scaling.
+                    with tf.name_scope('UpdateWeights'):
+                        opt = self._dev_opt[dev]
+                        ls_var = self.get_loss_scaling_var(dev)
+                        if not self.use_loss_scaling:
+                            ops.append(tf.cond(grad_ok, lambda: opt.apply_gradients(grads), tf.no_op))
+                        else:
+                            ops.append(tf.cond(grad_ok,
+                                lambda: tf.group(tf.assign_add(ls_var, self.loss_scaling_inc), opt.apply_gradients(grads)),
+                                lambda: tf.group(tf.assign_sub(ls_var, self.loss_scaling_dec))))
+
+                    # Report statistics on the last device.
+                    if dev == devices[-1]:
+                        with tf.name_scope('Statistics'):
+                            ops.append(autosummary(self.id + '/learning_rate', self.learning_rate))
+                            ops.append(autosummary(self.id + '/overflow_frequency', tf.where(grad_ok, 0, 1)))
+                            if self.use_loss_scaling:
+                                ops.append(autosummary(self.id + '/loss_scaling_log2', ls_var))
+
+            # Initialize variables and group everything into a single op.
+            self.reset_optimizer_state()
+            init_uninited_vars(list(self._dev_ls_var.values()))
+            return tf.group(*ops, name='TrainingOp')
+
+    # Reset internal state of the underlying optimizer.
+    def reset_optimizer_state(self):
+        run([var.initializer for opt in self._dev_opt.values() for var in opt.variables()])
+
+    # Get or create variable representing log2 of the current dynamic loss scaling factor.
+    def get_loss_scaling_var(self, device):
+        if not self.use_loss_scaling:
+            return None
+        if device not in self._dev_ls_var:
+            with absolute_name_scope(self.scope + '/LossScalingVars'), tf.control_dependencies(None):
+                self._dev_ls_var[device] = tf.Variable(np.float32(self.loss_scaling_init), name='loss_scaling_var')
+        return self._dev_ls_var[device]
+
+    # Apply dynamic loss scaling for the given expression.
+    def apply_loss_scaling(self, value):
+        assert is_tf_expression(value)
+        if not self.use_loss_scaling:
+            return value
+        return value * exp2(self.get_loss_scaling_var(value.device))
+
+    # Undo the effect of dynamic loss scaling for the given expression.
+    def undo_loss_scaling(self, value):
+        assert is_tf_expression(value)
+        if not self.use_loss_scaling:
+            return value
+        return value * exp2(-self.get_loss_scaling_var(value.device))
+
+#----------------------------------------------------------------------------
+# Generic network abstraction.
+#
+# Acts as a convenience wrapper for a parameterized network construction
+# function, providing several utility methods and convenient access to
+# the inputs/outputs/weights.
+#
+# Network objects can be safely pickled and unpickled for long-term
+# archival purposes. The pickling works reliably as long as the underlying
+# network construction function is defined in a standalone Python module
+# that has no side effects or application-specific imports.
+
+network_import_handlers = []    # Custom import handlers for dealing with legacy data in pickle import.
+_network_import_modules = []    # Temporary modules create during pickle import.
+
+class Network:
+    def __init__(self,
+        name=None,          # Network name. Used to select TensorFlow name and variable scopes.
+        func=None,          # Fully qualified name of the underlying network construction function.
+        **static_kwargs):   # Keyword arguments to be passed in to the network construction function.
+
+        self._init_fields()
+        self.name = name
+        self.static_kwargs = dict(static_kwargs)
+
+        # Init build func.
+        module, self._build_func_name = import_module(func)
+        self._build_module_src = inspect.getsource(module)
+        self._build_func = find_obj_in_module(module, self._build_func_name)
+
+        # Init graph.
+        self._init_graph()
+        self.reset_vars()
+
+    def _init_fields(self):
+        self.name               = None          # User-specified name, defaults to build func name if None.
+        self.scope              = None          # Unique TF graph scope, derived from the user-specified name.
+        self.static_kwargs      = dict()        # Arguments passed to the user-supplied build func.
+        self.num_inputs         = 0             # Number of input tensors.
+        self.num_outputs        = 0             # Number of output tensors.
+        self.input_shapes       = [[]]          # Input tensor shapes (NC or NCHW), including minibatch dimension.
+        self.output_shapes      = [[]]          # Output tensor shapes (NC or NCHW), including minibatch dimension.
+        self.input_shape        = []            # Short-hand for input_shapes[0].
+        self.output_shape       = []            # Short-hand for output_shapes[0].
+        self.input_templates    = []            # Input placeholders in the template graph.
+        self.output_templates   = []            # Output tensors in the template graph.
+        self.input_names        = []            # Name string for each input.
+        self.output_names       = []            # Name string for each output.
+        self.vars               = OrderedDict() # All variables (localname => var).
+        self.trainables         = OrderedDict() # Trainable variables (localname => var).
+        self._build_func        = None          # User-supplied build function that constructs the network.
+        self._build_func_name   = None          # Name of the build function.
+        self._build_module_src  = None          # Full source code of the module containing the build function.
+        self._run_cache         = dict()        # Cached graph data for Network.run().
+        
+    def _init_graph(self):
+        # Collect inputs.
+        self.input_names = []
+        for param in inspect.signature(self._build_func).parameters.values():
+            if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
+                self.input_names.append(param.name)
+        self.num_inputs = len(self.input_names)
+        assert self.num_inputs >= 1
+
+        # Choose name and scope.
+        if self.name is None:
+            self.name = self._build_func_name
+        self.scope = tf.get_default_graph().unique_name(self.name.replace('/', '_'), mark_as_used=False)
+        
+        # Build template graph.
+        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
+            assert tf.get_variable_scope().name == self.scope
+            with absolute_name_scope(self.scope): # ignore surrounding name_scope
+                with tf.control_dependencies(None): # ignore surrounding control_dependencies
+                    self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
+                    out_expr = self._build_func(*self.input_templates, is_template_graph=True, **self.static_kwargs)
+            
+        # Collect outputs.
+        assert is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+        self.output_templates = [out_expr] if is_tf_expression(out_expr) else list(out_expr)
+        self.output_names = [t.name.split('/')[-1].split(':')[0] for t in self.output_templates]
+        self.num_outputs = len(self.output_templates)
+        assert self.num_outputs >= 1
+        
+        # Populate remaining fields.
