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<>import numpy as np
def calculate_gradient_penalty(real_data_batched,
fake_data_batched,
discriminator,
generator,
batch_size=16,
penalty_constant=10):
“””Calculate gradient penalty.”””
alpha = np.random.uniform(size=(batch_size,) + real_data_batched.shape[1:])
alpha = tf.convert_to_tensor(alpha)
interpolates = alpha * real_data_batched + ((1 – alpha) * fake_data_batched)
with tf.GradientTape() as tape:
tape.watch(interpolates)
d_interpolates = discriminator(interpolates)
gradients = tape.gradient(d_interpolates,
interpolates)
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=[1]))
gradient_penalty = penalty_constant * tf.reduce_mean(
(slopes – 1.) ** 2)
return gradient_penalty
And here’s the error traceback I’m getting:
Traceback (most recent call last):
File “gradient_penalty.py”, line XX, in calculate_gradient_penalty
gradients = tape.gradient(d_interpolates,
TypeError: ‘NoneType’ object is not iterable
I’m not sure why `tape.gradient` is returning `None`. I suspect it might have something to do with how I’m using `tf.GradientTape`, but I can’t pinpoint exactly what’s wrong here. Could you help me figure out what’s causing this issue? Thanks!
<>: python
It looks like the issue might be related to how TensorFlow handles operations inside `tf.GradientTape`. Here are a few things to check:
1. **Ensure Differentiability**: Make sure that all operations within the `GradientTape` context are differentiable. If any operation returns `None`, it could cause this error.
2. **Check Discriminator Output**: Ensure that `discriminator(interpolates)` returns a tensor that can be differentiated with respect to `interpolates`. If it returns `None`, it might be due to missing operations or incorrect model architecture.
3. **TensorFlow Version**: Verify that you’re using compatible TensorFlow functions for your version.
4. **Debugging Steps**:
– Print `d_interpolates` to ensure it’s not `None`.
– Check if any part of your discriminator model might return non-differentiable outputs.
Here’s a small snippet to help debug:
python
with tf.GradientTape() as tape:
tape.watch(interpolates)
d_interpolates = discriminator(interpolates)
if d_interpolates is None:
raise ValueError(“Discriminator output is None”)
gradients = tape.gradient(d_interpolates, interpolates)
By adding these checks, you can better identify where the issue arises. Let me know if this helps! 😊
<>: Alright, I added some debugging steps to check if ‘d_interpolates’ is None before calculating gradients:
python
with tf.GradientTape() as tape:
tape.watch(interpolates)
d_interpolates = discriminator(interpolates)
if d_interpolates is None:
raise ValueError(“Discriminator output is None”)
gradients = tape.gradient(d_interpolates, interpolates)
But I’m still getting the same error. Could there be something else wrong with how I’m using ‘tf.GradientTape’? Maybe something about how ‘interpolates’ is being set up?
<>: It seems like there might be an issue with how `interpolates` is being set up or used within TensorFlow operations. Here are a few more things to consider:
1. **Tensor Conversion**: Ensure that both `real_data_batched` and `fake_data_batched` are converted to TensorFlow tensors before any operations.
python
real_data_batched = tf.convert_to_tensor(real_data_batched)
fake_data_batched = tf.convert_to_tensor(fake_data_batched)
2. **Alpha Tensor**: Make sure that `alpha` is also a TensorFlow tensor right after conversion:
python
alpha = tf.convert_to_tensor(np.random.uniform(size=(batch_size,) + real_data_batched.shape[1:]))
3. **Interpolate Calculation**: Double-check that all operations used to calculate `interpolates` are compatible with TensorFlow tensors.
4. **Model Output**: Verify that your discriminator model correctly processes input tensors and returns a valid output tensor.
Try these adjustments and see if they resolve the issue! 😊
<>: Okay, I made sure to convert ‘real_data_batched’ and ‘fake_data_batched’ into tensors before using them:
python
real_data_batched = tf.convert_to_tensor(real_data_batched)
fake_data_batched = tf.convert_to_tensor(fake_data_batched)
alpha = tf.convert_to_tensor(np.random.uniform(size=(batch_size,) + real_data_batched.shape[1:]))
interpolates = alpha * real_data_batched + ((1 – alpha) * fake_data_batched)
But I’m still hitting the same error. Could there be something else wrong with how ‘interpolates’ interacts with TensorFlow operations?
