fix conflict

gcvit/utils/gradcam.py

@@ -1,73 +1,3 @@
-<<<<<<< HEAD
-import tensorflow as tf
-import matplotlib.cm as cm
-import numpy as np
-try:
-    from tensorflow.keras.utils import array_to_img, img_to_array
-except:
-    from tensorflow.keras.preprocessing.image import array_to_img, img_to_array
-
-def process_image(img, size=(224, 224)):
-    img_array = tf.keras.applications.imagenet_utils.preprocess_input(img, mode='torch')
-    img_array = tf.image.resize(img_array, size,)[None,]
-    return img_array
-
-def get_gradcam_model(model):
-    inp = tf.keras.Input(shape=(224, 224, 3))
-    feats = model.forward_features(inp)
-    preds = model.forward_head(feats)
-    return tf.keras.models.Model(inp, [preds, feats])
-
-def get_gradcam_prediction(img, grad_model, process=True, decode=True, pred_index=None, cmap='jet', alpha=0.4):
-    """Grad-CAM for a single image
-
-    Args:
-        img (np.ndarray): process or raw image without batch_shape e.g. (224, 224, 3)
-        grad_model (tf.keras.Model): model with feature map and prediction
-        process (bool, optional): imagenet pre-processing. Defaults to True.
-        pred_index (int, optional): for particular calss. Defaults to None.
-        cmap (str, optional): colormap. Defaults to 'jet'.
-        alpha (float, optional): opacity. Defaults to 0.4.
-
-    Returns:
-        preds_decode: top5 predictions
-        heatmap: gradcam heatmap
-    """
-    # process image for inference
-    if process:
-        img_array = process_image(img)
-    else:
-        img_array = tf.convert_to_tensor(img)[None,]
-        if img.min()!=img.max():
-            img = (img - img.min())/(img.max() - img.min())
-            img = np.uint8(img*255.0)
-    # get prediction
-    with tf.GradientTape(persistent=True) as tape:
-        preds, feats = grad_model(img_array)
-        if pred_index is None:
-            pred_index = tf.argmax(preds[0])
-        class_channel = preds[:, pred_index]
-    # compute heatmap
-    grads = tape.gradient(class_channel, feats)
-    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-    feats = feats[0]
-    heatmap = feats @ pooled_grads[..., tf.newaxis]
-    heatmap = tf.squeeze(heatmap)
-    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
-    heatmap = heatmap.numpy()
-    heatmap = np.uint8(255 * heatmap)
-    # colorize heatmap
-    cmap = cm.get_cmap(cmap)
-    colors = cmap(np.arange(256))[:, :3]
-    heatmap = colors[heatmap]
-    heatmap = array_to_img(heatmap)
-    heatmap = heatmap.resize((img.shape[1], img.shape[0]))
-    heatmap = img_to_array(heatmap)
-    overlay = img + heatmap * alpha
-    overlay = array_to_img(overlay)
-    # decode prediction
-    preds_decode = tf.keras.applications.imagenet_utils.decode_predictions(preds.numpy())[0] if decode else preds
-=======
 import tensorflow as tf
 import matplotlib.cm as cm
 import numpy as np
@@ -136,5 +66,4 @@ def get_gradcam_prediction(img, grad_model, process=True, decode=True, pred_inde
     overlay = array_to_img(overlay)
     # decode prediction
     preds_decode = tf.keras.applications.imagenet_utils.decode_predictions(preds.numpy())[0] if decode else preds
->>>>>>> 094461a8d383ad2565311ea9a0094b5856887867
     return preds_decode, overlay

setup.py

@@ -1,50 +0,0 @@
-from setuptools import setup, find_packages
-from codecs import open
-from os import path
-
-here = path.abspath(path.dirname(__file__))
-
-# Get the long description from the README file
-with open(path.join(here, "README.md"), encoding="utf-8") as f:
-    long_description = f.read()
-
-with open(path.join(here, 'requirements.txt')) as f:
-    install_requires = [x for x in f.read().splitlines() if len(x)]
-    
-exec(open("gcvit/version.py").read())
-
-setup(
-    name="gcvit",
-    version=__version__,
-    description="Tensorflow 2.0 Implementation of GCViT: Global Context Vision Transformer. https://github.com/awsaf49/gcvit-tf",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/awsaf49/gcvit-tf",
-    author="Awsaf",
-    author_email="[email protected]",
-    classifiers=[
-        # How mature is this project? Common values are
-        #   3 - Alpha
-        #   4 - Beta
-        #   5 - Production/Stable
-        "Development Status :: 3 - Alpha",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: Apache Software License",
-        "Programming Language :: Python :: 3.6",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
-        "Topic :: Scientific/Engineering",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        "Topic :: Software Development",
-        "Topic :: Software Development :: Libraries",
-        "Topic :: Software Development :: Libraries :: Python Modules",
-    ],
-    # Note that this is a string of words separated by whitespace, not a list.
-    keywords="tensorflow computer_vision image classification transformer",
-    packages=find_packages(exclude=["tests"]),
-    include_package_data=True,
-    install_requires=install_requires,
-    python_requires=">=3.6",
-    license="MIT",
-)