main.py

from __future__ import print_function

from torch import nn
from torchvision.models import ResNet

dependencies = ['torch']



import torch
x = torch.rand(5, 3)
print(x)

model = torch.hub.load('pytorch/vision:v0.4.2', 'resnet50', pretrained=True)
#model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x8d_wsl')

# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.4.2', 'resnet34', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'resnet50', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'resnet101', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.4.2', 'resnet152', pretrained=True)
model.eval()

import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)

from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

modules=list(model.children())[:-2]
resnet50=nn.Sequential(*modules)

with torch.no_grad():
    output = resnet50(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print (output[0].shape)
#print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
#print(torch.nn.functional.softmax(output[0], dim=0))