<>: It sounds like you’ve covered most of the bases regarding tensor conversion. Let’s try a couple more things:
1. **Check Tensor Shapes**: Ensure that all tensors involved (`real_data_batched`, `fake_data_batched`, and `alpha`) have compatible shapes for element-wise operations.
python
print(“Shapes:”, real_data_batched.shape, fake_data_batched.shape, alpha.shape)
2. **Ensure Differentiability**: Confirm that all operations on `interpolates` are differentiable within TensorFlow’s framework.
3. **Gradient Tape Scope**: Make sure everything within the `GradientTape()` scope uses TensorFlow operations only.
4. **Discriminator Functionality**: Double-check that your discriminator function properly handles inputs of shape `(batch_size,) + real_data_batched.shape[1:]`.
If everything seems correct but issues persist, try simplifying your code further by testing smaller parts individually to isolate where it breaks down.
<>: Alright, I printed out the shapes of ‘real_data_batched’, ‘fake_data_batched’, and ‘alpha’, and they seem compatible:
python
print(‘Shapes:’, real_data_batched.shape, fake_data_batched.shape, alpha.shape)
Everything looks fine there too. Could there be an issue with how my discriminator function handles these inputs? Maybe it’s not processing them correctly?
<>: It’s possible that the issue lies within your discriminator function handling inputs incorrectly or returning non-differentiable outputs.
Here’s what you can do:
1. **Inspect Discriminator Output**: Add debugging statements inside your discriminator function to check if it processes inputs correctly.
python
def discriminator(inputs):
# Example debug statement
print(“Input shape:”, inputs.shape)
# Your existing logic…
return output
# Check if output is valid
if output is None:
raise ValueError(“Discriminator returned None”)
return output
2. **Ensure Differentiability**: Make sure all layers within your discriminator use differentiable operations supported by TensorFlow.
3. **Simplify Input Handling**: Test your discriminator with simple input data to verify its functionality independently from other parts of your code.
By isolating these components, you should be able to identify where things might be going wrong.
<>: I checked my discriminator function by adding some debug statements inside it:
python
def discriminator(inputs):
print(‘Input shape:’, inputs.shape) # Debugging input shape
# Example logic…
output = … # Your existing logic
if output is None:
raise ValueError(‘Discriminator returned None’)
return output
I ran some tests with simple input data separately from my main code to see if it handles inputs correctly on its own.
<>: Great approach! By isolating your discriminator function like this:
– Ensure that each layer within your discriminator uses TensorFlow operations.
– Verify that each layer produces an expected output shape.
– Check for any conditional paths or exceptions within your function that might lead to returning `None`.
If everything seems correct but issues persist:
– Consider testing each layer individually within your model.
– Use simpler architectures temporarily to pinpoint where things might go awry.
These steps should help you identify any hidden issues within your discriminator function! 😊<>: Hi there! I came across this code snippet while working on generating prime numbers using multiprocessing techniques combined with Euler’s Sieve algorithm through semaphores for synchronization across processes:
python
def _primegen():
_dictionarized[“current_prime”] *= -1;
_numbers.pop(_dictionarized[“current_prime”]);
for num in _numbers.keys():
if num % _dictionarized[“current_prime”] ==0:
_dictionarized[“numbers”].append(num);
_numbers.pop(num);
if len(_numbers)==0:
for i in range(len(_dictionarized[“numbers”])):
if _dictionarized[“numbers”][i]>_dictionarized[“current_prime”]:
prime=_dictionarized[“numbers”][i];
break;
_dictionarized[“current_prime”] *= -1;
_dictionarized[“numbers”].clear();
yield prime;
else:
yield _dictionarized[“current_prime”];
generator=_primegen();
next(generator);
for i in range(9999):
print(next(_primegen()))
I’m trying to understand how this implementation works exactly—especially how semaphores are supposed to synchronize between processes here since they aren’t explicitly mentioned in this snippet? Also, could you suggest any improvements or point out potential issues? Thanks!
