2-dl-container/Container-Root/job/resnet/direct_benchmark-gpu.py [47:62]:
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model_dict = dict()
model_dict['return_dict'] = False

# Load Images from the Folder
data_dir = './data'
img_preprocessed_list = []
jpg_file_list = os.listdir(data_dir)
jpg_file_list = [x for x in jpg_file_list if '.jpg' in x]
jpg_file_list_sample = random.sample(jpg_file_list, num_request_samples)

for cur_image_file in jpg_file_list_sample:
    cur_image = Image.open('%s/%s' % (data_dir, cur_image_file)).convert('RGB')

    cur_image_preprocessed = preprocess(cur_image)
    cur_image_preprocessed_unsqueeze = torch.unsqueeze(cur_image_preprocessed, 0)
    img_preprocessed_list.append(cur_image_preprocessed_unsqueeze)
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2-dl-container/Container-Root/job/resnet/direct_benchmark-inf.py [52:67]:
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model_dict = dict()
model_dict['return_dict'] = False

# Load Images from the Folder
data_dir = './data'
img_preprocessed_list = []
jpg_file_list = os.listdir(data_dir)
jpg_file_list = [x for x in jpg_file_list if '.jpg' in x]
jpg_file_list_sample = random.sample(jpg_file_list, num_request_samples)

for cur_image_file in jpg_file_list_sample:
    cur_image = Image.open('%s/%s' % (data_dir, cur_image_file)).convert('RGB')

    cur_image_preprocessed = preprocess(cur_image)
    cur_image_preprocessed_unsqueeze = torch.unsqueeze(cur_image_preprocessed, 0)
    img_preprocessed_list.append(cur_image_preprocessed_unsqueeze)
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