I'm a beginner in modifying YOLOv5 and I'd like to know how to detailed steps to use the varifocal loss from VarifocalNet and implement it to YOLOv5 (pytorch).
I putted a link here below which is the python file of the varifocal loss
Varifocal Loss
thank you in advance
copy builder and utils file from the following repository
https://github.com/hyz-xmaster/VarifocalNet
yow will find the builder file builder here and utils here. Then paste these two files in yolov5 "utils".
pip install mmcv you can find here
Pasts the following code Varifocal in the "loss.py" file. ( you can put below QFocalLoss Class).
Remove one dot (.) before builder in Varifocal code. Meaning that put from .builder import LOSSES instead from ..builder import LOSSES (because we put builder file in same folder)
Use these three lines:
Use this :
g = 2 # focal loss gamma
if g > 0:
BCEcls, BCEobj = VarifocalLoss(BCEcls), VarifocalLoss(BCEobj)
Instead of :
g = h['fl_gamma'] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
Related
I have an input dataset (let's name it ds), a function that passes in to encoder (model named embedder). I want to make a dataset of encodings and save it to file. What I tried to do:
Converter function:
def generate_embedding(image, label, embedder):
return (embedder(image)[0], label)
Converting:
embedding_ds = ds.map(lambda image, label: generate_embedding(image, label, embedder), num_parallel_calls=tf.data.AUTOTUNE)
Saving:
embedding_ds.save(path)
But I have a problem with embedding_ds, it's not tf.data.Dataset (which I expected), but tf.raw_ops.ParallelMapDataset, which don't have save method. Can anybody give an advice?
Looks like this problem is present on my tensorflow version (2.9.2) and not present on 2.11
Maybe update? In 2.11.0, it works:
import tensorflow as tf
ds = tf.data.Dataset.range(5)
tf.__version__ # 2.11.0
ds = ds.map(lambda e : (e + 3) % 5, num_parallel_calls=3)
ds.save('test') # works
Apologies in advance.
I am attempting to recreate this CNN (from the Keras Code Examples), with another dataset.
https://keras.io/examples/vision/image_classification_from_scratch/
The dataset I am using is one for retinal scans, and classifies images on a scale from 0-4. So, it's a multi-label image classification.
The Keras example used is binary classification (cats v dogs), though I would have hoped it wouldn't make much difference (maybe this is a big assumption on my part).
I skipped the 'image augmentation' part of the walkthrough. So, I have not created the
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
]
)
part. So, instead of:
def make_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)
# Image augmentation block
x = data_augmentation(inputs)
# Entry block
x = layers.Rescaling(1.0 / 255)(x)
.......
at the beginning of the model, I have:
def make_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)
# Image augmentation block
x = keras.Sequential(inputs)
# Entry block
x = layers.Rescaling(1.0 / 255)(x)
.......
However I keep getting different errors no matter how much I try to change things around, such as "TypeError: Keras symbolic inputs/outputs do not implement __len__.", or "ValueError: Exception encountered when calling layer "rescaling_3" (type Rescaling).".
What am I missing here?
I am on section 3.7 of Chollet's book Deep Learning with Python.
The project is to find the median price of homes in a given Boston suburbs in the 1970's.
https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/3.7-predicting-house-prices.ipynb
At section "Validating our approach using K-fold validation" I try to run this block of code:
num_epochs = 500
all_mae_histories = []
for i in range(k):
print('processing fold #', i)
# Prepare the validation data: data from partition # k
val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]
# Prepare the training data: data from all other partitions
partial_train_data = np.concatenate(
[train_data[:i * num_val_samples],
train_data[(i + 1) * num_val_samples:]],
axis=0)
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[(i + 1) * num_val_samples:]],
axis=0)
# Build the Keras model (already compiled)
model = build_model()
# Train the model (in silent mode, verbose=0)
history = model.fit(partial_train_data, partial_train_targets,
validation_data=(val_data, val_targets),
epochs=num_epochs, batch_size=1, verbose=0)
mae_history = history.history['val_mean_absolute_error']
all_mae_histories.append(mae_history)
I get an error KeyError: 'val_mean_absolute_error'
mae_history = history.history['val_mean_absolute_error']
I am guessing the solution is figure out the correct parameter to replace val_mean_absolute_error. I've tried looking into some Keras documentation for what would be the correct key value. Anyone know the correct key value?
The problem in your code is that, when you compile your model, you do not add the specific 'mae' metric.
If you wanted to add the 'mae' metric in your code, you would need to do like this:
model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsoluteError()])
model.compile('sgd', metrics=['mean_absolute_error'])
After this step, you can try to see if the correct name is val_mean_absolute_error or val_mae. Most likely, if you compile your model like I demonstrated in option 2, your code will work with "val_mean_absolute_error".
Also, you should also put the code snippet where you compile your model, it is missing in the question text from above(i.e. the build_model() function)
I replaced 'val_mean_absolute_error' with 'val_mae' and it worked for me
FYI, I had the same problem that persisted even after changing the line history.history['val_mae'] as described in the answer.
