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Make packages versions consistent
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README.md

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@@ -47,14 +47,19 @@ You can use our pretrained models in PyTorch to segment input image. The usage i
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```bash
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cd lib/nn/prroi_pool
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./travis.sh
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```
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```
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You may need ```nvcc``` for this step. If it cannot find the path to ```cuda.h```, do
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```bash
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export CPATH=/PATH/TO/YOUR/CUDA/include && ./travis.sh
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```
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2. Now you're good to go! Here is a simple demo to do inference on a single image:
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2. Install required packages
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```bash
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pip install -r requirements.txt
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```
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3. Now you're good to go! Here is a simple demo to do inference on a single image:
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```bash
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chmod +x demo_test.sh
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This script downloads trained models and a test image, runs the test script, and saves predicted segmentation (.png) to the working directory.
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3. Input arguments: (see full input arguments via python3 test.py -h)
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4. Input arguments: (see full input arguments via python3 test.py -h)
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```bash
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usage: test.py [-h] --test_img TEST_IMG --model_path MODEL_PATH
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[--suffix SUFFIX] [--arch_encoder ARCH_ENCODER]

lib/utils/data/dataloader.py

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import torch
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import torch.multiprocessing as multiprocessing
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from torch._C import _set_worker_signal_handlers, _update_worker_pids, \
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from torch._C import _set_worker_signal_handlers, _set_worker_pids, \
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_remove_worker_pids, _error_if_any_worker_fails
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from .sampler import SequentialSampler, RandomSampler, BatchSampler
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import signal
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w.daemon = True # ensure that the worker exits on process exit
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w.start()
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_update_worker_pids(id(self), tuple(w.pid for w in self.workers))
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_set_worker_pids(id(self), tuple(w.pid for w in self.workers))
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_set_SIGCHLD_handler()
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self.worker_pids_set = True
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requirements.txt

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numpy==1.16.6
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pillow==6.2.2
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opencv-python==4.2.0.32
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scipy==1.1.0
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torch==1.4.0
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torchvision==0.5.0
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tqdm==4.45.0
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colormath==3.0.0

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