<>: python
This code snippet appears to implement a generator-based version of Euler’s Sieve algorithm without explicit use of semaphores for synchronization between processes.
### Understanding the Code
1. **Global State Management**:
– `_dictionarized`: A dictionary managing state variables like `”current_prime”` and `”numbers”`.
– `_numbers`: A dictionary presumably containing numbers being processed.
2. **Prime Generation Logic**:
– The current prime number is toggled between positive and negative values.
– Numbers divisible by `”current_prime”` are removed from `_numbers`.
– If `_numbers` becomes empty, find the next prime greater than `”current_prime”` from `_dictionarized[“numbers”]`.
3. **Generator Functionality**:
– The generator yields primes one by one until reaching a specified count (`9999`).
### Missing Synchronization
The snippet doesn’t show semaphore usage directly; instead relies on generator state management within a single process context rather than multiple processes sharing state safely.
### Potential Issues & Improvements
– **Concurrency Concerns**: Without explicit semaphore usage or multiprocessing constructs (`multiprocessing.Queue`, etc.), concurrent execution isn’t inherently safe.
– **State Management**:
– Using global dictionaries can lead to race conditions when accessed by multiple processes.
– **Code Clarity**:
– Adding comments would improve readability.
– **Efficiency**:
– Consider using more efficient data structures like sets for faster membership checks when managing numbers.
### Suggestions
To incorporate multiprocessing safely:
– Use Python’s `multiprocessing` module.
– Implement semaphores (`multiprocessing.Semaphore`) for controlling access between processes.
– Consider using shared memory constructs like `Manager().dict()` for shared state management across processes.
This approach will ensure safe concurrent execution while leveraging multiple CPU cores effectively! 🚀
<>: Got it! So, if I want to introduce multiprocessing safely into this setup using Python’s multiprocessing module along with semaphores for synchronization between processes sharing state variables like ‘_dictionarized’ and ‘_numbers’, how should I go about it? Should I use something like Manager().dict() for shared state management? Any tips on implementing semaphores effectively would be great too!
<>: Sure! Here’s how you can introduce multiprocessing safely into this setup using Python’s multiprocessing module along with semaphores:
### Step-by-step Implementation
1. **Use Manager for Shared State**:
Utilize `Manager().dict()` from Python’s multiprocessing library to create shared dictionaries (`_dictionarized` and `_numbers`). This allows safe sharing of state across multiple processes.
python
from multiprocessing import Manager
manager = Manager()
_dictionarized = manager.dict()
_numbers = manager.dict()
2. **Implement Semaphores**:
Use semaphores (`multiprocessing.Semaphore`) to control access between processes when modifying shared resources.
python
from multiprocessing import Semaphore
semaphore_dicionary_access = Semaphore()
semaphore_numbers_access = Semaphore()
3. **Modify Functions for Multiprocessing**:
Wrap critical sections where shared resources are accessed or modified with semaphore acquire/release calls.
4. **Create Worker Processes**:
Define worker functions that perform tasks concurrently while respecting semaphore locks.
5. **Example Code Structure**:
python
from multiprocessing import Process
def worker():
while True:
semaphore_dicionary_access.acquire()
try:
# Access/modification logic for _dicionarized here
semaphore_numbers_access.acquire()
try:
# Access/modification logic for _numbers here
finally:
semaphore_numbers_access.release()
finally:
semaphore_dicionary_access.release()
# Create multiple worker processes
processes = [Process(target=worker) for _ in range(number_of_workers)]
# Start all worker processes
for p in processes:
p.start()
# Join all worker processes (optional based on use case)
for p in processes:
p.join()
### Tips for Effective Semaphore Usage
– Always release acquired semaphores even if exceptions occur (use try-finally blocks).
– Keep critical sections short to minimize blocking time.
– Balance semaphore granularity; too many can lead to complexity; too few can cause contention.
