def load_frozen_graph(pb_path): with tf.io.gfile.GFile(pb_path, "rb") as f: graph_def = tf.compat.v1.GraphDef() graph_def.ParseFromString(f.read())
import tensorflow as tf import cv2 import numpy as np
output = upscale("low_res.jpg", "fixed_edsr-x3.pb") cv2.imwrite("high_res_x3.png", output) Download Fixed Edsr-x3.pb
Make sure the filename in your code matches the downloaded file (e.g., EDSR_x3.pb
The Enhanced Deep Super-Resolution (EDSR) network remains a benchmark for single-image super-resolution (SISR). For deployment in production environments, the model is often converted to the TensorFlow .pb (protobuf) format. This note addresses the specific task of downloading the fixed EDSR-x3.pb model—a version with resolved tensor naming issues and shape inference bugs common in early exports. We provide the correct download source, verification steps, and a minimal code example for inference. def load_frozen_graph(pb_path): with tf
This is the most common technical reason users search for a "fixed" file. In the early days of AI upscaling, many models were distributed in the .pb (frozen graph) format. However, as machine learning frameworks evolved, the community shifted toward the format or the Prototxt/Pb combination.
graph = load_pb('EDSR_x3.pb') input_tensor = graph.get_tensor_by_name('input:0') output_tensor = graph.get_tensor_by_name('output:0') We provide the correct download source, verification steps,
def upscale(image_path, pb_path): graph = load_frozen_graph(pb_path) input_tensor = graph.get_tensor_by_name("input_1:0") # Check your model's naming output_tensor = graph.get_tensor_by_name("output_1:0")
The EDSR architecture [1], known for removing batch normalization layers for better performance, is widely used for upscaling images by factors of 2, 3, and 4. The x3 variant performs 3× super-resolution. However, naively converted .pb files often contain hardcoded input dimensions or broken rescaling nodes. The "fixed" version corrects these issues, accepting variable input sizes and properly outputting RGB images.