diff --git a/neuroencoders/fullEncoder/an_network_torch.py b/neuroencoders/fullEncoder/an_network_torch.py new file mode 100644 index 0000000..fc3c31c --- /dev/null +++ b/neuroencoders/fullEncoder/an_network_torch.py @@ -0,0 +1,371 @@ +import os +import time +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import wandb +import tensorflow as tf # For data loading only + +from neuroencoders.fullEncoder import nnUtils_torch as nnUtils +from neuroencoders.utils.global_classes import Project, Params +# Assuming these helper classes are pure python or can be reused. + + +class TFDataIterable(torch.utils.data.IterableDataset): + """ + Wrapper to convert a tf.data.Dataset into a PyTorch iterable. + """ + + def __init__(self, tf_dataset, device="cpu"): + super().__init__() + self.tf_dataset = tf_dataset + self.device = device + + def __iter__(self): + for batch in self.tf_dataset: + # batch is a tuple (inputs, outputs) or dict. + # In existing code: dataset = dataset.map(map_outputs) -> returns (inputs, outputs) + # inputs is a dict. + + inputs, targets = batch + + # Convert to torch + # We assume inputs is a dict of tensors + torch_inputs = {k: torch.from_numpy(v.numpy()) for k, v in inputs.items()} + torch_targets = {k: torch.from_numpy(v.numpy()) for k, v in targets.items()} + + yield torch_inputs, torch_targets + + +def to_device(data, device): + if isinstance(data, dict): + return {k: to_device(v, device) for k, v in data.items()} + if torch.is_tensor(data): + return data.to(device) + return data + + +class LSTMandSpikeNetwork(nn.Module): + """ + PyTorch implementation of LSTMandSpikeNetwork. + """ + + def __init__( + self, projectPath, params, deviceName="cpu", debug=False, phase=None, **kwargs + ): + super().__init__() + self.projectPath = projectPath + self.params = params + self.deviceName = deviceName # e.g. 'cuda:0' or 'cpu' + self.debug = debug + self.phase = phase + + self.nGroups = params.nGroups + self.nFeatures = params.nFeatures + self.learning_rate = kwargs.get("lr", params.learningRates[0]) + + # 1. Spike Nets (one per group) + self.spikeNets = nn.ModuleList( + [ + nnUtils.SpikeNet1D( + nChannels=params.nChannelsPerGroup[g], + nFeatures=params.nFeatures, + batch_normalization=True, # Default true in TF code + ) + for g in range(self.nGroups) + ] + ) + + # 2. Group Fusion + self.use_group_fusion = getattr(params, "use_group_attention_fusion", True) + if self.use_group_fusion: + self.group_fusion = nnUtils.GroupAttentionFusion( + n_groups=self.nGroups, embed_dim=self.nFeatures, num_heads=4 + ) + + # 3. Transformer / LSTM + self.isTransformer = kwargs.get("isTransformer", True) + self.dim_factor = getattr(params, "dim_factor", 1) + + # Determine the dimension coming OUT of fusion/concatenation + fusion_output_dim = self.nFeatures * self.nGroups + + # Determine the dimension expected by the Transformer + target_dim = ( + self.nFeatures * self.dim_factor + if getattr(params, "project_transformer", True) + else fusion_output_dim + ) + + self.project_transformer = getattr(params, "project_transformer", True) and ( + target_dim != fusion_output_dim + ) + + if self.project_transformer: + self.transformer_projection = nn.Linear(fusion_output_dim, target_dim) + self.activation_projection = nn.ReLU() # TF uses relu in Dense definition + + transformer_input_dim = target_dim + + if self.isTransformer: + self.pos_encoder = nnUtils.PositionalEncoding(d_model=transformer_input_dim) + self.transformer_blocks = nn.ModuleList( + [ + nnUtils.TransformerEncoderBlock( + d_model=transformer_input_dim, + num_heads=params.nHeads, + ff_dim1=params.ff_dim1, + ff_dim2=params.ff_dim2, + dropout_rate=params.dropoutLSTM, + residual=kwargs.get("transformer_residual", True), + ) + for _ in range(params.lstmLayers) + ] + ) + self.pooling = nnUtils.MaskedGlobalAveragePooling1D() + + # Dense layers after pooling + self.dense1 = nn.Linear( + transformer_input_dim, int(params.TransformerDenseSize1) + ) + self.dense2 = nn.Linear( + int(params.TransformerDenseSize1), int(params.TransformerDenseSize2) + ) + else: + # LSTM implementation + self.lstm = nn.LSTM( + input_size=transformer_input_dim, + hidden_size=params.lstmSize, + num_layers=params.lstmLayers, + batch_first=True, + dropout=params.dropoutLSTM, + ) + self.dense1 = nn.Linear( + params.lstmSize, int(params.TransformerDenseSize1) + ) # Just guessing connectivity + + # 4. Output Heads + # TF: denseFeatureOutput + dimOutput = params.dimOutput + # If heatmap, dimOutput might be different logic. + self.output_head = nn.Linear(int(params.TransformerDenseSize2), dimOutput) + + # Move to device + self.to(self.deviceName) + + # Optimizer + self.optimizer = optim.AdamW( + self.parameters(), + lr=self.learning_rate, + weight_decay=getattr(params, "weight_decay", 1e-4), + ) + + def forward(self, inputs): + """ + inputs: dict containing: + - group0, group1, ...: (Batch, nChannels, Time) + - groups: (Batch, TotalSpikes) ? Or just a list of tensors? + TF code had complex "gather" logic. + Here, we expect the data loader might yield slightly different structure or we adapt. + Proposed simplified flow: + We assume inputs['groupX'] contains the spike snippets for group X. + """ + + # 1. Run SpikeNets + # We need to handle the fact that spikes happen at specific times. + # In TF code, there was a "ZeroForGather" and "Indices" to scatter spikes into a time grid. + # This basically constructs a (Batch, MaxTime, nFeatures) tensor where features are at the spike time. + + # For this refactor, we will rely on inputs providing 'indices' similar to TF, + # or we assume inputs contain already dense-like structures if that's possible. + # But `dataset` yields 'indices' and 'groups'. + + # Let's assume we receive the raw spike waveforms and indices. + # group_features = List of (Batch, TotalSpikes, nFeatures) + # Wait, if we use the TF dataloader, it will give exactly what TF gives. + # TF gives: `inputsToSpikeNets` (Batch, Channels, 32). THIS IS WRONG. + # TF logic: `inputsToSpikeNets[group]` is (NbKeptSpikes, Channels, 32). + # It flattens batch and spikes. + + batch_size = inputs.get("batch_size", self.params.batchSize) + + group_features_list = [] + group_masks = [] + + # Reconstruct the time-grid features + # We need a canvas (Batch, MaxIndices, Features) initialized to 0. + # Then scatter the spike features into it using 'indices'. + + for g in range(self.nGroups): + # 1. Extract spike waveforms: (N_Spikes_Total, Channels, 32) + waveforms = inputs[f"group{g}"] # Should be float tensor + + # 2. Run feature extractor + # Output: (N_Spikes_Total, nFeatures) + features = self.spikeNets[g](waveforms) + + # 3. Scatter to (Batch, Time, nFeatures) + # indices[g] is (N_Spikes_Total,) containing the index in the flattened (Batch*Time) array? + # TF: "indices... contains the indices where to put the spikes... in the final tensor" + # TF: gather takes (zeroForGather + x) and indices. + + # In PyTorch, we can perform this scatter. + # We need the total size: Batch * MaxTime. + # Usually MaxTime is inferred or fixed. + # Let's assume we can deduce it from the max index in 'indices'. + + indices = inputs[f"indices{g}"].long() # (N_Spikes_Total) + + if indices.numel() == 0: + # No spikes for this group + # Look up max time from other groups or default? + # Handled below. + scattered = torch.zeros( + batch_size, 1, self.nFeatures, device=self.deviceName + ) + else: + max_idx = indices.max().item() + # We need to know the 'Time' dimension. + # TF reshapes to (Batch, -1, Features). + # We can try to guess Time from max_idx // batch_size? No. + # TF Code: "filledFeatureTrain = reshape(filled, (batch, -1, features))" + # This implies the flat index logic maps to linear batch*time. + + # We will construct a flat buffer. + # Size buffer: max_idx + 1 (or sufficient size). + # To be safe, we should probably find the global max index across all groups to define the Time dimension consistent. + # However, for now, let's just scatter into a sufficiently large buffer. + + # Better approach: The TF dataset creation likely defines a fixed max_len (maxNbOfSpikes). + # We can't easily see it here. But let's act dynamically. + + # Create a zero tensor of shape (max_idx + 1 + padding?, nFeatures) + # Scatter `features` into it at `indices`. + buffer_size = max_idx + 1 + container = torch.zeros( + buffer_size, self.nFeatures, device=self.deviceName + ) + container.index_add_(0, indices, features) + # Note: index_add_ sums if duplicate indices. TF logic was 'take', implying one spike per slot? + # TF: "kops.take( concatenated, indices )". + # Scatter is clearer. + + # Reshape to (Batch, Time, Features) + # We need to ensure buffer_size is divisible by batch_size + remainder = buffer_size % batch_size + if remainder != 0: + pad = batch_size - remainder + container = F.pad(container, (0, 0, 0, pad)) + buffer_size += pad + + scattered = container.view(batch_size, -1, self.nFeatures) + + group_features_list.append(scattered) + + # Mask + # 1 where there is a spike, 0 otherwise. + # We can deduce this from indices being non-zero/non-padding? + # TF: "indices == 0 implies zeroForGather". + # indices in TF input included 0 for "empty"? + # Actually indices map the spike to the position. + # The 'mask' is derived from `inputGroups`. + + # Ensure all groups have same time dimension + max_t = max([t.shape[1] for t in group_features_list]) + for i in range(len(group_features_list)): + t = group_features_list[i] + if t.shape[1] < max_t: + # Pad time dim + pad_t = max_t - t.shape[1] + group_features_list[i] = F.pad(t, (0, 0, 0, pad_t)) + + # 4. Fusion + if self.use_group_fusion: + # We need a generic mask. + # TF: "mymask = safe_mask_creation(batchedInputGroups)" + # `inputGroups` tensor in inputs dict has shape (TotalSpikes,). + # It maps which group the spike belongs to. + # This logic is redundant if we already have separated execution. + # The mask we really need is "where are the padding time steps?". + # In the TF code, `mymask` corresponds to valid time steps (vs padded batch). + + # Simplified: Assume all time steps valid for now or derive from indices? + # Let's default to full ones. + mask = torch.ones(batch_size, max_t, device=self.deviceName) + + fused_features = self.group_fusion(group_features_list, mask=None) + # output (B, T, G*F) + + # Flatten features? + all_features = fused_features.view(batch_size, max_t, -1) + else: + all_features = torch.cat(group_features_list, dim=2) + + # 5. Transformer + # Masking for padded time steps: + # If we had irregular sequences, we would need a mask. + # Here we derived max_t from indices. The gaps between actual spikes are 0s. + # Is that intended? Yes, it's a sparse spike train representation. + # But we DO need to mask the "padded batch" area if batches are padded? + # TF logic: "kops.where(expand(mask), allFeatures, 0)". + + # Let's apply Transformer + x = all_features + + if getattr(self, "project_transformer", False): + x = self.transformer_projection(x) + x = self.activation_projection(x) + + if self.isTransformer: + x = self.pos_encoder(x) + for block in self.transformer_blocks: + x = block(x, mask=mask) # mask used for attention + + # Pooling + x = self.pooling(x, mask=mask) + + x = F.relu(self.dense1(x)) + x = F.relu(self.dense2(x)) + else: + x, _ = self.lstm(x) + x = x[:, -1, :] # Last state? Or global pooling? + x = F.relu(self.dense1(x)) + + # 6. Output + out = self.output_head(x) + return out + + def train_epoch(self, dataloader): + self.train() + total_loss = 0 + steps = 0 + + for inputs, targets in dataloader: + inputs = to_device(inputs, self.deviceName) + targets = to_device(targets, self.deviceName) + + self.optimizer.zero_grad() + + preds = self(inputs) + + # Loss + # Basic MSE for now on 'pos' + # Look at targets['pos'] + true_pos = targets["myoutputPos"] # TF output name override + if true_pos is None: + true_pos = targets.get("pos") + + loss = F.mse_loss(preds, true_pos[:, :2]) # Assuming 2D pos + + loss.backward() + self.optimizer.step() + + total_loss += loss.item() + steps += 1 + + if steps % 10 == 0: + wandb.log({"train_loss": loss.item()}) + + return total_loss / steps diff --git a/neuroencoders/fullEncoder/nnUtils_torch.py b/neuroencoders/fullEncoder/nnUtils_torch.py new file mode 100644 index 0000000..3e526f6 --- /dev/null +++ b/neuroencoders/fullEncoder/nnUtils_torch.py @@ -0,0 +1,525 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class SpikeNet(nn.Module): + """ + Convolutional network for spike sequences. + Input: (Batch, N_Channels, Time) or something similar, tailored to match TF input. + TF input was (Batch, Channels, Time) but Conv2D in TF is (Batch, H, W, C). + Wait, let's re-read the TF code. + TF `spikeNet`: + Input is `input`. `expand_dims(input, axis=3)`. + TF Conv2D(8, [2, 3]) -> kernel size (2,3). + Input shape validation says (batch, time, channels). + So expanded is (batch, time, channels, 1). + Conv2D filter (2,3) usually means (height, width). + So it convolves over (Time, Channels). + + This is slightly unusual. Standard Conv2D in PyTorch expects (Batch, C, H, W). + If we map Time->H, Channels->W, Input Channels->1. + """ + + def __init__( + self, + nChannels=4, + nFeatures=128, + number="", + reduce_dense=False, + no_cnn=False, + batch_normalization=True, + ): + super().__init__() + self.nChannels = nChannels + self.nFeatures = nFeatures + self.reduce_dense = reduce_dense + self.no_cnn = no_cnn + self.batch_normalization = batch_normalization + + if not self.no_cnn: + # Conv layers + # In TF: Conv2D(8, [2, 3], padding="same") + # Kernel (2, 3) over (Time, Channels). + # Note: TF "same" padding is tricky to replicate exactly if strides > 1 or odd kernels. + # Here kernel is (2,3). + + # We will treat input as (Batch, 1, Time, Channels) + self.conv1 = nn.Conv2d(1, 8, kernel_size=(2, 3), padding="same") + self.conv2 = nn.Conv2d(8, 16, kernel_size=(2, 3), padding="same") + self.conv3 = nn.Conv2d(16, 32, kernel_size=(2, 3), padding="same") + + # MaxPool (1,2) -> Pool over Channels dimension only? + # TF: MaxPool2D([1, 2], [1, 2], padding="same") + # Pool size (1, 2), Strides (1, 2). + # This reduces the Channel dimension by factor of 2. + self.pool1 = nn.MaxPool2d( + kernel_size=(1, 2), stride=(1, 2), ceil_mode=True + ) # padding same-ish + self.pool2 = nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2), ceil_mode=True) + self.pool3 = nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2), ceil_mode=True) + + if self.batch_normalization: + self.bn1 = nn.BatchNorm2d(8) + self.bn2 = nn.BatchNorm2d(16) + self.bn3 = nn.BatchNorm2d(32) + + # Dense layers + self.flatten = nn.Flatten() + self.dropout = nn.Dropout(0.2) + + # We need to calculate the input feature size for the dense layer dynamically or hardcode if standard + # For now, we will assume it is dynamically calculated or we use LazyLinear + # But for robustness, let's use LazyLinear or similar if available, else Linear. + # Since we don't know the exact Time dimension after pooling (Time is not pooled), + # and Channels is pooled 3 times by factor 2. + + self.dense1 = nn.LazyLinear(nFeatures) + self.dense2 = nn.Linear(nFeatures, nFeatures) + # Final output layer + self.dense3 = nn.Linear(nFeatures, nFeatures) + + def forward(self, x): + # Input x: (Batch, nChannels, Time) or (Batch, Time, nChannels)? + # TF Code: "Expected input shape (batch, time, channels)" + # x is (Batch, Time, Channels) + + if self.no_cnn: + x = self.flatten(x) + x = self.dense3(x) + x = self.dropout(x) + return x + + # Torch expects (Batch, C_in, H, W). Let H=Time, W=Channels. + # Add channel dim: (Batch, 1, Time, Channels) + x = x.unsqueeze(1) + + x = self.conv1(x) + if self.batch_normalization: + x = self.bn1(x) + x = self.pool1(x) + + x = self.conv2(x) + if self.batch_normalization: + x = self.bn2(x) + x = self.pool2(x) + + x = self.conv3(x) + if self.batch_normalization: + x = self.bn3(x) + x = self.pool3(x) + + x = self.flatten(x) + + if not self.reduce_dense: + x = F.relu(self.dense1(x)) + x = self.dropout(x) + x = F.relu(self.dense2(x)) + x = F.relu(self.dense3(x)) + else: + x = F.relu(self.dense3(x)) + x = self.dropout(x) + + return x + + +class SpikeNet1D(nn.Module): + """ + Refined Spike Encoder. + Input shape: (Batch, Channels, Time) -> e.g., (128, 6, 32) + This version transposes the input so Conv1D operates on the Time axis (32) + while keeping the 6 channels separate (by processing them in batch). + """ + + def __init__( + self, nChannels=4, nFeatures=128, dropout_rate=0.2, batch_normalization=True + ): + super().__init__() + self.nChannels = nChannels + self.nFeatures = nFeatures + self.batch_normalization = batch_normalization + + # Backbone (Shared Weights) + # Conv1D in Torch: (Batch, C_in, L_in) -> we want to convolve over Time. + # TF: Conv1D(16, 3, padding="same"). + # So input channels=1, output=16. + self.conv1 = nn.Conv1d(1, 16, kernel_size=3, padding="same") + self.bn1 = nn.BatchNorm1d(16) + self.pool1 = nn.MaxPool1d(2, stride=2) + + self.conv2 = nn.Conv1d(16, 32, kernel_size=3, padding="same") + self.bn2 = nn.BatchNorm1d(32) + self.pool2 = nn.MaxPool1d(2, stride=2) + + self.conv3 = nn.Conv1d(32, 64, kernel_size=3, padding="same") + self.bn3 = nn.BatchNorm1d(64) + + # Global Average Pooling -> mean over the remaining time dimension + + self.dropout = nn.Dropout(dropout_rate) + + # Dense Fusion + # Input dim = nChannels * 64 + self.dense_fusion = nn.Linear(nChannels * 64, nFeatures * 2) + self.dense_out = nn.Linear(nFeatures * 2, nFeatures) + + def forward(self, x): + # x: (Batch, Channels, Time) + B, C, T = x.shape + + # Reshape to (Batch * Channels, 1, Time) to process each channel independently + x = x.view(B * C, 1, T) + + # Layer 1 + x = self.conv1(x) + if self.batch_normalization: + x = self.bn1(x) + x = F.relu(x) + x = self.pool1(x) + + # Layer 2 + x = self.conv2(x) + if self.batch_normalization: + x = self.bn2(x) + x = F.relu(x) + x = self.pool2(x) + + # Layer 3 + x = self.conv3(x) + if self.batch_normalization: + x = self.bn3(x) + x = F.relu(x) + + # Global Average Pool + # x is (B*C, 64, Time_Reduced) + x = x.mean(dim=-1) # (B*C, 64) + + # Concatenate channels back + # (B, C*64) + x = x.view(B, C * 64) + + # Dense Fusion + x = self.dense_fusion(x) + x = F.relu(x) + x = self.dropout(x) + + # Out + x = self.dense_out(x) + return x + + +class GroupAttentionFusion(nn.Module): + """ + Fuses features from multiple spike groups using Self-Attention. + """ + + def __init__(self, n_groups, embed_dim, num_heads=4): + super().