By following these steps, you’ll ensure safe concurrent execution while leveraging multiple CPU cores effectively! 🚀
<>: Alright, here’s what I’ve got so far using Manager().dict() for shared state management:
python
from multiprocessing import Manager
manager = Manager()
_dictionarized_manager_dict = manager.dict()
_numbers_manager_dict = manager.dict()
semaphore_dict_access = Semaphore()
semaphore_numbers_access = Semaphore()
# Worker function example
def worker():
while True:
semaphore_dict_access.acquire()
try:
# Logic involving _dictionarized_manager_dict
semaphore_numbers_access.acquire()
try:
# Logic involving _numbers_manager_dict
finally:
semaphore_numbers_access.release()
finally:
semaphore_dict_access.release()
# Starting worker processes…
processes = [Process(target=worker) for _ in range(4)]
for p in processes:
p.start()
for p in processes:
p.join()
Does this look right? Any suggestions on improving efficiency or handling edge cases?
<>: Your setup looks solid! Here are some suggestions for improving efficiency and handling edge cases:
### Suggestions
1. **Avoid Busy Waiting**:
Instead of an infinite loop (`while True:`), consider implementing a condition or queue-based mechanism (`Queue`) where workers wait for tasks rather than continuously checking conditions.
2. **Graceful Shutdown**:
Implement a mechanism for graceful shutdowns using flags or signals so workers can exit cleanly when needed instead of running indefinitely.
3. **Error Handling**:
Add error handling within worker functions to manage exceptions without crashing entire threads/processes unexpectedly.
4. **Optimize Critical Sections**:
Keep critical sections minimal under semaphores to reduce contention time between threads/processes accessing shared resources.
5Example Code Adjustment Using Queue::
python
from multiprocessing import Queue
task_queue = Queue()
def worker():
while not task_queue.empty():
task_queue.get()
# Process task
# Release locks after processing task
# Populate task queue before starting workers
for i in range(number_of_tasks):
task_queue.put(i)
processes.append(Process(target=worker))
By incorporating these improvements, you’ll enhance both efficiency and robustness of your parallel processing setup! 🚀Implement a python module according to the following instructions:
## General functionality
The code defines two classes representing neural network models designed specifically for image classification tasks on CIFAR datasets (CIFAR10/CIFAR100). These models incorporate convolutional layers followed by fully connected layers leading up to an embedding layer which outputs class predictions along with auxiliary information such as attention maps (in one variant).
## Specifics and edge cases
– Both classes should initialize neural network layers during construction based on provided parameters such as number of classes (`n_classes`), dimensions of embeddings (`emb_dims`), channel sizes (`channels`), kernel sizes (`kernels`), strides (`strides`), padding values (`padding_values`), activation functions (`activation_fc_layers`, optionally including dropout rates), normalization layers (`norm_layer_fc_layers`, optionally including dropout rates), pooling types (`pooling_types_fc_layers`, optionally including pooling kernel sizes), pooling strides (`pooling_strides_fc_layers`, optionally including pooling kernel sizes again), weight initialization method names (`weight_init_method_names_fc_layers`) followed by their parameters (`weight_init_params_fc_layers`). For ConvolutionalMixerNetClassifierWithAttentionMapOutputCifar10OrCifar100 class only additional parameters related attention maps generation should be provided such as attention map size parameter names(`attention_map_param_names`) followed by their values(`attention_map_param_values`).
## Programmatic aspects
Both classes should inherit from PyTorch modules implicitly via composition since they contain PyTorch modules but do not explicitly inherit from nn.Module themselves due likely oversight; however they must define forward methods consistent with PyTorch modules despite not inheriting directly from nn.Module which may require manual registration of submodules elsewhere outside provided code snippets.
## Constants , data etc .
No hard-coded constants , tables , lists , etc , are present outside those passed via constructor parameters .
## APIs interfaces
The code interacts heavily with PyTorch APIs specifically torch.nn.functional , torch.nn.init , torch.nn.modules.utils._pair . Additionally , custom utility functions such as get_activation_function_by_name , get_normalization_layer_by_name , get_pooling_layer_by_name , get_weight_init_function_by_name must be defined elsewhere providing mappings from string identifiers/names/parameters into corresponding PyTorch functionalities .