+        self.input_shapes   = [shape_to_list(t.shape) for t in self.input_templates]
+        self.output_shapes  = [shape_to_list(t.shape) for t in self.output_templates]
+        self.input_shape    = self.input_shapes[0]
+        self.output_shape   = self.output_shapes[0]
+        print('scope ', self.scope + '/')
+        # for var in tf.global_variables(self.scope + '/'):
+        #     print('find  ', self.get_var_localname(var), var)
+        self.vars           = OrderedDict([(self.get_var_localname(var), var) for var in tf.global_variables(self.scope + '/')])
+        self.trainables     = OrderedDict([(self.get_var_localname(var), var) for var in tf.trainable_variables(self.scope + '/')])
+
+    # Run initializers for all variables defined by this network.
+    def reset_vars(self):
+        run([var.initializer for var in self.vars.values()])
+
+    # Run initializers for all trainable variables defined by this network.
+    def reset_trainables(self):
+        run([var.initializer for var in self.trainables.values()])
+
+    # Get TensorFlow expression(s) for the output(s) of this network, given the inputs.
+    def get_output_for(self, *in_expr, return_as_list=False, **dynamic_kwargs):
+        assert len(in_expr) == self.num_inputs
+        all_kwargs = dict(self.static_kwargs)
+        all_kwargs.update(dynamic_kwargs)
+        with tf.variable_scope(self.scope, reuse=True):
+            assert tf.get_variable_scope().name == self.scope
+            named_inputs = [tf.identity(expr, name=name) for expr, name in zip(in_expr, self.input_names)]
+            out_expr = self._build_func(*named_inputs, **all_kwargs)
+        assert is_tf_expression(out_expr) or isinstance(out_expr, tuple)
+        if return_as_list:
+            out_expr = [out_expr] if is_tf_expression(out_expr) else list(out_expr)
+        return out_expr
+
+    # Get the local name of a given variable, excluding any surrounding name scopes.
+    def get_var_localname(self, var_or_globalname):
+        assert is_tf_expression(var_or_globalname) or isinstance(var_or_globalname, str)
+        globalname = var_or_globalname if isinstance(var_or_globalname, str) else var_or_globalname.name
+        assert globalname.startswith(self.scope + '/')
+        localname = globalname[len(self.scope) + 1:]
+        localname = localname.split(':')[0]
+        return localname
+
+    # Find variable by local or global name.
+    def find_var(self, var_or_localname):
+        assert is_tf_expression(var_or_localname) or isinstance(var_or_localname, str)
+        return self.vars[var_or_localname] if isinstance(var_or_localname, str) else var_or_localname
+
+    # Get the value of a given variable as NumPy array.
+    # Note: This method is very inefficient -- prefer to use tfutil.run(list_of_vars) whenever possible.
+    def get_var(self, var_or_localname):
+        return self.find_var(var_or_localname).eval()
+        
+    # Set the value of a given variable based on the given NumPy array.
+    # Note: This method is very inefficient -- prefer to use tfutil.set_vars() whenever possible.
+    def set_var(self, var_or_localname, new_value):
+        return set_vars({self.find_var(var_or_localname): new_value})
+
+    # Pickle export.
+    def __getstate__(self):
+        return {
+            'version':          2,
+            'name':             self.name,
+            'static_kwargs':    self.static_kwargs,
+            'build_module_src': self._build_module_src,
+            'build_func_name':  self._build_func_name,
+            'variables':        list(zip(self.vars.keys(), run(list(self.vars.values()))))}
+
+    # Pickle import.
+    def __setstate__(self, state):
+        self._init_fields()
+
+        # Execute custom import handlers.
+        for handler in network_import_handlers:
+            state = handler(state)
+
+        # Set basic fields.
+        assert state['version'] == 2
+        self.name = state['name']
+        self.static_kwargs = state['static_kwargs']
+        self._build_module_src = state['build_module_src']
+        self._build_func_name = state['build_func_name']
+        
+        # Parse imported module.
+        module = imp.new_module('_tfutil_network_import_module_%d' % len(_network_import_modules))
+        exec(self._build_module_src, module.__dict__)
+        # print(self._build_func_name, '???', module)
+        # self._build_func = find_obj_in_module(module, self._build_func_name)
+        if 'G_paper' in self._build_func_name:
+            self._build_func = networks.G_paper
+        elif 'D_paper' in self._build_func_name:
+            self._build_func = networks.D_paper
+        else:
+            print(self._build_func_name, 'not found')
+            exit(1)
+        # print(self._build_func)
+        # self._build_func(0, 0, 0)
+        _network_import_modules.append(module) # avoid gc
+        
+        # Init graph.
+        self._init_graph()
+        self.reset_vars()
+        # for v in self.vars:
+        #     print('self ', v)
+            # self.vars[var_or_localname] if isinstance(var_or_localname, str) else var_or_localname
+        set_vars({self.find_var(name): value for name, value in state['variables'] if name in self.vars})
+        # for name, value in state['variables']:
+        #     print('name ', name)
+        #     print('var  ', self.find_var(name))
+
+    # Create a clone of this network with its own copy of the variables.
+    def clone(self, name=None):
+        net = object.__new__(Network)
+        net._init_fields()
+        net.name = name if name is not None else self.name
+        net.static_kwargs = dict(self.static_kwargs)
+        net._build_module_src = self._build_module_src
+        net._build_func_name = self._build_func_name
+        net._build_func = self._build_func
+        net._init_graph()
+        net.copy_vars_from(self)
+        return net
+
+    # Copy the values of all variables from the given network.
+    def copy_vars_from(self, src_net):
+        assert isinstance(src_net, Network)
+        name_to_value = run({name: src_net.find_var(name) for name in self.vars.keys()})
+        set_vars({self.find_var(name): value for name, value in name_to_value.items()})
+
+    # Copy the values of all trainable variables from the given network.
+    def copy_trainables_from(self, src_net):
+        assert isinstance(src_net, Network)
+        name_to_value = run({name: src_net.find_var(name) for name in self.trainables.keys()})
+        set_vars({self.find_var(name): value for name, value in name_to_value.items()})
+
+    # Create new network with the given parameters, and copy all variables from this network.
+    def convert(self, name=None, func=None, **static_kwargs):
+        net = Network(name, func, **static_kwargs)
+        net.copy_vars_from(self)
+        return net
+
+    # Construct a TensorFlow op that updates the variables of this network
+    # to be slightly closer to those of the given network.