__init__() + self.n_groups = n_groups + self.embed_dim = embed_dim + self.num_heads = num_heads + + self.mha = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True) + self.norm = nn.LayerNorm(embed_dim) + self.dropout = nn.Dropout(0.1) + + # Learnable positional embedding for each group ID: (1, 1, n_groups, embed_dim) + # In PyTorch parameter: (1, 1, n_groups, embed_dim) + self.group_embeddings = nn.Parameter(torch.randn(1, 1, n_groups, embed_dim)) + + def forward(self, inputs, mask=None): + # inputs: List of tensors, each (Batch, Time, Features) -> we want stack -> (Batch, Time, Groups, Features) + # Note: Time here could be "maxNbSpikes". + + x = torch.stack(inputs, dim=2) # (B, T, G, F) + + # Add embeddings + x = x + self.group_embeddings # Broadcasts + + B, T, G, F = x.shape + # Flatten B and T for MHA: (B*T, G, F) + x_reshaped = x.view(B * T, G, F) + + attn_mask = None + key_padding_mask = None + + if mask is not None: + # mask shape (Batch, max_nSpikes, nGroups)? + # TF code: mask is (Batch, nGroups) expanded to match dimensions? + # Let's check TF code: + # "mask comes in as shape (Batch, max(nSpikes), n_groups)" + # "reshape to (Batch*Time, n_groups)" + # In PyTorch MultiheadAttention, key_padding_mask expected (N, S) where True is ignore. + # TF is_active (True if valid). So PyTorch padding_mask should be ~is_active (True if invalid). + + mask_reshaped = mask.view(B * T, G) + # Inverse boolean for PyTorch padding mask + key_padding_mask = ~mask_reshaped # (B*T, G) + + attn_out, _ = self.mha( + x_reshaped, x_reshaped, x_reshaped, key_padding_mask=key_padding_mask + ) + + x_reshaped = self.norm(x_reshaped + self.dropout(attn_out)) + + # Reshape back: (B, T, G*F) + output = x_reshaped.view(B, T, G * F) + + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, max_len=10000): + super().__init__() + pe = torch.zeros(max_len, d_model) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model) + ) + + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + self.register_buffer("pe", pe) + + def forward(self, x): + # x: (Batch, Seq_Len, Feature) + seq_len = x.size(1) + return x + self.pe[:seq_len, :] + + +class TransformerEncoderBlock(nn.Module): + def __init__( + self, + d_model=64, + num_heads=8, + ff_dim1=256, + ff_dim2=64, + dropout_rate=0.5, + residual=True, + ): + super().__init__() + self.residual = residual + self.norm1 = nn.LayerNorm(d_model) + self.mha = nn.MultiheadAttention(d_model, num_heads, batch_first=True) + self.dropout1 = nn.Dropout(dropout_rate) + + self.ff = nn.Sequential( + nn.Linear(d_model, ff_dim1), nn.ReLU(), nn.Linear(ff_dim1, ff_dim2) + ) + # Note: if ff_dim2 != d_model, the residual connection dimension won't match if we do x + ff_output. + # But in TF code: "TransformerEncoderBlock... output maintains info but with ff_dim2". + # Let's check TF code closely. + # "x = self.norm2(x + ff_output)" -> This implies ff_output has same shape as x. + # So ff_dim2 MUST equal d_model for the residual to work naturally, OR there is a projection. + # In TF code: `self.ff_layer2 = Dense(self.ff_dim2)`. + # And `x = self.norm2(x + ff_output)`. + # So yes, ff_dim2 MUST be equal to d_model for valid add. + # Unless the `d_model` passed to constructor IS `d_model`, and `ff_dim2` is intended to be output size? + # Re-reading TF `build`: "if feature_dim != self.d_model: raise..." + # So input is d_model. + # The output of this block is `ff_dim2` size. + # BUT `x + ff_output` is done. `x` is `d_model` size. + # Thus `ff_dim2` MUST equal `d_model`. + + self.norm2 = nn.LayerNorm(ff_dim2) + + def forward(self, x, mask=None): + # mask: (Batch, SeqLen) - True for valid. + + x_norm = self.norm1(x) + + key_padding_mask = None + if mask is not None: + # In PyTorch: True means ignore (pad). + key_padding_mask = ~mask.bool() + + attn_out, _ = self.mha( + x_norm, x_norm, x_norm, key_padding_mask=key_padding_mask + ) + attn_out = self.dropout1(attn_out) + + if self.residual: + x = x + attn_out + + ff_out = self.ff(x) + # Final residual + # Note: if ff_dim2 != d_model, this will fail. Assuming they are same. + x = self.norm2(x + ff_out) + return x + + +class MaskedGlobalAveragePooling1D(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, mask=None): + # x: (Batch, SeqLen, Features) + # mask: (Batch, SeqLen) - 1 for valid, 0 for pad + + if mask is None: + return x.mean(dim=1) + + mask = mask.unsqueeze(-1) # (B, S, 1) + masked_x = x * mask + sum_x = masked_x.sum(dim=1) + count_x = mask.sum(dim=1) + + return sum_x / count_x.clamp(min=1.0) + + +class LinearizationLayer(nn.Module): + """ + A simple layer to linearize Euclidean data into a maze-like linear track. + """ + + def __init__(self, maze_points, ts_proj, device="cpu"): + super().__init__() + self.device = device + # Register as buffers to be saved with state_dict but not trained + self.register_buffer( + "maze_points", torch.tensor(maze_points, dtype=torch.float32) + ) + self.register_buffer("ts_proj", torch.tensor(ts_proj, dtype=torch.float32)) + + def forward(self, euclidean_data): + # euclidean_data: (Batch, 2) + # maze_points: (J, 2) + + # Compute distances: ||x - p||^2 + # (Batch, 1, 2) - (1, J, 2) -> (Batch, J, 2) + diff = euclidean_data.unsqueeze(1) - self.maze_points.unsqueeze(0) + dists = torch.sum(diff**2, dim=2) # (Batch, J) + + # Argmin to find closest point + min_indices = torch.argmin(dists, dim=1) # (Batch,) + + # Gather projected points and linear positions + projected_pos = self.maze_points[min_indices] # (Batch, 2) + linear_pos = self.ts_proj[min_indices] # (Batch,) + + return projected_pos, linear_pos + + +class GaussianHeatmapLayer(nn.Module): + """ + Layer to produce a Gaussian heatmap from 2D positions or decode 2D positions from a heatmap. + """ + + def __init__(self, grid_size=(40, 40), std=2.0, device="cpu"): + super().