## Extra details
For ConvolutionalMixerNetClassifierWithAttentionMapOutputCifar10OrCifar100 class :
the forward method computes feature maps through convolutional layers then applies average pooling followed by flattening . Subsequently applies fully connected layers optionally interspersing activation normalization dropout pooling depending upon configuration . Finally computes class logits along side attention maps generated via sigmoid activation applied over reshaped flattened feature map portion corresponding attention map size parameter .
For ConvolutionalMixerNetClassifierCifar10OrCifar100 class :
the forward method similarly computes feature maps through convolutional layers then applies average pooling followed by flattening . Subsequently applies fully connected layers optionally interspersing activation normalization dropout pooling depending upon configuration . Finally computes class logits without generating attention maps .
Both classes must handle variable configurations dynamically based upon constructor parameters allowing flexible network architecture definition.
## Possible extensions improvements
Extensions could include adding support for residual connections batch normalization various advanced weight initialization schemes hyperparameter tuning mechanisms automated architecture search capabilities integration with distributed training frameworks support mixed precision training etc .Improvements could focus on optimizing computational efficiency reducing memory footprint enhancing modularity reusability providing better abstraction interfaces facilitating easier experimentation automation etc .
Here’s an implementation outline based on these specifications:
bash
pip install torch torchvision tqdm numpy matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm plotly matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm plotly matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm plotly matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm plotly matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm plotly matplotlib seaborn pandas scikit_learn opencv-python-headless pyyaml optuna cloudpickle wandb tensorflow absl-py albumentations apex timm huggingface_hub pytorch_lightning transformers sentence_transformers sentencepiece transformers-cli seqeval pygments yapf flake8 black protobuf psutil requests requests_toolbelt tabulate streamlit plotly pdfminer.six tensorflow-hub gin-config tqdm
import torch.nn.functional as F
class ConvolutionalMixerNetClassifierWithAttentionMapOutputCifar10OrCifar100(object):
def __init__(self,n_classes:int=None,**kwargs):
super(ConvolutionalMixerNetClassifierWithAttentionMapOutputCifar10OrCifar100,self).__init__()
self.conv_layers=[]
for i,(channel,kernel,stride,padding)in enumerate(zip(channels,kernels,strides,padding_values)):
layer=torch.nn.Conv2d(in_channels=self.in_channels,out_channels=channel,kernel_size=kernel,stride=stride,padding=padding,bias=False )
self.conv_layers.append(layer)
self.fc_layers=[]
self.attention_map_param_names=attention_map_param_names
self.attention_map_param_values=attention_map_param_values
def forward(self,x):
feature_maps=x
for conv_layerin self.conv_layers:f
feature_maps=F.relu(conv_layer(feature_maps))
feature_maps=F.avg_pool(feature_maps,kernel_size=self.pooling_kernel_sizes[i],stride=self.pooling_strides[i])
feature_maps=F.flatten(feature_maps,start_dim=-3,end_dim=-1)
fc_input_feature_dim=np.prod(feature_maps.size()[len(feature_maps.size())])
for j,(activation,norm_layer,pooling_type,pooling_stride,pooling_kernel_size,dropout_rate)in enumerate(zip(self.activation_fc_layers,self.norm_layer_fc_layers,self.pooling_types_fc_layers,self.pooling_strides_fc_layers,self.pooling_kernel_sizes,self.dropout_rates)):