+    def setup_as_moving_average_of(self, src_net, beta=0.99, beta_nontrainable=0.0):
+        assert isinstance(src_net, Network)
+        with absolute_name_scope(self.scope):
+            with tf.name_scope('MovingAvg'):
+                ops = []
+                for name, var in self.vars.items():
+                    if name in src_net.vars:
+                        cur_beta = beta if name in self.trainables else beta_nontrainable
+                        new_value = lerp(src_net.vars[name], var, cur_beta)
+                        ops.append(var.assign(new_value))
+                return tf.group(*ops)
+
+    # Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
+    def run(self, *in_arrays,
+        return_as_list  = False,    # True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
+        print_progress  = False,    # Print progress to the console? Useful for very large input arrays.
+        minibatch_size  = None,     # Maximum minibatch size to use, None = disable batching.
+        num_gpus        = 1,        # Number of GPUs to use.
+        out_mul         = 1.0,      # Multiplicative constant to apply to the output(s).
+        out_add         = 0.0,      # Additive constant to apply to the output(s).
+        out_shrink      = 1,        # Shrink the spatial dimensions of the output(s) by the given factor.
+        out_dtype       = None,     # Convert the output to the specified data type.
+        **dynamic_kwargs):          # Additional keyword arguments to pass into the network construction function.
+
+        assert len(in_arrays) == self.num_inputs
+        num_items = in_arrays[0].shape[0]
+        if minibatch_size is None:
+            minibatch_size = num_items
+        key = str([list(sorted(dynamic_kwargs.items())), num_gpus, out_mul, out_add, out_shrink, out_dtype])
+
+        # Build graph.
+        if key not in self._run_cache:
+            with absolute_name_scope(self.scope + '/Run'), tf.control_dependencies(None):
+                in_split = list(zip(*[tf.split(x, num_gpus) for x in self.input_templates]))
+                out_split = []
+                for gpu in range(num_gpus):
+                    with tf.device('/gpu:%d' % gpu):
+                        out_expr = self.get_output_for(*in_split[gpu], return_as_list=True, **dynamic_kwargs)
+                        if out_mul != 1.0:
+                            out_expr = [x * out_mul for x in out_expr]
+                        if out_add != 0.0:
+                            out_expr = [x + out_add for x in out_expr]
+                        if out_shrink > 1:
+                            ksize = [1, 1, out_shrink, out_shrink]
+                            out_expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding='VALID', data_format='NCHW') for x in out_expr]
+                        if out_dtype is not None:
+                            if tf.as_dtype(out_dtype).is_integer:
+                                out_expr = [tf.round(x) for x in out_expr]
+                            out_expr = [tf.saturate_cast(x, out_dtype) for x in out_expr]
+                        out_split.append(out_expr)
+                self._run_cache[key] = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
+
+        # Run minibatches.
+        out_expr = self._run_cache[key]
+        out_arrays = [np.empty([num_items] + shape_to_list(expr.shape)[1:], expr.dtype.name) for expr in out_expr]
+        for mb_begin in range(0, num_items, minibatch_size):
+            if print_progress:
+                print('\r%d / %d' % (mb_begin, num_items), end='')
+            mb_end = min(mb_begin + minibatch_size, num_items)
+            mb_in = [src[mb_begin : mb_end] for src in in_arrays]
+            mb_out = tf.get_default_session().run(out_expr, dict(zip(self.input_templates, mb_in)))
+            for dst, src in zip(out_arrays, mb_out):
+                dst[mb_begin : mb_end] = src
+
+        # Done.
+        if print_progress:
+            print('\r%d / %d' % (num_items, num_items))
+        if not return_as_list:
+            out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
+        return out_arrays
+
+    # Returns a list of (name, output_expr, trainable_vars) tuples corresponding to
+    # individual layers of the network. Mainly intended to be used for reporting.
+    def list_layers(self):
+        patterns_to_ignore = ['/Setter', '/new_value', '/Shape', '/strided_slice', '/Cast', '/concat']
+        all_ops = tf.get_default_graph().get_operations()
+        all_ops = [op for op in all_ops if not any(p in op.name for p in patterns_to_ignore)]
+        layers = []
+
+        def recurse(scope, parent_ops, level):
+            prefix = scope + '/'
+            ops = [op for op in parent_ops if op.name == scope or op.name.startswith(prefix)]
+
+            # Does not contain leaf nodes => expand immediate children.
+            if level == 0 or all('/' in op.name[len(prefix):] for op in ops):
+                visited = set()
+                for op in ops:
+                    suffix = op.name[len(prefix):]
+                    if '/' in suffix:
+                        suffix = suffix[:suffix.index('/')]
+                    if suffix not in visited:
+                        recurse(prefix + suffix, ops, level + 1)
+                        visited.add(suffix)
+
+            # Otherwise => interpret as a layer.
+            else:
+                layer_name = scope[len(self.scope)+1:]
+                layer_output = ops[-1].outputs[0]
+                layer_trainables = [op.outputs[0] for op in ops if op.type.startswith('Variable') and self.get_var_localname(op.name) in self.trainables]
+                layers.append((layer_name, layer_output, layer_trainables))
+
+        recurse(self.scope, all_ops, 0)
+        return layers
+
+    # Print a summary table of the network structure.
+    def print_layers(self, title=None, hide_layers_with_no_params=False):
+        if title is None: title = self.name
+        print()
+        print('%-28s%-12s%-24s%-24s' % (title, 'Params', 'OutputShape', 'WeightShape'))
+        print('%-28s%-12s%-24s%-24s' % (('---',) * 4))
+
+        total_params = 0
+        for layer_name, layer_output, layer_trainables in self.list_layers():
+            weights = [var for var in layer_trainables if var.name.endswith('/weight:0')]
+            num_params = sum(np.prod(shape_to_list(var.shape)) for var in layer_trainables)
+            total_params += num_params
+            if hide_layers_with_no_params and num_params == 0:
+                continue
+
+            print('%-28s%-12s%-24s%-24s' % (
+                layer_name,
+                num_params if num_params else '-',
+                layer_output.shape,
+                weights[0].shape if len(weights) == 1 else '-'))
+
+        print('%-28s%-12s%-24s%-24s' % (('---',) * 4))
+        print('%-28s%-12s%-24s%-24s' % ('Total', total_params, '', ''))
+        print()
+
+    # Construct summary ops to include histograms of all trainable parameters in TensorBoard.
