Upload app.py

app.py

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+# Imports
+import numpy as np
+import matplotlib.pyplot as plt
+
+import tensorflow as tf
+from tensorflow import keras
+
+import streamlit as st
+
+from app_utils import *
+
+# The functions (except main) are taken straight from Keras Example
+def compute_loss(feature_extractor, input_image, filter_index):
+    activation = feature_extractor(input_image)
+    # We avoid border artifacts by only involving non-border pixels in the loss.
+    filter_activation = activation[:, 2:-2, 2:-2, filter_index]
+    return tf.reduce_mean(filter_activation)
+
+
+@tf.function
+def gradient_ascent_step(feature_extractor, img, filter_index, learning_rate):
+    with tf.GradientTape() as tape:
+        tape.watch(img)
+        loss = compute_loss(feature_extractor, img, filter_index)
+    # Compute gradients.
+    grads = tape.gradient(loss, img)
+    # Normalize gradients.
+    grads = tf.math.l2_normalize(grads)
+    img += learning_rate * grads
+    return loss, img
+
+
+def initialize_image():
+    # We start from a gray image with some random noise
+    img = tf.random.uniform((1, IMG_WIDTH, IMG_HEIGHT, 3))
+    # ResNet50V2 expects inputs in the range [-1, +1].
+    # Here we scale our random inputs to [-0.125, +0.125]
+    return (img - 0.5) * 0.25
+
+
+def visualize_filter(feature_extractor, filter_index):
+    # We run gradient ascent for 20 steps
+    img = initialize_image()
+    for _ in range(ITERATIONS):
+        loss, img = gradient_ascent_step(
+            feature_extractor, img, filter_index, LEARNING_RATE
+        )
+
+    # Decode the resulting input image
+    img = deprocess_image(img[0].numpy())
+    return loss, img
+
+
+def deprocess_image(img):
+    # Normalize array: center on 0., ensure variance is 0.15
+    img -= img.mean()
+    img /= img.std() + 1e-5
+    img *= 0.15
+
+    # Center crop
+    img = img[25:-25, 25:-25, :]
+
+    # Clip to [0, 1]
+    img += 0.5
+    img = np.clip(img, 0, 1)
+
+    # Convert to RGB array
+    img *= 255
+    img = np.clip(img, 0, 255).astype("uint8")
+    return img
+
+
+# The visualization function
+def main():
+    # Model selector
+    mn_option = st.selectbox("Select the model for visualization -", AVAILABLE_MODELS)
+
+    # Check to not load the model for ever layer change
+    if mn_option != st.session_state.model_name:
+        model = getattr(keras.applications, mn_option)(
+            weights="imagenet", include_top=False
+        )
+        st.session_state.layer_list = ["<select layer>"] + [
+            layer.name for layer in model.layers
+        ]
+        st.session_state.model = model
+        st.session_state.model_name = mn_option
+
+    # Layer selector, saves the feature selector in case 64 filters are to be seen
+    if st.session_state.model_name:
+        ln_option = st.selectbox(
+            "Select the target layer (best to pick somewhere in the middle of the model) -",
+            st.session_state.layer_list,
+        )
+        if ln_option != "<select layer>":
+            if ln_option != st.session_state.layer_name:
+                layer = st.session_state.model.get_layer(name=ln_option)
+                st.session_state.feat_extract = keras.Model(
+                    inputs=st.session_state.model.inputs, outputs=layer.output
+                )
+                st.session_state.layer_name = ln_option
+
+    # Filter index selector
+    if st.session_state.layer_name:
+        filter_select = st.selectbox("Visualize -", VIS_OPTION.keys())
+
+        if VIS_OPTION[filter_select] == 0:
+            loss, img = visualize_filter(st.session_state.feat_extract, 0)
+            st.image(img)
+        else:
+            st.warning(":exclamation: Calculating the gradients can take a while..")
+            prog_bar = st.progress(0)
+            fig, axis = plt.subplots(nrows=8, ncols=8, figsize=(14, 14))
+            for filter_index, ax in enumerate(axis.ravel()):
+                prog_bar.progress((filter_index + 1) / 64)
+                if filter_index < 65:
+                    loss, img = visualize_filter(
+                        st.session_state.feat_extract, filter_index
+                    )
+                    ax.imshow(img)
+                    ax.set_title(filter_index + 1)
+                    ax.set_axis_off()
+                else:
+                    ax.set_axis_off()
+
+            st.write(fig)
+
+
+if __name__ == "__main__":
+
+    with open("model_names.txt", "r") as op:
+        AVAILABLE_MODELS = [i.strip() for i in op.readlines()]
+
+    st.set_page_config(layout="wide")
+
+    st.title(title)
+    st.write(info_text)
+    st.info(f"{credits}\n\n{replicate}\n\n{vit_info}")
+
+    main()