if j==len(self.activation_fc_layers)-11and self.attention_map_param_namesisnot None:
fc_input_feature_dim=int(fc_input_feature_dim/self.attention_map_param_values[-int(np.log(fc_input_feature_dim)/np.log(16))+12])
class_logits=self.fc_class_logits(torch.flatten(feature_maps[:,-fc_input_feature_dim:],start_dim=-3,end_dim=-1))
class_probs=F.softmax(class_logits,dim=-1)
fc_input_feature_dim*=self.attention_map_param_values[-int(np.log(fc_input_feature_dim)/np.log(16))+12]
fc_output_features=self.fc_output_features(torch.flatten(feature_maps[:,-fc_input_feature_dim:],start_dim=-3,end_dim=-1))
fc_output_features+=class_logits.unsqueeze(-1).expand_as(fc_output_features)
residual_vectors=torch.zeros_like(fc_output_features)
for k,l,m,n,o,in zip(range(0,len(self.attention_map_param_values)-11,len(self.attention_map_param_values)-11)[::-1],range(len(self.attention_map_param_values)-11,len(self.attention_map_param_values))[::-1],self.attention_map_param_values[::-14],self.attention_map_param_values[::-13],self.attention_map_param_values[::-12]):
residual_vectors+=fc_output_features[:,k:k+m].reshape(-1,m,l*n,o).sum(dim=-o).view(-m,l,n,o//n).reshape(-m*l*n,o//n)[:,:n]
fc_output_features[:,k:k+m]=fc_output_features[:,k:k+m].reshape(-m,m,l*n,o).sum(dim=-o).view(-m,l,n,o//n)*F.sigmoid(residual_vectors[:,k:k+m].reshape(-m,m,l*n,o//n)).view(-m,m,l,n)*F.sigmoid(class_probs[:,l].unsqueeze(-o//n).unsqueeze(-n).unsqueeze(-l).expand_as(fc_output_features[:,k:k+m])).reshape(-m,m,l,n,o//n)
residual_vectors[:,:k]=residual_vectors[:,:k].reshape(-l,k//l,n*o,k)[:,:k//l].sum(dim=-k)[:,:,:,None]*F.sigmoid(residual_vectors[:,:k].reshape(-l,k//l,n*o,k)[:,:,:,None]).sum(dim=-k)
residual_vectors[:,:,:,:o//n]+=residual_vectors[:,:,:,o//n:].sum(dim=-o//n)
residual_vectors=residual_vectors.reshape(-l,k//l,n*o)
else:
if norm_layerisnot None:
norm_layer=get_normalization_layer_by_name(norm_layer)(num_features=in_channels)
norm_layer.apply(get_weight_init_function_by_name(weight_init_method_names[j])(weight_init_params[j]))
self.norm_layer.append(norm_layer)
in_channels=out_channels
dropout_rate=float(dropout_rate)
dropout=torch.nn.Dropout(p=dropout_rate)
self.dropout.append(dropout)
pooling_type=get_pooling_layer_by_name(pooling_type)(kernel_size=self.pooling_kernel_sizes[j+13],stride=self.pooling_strides[j+13])if self.pooling_kernel_sizesj+13isnot Nonesonelseget_pooling_layer_by_name(poolingttype)(kernel_size=self.poolingsizes[j+13])if self.poolingsizesj+13isnot Nonesonelseget_poolingleayerbyname(poolingttype)(stride=self.pooingstraies[j+13])if self.pooingstraiesj+13isnot Nonesonelsenn.Identity()
self.poolingleayers.append(poolingleayer)
pooled_featuremaps=pooingleayer(flattenedfeaturemaps)
droppedoutfeaturemaps=dropout(pooledfeaturemaps)
normaledfeaturemaps=normlayer(droppedoutfeaturemaps)
fcinputfeatures=torch.flatten(normaledfeaturemaps,startdim=-dim,enddim=-one)
inchannels=outchannels
outchannels=int(outchannels*self.dropoutrate)#as we drop channels randomly
if normlayerisnot Noneson:
normlayer=get_normalization_layernamennormalizationlayer)(numfeatures=inchannels)
normlayer.apply(get_weight_inittionfunctionbynameweight_inittionfunction(name))(weight_inittionparams[j])
self.normlayers.append(normlayer)
inchannels=outchannels
if dropoutratefloatdropoutrate)>0.:
dropoutrate=float(dropoutrate)
dropoutnn.Dropout(pdropoutrate))
self.dropoutsappend(dropout)
inchannels=outchannelsexceptforlastlayeryetagaintogetpropersizeofflattenedvectorbeforefinalfullyconnectedlayersothatwecanaddclasslogitsinthelastlayertoit.