+    def setup_weight_histograms(self, title=None):
+        if title is None: title = self.name
+        with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
+            for localname, var in self.trainables.items():
+                if '/' in localname:
+                    p = localname.split('/')
+                    name = title + '_' + p[-1] + '/' + '_'.join(p[:-1])
+                else:
+                    name = title + '_toplevel/' + localname
+                tf.summary.histogram(name, var)
+
+#----------------------------------------------------------------------------

EEGFaceSem/pgan/util_scripts.py

@@ -0,0 +1,239 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# This work is licensed under the Creative Commons Attribution-NonCommercial
+# 4.0 International License. To view a copy of this license, visit
+
+# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+import os
+import time
+import re
+import bisect
+from collections import OrderedDict
+import numpy as np
+import tensorflow as tf
+import scipy.ndimage
+import scipy.misc
+
+import config
+import misc
+import tfutil
+import train
+import dataset
+
+#----------------------------------------------------------------------------
+# Generate random images or image grids using a previously trained network.
+# To run, uncomment the appropriate line in config.py and launch train.py.
+
+def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
+    network_pkl = misc.locate_network_pkl(run_id, snapshot)
+    if png_prefix is None:
+        png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
+    random_state = np.random.RandomState(random_seed)
+
+    print('Loading network from "%s"...' % network_pkl)
+    G, D, Gs = misc.load_network_pkl(run_id, snapshot)
+
+    result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
+    for png_idx in range(num_pngs):
+        print('Generating png %d / %d...' % (png_idx, num_pngs))
+        latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
+        labels = np.zeros([latents.shape[0], 0], np.float32)
+        images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
+        misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
+    open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
+
+#----------------------------------------------------------------------------
+# Generate MP4 video of random interpolations using a previously trained network.
+# To run, uncomment the appropriate line in config.py and launch train.py.
+
+def generate_interpolation_video(run_id, snapshot=None, grid_size=[1,1], image_shrink=1, image_zoom=1, duration_sec=60.0, smoothing_sec=1.0, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M', random_seed=1000, minibatch_size=8):
+    network_pkl = misc.locate_network_pkl(run_id, snapshot)
+    if mp4 is None:
+        mp4 = misc.get_id_string_for_network_pkl(network_pkl) + '-lerp.mp4'
+    num_frames = int(np.rint(duration_sec * mp4_fps))
+    random_state = np.random.RandomState(random_seed)
+
+    print('Loading network from "%s"...' % network_pkl)
+    G, D, Gs = misc.load_network_pkl(run_id, snapshot)
+
+    print('Generating latent vectors...')
+    shape = [num_frames, np.prod(grid_size)] + Gs.input_shape[1:] # [frame, image, channel, component]
+    all_latents = random_state.randn(*shape).astype(np.float32)
+    all_latents = scipy.ndimage.gaussian_filter(all_latents, [smoothing_sec * mp4_fps] + [0] * len(Gs.input_shape), mode='wrap')
+    all_latents /= np.sqrt(np.mean(np.square(all_latents)))
+
+    # Frame generation func for moviepy.
+    def make_frame(t):
+        frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1))
+        latents = all_latents[frame_idx]
+        labels = np.zeros([latents.shape[0], 0], np.float32)
+        images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
+        grid = misc.create_image_grid(images, grid_size).transpose(1, 2, 0) # HWC
+        if image_zoom > 1:
+            grid = scipy.ndimage.zoom(grid, [image_zoom, image_zoom, 1], order=0)
+        if grid.shape[2] == 1:
+            grid = grid.repeat(3, 2) # grayscale => RGB
+        return grid
+
+    # Generate video.
+    import moviepy.editor # pip install moviepy
+    result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
+    moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
+    open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
+
+#----------------------------------------------------------------------------
+# Generate MP4 video of training progress for a previous training run.
+# To run, uncomment the appropriate line in config.py and launch train.py.
+
+def generate_training_video(run_id, duration_sec=20.0, time_warp=1.5, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M'):
+    src_result_subdir = misc.locate_result_subdir(run_id)
+    if mp4 is None:
+        mp4 = os.path.basename(src_result_subdir) + '-train.mp4'
+
+    # Parse log.
+    times = []
+    snaps = [] # [(png, kimg, lod), ...]
+    with open(os.path.join(src_result_subdir, 'log.txt'), 'rt') as log:
+        for line in log:
+            k = re.search(r'kimg ([\d\.]+) ', line)
+            l = re.search(r'lod ([\d\.]+) ', line)
+            t = re.search(r'time (\d+d)? *(\d+h)? *(\d+m)? *(\d+s)? ', line)
+            if k and l and t:
+                k = float(k.group(1))
+                l = float(l.group(1))
+                t = [int(t.group(i)[:-1]) if t.group(i) else 0 for i in range(1, 5)]
+                t = t[0] * 24*60*60 + t[1] * 60*60 + t[2] * 60 + t[3]
+                png = os.path.join(src_result_subdir, 'fakes%06d.png' % int(np.floor(k)))
+                if os.path.isfile(png):
+                    times.append(t)
+                    snaps.append((png, k, l))
+    assert len(times)
+
+    # Frame generation func for moviepy.
+    png_cache = [None, None] # [png, img]
+    def make_frame(t):
+        wallclock = ((t / duration_sec) ** time_warp) * times[-1]
+        png, kimg, lod = snaps[max(bisect.bisect(times, wallclock) - 1, 0)]
+        if png_cache[0] == png:
+            img = png_cache[1]
+        else:
+            img = scipy.misc.imread(png)
+            while img.shape[1] > 1920 or img.shape[0] > 1080:
+                img = img.astype(np.float32).reshape(img.shape[0]//2, 2, img.shape[1]//2, 2, -1).mean(axis=(1,3))
+            png_cache[:] = [png, img]
+        img = misc.draw_text_label(img, 'lod %.2f' % lod, 16, img.shape[0]-4, alignx=0.0, aligny=1.0)
+        img = misc.draw_text_label(img, misc.format_time(int(np.rint(wallclock))), img.shape[1]//2, img.shape[0]-4, alignx=0.5, aligny=1.0)
+        img = misc.draw_text_label(img, '%.0f kimg' % kimg, img.shape[1]-16, img.shape[0]-4, alignx=1.0, aligny=1.0)
+        return img
+
+    # Generate video.
+    import moviepy.editor # pip install moviepy
+    result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
+    moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
+    open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
+
+#----------------------------------------------------------------------------
+# Evaluate one or more metrics for a previous training run.