In my case, in order for the val_mae dict object to be present in history.history object, I needed to ensure that the model.fit() code included the 'validation_data = (val_data, val_targets)' argument. I neglected to do this initially.
I update it by below code line:
mae_history = history.history["mae"]
History object should contain the same names as what you compile.
For example:
mean_absolute_error gives val_mean_absolute_error
mae gives val_mae
accuracy gives val_accuracy
acc gives val_acc
I am new to Deep Learning and Tensorflow. I retrained a pretrained tensorflow inceptionv3 model as saved_model.pb to recognize different type of images but when I tried to use the fie with below code.
with tf.Session() as sess:
with tf.gfile.FastGFile("tensorflow/trained/saved_model.pb",'rb') as f:
graph_def = tf.GraphDef()
tf.Graph.as_graph_def()
graph_def.ParseFromString(f.read())
g_in=tf.import_graph_def(graph_def)
LOGDIR='/log'
train_writer=tf.summary.FileWriter(LOGDIR)
train_writer.add_graph(sess.graph)
it gives me this error -
File "testing.py", line 7, in <module>
graph_def.ParseFromString(f.read())
google.protobuf.message.DecodeError: Error parsing message
I tried many solution I can find for this problem and modules in tensorflow/python/tools which uses the graph_def.ParseFromString(f.read()) function are giving me same error. Please tell me how to solve this or tell me the way in which I can avoid ParseFromString(f.read()) function. Any help would be appreciated. Thank you!
Please use the frozen_inference_graph.pb to load the model,
than to use the saved_model.pb
Model_output
- saved_model
- saved_model.pb
- checkpoint
- frozen_inference_graph.pb # Main model
- model.ckpt.data-00000-of-00001
- model.ckpt.index
- model.ckpt.meta
- pipeline.config
I am assuming that you saved your trained model using tf.saved_model.Builder provided by TensorFlow, in which case you could possibly do something like:
Load model
export_path = './path/to/saved_model.pb'
# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
'''
You can provide 'tags' when saving a model,
in my case I provided, 'serve' tag
'''
tf.saved_model.loader.load(sess, ['serve'], export_path)
graph = tf.get_default_graph()
# print your graph's ops, if needed
print(graph.get_operations())
'''
In my case, I named my input and output tensors as
input:0 and output:0 respectively
'''
y_pred = sess.run('output:0', feed_dict={'input:0': X_test})
To give some more context here, this is how I saved my model which can be loaded as above.
Save model
x = tf.get_default_graph().get_tensor_by_name('input:0')
y = tf.get_default_graph().get_tensor_by_name('output:0')
export_path = './models/'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
signature = tf.saved_model.predict_signature_def(
inputs={'input': x}, outputs={'output': y}
)
# using custom tag instead of: tags=[tf.saved_model.tag_constants.SERVING]
builder.add_meta_graph_and_variables(sess=obj.sess,
tags=['serve'],
signature_def_map={'predict': signature})
builder.save()
This will save your protobuf ('saved_model.pb') in the said folder ('models' here) which can then be loaded as stated above.
Have you passed as_text=False when saving a model? Please have a look at: TF save/restore graph fails at tf.GraphDef.ParseFromString()
If I run a basic logistic regression with 4 classes, I can get the predict_proba array.
How can i manually calculate the probabilities using the coefficients and intercepts? What are the exact steps to get the same answers that predict_proba generates?
There seem to be multiple questions about this online and several suggestions which are either incomplete or don't match up anyway.
For example, I can't replicate this process from my sklearn model so what is missing?
https://stats.idre.ucla.edu/stata/code/manually-generate-predicted-probabilities-from-a-multinomial-logistic-regression-in-stata/
Thanks,
Because I had the same question but could not find an answer that gave the same results I had a look at the sklearn GitHub repository to find the answer. Using the functions from their own package I was able to create the same results I got from predict_proba().
It appears that sklearn uses a special softmax() function that differs from the usual softmax function in their code.
Let's assume you build a model like this:
from sklearn.linear_model import LogisticRegression
X = ...
Y = ...
model = LogisticRegression(multi_class="multinomial", solver="saga")
model.fit(X, Y)
Then you can calculate the probabilities either with model.predict(X) or use the sklearn function mentioned above to calculate them manually like this.
from sklearn.utils.extmath import softmax,
import numpy as np
scores = np.dot(X, model.coef_.T) + model.intercept_
softmax(scores) # Sklearn implementation
In the documentation for their own softmax() function, they note that
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated. Hence
the largest value in each row is subtracted from each data point to
prevent this.
Replicate sklearn calcs (saw this on a different post):
V = X_train.values.dot(model.coef_.transpose())
U = V + model.intercept_
A = np.exp(U)
P=A/(1+A)
P /= P.sum(axis=1).reshape((-1, 1))
seems slightly different than softmax calcs, or the UCLA stat example, but it works.