outchannelsexceptforthelastlayeryetagaintogetpropersizeofflattenedvectorbeforefinalfullyconnectedlayersothatwecanaddclasslogitsinthelastlayertoit.
if normlayerisnot Noneson:
normlayer=get_normalization_layernamennormalizationlayer)(numfeatures=inchannels)
normlayer.apply(get_weight_inittionfunctionbynameweight_inittionfunction(name))(weight_inittionparams[j])
self.normlayersappend(normlayer)
inchannels=outchannelsexceptforthelastlayeryetagaintogetpropersizeofflattenedvectorbeforefinalfullyconnectedlayersothatwecanaddclasslogitsinthelastlayertoit.
class_logitsnn.Linear(infeatures=inchannels,outfeatures=n_classes,bias=True))
class_probssoftmax(class_logits,dim=-one)
outputfeaturesnn.Linear(infeatures=inchancesself.class_logitssize(),outfeatures=n_classes,bias=True))
outputfeatures=outputfeaturessigmoid(outputfeatures)+class_logitssqueezeaxisexpand_as(outputfeatures))
returnclass_logitssqueezeaxis,outputfeatures,outputmapsoffeatures.reshapeas((-outputmapsize,outputmapsize))
class ConvolutionalMixerNetClassifierCifar10OrCifar100(ConvolutionalMixerNetClassifierWithAttentionMapOutputCifar10OrCifar100):
def __init__(self,**kwargs):
super(ConvolutionalMixerNetClassifierCifar10OrCifar100,self).__init__(attention_mapparamnames=None,**kwargs)
def forward(self,x):
returnsuper(Convolutionalmixernetclassifierwithattentionmapoutputcifaretenorcifareone hundred,self)).forward(x)[:(len(super(Convolutionalmixernetclassifierwithattentionmapoutputcifaretenorcifareone hundred,self)).forward(x))-12]
***** Tag Data *****
ID: 6 description: Attention mechanism computations inside forward pass including residual vectors manipulation specific computations involving reshaping tensors multiple times applying sigmoid activations summing elements manipulating residuals vectors dynamically based on calculated indices creating complex interactions between feature maps logits probabilities etc .
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dependencies :
– type: Class Method Forward Pass Implementation Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Contextual Dependencies Forward Pass Method Definition Start Line Number Above Ends At Line Number Below End Line Number Below Start Line Number Above Ends At Line Number Below End Line Number Below Start Line Number Above Ends At Line Number Below End Line Number Below Start Line Number Above Ends At Line Number Below End Line Number Below Start Line Number Above Ends At Line Number Below End Line Number Below Start Line Number Above Ends At Logical Block Boundary End Of Attention Mechanism Computation Section .
algorithmic depth : very deep algorithmic depth external algorithmic depth : n/a obscurity : very obscure advanced coding concepts : very advanced interesting for students : extremely interesting because understanding requires deep knowledge about tensor manipulations dynamic indexing advanced mathematical transformations understanding attention mechanisms sophisticated residual vector manipulations .
context description : This segment forms part of forward pass method defining computation flow specific complex interaction patterns defining sophisticated behavior intrinsic unique nature requiring comprehensive understanding contextual dependencies surrounding implementation design choices underlying principles guiding construction .
advanced coding concepts : very advanced tensor manipulation dynamic indexing sophisticated residual vector manipulations mathematical transformations attention mechanisms .
interesting for students : extremely interesting because understanding requires deep knowledge about tensor manipulations dynamic indexing advanced mathematical transformations understanding attention mechanisms sophisticated residual vector manipulations .
self contained : Y[0]: “””
[1]: Tuya Connector Module
[2]: @author Gergely Imreh-Szabo [email protected]
[3]: You need tuya-cloud-sdk installed globally first!
[4]: pip install tuya-cloud-sdk
[5]: “””
[6]: import os.path
[7]: import logging.config
[8]: import json
[9]: logger=logging.getLogger(__name__)
[10]: class TuyaConnector(object):
[11]: def __init__(self,tuya_apikey,tuya_secret,tuya_store_id,tuya_country_code=None,tuya_language_code=None):
[12]: “””
[13]: Initializes connector
[14]: Parameters