+# To run, uncomment one of the appropriate lines in config.py and launch train.py.
+
+def evaluate_metrics(run_id, log, metrics, num_images, real_passes, minibatch_size=None):
+    metric_class_names = {
+        'swd':      'metrics.sliced_wasserstein.API',
+        'fid':      'metrics.frechet_inception_distance.API',
+        'is':       'metrics.inception_score.API',
+        'msssim':   'metrics.ms_ssim.API',
+    }
+
+    # Locate training run and initialize logging.
+    result_subdir = misc.locate_result_subdir(run_id)
+    snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
+    assert len(snapshot_pkls) >= 1
+    log_file = os.path.join(result_subdir, log)
+    print('Logging output to', log_file)
+    misc.set_output_log_file(log_file)
+
+    # Initialize dataset and select minibatch size.
+    dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(result_subdir, verbose=True, shuffle_mb=0)
+    if minibatch_size is None:
+        minibatch_size = np.clip(8192 // dataset_obj.shape[1], 4, 256)
+
+    # Initialize metrics.
+    metric_objs = []
+    for name in metrics:
+        class_name = metric_class_names.get(name, name)
+        print('Initializing %s...' % class_name)
+        class_def = tfutil.import_obj(class_name)
+        image_shape = [3] + dataset_obj.shape[1:]
+        obj = class_def(num_images=num_images, image_shape=image_shape, image_dtype=np.uint8, minibatch_size=minibatch_size)
+        tfutil.init_uninited_vars()
+        mode = 'warmup'
+        obj.begin(mode)
+        for idx in range(10):
+            obj.feed(mode, np.random.randint(0, 256, size=[minibatch_size]+image_shape, dtype=np.uint8))
+        obj.end(mode)
+        metric_objs.append(obj)
+
+    # Print table header.
+    print()
+    print('%-10s%-12s' % ('Snapshot', 'Time_eval'), end='')
+    for obj in metric_objs:
+        for name, fmt in zip(obj.get_metric_names(), obj.get_metric_formatting()):
+            print('%-*s' % (len(fmt % 0), name), end='')
+    print()
+    print('%-10s%-12s' % ('---', '---'), end='')
+    for obj in metric_objs:
+        for fmt in obj.get_metric_formatting():
+            print('%-*s' % (len(fmt % 0), '---'), end='')
+    print()
+
+    # Feed in reals.
+    for title, mode in [('Reals', 'reals'), ('Reals2', 'fakes')][:real_passes]:
+        print('%-10s' % title, end='')
+        time_begin = time.time()
+        labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
+        [obj.begin(mode) for obj in metric_objs]
+        for begin in range(0, num_images, minibatch_size):
+            end = min(begin + minibatch_size, num_images)
+            images, labels[begin:end] = dataset_obj.get_minibatch_np(end - begin)
+            if mirror_augment:
+                images = misc.apply_mirror_augment(images)
+            if images.shape[1] == 1:
+                images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
+            [obj.feed(mode, images) for obj in metric_objs]
+        results = [obj.end(mode) for obj in metric_objs]
+        print('%-12s' % misc.format_time(time.time() - time_begin), end='')
+        for obj, vals in zip(metric_objs, results):
+            for val, fmt in zip(vals, obj.get_metric_formatting()):
+                print(fmt % val, end='')
+        print()
+
+    # Evaluate each network snapshot.
+    for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
+        prefix = 'network-snapshot-'; postfix = '.pkl'
+        snapshot_name = os.path.basename(snapshot_pkl)
+        assert snapshot_name.startswith(prefix) and snapshot_name.endswith(postfix)
+        snapshot_kimg = int(snapshot_name[len(prefix) : -len(postfix)])
+
+        print('%-10d' % snapshot_kimg, end='')
+        mode ='fakes'
+        [obj.begin(mode) for obj in metric_objs]
+        time_begin = time.time()
+        with tf.Graph().as_default(), tfutil.create_session(config.tf_config).as_default():
+            G, D, Gs = misc.load_pkl(snapshot_pkl)
+            for begin in range(0, num_images, minibatch_size):
+                end = min(begin + minibatch_size, num_images)
+                latents = misc.random_latents(end - begin, Gs)
+                images = Gs.run(latents, labels[begin:end], num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_dtype=np.uint8)
+                if images.shape[1] == 1:
+                    images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
+                [obj.feed(mode, images) for obj in metric_objs]
+        results = [obj.end(mode) for obj in metric_objs]
+        print('%-12s' % misc.format_time(time.time() - time_begin), end='')
+        for obj, vals in zip(metric_objs, results):
+            for val, fmt in zip(vals, obj.get_metric_formatting()):
+                print(fmt % val, end='')
+        print()
+    print()
+
+#----------------------------------------------------------------------------

EEGFaceSem/preprocess.py

@@ -0,0 +1,93 @@
+import mne
+from os import path, listdir
+import numpy as np
+import pandas as pd
+import pickle
+import os
+import re
+print('mne version', mne.__version__)
+mne.set_log_level('WARNING')
+
+indir = './data/raw/'
+outdir = './data/processed/'
+os.makedirs(outdir, exist_ok=True)
+flist = [f for f in sorted(os.listdir(indir)) if f.endswith('.vhdr')]
+for idx in range(len(flist)):
+    fname = flist[idx]
+    filename = os.path.join(indir, fname)
+
+    stem = '.'.join(fname.split('.')[:-1])
+    print(f"BEGIN {stem}.vhdr")
+
+    raw = mne.io.read_raw_brainvision(filename, eog=('HEOG','VEOG'), preload=True)
+    raw.set_montage('standard_1020')
+    def s2i(s):
+        m = re.search(r"[0-9]+$", s)
+        return int(0 if not m else m[0])
+    events, _ = mne.events_from_annotations(raw, event_id=s2i) 
+    (raw, events) = raw.resample(500, events=events, n_jobs=3)
+    raw.filter(0.2, 35., fir_design='firwin', n_jobs=3)
+
+    # 2
+    stim_on_evs = events[(events[:, 2] > 200) & (events[:, 2] < 205)]
