def main():
start_time = time()
# Handle Arguments
in_arg = get_input_args_predict()
print(in_arg)
# Load checkpoint and rebuild network
model = model_utils.load_checkpoint(in_arg.input, in_arg.gpu)
# Process image
image = data_image_utils.process_image(in_arg.image_path)
# Label mapping
cat_to_name = data_image_utils.get_label_mapping(in_arg.category_names)
# Predict
probs, classes = model_utils.predict(Variable(image).unsqueeze(0), model, in_arg.top_k)
model_utils.print_prediction(classes, probs, model.class_to_idx, cat_to_name)
tot_time = time()- start_time
print("\n** Total Elapsed Runtime:",
str(int((tot_time/3600)))+":"+str(int((tot_time%3600)/60))+":"
+str(int((tot_time%3600)%60)) )
def main():
# command line arguments
c_img_path = None
c_chk_path = None
c_top_k = None
c_mapping_path = None
c_use_gpu = False
# Get command line arguments
in_args = get_input_args_predict()
c_img_path = in_args.img_path
c_chk_path = in_args.checkpoint
c_top_k = in_args.top_k
c_mapping_path = in_args.category_names
c_use_gpu = in_args.gpu
print("Running predict.py with the following arguments: ")
print("Image Path: {}\nCheckpoint: {}\nTop K: {}\nMapping: {}\nGPU: {}".
format(c_img_path, c_chk_path, c_top_k, c_mapping_path, c_use_gpu))
# load the checkpoint
model = checkpoint_manager.load_checkpoint(c_chk_path)
# set the device (gpu or cpu)
device = torch.device(
"cuda" if torch.cuda.is_available() and c_use_gpu else "cpu")
# call the predict function and get the topK (c_top_k) classes and probabilities
probs, classes = predict(c_img_path, model, device, c_top_k)
# check to see if we want to map the classes to the category names (one of the command
# line arguments)
if c_mapping_path is not None:
cat_to_name = hlp.open_label_mapping_file(c_mapping_path)
classes = map_cat_to_real_names(classes, cat_to_name)
# print the results
print_results(probs, classes)
def main():
# data_dir = 'flowers'
# train_dir = data_dir + '/train'
# valid_dir = data_dir + '/valid'
# test_dir = data_dir + '/test'
# load arguments
in_arg = get_input_args_predict()
# load the model and optimizer we already used
model = load_checkpoint(in_arg.checkpoint)
# print(model)
# initialize device as CPU
device = torch.device("cpu")
# set up cuda or cpu since we will need some gpu time
if in_arg.gpu:
if torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
print("GPU selected, but there is no GPU available.")
print("Model running on: ", device)
image_path = in_arg.input
topk = in_arg.top_k
probs, classes = predict(image_path, model, topk, device)
print("Probabilities: ", probs)
print("Classes: ", classes)
if (in_arg.category_names != None): # we have a file path
category_path = in_arg.category_names
flowers = category_mapper(probs, classes, category_path)
def main():
# Get the arguments passed to the python script
in_arg = get_input_args_predict()
# Load check point from pass
model = load_checkpoint(in_arg.checkpoint)
# Send model to gpu
model_gpu, device = hw_control(model, in_arg.gpu)
# Perform the prediction
probs, classes = predict(in_arg.path, model, in_arg.top_k, in_arg.gpu)
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
Class_names = [cat_to_name[str(class_name)] for class_name in classes]
print("Most probable cat.:\t", Class_names)
print("Probalbilities \t", probs)
return probs, classes
#PROGRAMMER: Joshua Cayanan
# DATE CREATED: June 29, 2020
# REVISED DATE:
# PURPOSE: Feed an image into classifier neural network and return prediction with associated probability
# Example call:
# python predict.py --image 07573.jpg --model checkpoint --mapping cat_to_name.json
#Import functions created for this program
from get_input_args import get_input_args_predict
from prediction_model import predict
#Funnel command line arguments into program
in_arg = get_input_args_predict()
#Assign input arguments to variables
test_image = in_arg.test_image
checkpoint = in_arg.checkpoint
top_k = in_arg.top_k
category_names = in_arg.category_names
gpu = in_arg.gpu
if gpu:
gpu = 'cuda'
else:
gpu = 'cpu'
#Call the prediction model function
flower_names, probs = predict(test_image, checkpoint, top_k, category_names, gpu)
print(flower_names)
print(probs)
import torch
from torchvision import datasets, transforms, models
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import json
import get_input_args as args
from torch import nn
from collections import OrderedDict
#import train
#Load predict command line args
arg = args.get_input_args_predict()
image_path = arg.image_path
checkpoint = arg.checkpoint #Name checkpoint file, e.g. checkpoint.pth
topk = arg.topk
cat_to_name = arg.cat_to_name
gpu = arg.gpu
arch = arg.arch
#Gives a dictionary, mapping the integer encoded categories to the actual names of the flowers. Used in predict()
with open(cat_to_name, 'r') as f:
cat_to_name = json.load(f)
cat_to_name
def build_network(input_size,
hidden_units,
model,
output_size=102,
drpout=0.2,
def main():
# read initial inputs
in_args = get_input_args_predict()
# train neural network
predict(in_args)