__init__() + self.grid_size = grid_size + self.std = std + self.device = device + + # Create grid coordinates + H, W = grid_size + # Assuming grid covers [0, 1] x [0, 1] ?? + # Or [0, W] x [0, H]? + # TF code usually implies scaling. Let's assume normalized 0-1 or pixel coordinates. + # If input pos is 0-1, we need to map to grid indices. + # Let's assume input 'pos' is normalized [0,1]. + + yy, xx = torch.meshgrid(torch.arange(H), torch.arange(W), indexing="ij") + self.register_buffer("grid_xx", xx.float()) + self.register_buffer("grid_yy", yy.float()) + + def gaussian_heatmap_targets(self, true_pos): + """ + Generate target heatmaps from true positions. + true_pos: (Batch, 2) in range [0, 1] ? + """ + B = true_pos.shape[0] + H, W = self.grid_size + + # Scale pos to grid coords + # pos x -> W, pos y -> H + # assuming pos is (x, y) + target_x = true_pos[:, 0] * W + target_y = true_pos[:, 1] * H + + # (Batch, H, W) + # exp( -((x-mu_x)^2 + (y-mu_y)^2) / (2*std^2) ) + + grid_x = self.grid_xx.unsqueeze(0).expand(B, -1, -1) + grid_y = self.grid_yy.unsqueeze(0).expand(B, -1, -1) + + t_x = target_x.reshape(B, 1, 1) + t_y = target_y.reshape(B, 1, 1) + + dist_sq = (grid_x - t_x) ** 2 + (grid_y - t_y) ** 2 + heatmap = torch.exp(-dist_sq / (2 * self.std**2)) + + # Normalize sum to 1? Or max to 1? + # Usually target heatmaps max is 1. + return heatmap + + def forward(self, x, flatten=True): + # x input is usually the Dense layer output of size H*W + # reshape to (B, H, W) + B = x.shape[0] + H, W = self.grid_size + heatmap = x.view(B, H, W) + + # Use softmax? Or sigmoid? + # TF code: "activation=None" in dense output, then maybe applied later? + # Usually heatmap regression uses linear output trained with MSE/BCE. + # Let's assume raw logits. + + if flatten: + return heatmap.view(B, -1) + return heatmap + + +class GaussianHeatmapLosses(nn.Module): + def __init__(self, loss_type="safe_kl"): + super().__init__() + self.loss_type = loss_type + + def forward(self, inputs): + # inputs dict: 'logits' (Batch, H, W), 'targets' (Batch, H, W) + logits = inputs["logits"] + targets = inputs["targets"] + + if self.loss_type == "mse": + return F.mse_loss(logits, targets) + elif self.loss_type == "safe_kl": + # Softmax logits to get probability distribution + # KLDivLoss expects log-probabilities + + # Flatten spatial dims + B = logits.shape[0] + log_probs = F.log_softmax(logits.view(B, -1), dim=1) + target_probs = targets.view(B, -1) + + # Normalize target to be proper distribution + target_probs = target_probs / (target_probs.sum(dim=1, keepdim=True) + 1e-8) + + return F.kl_div(log_probs, target_probs, reduction="batchmean") + + return F.mse_loss(logits, targets) + + +class ContrastiveLossLayer(nn.Module): + def __init__(self, margin=1.0): + super().__init__() + self.margin = margin + + def forward(self, inputs): + # inputs: [predicted_pos, linearized_pos] + # This seems to implement a specific regularizer. + # "ContrastiveLossLayer... weights the first 2 dimensions by linPos?" + # Wait, the TF code usage was: + # projected_pos, linear_pos = l_function(truePos) + # regression_loss = regression_loss_layer([myoutputPos, projected_pos]) + + # It encourages predicted pos to be close to the manifold (projected pos). + + pred = inputs[0] + target = inputs[1] + + dist = F.pairwise_distance(pred, target) + loss = torch.mean(dist**2) + return loss diff --git a/pyproject.toml b/pyproject.toml index f1ec808..bfa1d00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,6 +65,7 @@ dependencies = [ "umap-learn[plot]>=0.5.9.post2", "wandb>=0.20.1", "xlsxwriter>=3.2.5", + "torch>=2.0.0", ] [dependency-groups] @@ -88,6 +89,7 @@ dev = [ "scipy>=1.15.3", "spikeinterface[full]>=0.103.0", "xlsxwriter>=3.2.5", + "pytest>=7.0.0", ] [tool.ruff] diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..6d60b45 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,91 @@ +import pytest +import numpy as np +import os +import shutil +import tensorflow as tf +import torch + + +class MockParams: + def __init__(self): + self.nGroups = 2 + self.nChannelsPerGroup = [2, 2] # 2 groups, 2 channels each + self.nFeatures = 64 + self.nHeads = 4 + self.ff_dim1 = 128 + self.ff_dim2 = 128 + self.dropoutLSTM = 0.1 + self.lstmLayers = 2 + self.lstmSize = 64 + self.TransformerDenseSize1 = 64 + self.TransformerDenseSize2 = 32 + self.dimOutput = 2 + self.batchSize = 4 + self.windowLength = 0.2 + self.dim_factor = 2 + self.project_transformer = True + self.use_group_attention_fusion = True + self.weight_decay = 1e-4 + self.learningRates = [1e-3, 1e-4] + self.target = "pos" + self.usingMixedPrecision = False + self.windowSize = 0.036 + self.windowSizeMS = 36 + self.denseweight = False + self.GaussianHeatmap = False + self.OversamplingResampling = False + + self.resultsPath = "test_results" + self.use_conv2d = False + self.isTransformer = True + self.GaussianGridSize = (45, 45) + self.GaussianSigma = 0.05 + self.GaussianEps = 1e-6 + self.nDenseLayers = 2 + self.featureActivation = None + self.dropoutCNN = 0.35 + self.reduce_dense = False + self.no_cnn = False + self.loss = "mse" + self.contrastive_loss = False + self.alpha = 1.3 + self.delta = 0.5 + self.transform_w_log = False + self.mixed_loss = False + self.dataAugmentation = False + self.nSteps = 10 + self.heatmap_weight = 1.0 + self.others_weight = 1.0 + + +@pytest.fixture +def mock_params(): + return MockParams() + + +@pytest.fixture +def temp_project_dir(tmp_path): + """Creates a temporary project structure.""" + project_dir = tmp_path / "test_project" + project_dir.mkdir() + + # Create necessary subdirectories + (project_dir / "dataset").mkdir() + (project_dir / "Network").mkdir() + (project_dir / "Network" / "models").mkdir() + + # Create fake XML + xml_path = project_dir / "test.xml" + xml_path.write_text("Mock XML") + + # Create fake data file + dat_path = project_dir / "test.dat" + dat_path.write_text("fake binary content") + + return str(project_dir), str(xml_path) + + +@pytest.fixture +def run_both_backends(): + """Fixture to parameterize tests for both TF and Torch backends.""" + return ["tensorflow", "pytorch"] diff --git a/tests/test_bayes.py b/tests/test_bayes.py new file mode 100644 index 0000000..9eb2710 --- /dev/null +++ b/tests/test_bayes.py @@ -0,0 +1,103 @@ +import pytest +import numpy as np +import os +from unittest.mock import MagicMock, patch +from neuroencoders.simpleBayes.decode_bayes import Trainer, DecoderConfig + + +@pytest.fixture +def mock_trainer_deps(): + with patch( + "neuroencoders.importData.import_clusters.load_spike_sorting" + ) as mock_load: + mock_load.return_value = { + "Spike_labels": [np.array([[1], [0]])], + "Spike_times": [np.array([[0.1], [0.2]])], + "Spike_positions": [np.array([[0.5, 0.5], [0.6, 0.6]])], + } + yield mock_load + + +def test_trainer_init(mock_trainer_deps, temp_project_dir): + project_dir, xml_path = temp_project_dir + project = MagicMock() + project.experimentPath = project_dir + project.folderResult = os.path.join(project_dir, "results") + project.folderResultSleep = os.path.join(project_dir, "results_Sleep") + + trainer = Trainer(projectPath=project, phase="pre") + + assert trainer.phase == "pre" + assert trainer.projectPath == project + assert os.path.exists(trainer.folderResult) + + +def test_build_occupation_map(mock_trainer_deps, temp_project_dir): + project_dir, xml_path = temp_project_dir + project = MagicMock() + project.experimentPath = project_dir + + trainer = Trainer(projectPath=project) + + # Create some mock positions + positions = np.random.rand(100, 2) + + inv_occ, occ, grid = trainer._build_occupation_map(positions) + + assert occ.shape == (trainer.GRID_H, trainer.GRID_W) + assert inv_occ.shape == (trainer.GRID_H, trainer.GRID_W) + # Check that forbidden regions are masked in inverse occupation + # (assuming default MAZE_COORDS) + # Gap is 0.35 to 0.65, Y < 0.75. + # Meshgrid 'xy' indexing: X is columns, Y is rows. + # Let's check a point in the gap: (0.5, 0.1) + + # grid[0] is Xc, grid[1] is Yc + # Find index closest to (0.5, 0.1) + x_idx = np.argmin(np.abs(grid[0][0, :] - 0.5)) + y_idx = np.argmin(np.abs(grid[1][:, 0] - 0.1)) + + assert inv_occ[y_idx, x_idx] == 0.0 # Should be masked + + +def test_compute_rate_function(mock_trainer_deps, temp_project_dir): + project_dir, xml_path = temp_project_dir + project = MagicMock() + project.experimentPath = project_dir + + trainer = Trainer(projectPath=project) + # Set bandwidth explicitly for testing + trainer.config.bandwidth = 0.1 + + spike_positions = np.random.rand(50, 2) + grid_feature = [trainer.Xc_np, trainer.Yc_np] + final_occ = np.ones((trainer.GRID_H, trainer.GRID_W)) + + rate_map = trainer._compute_rate_function( + spike_positions, grid_feature, final_occ, len(spike_positions), 100.0 + ) + + assert rate_map.shape == (trainer.GRID_H, trainer.GRID_W) + assert not np.isnan(rate_map).any() + + +def test_align_speed_filters(mock_trainer_deps, temp_project_dir): + project_dir, xml_path = temp_project_dir + project = MagicMock() + project.experimentPath = project_dir + + trainer = Trainer(projectPath=project) + + behaviorData = { + "positionTime": np.array([[0.1], [0.2], [0.3]]), + "Times": {"speedFilter": np.array([True, False, True])}, + } + + # Trainer clusterData is mocked by fixture: Spike_times: [0.1, 0.2] + speed_filters = trainer._align_speed_filters(behaviorData) + + assert len(speed_filters) == 1 + # Spike at 0.1 should match pos at 0.1 -> speed True (1) + # Spike at 0.2 should match pos at 0.2 -> speed False (0) + assert speed_filters[0][0] == 1 + assert speed_filters[0][1] == 0 diff --git a/tests/test_data_helper.py b/tests/test_data_helper.py new file mode 100644 index 0000000..61cf750 --- /dev/null +++ b/tests/test_data_helper.py @@ -0,0 +1,146 @@ +import pytest +import os +import numpy as np +from unittest.mock import MagicMock, patch +from neuroencoders.utils.global_classes import DataHelper, Project, is_in_zone, ZONEDEF + + +def test_project_init(temp_project_dir): + project_dir, xml_path = temp_project_dir + + prj = Project(str(xml_path), nameExp="Network") + + assert prj.xml == str(xml_path) + assert prj.baseName == str(xml_path)[:-4] + assert prj.experimentPath == os.path.join(project_dir, "Network") + + +def test_is_in_zone(): + # ZoneDef: [[x_min, x_max], [y_min, y_max]] + # shock zone: [[0, 0.35], [0, 0.43]] + zone = ZONEDEF[0] + + # Point inside + p_in = np.array([[0.1, 0.1]]) + assert is_in_zone(p_in, zone).all() + + # Point outside + p_out = np.array([[0.5, 0.5]]) + assert not is_in_zone(p_out, zone).any() + + +def test_dist2wall(): + # We can mock DataHelper without loading from disk by patching __new__ or __init__? + # Or just use a dummy subclass. + + # Let's create a minimal DataHelper stub that has dist2wall + # DataHelper.dist2wall(positions) + + # Creating a minimal instance without calling actual __init__ + dh = DataHelper.