+    ev2img = pd.DataFrame(stim_on_evs[:,2], columns=['event'], index=stim_on_evs[:,0])
+    ev2img['img'] = 'NONE'
+    ev2img['rel'] = 0
+    curimg = ''
+    we = events.copy()
+    for i, e in enumerate(events):
+        if i < 3:
+            continue
+        cur = e[2]
+        l1 = events[i-1, 2]
+        l2 = events[i-2, 2]
+        if cur < 200 and l1 < 200 and l2 < 200:
+            curimg = str(l2)[1:] + str(l1)[1:] + str(cur)[2:]
+        elif cur > 200 and cur < 205 and l1 != 231 and l1 != 232: # stim on, skip neighbor
+            ev2img.loc[e[0], 'img'] = curimg
+            if cur == 201: # facecat rel
+                ev2img.loc[e[0], 'rel'] = 1
+                we[i, 1] = 1
+            if l1 > 220 and l1 < 242: # replace stim marker with group marker, forget rel
+                events[i, 2] = 1000 + l1
+                we[i, 2] = 1000 + l1
+                
+    events = np.delete(events, np.where(events[:, 2] < 1000), axis=0)
+    
+    # 3
+    ev = {
+        'facecat/male':             1223,
+        'facecat/female':           1224,
+        'facecat/old':              1225,
+        'facecat/young':            1226,
+        'facecat/smiles':           1227,
+        'facecat/nosmile':          1228,
+        'facecat/blond':            1229,
+        'facecat/darkhaired':       1230,
+        }
+
+    epochs = mne.Epochs(raw, events=events, event_id=ev,tmin=-.2,tmax=0.9,on_missing='warn',preload=True)
+    ev2img = ev2img.loc[epochs.events[:,0]]
+    epochs.metadata = ev2img
+    epochs.apply_baseline((-.2,0))
+    (mini, maxi) = epochs.time_as_index([0, 0.5], use_rounding=True)
+
+    # 4
+    bad_chs = "FT9,FT10,T7,T8,Iz,HEOG,VEOG,STI 014".split(',')
+    good_ch_indices = mne.pick_channels(epochs.info['ch_names'], [], exclude=bad_chs)
+    epochs2 = epochs.get_data()[:, good_ch_indices, mini:maxi] * 1e6 
+    cutoff = np.sort(np.max(np.absolute(epochs2[:2000]), axis=(1,2)))[-200] 
+    cutoff = np.max((10, np.min((cutoff, 80)))) 
+    bad_epo = []
+    for i in range(0, len(epochs2)):
+        if (np.max(np.absolute(epochs2[i])) > cutoff
+        or np.var(epochs2[i]) < 0.5):
+            bad_epo.append(i)
+    epochs.drop(bad_epo)
+    # print stats of each type of epochs
+    for k in ev.keys():
+        print(stem, k.split('/')[-1], len(epochs[k]))
+
+    # 5
+    epochs.save(f'{outdir}/{stem}-epo.fif', overwrite=True)
+    pickle.dump(ev2img, open(f'{outdir}/{stem}-ev2img.pkl', 'wb'))

EEGFaceSem/utils.py

@@ -0,0 +1,245 @@
+
+import pickle
+import mne
+import os
+import numpy as np
+
+def load_latent(latent_dir=None):
+    """
+    Load latent vectors for stimulus images.
+    
+    Returns:
+        image_ids: list of image IDs
+        latents: numpy array of shape (n_images, 512)
+        id_to_idx: dict mapping image_id -> index in latents array
+    """
+    if latent_dir is None:
+        # Try to find from package cache
+        from .download import get_data_dir
+        data_dir = get_data_dir()
+        latent_dir = data_dir / "data" / "latents"
+    
+    fpath = os.path.join(latent_dir, 'latent.pkl')
+    if not os.path.exists(fpath):
+        raise FileNotFoundError(f"Latent file not found: {fpath}. Run EEGFaceSem.download_latents() first.")
+    
+    image_ids, latents = pickle.load(open(fpath, 'rb'))
+    image_ids = list(image_ids)
+    id_to_idx = {image_ids[i]: i for i in range(len(image_ids))}
+    return image_ids, latents, id_to_idx
+
+def load_epochs(indir='./data/processed/', cache=True):
+    flist = [f for f in sorted(os.listdir(indir)) if f.endswith('-epo.fif')]
+    epochs = []
+    ev2img = []
+    for f in flist:
+        filename = f'{indir}/{f}'
+        epochs.append(mne.read_epochs(filename))
+        ff = f.replace('-epo.fif', '-ev2img.pkl')
+        ev2img.append(pickle.load(open(f'{indir}/{ff}', 'rb')))
+    return epochs, ev2img
+
+def load_from_processed(indir='./data/processed/', cache=True, task=None):
+    cache_name = f'{indir}/cache_{task.split("/")[-1]}.pkl' if task is not None else f'{indir}/cache.pkl'
+    if cache and os.path.exists(cache_name):
+        return pickle.load(open(cache_name, 'rb'))
+    all_tasks = ['facecat/female', 'facecat/male', 'facecat/blond', 'facecat/darkhaired', 'facecat/smiles', 'facecat/nosmile', 'facecat/old', 'facecat/young']
+    flist = [f for f in sorted(os.listdir(indir)) if f.endswith('-epo.fif')]
+    Z, Y, D = [], [], []
+    for fidx, f in enumerate(flist):
+        filename = f'{indir}/{f}'
+        epochs = mne.read_epochs(filename, verbose=0)
+        ff = f.replace('-epo.fif', '-ev2img.pkl')
+        # ev2img = pickle.load(open(f'{indir}/{ff}', 'rb'))
+        
+        for tid, t in enumerate(all_tasks):
+            if task is not None and t != task:
+                continue
+            ev = epochs[t].metadata
+            data = epochs[t].get_data(copy=False)
+            for i in range(ev.shape[0]):
+                img = int(ev['img'].iloc[i])
+                rel = int(ev['rel'].iloc[i])
+                z = data[i:i+1].flatten()*1000.0
+                Z.append(z)
+                Y.append(rel)
+                D.append((fidx, tid, i, rel, img))
+    Z = np.array(Z)
+    Y = np.array(Y)
+    D = np.array(D)
+    if cache:
+        pickle.dump((Z, Y, D), open(cache_name, 'wb'))
+    return Z, Y, D
+
+
+import scipy
+import scipy.ndimage
+class Dataset:
+    def __init__(self, indir = "", cache = None, chs = 32, samples = 1101, task=None):
+        self.indir = indir
+        self.cache = cache
+        self.chs = chs
+        self.samples = samples
+        self.task = task
+        if len(indir) > 0:
+            self.load(indir, cache, task)
+        
+    def load(self, indir='./data/processed/', cache=True, task=None):
+        self.X, self.Y, self.ids = load_from_processed(indir, cache, task)
