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Jinny Kim chjinny

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chjinny / image_transformatation.py
Created December 12, 2021 11:51 — forked from daino3/image_transformatation.py
Converting an image data uri to (28, 28) numpy array and writing to csv
from PIL import Image
import base64
import numpy
from io import BytesIO
data_uri = "data:image/png;base64,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
@chjinny
chjinny / gradient_accumulation.py
Created July 31, 2021 05:32 — forked from thomwolf/gradient_accumulation.py
PyTorch gradient accumulation training loop
model.zero_grad() # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
predictions = model(inputs) # Forward pass
loss = loss_function(predictions, labels) # Compute loss function
loss = loss / accumulation_steps # Normalize our loss (if averaged)
loss.backward() # Backward pass
if (i+1) % accumulation_steps == 0: # Wait for several backward steps
optimizer.step() # Now we can do an optimizer step
model.zero_grad() # Reset gradients tensors
if (i+1) % evaluation_steps == 0: # Evaluate the model when we...
@chjinny
chjinny / form_to_opencv.py
Created January 25, 2021 05:36 — forked from mjul/form_to_opencv.py
Decode Python (Flask or Werkzeug) photo file uploaded via HTTP POST request in-memory to an OpenCV matrix.
#
# Example from code built on the Flask web framework (and Werkzeug)
# Accepts uploading a photo file in the 'photo' form member, then
# copies it into a memory byte array and converts it to a numpy array
# which in turn can be decoded by OpenCV.
#
# Beware that this increases the memory pressure and you should
# configure a max request size before doing so.
#
# It saves a round-trip to a temporary file, though.