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Create app.py

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  1. app.py +222 -0
app.py ADDED
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+ import tensorflow as tf
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+ import tensorflow_hub as hub
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+
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+ import requests
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+ from PIL import Image
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+ from io import BytesIO
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+
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+ import matplotlib.pyplot as plt
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+ import numpy as np
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+ import gradio as gr
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+
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+ #@title Helper functions for loading image (hidden)
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+
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+ original_image_cache = {}
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+
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+ def preprocess_image(image):
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+ image = np.array(image)
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+ # reshape into shape [batch_size, height, width, num_channels]
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+ img_reshaped = tf.reshape(image, [1, image.shape[0], image.shape[1], image.shape[2]])
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+ # Use `convert_image_dtype` to convert to floats in the [0,1] range.
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+ image = tf.image.convert_image_dtype(img_reshaped, tf.float32)
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+ return image
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+
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+ def load_image_from_url(img_url):
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+ """Returns an image with shape [1, height, width, num_channels]."""
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+ user_agent = {'User-agent': 'Colab Sample (https://tensorflow.org)'}
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+ response = requests.get(img_url, headers=user_agent)
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+ image = Image.open(BytesIO(response.content))
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+ image = preprocess_image(image)
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+ return image
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+
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+ def load_image(image_url, image_size=256, dynamic_size=False, max_dynamic_size=512):
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+ """Loads and preprocesses images."""
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+ # Cache image file locally.
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+ if image_url in original_image_cache:
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+ img = original_image_cache[image_url]
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+ elif image_url.startswith('https://'):
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+ img = load_image_from_url(image_url)
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+ else:
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+ fd = tf.io.gfile.GFile(image_url, 'rb')
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+ img = preprocess_image(Image.open(fd))
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+ original_image_cache[image_url] = img
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+ # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
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+ img_raw = img
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+ if tf.reduce_max(img) > 1.0:
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+ img = img / 255.
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+ if len(img.shape) == 3:
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+ img = tf.stack([img, img, img], axis=-1)
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+ if not dynamic_size:
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+ img = tf.image.resize_with_pad(img, image_size, image_size)
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+ elif img.shape[1] > max_dynamic_size or img.shape[2] > max_dynamic_size:
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+ img = tf.image.resize_with_pad(img, max_dynamic_size, max_dynamic_size)
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+ return img, img_raw
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+
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+
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+
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+ image_size = 224
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+ dynamic_size = False
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+
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+ model_name = "efficientnetv2-s"
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+
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+ model_handle_map = {
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+ "efficientnetv2-s": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2",
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+ "efficientnetv2-m": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_m/classification/2",
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+ "efficientnetv2-l": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/classification/2",
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+ "efficientnetv2-s-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_s/classification/2",
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+ "efficientnetv2-m-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_m/classification/2",
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+ "efficientnetv2-l-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_l/classification/2",
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+ "efficientnetv2-xl-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_xl/classification/2",
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+ "efficientnetv2-b0-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b0/classification/2",
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+ "efficientnetv2-b1-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b1/classification/2",
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+ "efficientnetv2-b2-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b2/classification/2",
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+ "efficientnetv2-b3-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b3/classification/2",
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+ "efficientnetv2-s-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_s/classification/2",
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+ "efficientnetv2-m-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_m/classification/2",
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+ "efficientnetv2-l-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_l/classification/2",
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+ "efficientnetv2-xl-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_xl/classification/2",
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+ "efficientnetv2-b0-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b0/classification/2",
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+ "efficientnetv2-b1-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b1/classification/2",
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+ "efficientnetv2-b2-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b2/classification/2",
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+ "efficientnetv2-b3-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b3/classification/2",
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+ "efficientnetv2-b0": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/classification/2",
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+ "efficientnetv2-b1": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b1/classification/2",
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+ "efficientnetv2-b2": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b2/classification/2",
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+ "efficientnetv2-b3": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b3/classification/2",
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+ "efficientnet_b0": "https://tfhub.dev/tensorflow/efficientnet/b0/classification/1",
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+ "efficientnet_b1": "https://tfhub.dev/tensorflow/efficientnet/b1/classification/1",
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+ "efficientnet_b2": "https://tfhub.dev/tensorflow/efficientnet/b2/classification/1",
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+ "efficientnet_b3": "https://tfhub.dev/tensorflow/efficientnet/b3/classification/1",
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+ "efficientnet_b4": "https://tfhub.dev/tensorflow/efficientnet/b4/classification/1",
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+ "efficientnet_b5": "https://tfhub.dev/tensorflow/efficientnet/b5/classification/1",
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+ "efficientnet_b6": "https://tfhub.dev/tensorflow/efficientnet/b6/classification/1",
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+ "efficientnet_b7": "https://tfhub.dev/tensorflow/efficientnet/b7/classification/1",
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+ "bit_s-r50x1": "https://tfhub.dev/google/bit/s-r50x1/ilsvrc2012_classification/1",
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+ "inception_v3": "https://tfhub.dev/google/imagenet/inception_v3/classification/4",
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+ "inception_resnet_v2": "https://tfhub.dev/google/imagenet/inception_resnet_v2/classification/4",
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+ "resnet_v1_50": "https://tfhub.dev/google/imagenet/resnet_v1_50/classification/4",
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+ "resnet_v1_101": "https://tfhub.dev/google/imagenet/resnet_v1_101/classification/4",
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+ "resnet_v1_152": "https://tfhub.dev/google/imagenet/resnet_v1_152/classification/4",
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+ "resnet_v2_50": "https://tfhub.dev/google/imagenet/resnet_v2_50/classification/4",
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+ "resnet_v2_101": "https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4",