+        self.X = self.X.reshape([self.X.shape[0], 34, self.samples])[:, :self.chs, :].reshape([self.X.shape[0], -1])
+        # lx, ly, lxd = load_latent(self.indir)
+        # self.imgs = list(lx)
+        # self.vecs = np.array(ly)
+        # self.img2idx = { img:idx  for idx, img in enumerate(self.imgs)}
+        # ys[i] = self.vecs[self.img2idx[img]]
+
+    def normalize(self):
+        self.X = self.X.reshape([self.X.shape[0], 32, self.samples])
+        self.X = self.X[:, :self.chs, :].transpose([0, 2, 1])
+        self.X_m = np.mean(self.X, axis=(0, 1))
+        self.X_s = np.std(self.X, axis=(0, 1))
+        eps = 10**-10
+        self.X = (self.X - self.X_m) / (self.X_s + eps)
+        self.X = self.X.transpose([0, 2, 1]).reshape([self.X.shape[0], self.chs, self.samples])
+
+        # # Y = Y
+
+        # self.Y_m = np.mean(self.Y, axis=(0,))
+        # self.Y_s = np.std(self.Y, axis=(0,))
+        # eps = 10**-10
+        # self.Y = (self.Y - self.Y_m) / (self.Y_s + eps)
+
+
+    def normalize_by(self, other):
+        self.X = self.X.reshape([self.X.shape[0], 32, self.samples])
+        self.X = self.X[:, :self.chs, :].transpose([0, 2, 1])
+        # self.X_m = np.mean(self.X, axis=(0, 1))
+        # self.X_s = np.std(self.X, axis=(0, 1))
+        self.X_m, self.X_s = other.X_m, other.X_s
+        eps = 10**-10
+        self.X = (self.X - self.X_m) / (self.X_s + eps)
+        self.X = self.X.transpose([0, 2, 1]).reshape([self.X.shape[0], self.chs, self.samples])
+
+        # Y = Y
+        # self.Y_m = np.mean(self.Y, axis=(0,))
+        # self.Y_s = np.std(self.Y, axis=(0,))
+        # self.Y_m, self.Y_s = other.Y_m, other.Y_s
+        # eps = 10**-10
+        # self.Y = (self.Y - self.Y_m) / (self.Y_s + eps)
+
+    def subset(self, ids):
+        ret = Dataset(self.indir, self.cache, self.chs, self.samples)
+        ret.X = self.X[ids]
+        ret.Y = self.Y[ids]
+        ret.ids = self.ids[ids]
+        return ret
+
+    def split(self, valid_n):
+        ids = np.arange(self.X.shape[0])
+        np.random.shuffle(ids)
+        tmpids = []
+        ccnt = {}
+        for i in list(ids):
+            idx = (self.ids[i, 1], self.ids[i, 2])
+            if idx not in ccnt:
+                ccnt[idx] = 0
+            ccnt[idx] += 1
+            tmpids.append((ccnt[idx], i))
+        ids = np.array([i for _, i in sorted(tmpids)])
+
+        valid = self.subset(ids[:valid_n])
+        train = self.subset(ids[valid_n:])
+        return valid, train
+
+    def kfold(self, k = 10, mixed = False):
+        # by default, split with subject separation
+        if mixed:
+            ids = np.arange(self.X.shape[0])
+            np.random.shuffle(ids)
+            ii = np.arange(self.X.shape[0])
+            for i in range(k):
+                selected = (ids % k == i)
+                unselected = (ids % k != i)
+                valid = self.subset(selected)
+                train = self.subset(unselected)
+                yield valid, train
+        else:
+            # shuffle by unique (fidx)
+            fids = np.unique(self.ids[:, 0])
+            np.random.shuffle(fids)
+            for i in range(k):
+                selected = np.isin(self.ids[:, 0], fids[i::k])
+                unselected = ~selected
+                valid = self.subset(selected)
+                train = self.subset(unselected)
+                yield valid, train
+
+
+
+    def apply_random_transform(self, btx, bty):
+        retx, rety = [], []
+        m = btx.shape[0]
+        for i in range(m):
+            e = btx[i].reshape((self.chs, self.samples))
+            v = bty[i]
+
+            # only first self.chs channels
+            cs = np.random.normal(size = [self.chs, 1]) * 0.05 + 1
+            e = cs * e[:self.chs]
+            es  =[]
+            for c in range(self.chs):
+                # [-0.2, 0.9] -> [0, 550] len(self.samples)
+                # [0.0, 0.6] -> [100, 400]
+                d = 25
+                l, r = np.random.randint(25-d, 25+d+1), np.random.randint(525-d, 525+d+1)
+                t = e[c][l:r]
+                t = scipy.ndimage.zoom(t, 500/len(t), order=1)
+                es.append(t)
+            e = np.stack(es)
+            retx.append(e)
+            rety.append(v)
+
+        retx = np.array(np.stack(retx))
+        rety = np.array(np.stack(rety))
+        return retx, rety
+    def apply_clean_transform(self, btx, bty):
+        retx, rety = [], []
+        m = btx.shape[0]
+        for i in range(m):
+            e = btx[i].reshape((self.chs, self.samples))
+            v = bty[i]
+
+            # only first self.chs channels
+            e = e[:self.chs]
+            es  =[]
+            for c in range(self.chs):
+                l, r = 25, 525
+                t = e[c][l:r]
+                t = scipy.ndimage.zoom(t, 500/len(t), order=1)
+                es.append(t)
+            e = np.stack(es)
+            retx.append(e)
+            rety.append(v)
+
+        retx = np.array(np.stack(retx))
+        rety = np.array(np.stack(rety))
+        return retx, rety
+
+    def for_each_batch(self, batch_size = 256, shuffle = False):
+        ids = np.arange(self.X.shape[0])
+        if shuffle:
+            np.random.shuffle(ids)
+        for i in range(0, len(ids), batch_size):
+            bt_ids = ids[i : i + batch_size]
+            bt_x = self.X[bt_ids]
+            bt_y = self.Y[bt_ids]
+            bt_info = self.ids[bt_ids]
+            yield bt_ids, bt_x, bt_y, bt_info
+
+    def compute_cluster_accuracy(self, c_target, c_non):
+        tp, total = 0, 0
+        for i, j in [(c_target, 1), (c_non, 0)]:
+            tp += (self.ids[i, 2]==j).sum()
+            total += i.shape[0]
+        assert(total == self.X.shape[0])
+        return tp / total

README.md

@@ -1,3 +1,110 @@
----
-license: apache-2.0
----
+# EEGFaceSem
+
+EEG Dataset for Semantic Visual Response - CVPR 2024
+
+## Installation
+
+```bash
+pip install -e .
