Tony607

from IPython.display import clear_output, Image, display, HTML
import numpy as np
import tensorflow as tf
def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':

Tony607

os.makedirs("data/VOCdevkit", exist_ok=True)
voc2007_dir = os.path.join(project_name, "data/VOC2007")
os.system("ln -s {} data/VOCdevkit".format(voc2007_dir))

Tony607

%cd {mmdetection_dir}
from mmcv.runner import load_checkpoint
from mmdet.apis import inference_detector, show_result, init_detector
checkpoint_file = os.path.join(mmdetection_dir, work_dir, "latest.pth")
score_thr = 0.8
# build the model from a config file and a checkpoint file
model = init_detector(config_fname, checkpoint_file)
# test a single image and show the results
img = 'data/VOCdevkit/VOC2007/JPEGImages/15.jpg'
result = inference_detector(model, img)

Tony607

import numpy as np

interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]

def eval_model(interpreter, x_test, y_test):
  total_seen = 0
  num_correct = 0

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# Create the .tflite file
tflite_model_file = "/tmp/sparse_mnist.tflite"
converter = tf.lite.TFLiteConverter.from_keras_model_file(pruned_keras_file)
tflite_model = converter.convert()
with open(tflite_model_file, "wb") as f:
    f.write(tflite_model)

Tony607

import tempfile
import zipfile

_, new_pruned_keras_file = tempfile.mkstemp(".h5")
print("Saving pruned model to: ", new_pruned_keras_file)
tf.keras.models.save_model(final_model, new_pruned_keras_file, include_optimizer=False)

# Zip the .h5 model file
_, zip3 = tempfile.mkstemp(".zip")
with zipfile.ZipFile(zip3, "w", compression=zipfile.ZIP_DEFLATED) as f:

Tony607

from tensorflow.keras.models import load_model

model = load_model(final_model)
import numpy as np

for i, w in enumerate(model.get_weights()):
    print(
        "{} -- Total:{}, Zeros: {:.2f}%".format(
            model.weights[i].name, w.size, np.sum(w == 0) / w.size * 100
        )

Tony607

# Add a pruning step callback to peg the pruning step to the optimizer's
# step. Also add a callback to add pruning summaries to tensorboard
callbacks = [
    sparsity.UpdatePruningStep(),
    sparsity.PruningSummaries(log_dir=logdir, profile_batch=0)
]

new_pruned_model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,

Tony607

import numpy as np
import tensorflow as tf
from tensorflow_model_optimization.sparsity import keras as sparsity

# Backend agnostic way to save/restore models
# _, keras_file = tempfile.mkstemp('.h5')
# print('Saving model to: ', keras_file)
# tf.keras.models.save_model(model, keras_file, include_optimizer=False)

# Load the serialized model

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import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms

# Writer will output to ./runs/ directory by default
writer = SummaryWriter()

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
trainset = datasets.MNIST('mnist_train', train=True, download=True, transform=transform)