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+ "resnet_v2_152": "https://tfhub.dev/google/imagenet/resnet_v2_152/classification/4",
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+ "nasnet_large": "https://tfhub.dev/google/imagenet/nasnet_large/classification/4",
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+ "nasnet_mobile": "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4",
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+ "pnasnet_large": "https://tfhub.dev/google/imagenet/pnasnet_large/classification/4",
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+ "mobilenet_v2_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4",
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+ "mobilenet_v2_130_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4",
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+ "mobilenet_v2_140_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/4",
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+ "mobilenet_v3_small_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5",
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+ "mobilenet_v3_small_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/classification/5",
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+ "mobilenet_v3_large_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/classification/5",
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+ "mobilenet_v3_large_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5",
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+ }
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+
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+ model_image_size_map = {
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+ "efficientnetv2-s": 384,
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+ "efficientnetv2-m": 480,
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+ "efficientnetv2-l": 480,
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+ "efficientnetv2-b0": 224,
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+ "efficientnetv2-b1": 240,
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+ "efficientnetv2-b2": 260,
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+ "efficientnetv2-b3": 300,
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+ "efficientnetv2-s-21k": 384,
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+ "efficientnetv2-m-21k": 480,
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+ "efficientnetv2-l-21k": 480,
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+ "efficientnetv2-xl-21k": 512,
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+ "efficientnetv2-b0-21k": 224,
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+ "efficientnetv2-b1-21k": 240,
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+ "efficientnetv2-b2-21k": 260,
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+ "efficientnetv2-b3-21k": 300,
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+ "efficientnetv2-s-21k-ft1k": 384,
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+ "efficientnetv2-m-21k-ft1k": 480,
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+ "efficientnetv2-l-21k-ft1k": 480,
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+ "efficientnetv2-xl-21k-ft1k": 512,
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+ "efficientnetv2-b0-21k-ft1k": 224,
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+ "efficientnetv2-b1-21k-ft1k": 240,
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+ "efficientnetv2-b2-21k-ft1k": 260,
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+ "efficientnetv2-b3-21k-ft1k": 300,
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+ "efficientnet_b0": 224,
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+ "efficientnet_b1": 240,
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+ "efficientnet_b2": 260,
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+ "efficientnet_b3": 300,
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+ "efficientnet_b4": 380,
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+ "efficientnet_b5": 456,
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+ "efficientnet_b6": 528,
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+ "efficientnet_b7": 600,
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+ "inception_v3": 299,
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+ "inception_resnet_v2": 299,
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+ "mobilenet_v2_100_224": 224,
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+ "mobilenet_v2_130_224": 224,
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+ "mobilenet_v2_140_224": 224,
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+ "nasnet_large": 331,
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+ "nasnet_mobile": 224,
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+ "pnasnet_large": 331,
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+ "resnet_v1_50": 224,
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+ "resnet_v1_101": 224,
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+ "resnet_v1_152": 224,
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+ "resnet_v2_50": 224,
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+ "resnet_v2_101": 224,
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+ "resnet_v2_152": 224,
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+ "mobilenet_v3_small_100_224": 224,
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+ "mobilenet_v3_small_075_224": 224,
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+ "mobilenet_v3_large_100_224": 224,
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+ "mobilenet_v3_large_075_224": 224,
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+ }
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+
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+ model_handle = model_handle_map[model_name]
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+
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+
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+ max_dynamic_size = 512
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+ if model_name in model_image_size_map:
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+ image_size = model_image_size_map[model_name]
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+ dynamic_size = False
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+ print(f"Images will be converted to {image_size}x{image_size}")
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+ else:
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+ dynamic_size = True
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+ print(f"Images will be capped to a max size of {max_dynamic_size}x{max_dynamic_size}")
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+
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+ labels_file = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
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+
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+ #download labels and creates a maps
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+ downloaded_file = tf.keras.utils.get_file("labels.txt", origin=labels_file)
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+
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+ classes = []
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+
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+ with open(downloaded_file) as f:
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+ labels = f.readlines()
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+ classes = [l.strip() for l in labels]
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+
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+
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+ classifier = hub.load(model_handle)
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+
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+
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+ def inference(img):
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+ image, original_image = load_image(img, image_size, dynamic_size, max_dynamic_size)
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+
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+
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+ input_shape = image.shape
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+ warmup_input = tf.random.uniform(input_shape, 0, 1.0)
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+ warmup_logits = classifier(warmup_input).numpy()
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+
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+ # Run model on image
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+ probabilities = tf.nn.softmax(classifier(image)).numpy()
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+
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+ top_5 = tf.argsort(probabilities, axis=-1, direction="DESCENDING")[0][:5].numpy()
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+ np_classes = np.array(classes)
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+
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+ # Some models include an additional 'background' class in the predictions, so
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+ # we must account for this when reading the class labels.
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+ includes_background_class = probabilities.shape[1] == 1001
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+ result = {}
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+ for i, item in enumerate(top_5):
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+ class_index = item if includes_background_class else item + 1
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+ line = f'({i+1}) {class_index:4} - {classes[class_index]}: {probabilities[0][top_5][i]}'
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+ result[classes[class_index]] = probabilities[0][top_5][i].item()
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+ return result
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+
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+ title="efficientnetv2-s"
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+ description="Gradio Demo for efficientnetv2-s: EfficientNet V2 are a family of image classification models, which achieve better parameter efficiency and faster training speed than prior arts. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below"
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+ article = "<p style='text-align: center'><a href='https://tfhub.dev/google/collections/efficientnet_v2/1' target='_blank'>Tensorflow Hub</a></p>"
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+ examples=[['apple1.jpg']]
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+ gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)