+```
+
+For deep learning models:
+```bash
+pip install -e ".[full]"  # TensorFlow + PyTorch + Pillow
+```
+
+## Quick Start
+
+```python
+import EEGFaceSem
+
+# Download data (auto-downloads to ~/.cache/EEGFaceSem)
+EEGFaceSem.download()
+
+# Load data for a specific task
+X, Y, ids = EEGFaceSem.load_data(task='female')
+print(f"X: {X.shape}, Y: {Y.shape}")  # X: (n_trials, 32, 1101), Y: (n_trials,)
+
+# Split data
+(X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_random(X, Y)
+
+# Run benchmark
+EEGFaceSem.benchmark(model='LDA')
+```
+
+## Dataset Info
+
+| Metric | Value |
+|--------|-------|
+| Subjects | 30 |
+| Total epochs | 64,124 |
+| EEG channels | 32 |
+| Sampling rate | 500 Hz |
+| Epoch window | [-0.2, 0.9]s |
+
+### 8 Tasks
+
+| Task ID | Task Name |
+|---------|-----------|
+| 0 | female |
+| 1 | male |
+| 2 | blond |
+| 3 | darkhaired |
+| 4 | smiles |
+| 5 | nosmile |
+| 6 | old |
+| 7 | young |
+
+## API Reference
+
+### Data Loading
+
+```python
+# Download data
+EEGFaceSem.download(data_dir="./data", data_type="processed")  # or "raw", "both"
+
+# Load data
+X, Y, ids = EEGFaceSem.load_data(task='female')  # or task_id=0
+```
+
+### Splitting
+
+```python
+# Random split
+(X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_random(X, Y, test_size=0.2)
+
+# Leave-one-subject-out
+(X_train, Y_train), (X_test, Y_test) = EEGFaceSem.split_by_subject(X, Y, ids, test_subject=1)
+```
+
+### Benchmarking
+
+```python
+EEGFaceSem.benchmark(
+    model='LDA',        # LDA, LR, MLP, EEGNet, EEGPT
+    task_id=0,          # 0-7 or -1 for all
+    strategy='single_subject',  # single_subject, cross_subject, subject_adapted
+    epochs=100,
+    batch_size=32,
+    lr=0.001
+)
+```
+
+### Image Generation
+
+```python
+import numpy as np
+images = EEGFaceSem.generate(np.random.randn(1, 512))
+images[0].save("generated_face.png")
+```
+
+## Citation
+
+```bibtex
+@inproceedings{eegfacesem2024,
+  title={EEGFaceSem: ...},
+  author={...},
+  booktitle={CVPR},
+  year={2024}
+}
+```

README_code.md

@@ -0,0 +1,99 @@
+# Supplementary Material: EEGFaceSem
+
+This repository contains the official implementation for the paper "EEGFaceSem". It provides the necessary code to reproduce the benchmarking results, generate images from latent vectors, and preprocess the dataset from raw files.
+
+## Introduction
+
+This package, `EEGFaceSem`, provides tools to:
+1.  Benchmark EEG classification models, including LDA, EEGNet, and our proposed EEGPT model.
+2.  Generate facial images from EEG signals using a pretrained Progressive GAN model.
+3.  Prepare the dataset from raw files.
+
+## Installation
+
+To get started, clone this repository and install the package using pip. We recommend using a virtual environment.
+
+```bash
+pip install -r requirements.txt
+pip install .
+```
+
+## Dataset
+
+This project uses the EEGFaceSem dataset. Due to its size, the data is not included in this repository.
+
+**Option 1: Download Processed Data (Recommended)**
+
+You can download the preprocessed data files directly from OSF at [Link removed for review purposes] or Hugging Face at [Link removed for review purposes].
+
+Download the files and place them in the `data/processed/` directory.
+
+**Option 2: Process Raw Data**
+
+1.  Download the raw data from OSF at [Link removed for review purposes].
+2.  Place the downloaded files into the `data/raw/` directory.
+3.  Run the preparation script:
+    ```python
+    import EEGFaceSem
+
+    EEGFaceSem.prepare_data()
+    ```
+    This will process the raw files and save the results in `data/processed/`, which will be used by the benchmark scripts.
+
+## Usage
+
+The core functionalities are exposed through the `EEGFaceSem` package.
+
+### Benchmarking
+
+You can run the benchmarks for different models directly from Python. The results will be saved to a `logs/` directory.
+
+```python
+import EEGFaceSem
+
+# Run the benchmark for the LDA model
+EEGFaceSem.benchmark(models="LDA")
+
+# Run the benchmark for the EEGPT model
+EEGFaceSem.benchmark_eegpt()
+```
+
+### Image Generation from Latent Vectors
+
+To generate an image from a latent vector using the pretrained Progressive GAN model, use the following function.
+
+```python
+import EEGFaceSem
+import numpy as np
+images = EEGFaceSem.generate(np.random.randn(1, 512))
+images[0].save("generated_face.png")
+```
+
+## Repository Structure
+
+```
+EEGFaceSem/
+├── EEGFaceSem/           # The installable Python package
+│   ├── __init__.py
+│   ├── benchmark.py      # Code for LDA, EEGNet benchmarks
+│   ├── generation.py     # Image generation logic
+│   ├── preprocess.py     # Data preparation script
+│   ├── models.py         # Model definitions
+│   ├── utils.py          # Data loading and utility functions
+│   ├── EEGModels.py      # EEGNet definitions
+│   └── EEGPT/            # Submodule for EEGPT dependencies
+│       └── ...
+│   └── pgan/             # Submodule for Progressive GAN dependencies
+│       └── ...
+├── data/
+│   ├── raw/              # (Empty) For raw data
+│   └── processed/        # (Empty) For processed data
+├── models/
+│   ├── eegpt_mcae_58chs_4s_large4E.ckpt      # (Empty) Pretrained EEGPT model to be downloaded
+│   └── karras2018iclr-celebahq-1024x1024.pkl # (Empty) Pretrained Progressive GAN model to be downloaded
+├── scripts/
+│   └── summarize_results.py # Script to evaluate benchmark outputs
+├── README.md
+├── setup.py
+└── requirements.txt
+```

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