__new__(DataHelper) + # Positions needs to trigger get_maze_limits logic: + # lower_mask = (y < 0.75) & (x < 0.5) + # upper_mask = (y < 0.75) & (x > 0.5) + # We need points in these regions. + dh.positions = np.array( + [ + [0.1, 0.1], # Lower region -> sets lower_x + [0.9, 0.1], # Upper region -> sets upper_x + [0.5, 0.8], # Other + ] + ) + dh.old_positions = dh.positions + + # Mock maze limits for standard unit square + # We can rely on get_maze_limits to run or just set them? + # The failing test called get_maze_limits explicitly implicitly via dist2wall -> get_maze_limits + # ensuring get_maze_limits works with above positions. + + dh.lower_x = 0.35 + dh.upper_x = 0.65 + dh.ylim = 0.75 + + # We also need to mock create_polygon and shapePoints logic if we don't assume real DataHelper methods work fully. + # But dist2wall calls them. + # We need to make sure global_classes imports standard libs correctly. + + # If we trust get_maze_limits works with above data, we are good. + # Let's just mock get_maze_limits to return fixed values to avoid the auto-detection logic being the point of failure for dist2wall test. + + dh.get_maze_limits = MagicMock(return_value=([0.35, 0.65], 0.75)) + + dh.maze_coords = [ + [0, 0], + [0, 1], + [1, 1], + [1, 0], + [dh.upper_x, 0], + [dh.upper_x, dh.ylim], + [dh.lower_x, dh.ylim], + [dh.lower_x, 0], + [0, 0], + ] + + # Test valid point + pos = np.array([[0.5, 0.8]]) # Center top + # The maze has a hole in the middle? + # Let's look at `_define_maze_zones`. + # It creates a polygon. + + dist = dh.dist2wall(pos, show=False) + assert isinstance(dist, np.ndarray) + assert dist.shape == (1,) + # Should be positive distance to boundary + assert dist[0] >= 0 + + +def test_helper_linearization_target(): + # Mock l_function + def mock_l_func(pos): + # map x coordinate to linear pos + return None, pos[:, 0] + + dh = DataHelper.__new__(DataHelper) + dh.positions = np.array([[0.2, 0.2]]) + dh.target = "lin" + + # Mock get_maze_limits to avoid it failing + dh.get_maze_limits = MagicMock(return_value=[0, 1]) + + res = dh.get_true_target(l_function=mock_l_func, in_place=False) + + assert res.shape == (1,) + assert res[0] == 0.2 + + +def test_data_helper_persistence(temp_project_dir): + project_dir, _ = temp_project_dir + save_path = os.path.join(project_dir, "dh.pkl") + + dh = DataHelper.__new__(DataHelper) + dh.custom_attr = "hello" + dh.positions = np.array([[1, 2]]) + + # Save + import dill as pickle + + with open(save_path, "wb") as f: + pickle.dump(dh, f) + + # Load + dh_loaded = DataHelper.load(save_path) + + assert dh_loaded.custom_attr == "hello" + assert np.array_equal(dh_loaded.positions, dh.positions) + assert getattr(dh_loaded, "_loaded_from_pickle", True) + + +def test_project_paths(temp_project_dir): + project_dir, xml_path = temp_project_dir + + # Test normalization of paths + prj = Project(xmlPath=xml_path, nameExp="TestExp") + + assert prj.experimentPath == os.path.join(project_dir, "TestExp") + # Verify subfolders are set + assert hasattr(prj, "experimentPath") diff --git a/tests/test_losses.py b/tests/test_losses.py new file mode 100644 index 0000000..3271c0c --- /dev/null +++ b/tests/test_losses.py @@ -0,0 +1,95 @@ +import pytest +import torch +import numpy as np +from neuroencoders.fullEncoder.nnUtils_torch import ( + LinearizationLayer, + GaussianHeatmapLayer, + GaussianHeatmapLosses, + ContrastiveLossLayer, +) + + +def test_linearization_layer(): + # Simple linear track: (0,0) -> (1,0) + maze_points = np.array([[0.0, 0.0], [0.5, 0.0], [1.0, 0.0]]) + ts_proj = np.array([0.0, 0.5, 1.0]) + + linearizer = LinearizationLayer(maze_points, ts_proj) + + # Test point close to start + test_pt = torch.tensor([[0.1, 0.1]]) # Should map to (0,0) -> 0.0 + proj, lin = linearizer(test_pt) + + # Distance to (0,0) is 0.02, to (0.5,0) is 0.4^2 + 0.1^2 = 0.17 + # So should map to index 0 + assert torch.allclose(proj, torch.tensor([[0.0, 0.0]])) + assert torch.allclose(lin, torch.tensor([0.0])) + + # Test point close to end + test_pt2 = torch.tensor([[0.9, 0.0]]) # Should map to (1,0) -> 1.0 + proj2, lin2 = linearizer(test_pt2) + assert torch.allclose(proj2, torch.tensor([[1.0, 0.0]])) + assert torch.allclose(lin2, torch.tensor([1.0])) + + +def test_gaussian_heatmap_layer(): + grid_size = (10, 10) + std = 1.0 + layer = GaussianHeatmapLayer(grid_size=grid_size, std=std) + + # Target in middle of grid + true_pos = torch.tensor([[0.5, 0.5]]) # Corresponds to index (5, 5) + + # Generate heatmap + heatmap = layer.gaussian_heatmap_targets(true_pos) + + # Shape check + assert heatmap.shape == (1, 10, 10) + + # Max value should be at (5, 5) or nearby + # (0.5 * 10 = 5.0). Grid indices are 0..9. + # Closest indices are 5. + + max_idx = torch.argmax(heatmap.view(-1)) + # 5 * 10 + 5 = 55 + assert max_idx.item() == 55 + + # Test forward (reshape) + flat_input = torch.randn(2, 100) + out = layer(flat_input, flatten=False) + assert out.shape == (2, 10, 10) + + +def test_gaussian_heatmap_losses(): + loss_fn = GaussianHeatmapLosses(loss_type="mse") + + logits = torch.zeros(2, 10, 10) + targets = torch.zeros(2, 10, 10) + + # MSE of zeros should be 0 + loss = loss_fn({"logits": logits, "targets": targets}) + assert loss.item() == 0.0 + + # Test safe_kl + loss_fn_kl = GaussianHeatmapLosses(loss_type="safe_kl") + # Perfect match (uniform) + loss_kl = loss_fn_kl({"logits": logits, "targets": torch.ones(2, 10, 10) / 100}) + # Softmax of 0s is uniform. Target is uniform. KL should be 0. + assert abs(loss_kl.item()) < 1e-6 + + +def test_contrastive_loss(): + loss_fn = ContrastiveLossLayer() + + # Pred = Target -> Loss 0 + p1 = torch.tensor([[0.5, 0.5]]) + p2 = torch.tensor([[0.5, 0.5]]) + loss = loss_fn([p1, p2]) + assert loss.item() == pytest.approx(0.0, abs=1e-6) + + # Pred != Target + p3 = torch.tensor([[0.0, 0.0]]) + # Dist = sqrt(0.5^2 + 0.5^2) = sqrt(0.5) ~ 0.707 + # Loss = mean(dist^2) = 0.5 + loss2 = loss_fn([p1, p3]) + assert loss2.item() == pytest.approx(0.5, abs=1e-5) diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..8f7b62b --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,154 @@ +import os +import pytest +import numpy as np +import torch +import tensorflow as tf +from unittest.mock import MagicMock, patch + +# Import implementation classes +from neuroencoders.fullEncoder.an_network_torch import LSTMandSpikeNetwork as TorchNet + +# Assuming original TF class is importable +try: + from neuroencoders.fullEncoder.an_network import LSTMandSpikeNetwork as TFNet +except ImportError: + TFNet = None + + +def get_mock_inputs( + backend, batch_size=2, n_groups=2, n_channels=[2, 2], n_features=64 +): + """Generates mock inputs for the specified backend.""" + inputs = {} + + if backend == "pytorch": + for g in range(n_groups): + indices = torch.arange(batch_size).repeat_interleave(n_channels[g]) + inputs[f"indices{g}"] = indices + inputs[f"group{g}"] = torch.randn(len(indices), n_channels[g], 32) + inputs["batch_size"] = batch_size + + elif backend == "tensorflow": + for g in range(n_groups): + # Group voltage inputs: (Batch, Channels, Time) + inputs[f"group{g}"] = tf.random.normal((batch_size, n_channels[g], 32)) + # Indices for gathering: (Batch,) + inputs[f"indices{g}"] = tf.zeros((batch_size,), dtype=tf.int32) + + inputs["groups"] = tf.zeros((batch_size,), dtype=tf.int32) + inputs["zeroForGather"] = tf.zeros((batch_size, n_features)) + inputs["pos"] = tf.zeros((batch_size, 2)) + + return inputs + + +def get_mock_behavior_data(n_samples=100): + return { + "Times": { + "speedFilter": np.ones((n_samples,), dtype=bool), + "trainEpochs": np.array([[0, n_samples]]), + }, + "positionTime": np.linspace(0, n_samples, n_samples)[:, None], + "Positions": np.random.rand(n_samples, 2), + } + + +@pytest.fixture +def mock_project(temp_project_dir): + project_dir, xml_path = temp_project_dir + project = MagicMock() + project.experimentPath = project_dir + project.xml = xml_path + project.folderResult = os.path.join(project_dir, "Network") + project.folderResultSleep = os.path.join(project_dir, "Network", "results_Sleep") + return project + + +@pytest.mark.parametrize("backend", ["tensorflow", "pytorch"]) +def test_model_instantiation(backend, mock_params, mock_project): + behavior_data = get_mock_behavior_data() + if backend == "pytorch": + model = TorchNet(projectPath=mock_project, params=mock_params) + assert model is not None + assert isinstance(model, torch.nn.Module) + elif backend == "tensorflow": + if TFNet is None: + pytest.skip("TFNet not available") + model = TFNet( + projectPath=mock_project, params=mock_params, behaviorData=behavior_data + ) + assert model.model is not None + assert isinstance(model.model, tf.keras.Model) + + +@pytest.mark.parametrize("backend", ["tensorflow", "pytorch"]) +def test_model_forward(backend, mock_params, mock_project): + behavior_data = get_mock_behavior_data() + inputs = get_mock_inputs( + backend, + batch_size=mock_params.batchSize, + n_groups=mock_params.nGroups, + n_channels=mock_params.nChannelsPerGroup, + n_features=mock_params.nFeatures, + ) + + if backend == "pytorch": + model = TorchNet(projectPath=mock_project, params=mock_params) + output = model(inputs) + assert output.shape == (mock_params.batchSize, mock_params.dimOutput) + assert not torch.isnan(output).any() + elif backend == "tensorflow": + if TFNet is None: + pytest.skip("TFNet not available") + model_obj = TFNet( + projectPath=mock_project, params=mock_params, behaviorData=behavior_data + ) + output = model_obj.model(inputs) + + if isinstance(output, (list, tuple)): + pos_out = output[0] + else: + pos_out = output + + assert pos_out.shape == (mock_params.batchSize, mock_params.dimOutput) + + +@pytest.mark.parametrize("backend", ["tensorflow", "pytorch"]) +def test_train_step(backend, mock_params, mock_project): + behavior_data = get_mock_behavior_data() + inputs = get_mock_inputs( + backend, + batch_size=mock_params.batchSize, + n_groups=mock_params.nGroups, + n_channels=mock_params.nChannelsPerGroup, + n_features=mock_params.nFeatures, + ) + + if backend == "pytorch": + model = TorchNet(projectPath=mock_project, params=mock_params) + targets = {"pos": torch.randn(mock_params.batchSize, 2)} + model.train() + model.optimizer.zero_grad() + preds = model(inputs) + loss = torch.nn.functional.mse_loss(preds, targets["pos"]) + loss.backward() + model.optimizer.step() + assert not torch.isnan(loss).any() + elif backend == "tensorflow": + if TFNet is None: + pytest.skip("TFNet not available") + model_obj = TFNet( + projectPath=mock_project, params=mock_params, behaviorData=behavior_data + ) + + targets = { + "myoutputPos": np.random.randn( + mock_params.batchSize, mock_params.dimOutput + ), + "posLoss": np.zeros((mock_params.batchSize,)), + } + for name in model_obj.outNames[2:]: + targets[name] = np.zeros((mock_params.batchSize,)) + + loss = model_obj.model.train_on_batch(inputs, targets) + assert loss is not None diff --git a/tests/test_plotting.py b/tests/test_plotting.py new file mode 100644 index 0000000..95d0d92 --- /dev/null +++ b/tests/test_plotting.py @@ -0,0 +1,44 @@ +import pytest +from unittest.mock import MagicMock, patch +import numpy as np + +# We need to mock matplotlib BEFORE importing modules that might use it +with patch("matplotlib.pyplot.show"): + import neuroencoders.resultAnalysis.paper_figures as paper_figures + + +def test_plotting_imports(): + # If this runs, imports worked + assert paper_figures is not None + + +@patch("matplotlib.pyplot.figure") +@patch("matplotlib.pyplot.subplot") +@patch("matplotlib.pyplot.plot") +def test_basic_plot_calls(mock_plot, mock_subplot, mock_figure): + # Test a simple function that does plotting + # Assuming there's a function that takes simple inputs + + # Let's find a simple plotting function in paper_figures + # for now we just verify we can mock and call something if it exists. + pass + + +@patch("matplotlib.pyplot.imshow") +def test_plot_heatmaps_mock(mock_imshow): + # Mocking data for some hypothetical plotting function + data = np.random.rand(10, 10) + + import matplotlib.pyplot as plt + + plt.imshow(data) + mock_imshow.assert_called_once() + + +def test_paper_figures_gui_import(): + try: + from neuroencoders.importData import gui_elements + + assert gui_elements is not None + except ImportError as e: + pytest.skip(f"GUI elements might depend on system libs: {e}") diff --git a/uv.lock b/uv.lock index 74438b9..3a9bb79 100644 --- a/uv.lock +++ b/uv.lock @@ -2331,6 +2331,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656 }, ] +[[package]] +name = "iniconfig" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 }, 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